Human iPSC and CRISPR targeted gene knock-in strategy for studying the somatic TIE2L914F mutation in endothelial cells.

Induced pluripotent stem cell (iPSC) derived endothelial cells (iECs) have emerged as a promising tool for studying vascular biology and providing a platform for modelling various vascular diseases, including those with genetic origins. Currently, primary ECs are the main source for disease modelling in this field. However, they are difficult to edit and have a limited lifespan. To study the effects of targeted mutations on an endogenous level, we generated and characterized an iPSC derived model for venous malformations (VMs). CRISPR-Cas9 technology was used to generate a novel human iPSC line with an amino acid substitution L914F in the TIE2 receptor, known to cause VMs. This enabled us to study the differential effects of VM causative mutations in iECs in multiple in vitro models and assess their ability to form vessels in vivo. The analysis of TIE2 expression levels in TIE2L914F iECs showed a significantly lower expression of TIE2 on mRNA and protein level, which has not been observed before due to a lack of models with endogenous edited TIE2L914F and sparse patient data. Interestingly, the TIE2 pathway was still significantly upregulated and TIE2 showed high levels of phosphorylation. TIE2L914F iECs exhibited dysregulated angiogenesis markers and upregulated migration capability, while proliferation was not affected. Under shear stress TIE2L914F iECs showed reduced alignment in the flow direction and a larger cell area than TIE2WT iECs. In summary, we developed a novel TIE2L914F iPSC-derived iEC model and characterized it in multiple in vitro models. The model can be used in future work for drug screening for novel treatments for VMs.

Assessment of longitudinal dispersion and flow resistance near floodplains through riparian woodlands.

Riparian woodlands prevent bank erosions, recycle minerals, sustain biodiversity, act as flow resistance on floodplains, and filter pollutants. The emergent trees characterize woodlands with different spacing arrangements that dictate flow resistance and longitudinal dispersion of the pollutants in compound channel flow. The single- and multistage compound channels exist in urban and natural watercourses with riparian and transplanted trees on different stages of the floodplain. This study numerically validates the planting of vegetation in lines on single- and multistage floodplains using a wall-modeled large-eddy simulation model. Post-validation, the focus of the study was to assess the hydrodynamic behavior and mixing around the floodplain and main channel section of different tested configurations. The approximation of flow structures for the various configurations of tree plantations shows stronger vortices with significant characteristic length scales for floodplains closer to the main channel. The intensity of the secondary current is higher for denser planted trees at junctions of floodplains. For higher flow events, drag force contributions for staged floodplains with trees on both stages are 45-41%, and trees on the top stage contribute 27-22% to the total frictional force budget. The subsequent investigation shows that the in-line trees geometrical configuration and spacing arrangement on the floodplain dictates flow resistance and longitudinal dispersion of the pollutants and contamination in channel flow. The results show that the overall reduction in discharge for floodplains with tree planting is 19.8-36.2% for single-stage and 10.4-23.6% for multistage compound channels. The longitudinal dispersion coefficients for each multi-zone model predict a 61% and 41% dispersion reduction, respectively, in single- and multistage floodplains with planted trees. Floodplains with denser tree spacing have a maximum zonal discharge reduction of 45% for a single-stage and 27.2% and 28.0% for multistage channels. These findings strongly suggest that the planting parameters of spacing-to-diameter ratio and floodplain geometry play a pivotal role in floodplain management from the perspective of contaminant dispersion and flood risk reduction during high-flow events.

Human-derived air-liquid interface cultures decipher Alzheimer's disease-SARS-CoV-2 crosstalk in the olfactory mucosa.

BACKGROUND: The neurological effects of the coronavirus disease of 2019 (COVID-19) raise concerns about potential long-term consequences, such as an increased risk of Alzheimer's disease (AD). Neuroinflammation and other AD-associated pathologies are also suggested to increase the risk of serious SARS-CoV-2 infection. Anosmia is a common neurological symptom reported in COVID-19 and in early AD. The olfactory mucosa (OM) is important for the perception of smell and a proposed site of viral entry to the brain. However, little is known about SARS-CoV-2 infection at the OM of individuals with AD. METHODS: To address this gap, we established a 3D in vitro model of the OM from primary cells derived from cognitively healthy and AD individuals. We cultured the cells at the air-liquid interface (ALI) to study SARS-CoV-2 infection under controlled experimental conditions. Primary OM cells in ALI expressed angiotensin-converting enzyme 2 (ACE-2), neuropilin-1 (NRP-1), and several other known SARS-CoV-2 receptor and were highly vulnerable to infection. Infection was determined by secreted viral RNA content and confirmed with SARS-CoV-2 nucleocapsid protein (NP) in the infected cells by immunocytochemistry. Differential responses of healthy and AD individuals-derived OM cells to SARS-CoV-2 were determined by RNA sequencing. RESULTS: Results indicate that cells derived from cognitively healthy donors and individuals with AD do not differ in susceptibility to infection with the wild-type SARS-CoV-2 virus. However, transcriptomic signatures in cells from individuals with AD are highly distinct. Specifically, the cells from AD patients that were infected with the virus showed increased levels of oxidative stress, desensitized inflammation and immune responses, and alterations to genes associated with olfaction. These results imply that individuals with AD may be at a greater risk of experiencing severe outcomes from the infection, potentially driven by pre-existing neuroinflammation. CONCLUSIONS: The study sheds light on the interplay between AD pathology and SARS-CoV-2 infection. Altered transcriptomic signatures in AD cells may contribute to unique symptoms and a more severe disease course, with a notable involvement of neuroinflammation. Furthermore, the research emphasizes the need for targeted interventions to enhance outcomes for AD patients with viral infection. The study is crucial to better comprehend the relationship between AD, COVID-19, and anosmia. It highlights the importance of ongoing research to develop more effective treatments for those at high risk of severe SARS-CoV-2 infection.

Functional Characterization of Mechanosensitive Piezo1 Channels in Trigeminal and Somatic Nerves in a Neuron-on-Chip Model.

Mechanosensitive ion channels, Piezo1 and 2, are activated by pressure and involved in diverse physiological functions, including senses of touch and pain, proprioception and many more. Understanding their function is important for elucidating the mechanosensitive mechanisms of a range of human diseases. Recently, Piezo channels were suggested to be contributors to migraine pain generation. Migraine is typically characterized by allodynia and mechanical hyperalgesia associated with the activation and sensitization of trigeminal ganglion (TG) nerve fibers. Notably, migraine specific medicines are ineffective for other types of pain, suggesting a distinct underlying mechanism. To address, in a straightforward manner, the specificity of the mechanosensitivity of trigeminal vs. somatic nerves, we compared the activity of Piezo1 channels in mouse TG neurons vs. dorsal root ganglia (DRG) neurons. We assessed the functional expression of Piezo1 receptors using a conventional live calcium imaging setup equipped with a multibarrel application system and utilizing a microfluidic chip-based setup. Surprisingly, the TG neurons, despite higher expression of the Piezo1 gene, were less responsive to Piezo1 agonist Yoda1 than the DRG neurons. This difference was more prominent in the chip-based setup, suggesting that certain limitations of the conventional approach, such as turbulence, can be overcome by utilizing microfluidic devices with laminar solution flow.

Characterization of nucleic acids from extracellular vesicle-enriched human sweat.

BACKGROUND: The human sweat is a mixture of secretions from three types of glands: eccrine, apocrine, and sebaceous. Eccrine glands open directly on the skin surface and produce high amounts of water-based fluid in response to heat, emotion, and physical activity, whereas the other glands produce oily fluids and waxy sebum. While most body fluids have been shown to contain nucleic acids, both as ribonucleoprotein complexes and associated with extracellular vesicles (EVs), these have not been investigated in sweat. In this study we aimed to explore and characterize the nucleic acids associated with sweat particles. RESULTS: We used next generation sequencing (NGS) to characterize DNA and RNA in pooled and individual samples of EV-enriched sweat collected from volunteers performing rigorous exercise. In all sequenced samples, we identified DNA originating from all human chromosomes, but only the mitochondrial chromosome was highly represented with 100% coverage. Most of the DNA mapped to unannotated regions of the human genome with some regions highly represented in all samples. Approximately 5 % of the reads were found to map to other genomes: including bacteria (83%), archaea (3%), and virus (13%), identified bacteria species were consistent with those commonly colonizing the human upper body and arm skin. Small RNA-seq from EV-enriched pooled sweat RNA resulted in 74% of the trimmed reads mapped to the human genome, with 29% corresponding to unannotated regions. Over 70% of the RNA reads mapping to an annotated region were tRNA, while misc. RNA (18,5%), protein coding RNA (5%) and miRNA (1,85%) were much less represented. RNA-seq from individually processed EV-enriched sweat collection generally resulted in fewer percentage of reads mapping to the human genome (7-45%), with 50-60% of those reads mapping to unannotated region of the genome and 30-55% being tRNAs, and lower percentage of reads being rRNA, LincRNA, misc. RNA, and protein coding RNA. CONCLUSIONS: Our data demonstrates that sweat, as all other body fluids, contains a wealth of nucleic acids, including DNA and RNA of human and microbial origin, opening a possibility to investigate sweat as a source for biomarkers for specific health parameters.

Telomere repeat-binding factor 2 binds extensively to extra-telomeric G-quadruplexes and regulates the epigenetic status of several gene promoters.

The role of the telomere repeat-binding factor 2 (TRF2) in telomere maintenance is well-established. However, recent findings suggest that TRF2 also functions outside telomeres, but relatively little is known about this function. Herein, using genome-wide ChIP-Seq assays of TRF2-bound chromatin from HT1080 fibrosarcoma cells, we identified thousands of TRF2-binding sites within the extra-telomeric genome. In light of this observation, we asked how TRF2 occupancy is organized within the genome. Interestingly, we found that extra-telomeric TRF2 sites throughout the genome are enriched in potential G-quadruplex-forming DNA sequences. Furthermore, we validated TRF2 occupancy at several promoter G-quadruplex motifs, which did adopt quadruplex forms in solution. TRF2 binding altered expression and the epigenetic state of several target promoters, indicated by histone modifications resulting in transcriptional repression of eight of nine genes investigated here. Furthermore, TRF2 occupancy and target gene expression were also sensitive to the well-known intracellular G-quadruplex-binding ligand 360A. Together, these results reveal an extensive genome-wide association of TRF2 outside telomeres and that it regulates gene expression in a G-quadruplex-dependent fashion.

Temperature effect on the build-up of exponentially growing polyelectrolyte multilayers. An exponential-to-linear transition point.

In this study, the effect of temperature on the build-up of exponentially growing polyelectrolyte multilayer films was investigated. It aims at understanding the multilayer growth mechanism as crucially important for the fabrication of tailor-made multilayer films. Model poly(L-lysine)/hyaluronic acid (PLL/HA) multilayers were assembled in the temperature range of 25-85 °C by layer-by-layer deposition using a dipping method. The film growth switches from the exponential to the linear regime at the transition point as a result of limited polymer diffusion into the film. With the increase of the build-up temperature the film growth rate is enhanced in both regimes; the position of the transition point shifts to a higher number of deposition steps confirming the diffusion-mediated growth mechanism. Not only the faster polymer diffusion into the film but also more porous/permeable film structure are responsible for faster film growth at higher preparation temperature. The latter mechanism is assumed from analysis of the film growth rate upon switching of the preparation temperature during the film growth. Interestingly, the as-prepared films are equilibrated and remain intact (no swelling or shrinking) during temperature variation in the range of 25-45 °C. The average activation energy for complexation between PLL and HA in the multilayers calculated from the Arrhenius plot has been found to be about 0.3 kJ mol(-1) for monomers of PLL. Finally, the following processes known to be dependent on temperature are discussed with respect to the multilayer growth: (i) polymer diffusion, (ii) polymer conformational changes, and (iii) inter-polymer interactions.

PgxSAVy: A tool for comprehensive evaluation of variant peptide quality in proteogenomics - catching the (un)usual suspects.

Variant peptides resulting from single nucleotide polymorphisms (SNPs) can lead to aberrant protein functions and have translational potential for disease diagnosis and personalized therapy. Variant peptides detected by proteogenomics are fraught with high number of false positives, but there is no uniform and comprehensive approach to assess variant quality across analysis pipelines. Despite class-specific FDR along with ad-hoc filters, the problem is far from solved. These protocols are typically manual and tedious, and thus not uniform across labs. We demonstrate that variant peptide rescoring, integrated with intensity, variant event information and search result features, allows better discrimination of correct variant peptides. Implemented into PgxSAVy - a tool for quality control of variant peptides, this method can tackle the high rate of false positives. PgxSAVy provides a rigorous framework for quality control and annotations of variant peptides on the basis of (i) variant quality, (ii) isobaric masses, and (iii) disease annotation. PgxSAVy demonstrated high accuracy by identifying true variants with 98.43% accuracy on simulated data. Large-scale proteogenomic reanalysis of ∼2.8 million spectra (PXD004010 and PXD001468) resulted in 12,705 variant peptide spectrum matches (PSMs), of which PgxSAVy evaluated 3028 (23.8%), 1409 (11.1%) and 8268 (65.1%) as confident, semi-confident and doubtful respectively. PgxSAVy also annotates the variants based on their pathogenicity and provides support for assisted manual validation. The analysis of proteins carrying variants can provide fine granularity in discovering important pathways. PgxSAVy will advance personalized medicine by providing a comprehensive framework for quality control and prioritization of proteogenomics variants. PgxSAVy is freely available at https://pgxsavy.igib.res.in/ as a webserver and https://github.com/anuragraj/PgxSAVy as a stand-alone tool.

Enrichment of sweat-derived extracellular vesicles of human and bacterial origin for biomarker identification.

Sweat contains biomarkers for real-time non-invasive health monitoring, but only a few relevant analytes are currently used in clinical practice. In the present study, we investigated whether sweat-derived extracellular vesicles (EVs) can be used as a source of potential protein biomarkers of human and bacterial origin. Methods: By using ExoView platform, electron microscopy, nanoparticle tracking analysis and Western blotting we characterized EVs in the sweat of eight volunteers performing rigorous exercise. We compared the presence of EV markers as well as general protein composition of total sweat, EV-enriched sweat and sweat samples collected in alginate skin patches. Results: We identified 1209 unique human proteins in EV-enriched sweat, of which approximately 20% were present in every individual sample investigated. Sweat derived EVs shared 846 human proteins (70%) with total sweat, while 368 proteins (30%) were captured by medical grade alginate skin patch and such EVs contained the typical exosome marker CD63. The majority of identified proteins are known to be carried by EVs found in other biofluids, mostly urine. Besides human proteins, EV-enriched sweat samples contained 1594 proteins of bacterial origin. Bacterial protein profiles in EV-enriched sweat were characterized by high interindividual variability, that reflected differences in total sweat composition. Alginate-based sweat patch accumulated only 5% proteins of bacterial origin. Conclusion: We showed that sweat-derived EVs provide a rich source of potential biomarkers of human and bacterial origin. Use of commercially available alginate skin patches selectively enrich for human derived material with very little microbial material collected.

Highly scalable and standardized organ-on-chip platform with TEER for biological barrier modeling.

The development of new therapies is hampered by the lack of predictive, and patient-relevant in vitro models. Organ-on-chip (OOC) technologies can potentially recreate physiological features and hold great promise for tissue and disease modeling. However, the non-standardized design of these chips and perfusion control systems has been a barrier to quantitative high-throughput screening (HTS). Here we present a scalable OOC microfluidic platform for applied kinetic in vitro assays (AKITA) that is applicable for high, medium, and low throughput. Its standard 96-well plate and 384-well plate layouts ensure compatibility with existing laboratory workflows and high-throughput data collection and analysis tools. The AKITA plate is optimized for the modeling of vascularized biological barriers, primarily the blood-brain barrier, skin, and lung, with precise flow control on a custom rocker. The integration of trans-epithelial electrical resistance (TEER) sensors allows rapid and repeated monitoring of barrier integrity over long time periods. Together with automated liquid handling and compound permeability testing analyses, we demonstrate the flexibility of the AKITA platform for establishing human-relevant models for preclinical drug and precision medicine's efficacy, toxicity, and permeability under near-physiological conditions.

Topical Anti-ulcerogenic Effect of the Beta-adrenergic Blockers on Diabetic Foot Ulcers: Recent Advances and Future Prospectives.

BACKGROUND: Patients with diabetes suffer from major complications like Diabetic Retinopathy, Diabetic Coronary Artery Disease, and Diabetic Foot ulcers (DFUs). Diabetes complications are a group of ailments whose recovery time is especially delayed, irrespective of the underlying reason. The longer duration of wound healing enhances the probability of problems like sepsis and amputation. The delayed healing makes it more critical for research focus. By understanding the molecular pathogenesis of diabetic wounds, it is quite easy to target the molecules involved in the healing of wounds. Recent research on beta-adrenergic blocking drugs has revealed that these classes of drugs possess therapeutic potential in the healing of DFUs. However, because the order of events in defective healing is adequately defined, it is possible to recognize moieties that are currently in the market that are recognized to aim at one or several identified molecular processes. OBJECTIVE: The aim of this study was to explore some molecules with different therapeutic categories that have demonstrated favorable effects in improving diabetic wound healing, also called the repurposing of drugs. METHOD: Various databases like PubMed/Medline, Google Scholar and Web of Science (WoS) of all English language articles were searched, and relevant information was collected regarding the role of beta-adrenergic blockers in diabetic wounds or diabetic foot ulcers (DFUs) using the relevant keywords for the literature review. RESULT: The potential beta-blocking agents and their mechanism of action in diabetic foot ulcers were studied, and it was found that these drugs have a profound effect on diabetic foot ulcer healing as per reported literatures. CONCLUSION: There is a need to move forward from preclinical studies to clinical studies to analyze clinical findings to determine the effectiveness and safety of some beta-antagonists in diabetic foot ulcer treatment.

Publishing your research work: Updated concepts and nuances of few metrics used to assess journal quality.

Authors have a multitude of options for journals for publishing their research. However, their choice is mostly based on academic credits required for promotion, cost of publication, timeliness of process, etc., The purpose of this narrative review is to enlighten the authors about some other journal metrics used to assess journal ranking and quality in international scenario. The main concepts discussed in this paper are the impact factor and cite score. The paper includes an explanation of terms like web of science, journal citation reports, and how they are related to impact factor. This will help the authors to make the right decision about choosing the right journal for publishing their research. Along with the historic concepts we have included the latest updates about changes being made to the journal citation report and impact factor released in 2023 June. Hopefully with the review paper, we will be able to encourage the inclusion of such concepts and curriculum of post-graduation courses considering publishing a paper and choosing a journal are an integral aspect of a researcher's work life.

Investigation of serum proteome alterations in human glioblastoma multiforme.

Glioblastoma multiforme (GBM) or grade IV astrocytoma is the most common and lethal adult malignant brain tumor. The present study was conducted to investigate the alterations in the serum proteome in GBM patients compared to healthy controls. Comparative proteomic analysis was performed employing classical 2DE and 2D-DIGE combined with MALDI TOF/TOF MS and results were further validated through Western blotting and immunoturbidimetric assay. Comparison of the serum proteome of GBM and healthy subjects revealed 55 differentially expressed and statistically significant (p <0.05) protein spots. Among the identified proteins, haptoglobin, plasminogen precursor, apolipoprotein A-1 and M, and transthyretin are very significant due to their functional consequences in glioma tumor growth and migration, and could further be studied as glioma biomarkers and grade-specific protein signatures. Analysis of the lipoprotein pattern indicated elevated serum levels of cholesterol, triacylglycerol, and low-density lipoproteins in GBM patients. Functional pathway analysis was performed using multiple software including ingenuity pathway analysis (IPA), protein analysis through evolutionary relationships (PANTHER), database for annotation, visualization and integrated discovery (DAVID), and GeneSpring to investigate the biological context of the identified proteins, which revealed the association of candidate proteins in a few essential physiological pathways such as intrinsic prothrombin activation pathway, plasminogen activating cascade, coagulation system, glioma invasiveness signaling, and PI3K signaling in B lymphocytes. A subset of the differentially expressed proteins was applied to build statistical sample class prediction models for discrimination of GBM patients and healthy controls employing partial least squares discriminant analysis (PLS-DA) and other machine learning methods such as support vector machine (SVM), Decision Tree and Naïve Bayes, and excellent discrimination between GBM and control groups was accomplished.

Evaluation of transgenic chickpea harboring codon-modified Vip3Aa against gram pod borer (Helicoverpa armigera H.).

The gram pod borer is a major pest of chickpea, accounting for average annual yield losses to the tune of 40-50%. VIP3Aa, a class of insecticidal protein with different receptor binding site in the insect's midgut compared to Bt-crystal protein, offers an alternative protection strategy against Lepidopteran insects. Here, we report evaluation of genetically engineered chickpea lines harboring codon modified Vip3Aa (cmVip3Aa) against the Lepidopteran insect pest, gram pod borer. The synthetic codon modified, cmVip3Aa gene of 2,370 bp was sub-cloned in modified plant expression vector and used for direct transformation of embryonic axis explants of chickpea (cv. DCP 92-3), with transformation efficiency of 4.30%. Presence and transmission of transgene across two generations were confirmed by PCR and Southern blot analyses in the five selected transgenic chickpea lines. Real Time PCR analyses indicated variable levels of cmVip3Aa expression in the transgenic chickpea lines (average Cq values 15.01±0.86 to 19.32±0.10), which were absent in the non-transgenic counterpart. Detached leaf insect bioassay indicate larval mortality (up to 39.75%), reduced larval feeding (up to 82.91%) and reduced larval weight gain (up to 68.23%), compared to control lines. Evaluation of gene offers a platform to identify efficacious insecticidal gene that can be used for insect resistance management in chickpea.

Disassembly of α6ß4-mediated hemidesmosomal adhesions promotes tumorigenesis in PTEN-negative prostate cancer by targeting plectin to focal adhesions.

Loss of α6ß4-dependent hemidesmosomal adhesions has been observed during prostate cancer progression. However, the significance and underlying mechanisms by which aberrant hemidesmosome assembly may modulate tumorigenesis remain elusive. Using an extensive CRISPR/Cas9-mediated genetic engineering approaches in different prostate cancer cell lines combined with in vivo tumorigenesis studies in mice, bone marrow-on-chip assays and bioinformatics, as well as histological analysis of prostate cancer patient cohorts, we demonstrated that simultaneous loss of PTEN and hemidesmosomal adhesions induced several tumorigenic properties including proliferation, migration, resistance to anoikis, apoptosis, and drug treatment in vitro, and increased metastatic capacity in vivo. These effects were plectin-depended and plectin was associated with actin-rich adhesions upon hemidesmosome disruption in PTEN-negative prostate cancer cells leading to activation of EGFR/PI3K/Akt- and FAK/Src-pathways. These results suggest that analysis of PTEN and hemidesmosomal proteins may have diagnostic value helping to stratify prostate cancer patients with high risk for development of aggressive disease and highlight actin-associated plectin as a potential therapeutic target specifically in PTEN/hemidesmosome dual-negative prostate cancer.

Enrichment of bovine milk-derived extracellular vesicles using surface-functionalized cellulose nanofibers.

The isolation of extracellular vesicles (EVs) from milk, a complex mixture of colloidal structures having a comparable size to EVs, is challenging. Although ultracentrifugation (UC) has been widely used for EV isolation, this has significant limitations, including a long processing time at high g-force conditions and large sample volume requirements. We introduced a new approach based on nature nanoentities cellulose nanofibers (CNFs) and short time and low g-force centrifugation to isolate EVs from various milk fractions. The flexible and entangled network of CNFs forms nanoporous, which entraps the EVs. Further, positively charged CNFs interact with anionic EVs through an electrostatic attraction, promoting their isolation with efficiency comparable with UC. The functionality and toxicity of isolated milk EVs were tested in Caco2 cells. Overall, the newly developed approach provides straightforward isolation and biocompatibility and preserves the natural properties of the isolated EVs, enabling further applications.

A novel epidemiological scoring system for the prediction of mortality in COVID-19 patients.

BACKGROUND: Most of the reported risk score models for coronavirus disease 2019 (COVID-19) mortality are based on the levels of inflammatory markers, comorbidities or various treatment modalities, and there is a paucity of risk score models based on clinical symptoms and comorbidities. METHODS: To address this need, age, clinical symptoms and comorbidities were used to develop a COVID-19 scoring system (CSS) for early prediction of mortality in severe COVID-19 patients. The CSS was developed with scores ranging from 0 to 9. A higher score indicates higher risk with good discrimination quality presented by Mann Whitney U test and area under receiver operating characteristic curve (AUROC). RESULTS: Patient age of ≥60 y, cough, breathlessness, diabetes and any other comorbidity (with or without diabetes) are significant and independent risk factors for non-survival among COVID-19 patients. The CSS showed good sensitivity and specificity (i.e. 74.1% and 78.5% at CSS≥5, respectively), with an overall diagnostic accuracy of 82.8%, which was close to the diagnostic accuracy detected in the validation cohort (81.9%). In the validation cohort, high (8-9), medium (5-7) and low (0-4) CSS groups had 54.80%, 28.60% and 6.5% observed mortality, respectively, which was very close to the predicted mortality (62.40%, 27.60% and 5.2%, respectively, by scoring cohort). CONCLUSIONS: The CSS shows a positive relationship between a higher score and proportion of mortality and, as its validation showed, it is useful for the prediction of risk of mortality in COVID-19 patients at an early stage, so that referral for triage and admission can be predetermined even before admission to hospital.

High failure rate of ChAdOx1-nCoV19 immunization against asymptomatic infection in healthcare workers during a Delta variant surge.

Immunization is expected to confer protection against infection and severe disease for vaccines while reducing risks to unimmunized populations by inhibiting transmission. Here, based on serial serological studies of an observational cohort of healthcare workers, we show that during a Severe Acute Respiratory Syndrome -Coronavirus 2 Delta-variant outbreak in Delhi, 25.3% (95% Confidence Interval 16.9-35.2) of previously uninfected, ChAdOx1-nCoV19 double vaccinated, healthcare workers were infected within less than two months, based on serology. Induction of anti-spike response was similar between groups with breakthrough infection (541 U/ml, Inter Quartile Range 374) and without (342 U/ml, Inter Quartile Range 497), as was the induction of neutralization activity to wildtype. This was not vaccine failure since vaccine effectiveness estimate based on infection rates in an unvaccinated cohort were about 70% and most infections were asymptomatic. We find that while ChAdOx1-nCoV19 vaccination remains effective in preventing severe infections, it is unlikely to be completely able to block transmission and provide herd immunity.

A machine learning-based approach to determine infection status in recipients of BBV152 (Covaxin) whole-virion inactivated SARS-CoV-2 vaccine for serological surveys.

Data science has been an invaluable part of the COVID-19 pandemic response with multiple applications, ranging from tracking viral evolution to understanding the vaccine effectiveness. Asymptomatic breakthrough infections have been a major problem in assessing vaccine effectiveness in populations globally. Serological discrimination of vaccine response from infection has so far been limited to Spike protein vaccines since whole virion vaccines generate antibodies against all the viral proteins. Here, we show how a statistical and machine learning (ML) based approach can be used to discriminate between SARS-CoV-2 infection and immune response to an inactivated whole virion vaccine (BBV152, Covaxin). For this, we assessed serial data on antibodies against Spike and Nucleocapsid antigens, along with age, sex, number of doses taken, and days since last dose, for 1823 Covaxin recipients. An ensemble ML model, incorporating a consensus clustering approach alongside the support vector machine model, was built on 1063 samples where reliable qualifying data existed, and then applied to the entire dataset. Of 1448 self-reported negative subjects, our ensemble ML model classified 724 to be infected. For method validation, we determined the relative ability of a random subset of samples to neutralize Delta versus wild-type strain using a surrogate neutralization assay. We worked on the premise that antibodies generated by a whole virion vaccine would neutralize wild type more efficiently than delta strain. In 100 of 156 samples, where ML prediction differed from self-reported uninfected status, neutralization against Delta strain was more effective, indicating infection. We found 71.8% subjects predicted to be infected during the surge, which is concordant with the percentage of sequences classified as Delta (75.6%-80.2%) over the same period. Our approach will help in real-world vaccine effectiveness assessments where whole virion vaccines are commonly used.