In vitro antioxidant and anticancer potential of intra-cellular ethyl acetate extract of marine-derived fungus Talaromyces tratensis SS10

Marine fungi are well-known for producing structurally distinct secondary metabolites, making them potential sources of novel therapies. The present investigation aims to study the in vitro antioxidant and anticancer potential of intra-cellular crude ethyl acetate extracts of Talaromyces tratensis SS10. In the present study, qualitative and quantitative phytochemical studies of various solvent extracts of T. tratensis have been carried out using standard protocols. Later, ethyl acetate extract of T. tratensis was analyzed for phytochemicals using Gas Chromatography Mass Spectrometry (GC-MS). Further, the antioxidant properties of the T. tratensis ethyl acetate extract have been done by Ferric reducing antioxidant power assay (FRAP). Further, the anticancer potential of this extract has been carried out by MTT assay against human cancer cells such as MDA MB 231, HeLa, and HT-29. Ethyl acetate exhibited a higher yield of chemical extraction than the other solvents used. The GCMS analysis of T. tratensis ethyl acetate extract revealed major phytoconstituents such as N-(1,1-Dimethylpropyl)-2,2,3-trimethylaziridine-1-carboxamide, 1-Undecanol, 5,5 Dimethyl-3-vinyl cyclohex-2-en-1-one, 1,2-Benzenedicarboxylic acid, bis (2-methyl propyl) ester. T. tratensis ethyl acetate extract showed the highest percentage of Fe3+ reduction (48.093±1.469%) at 120 μg/mL, with an IC50 value of 157.26 μg/mL concentration. Furthermore, 100 μg/mL of the extract showed significant cell death rates in cytotoxic assays, indicating a low percentage of viable cells for all three examined cell lines. The T. tratensis ethyl acetate extract has shown a dose-dependent cytotoxic effect against all tested cancer cell lines. The better IC50 value (6.25 μg/mL) was recorded in the case of HeLa cell lines followed by 12.5 μg/mL for both MDA MB 231 and HT-29 cell lines. The presence of bioactive compounds such as Benzeneethanamine, N-[(pentafluorophenyl)methylene]-beta.,3,4-tris[(trimethylsilyl) oxy]-, 1,2-Benzenedicarboxylic acid, bis(2-methylpropyl) ester, and cyclononasiloxane, octadecamethyl- may have contributed to the ethyl acetate extracts’ strong antioxidant and anticancer properties. The current study’s findings show that T. tratensis SS10 has the potential for drug development due to its chemical constituents, which possess various biological activities

T cell memory to evolutionarily conserved and shared hemagglutinin epitopes of H1N1 viruses: a pilot scale study

Abstract Background The 2009 pandemic influenza was milder than expected. Based on the apparent lack of pre-existing cross-protective antibodies to the A (H1N1)pdm09 strain, it was hypothesized that pre-existing CD4+ T cellular immunity provided the crucial immunity that led to an attenuation of disease severity. We carried out a pilot scale study by conducting in silico and in vitro T cellular assays in healthy population, to evaluate the pre-existing immunity to A (H1N1)pdm09 strain. Methods Large-scale epitope prediction analysis was done by examining the NCBI available (H1N1) HA proteins. NetMHCIIpan, an eptiope prediction tool was used to identify the putative and shared CD4+ T cell epitopes between seasonal H1N1 and A (H1N1)pdm09 strains. To identify the immunogenicity of these putative epitopes, human IFN-γ-ELISPOT assays were conducted using the peripheral blood mononuclear cells from fourteen healthy human donors. All donors were screened for the HLA-DRB1 alleles. Results Epitope-specific CD4+ T cellular memory responses (IFN-γ) were generated to highly conserved HA epitopes from majority of the donors (93%). Higher magnitude of the CD4+ T cell responses was observed in the older adults. The study identified two HA2 immunodominant CD4+ T cell epitopes, of which one was found to be novel. Conclusions The current study provides a compelling evidence of HA epitope specific CD4+ T cellular memory towards A (H1N1)pdm09 strain. These well-characterized epitopes could recruit alternative immunological pathways to overcome the challenge of annual seasonal flu vaccine escape

Diagnostic Coding Intensity among a Pneumonia Inpatient Cohort Using a Risk-Adjustment Model and Claims Data: A U.S. Population-Based Study

Hospital payments depend on the Medicare Severity Diagnosis-Related Group’s estimated cost and the set of diagnoses identified during inpatient stays. However, over-coding and under-coding diagnoses can occur for different reasons, leading to financial and clinical consequences. We provide a novel approach to measure diagnostic coding intensity, built on commonly available administrative claims data, and demonstrated through a 2019 pneumonia acute inpatient cohort (N = 182,666). A Poisson additive model (PAM) is proposed to model risk-adjusted additional coded diagnoses. Excess coding intensity per patient visit was estimated as the difference between the observed and PAM-based expected counts of secondary diagnoses upon risk adjustment by patient-level characteristics. Incidence rate ratios were extracted for patient-level characteristics and further adjustments were explored by facility-level characteristics to account for facility and geographical differences. Facility-level factors contribute substantially to explain the remaining variability in excess diagnostic coding, even upon adjusting for patient-level risk factors. This approach can provide hospitals and stakeholders with a tool to identify outlying facilities that may experience substantial differences in processes and procedures compared to peers or general industry standards. The approach does not rely on the availability of clinical information or disease-specific markers, is generalizable to other patient cohorts, and can be expanded to use other sources of information, when available

Preexisting CD4+ T-Cell Immunity in Human Population to Avian Influenza H7N9 Virus: Whole Proteome-Wide Immunoinformatics Analyses

<div><p>In 2013, a novel avian influenza H7N9 virus was identified in human in China. The antigenically distinct H7N9 surface glycoproteins raised concerns about lack of cross-protective neutralizing antibodies. Epitope-specific preexisting T-cell immunity was one of the protective mechanisms in pandemic 2009 H1N1 even in the absence of cross-protective antibodies. Hence, the assessment of preexisting CD4+ T-cell immunity to conserved epitopes shared between H7N9 and human influenza A viruses (IAV) is critical. A comparative whole proteome-wide immunoinformatics analysis was performed to predict the CD4+ T-cell epitopes that are commonly conserved within the proteome of H7N9 in reference to IAV subtypes (H1N1, H2N2, and H3N2). The CD4+ T-cell epitopes that are commonly conserved (∼556) were further screened against the Immune Epitope Database (IEDB) to validate their immunogenic potential. This analysis revealed that 45.5% (253 of 556) epitopes are experimentally proven to induce CD4+ T-cell memory responses. In addition, we also found that 23.3% of CD4+ T-cell epitopes have ≥90% of sequence homology with experimentally defined CD8+ T-cell epitopes. We also conducted the population coverage analysis across different ethnicities using commonly conserved CD4+ T-cell epitopes and corresponding HLA-DRB1 alleles. Interestingly, the indigenous populations from Canada, United States, Mexico and Australia exhibited low coverage (28.65% to 45.62%) when compared with other ethnicities (57.77% to 94.84%). In summary, the present analysis demonstrate an evidence on the likely presence of preexisting T-cell immunity in human population and also shed light to understand the potential risk of H7N9 virus among indigenous populations, given their high susceptibility during previous pandemic influenza events. This information is crucial for public health policy, in targeting priority groups for immunization programs.</p></div

The role of cellular immunity in Influenza H1N1 population dynamics

Abstract Background Pre-existing cellular immunity has been recognized as one of the key factors in determining the outcome of influenza infection by reducing the likelihood of clinical disease and mitigates illness. Whether, and to what extent, the effect of this self-protective mechanism can be captured in the population dynamics of an influenza epidemic has not been addressed. Methods We applied previous findings regarding T-cell cross-reactivity between the 2009 pandemic H1N1 strain and seasonal H1N1 strains to investigate the possible changes in the magnitude and peak time of the epidemic. Continuous Monte-Carlo Markov Chain (MCMC) model was employed to simulate the role of pre-existing immunity on the dynamical behavior of epidemic peak. Results From the MCMC model simulations, we observed that, as the size of subpopulation with partially effective pre-existing immunity increases, the mean magnitude of the epidemic peak decreases, while the mean time to reach the peak increases. However, the corresponding ranges of these variations are relatively small. Conclusions Our study concludes that the effective role of pre-existing immunity in alleviating disease outcomes (e.g., hospitalization) of novel influenza virus remains largely undetectable in population dynamics of an epidemic. The model outcome suggests that rapid clinical investigations on T-cell assays remain crucial for determining the protection level conferred by pre-existing cellular responses in the face of an emerging influenza virus

Comparison of amino acid sequence identity of 11 protein segments of newly emerged avian influenza (H7N9) viruses in China with H1N1, H2N2, and H3N2 virus subtypes.

<p>PB2: RNA polymerase subunit B2; PB1: RNA polymerase subunit B1; PA: RNA polymerase subunit A; HA: hemagglutinin; NP: nucleoprotein; NA: neuraminidase; M1 and M2: matrix proteins; NS1 and NS2: nonstructural protein 1 and 2. ^#: partial genes available.</p

Information CD4+ T-cell epitopes that are commonly conserved between H7N9 viruses and human influenza A virus (IAV) subtypes (H1N1, H2N2, H3N2).

<p>Italicized numbers refer to conserved regions between H7N9 and each of 2009 H1N1 and H3N2.</p

HLA-DRB1 alleles restriction of predicted commonly conserved CD4+ T-cell epitopes.

<p>Shown in each panel from A to I are commonly conserved CD4+ T-cell epitopes of nine influenza A virus proteins. Shown on the X-axis are fourteen HLA-DRB1 alleles. On Y-axis are the numbers of predicted CD4+ T-cell epitopes. Each solid circle denotes the HLA-DRB1 allele restriction and promiscuity of identified epitopes.</p

Population coverage analysis of identified commonly conserved CD4+ T-cell epitopes - 3A) all ethnicities groups, and 3B) only indigenous groups.

<p>The identified commonly conserved CD4+ T-cell epitopes provide broad population coverage. Based on the binding data for each HLA class II-restricted DRB1 alleles, theoretical population coverage was calculated. The number of possible epitope-HLA allele combinations as a function of the fraction of each ethnic population (%) is shown.</p

Interactive precision medicine revolution: unleashing a deep learning framework for drug response and recommendation

Liver cancer ranks as the third most common cause of cancer-related death rate, resulting in an estimated 830,180 deaths globally. Recent, 2023 estimation of the American Cancer Society predicted around 29,380 deaths due to this disease in America alone. However, with early detection and proper drug management the survival rate can be greatly extended. Significantly, the noteworthy observation pertains to the absence of consistent drug responses among patients with identical cancer types at the same stage. Hence the need of the hour is the precision medicine that predict patient specific drug response based on molecular data genome expression. In this regard, we propose drug response system that uses deep learning framework that predicts drug response in new cell lines or patient with the aim of accurate prediction and suggesting précised medicine to that particular cancer patient. The prediction of the framework is on the lines of recommended system’s projection of drugs and cell lines into latent pharmacogenomic space. During our experimentation, we utilized the Genomics of Drug Sensitivity in Cancer dataset and observed that Cancer Drug Response and Patient Specific Recommender System (CDRP-PRS) consistently delivers robust predictions and effective response, even when applied to previously unseen patient-derived cell line datasets. CDRP-PRS inferences in the pharmacogenomic space are helpful to understand the drug mechanism to identify the cellular subtypes and their categorized associations. Further it is aimed is to predict the accuracy of drug responses for patients and suggest precise medications tailored to their specific cancer