Advancing drug-response prediction using multi-modal and -omics machine learning integration (MOMLIN): a case study on breast cancer clinical data.

The inherent heterogeneity of cancer contributes to highly variable responses to any anticancer treatments. This underscores the need to first identify precise biomarkers through complex multi-omics datasets that are now available. Although much research has focused on this aspect, identifying biomarkers associated with distinct drug responders still remains a major challenge. Here, we develop MOMLIN, a multi-modal and -omics machine learning integration framework, to enhance drug-response prediction. MOMLIN jointly utilizes sparse correlation algorithms and class-specific feature selection algorithms, which identifies multi-modal and -omics-associated interpretable components. MOMLIN was applied to 147 patients' breast cancer datasets (clinical, mutation, gene expression, tumor microenvironment cells and molecular pathways) to analyze drug-response class predictions for non-responders and variable responders. Notably, MOMLIN achieves an average AUC of 0.989, which is at least 10% greater when compared with current state-of-the-art (data integration analysis for biomarker discovery using latent components, multi-omics factor analysis, sparse canonical correlation analysis). Moreover, MOMLIN not only detects known individual biomarkers such as genes at mutation/expression level, most importantly, it correlates multi-modal and -omics network biomarkers for each response class. For example, an interaction between ER-negative-HMCN1-COL5A1 mutations-FBXO2-CSF3R expression-CD8 emerge as a multimodal biomarker for responders, potentially affecting antimicrobial peptides and FLT3 signaling pathways. In contrast, for resistance cases, a distinct combination of lymph node-TP53 mutation-PON3-ENSG00000261116 lncRNA expression-HLA-E-T-cell exclusions emerged as multimodal biomarkers, possibly impacting neurotransmitter release cycle pathway. MOMLIN, therefore, is expected advance precision medicine, such as to detect context-specific multi-omics network biomarkers and better predict drug-response classifications.

Incredible affinity of Kattosh with PPAR-γ receptors attenuates STZ-induced pancreas and kidney lesions evidenced in chemicobiological interactions.

Since ancient times, plants have been used as green bioresources to ensure a healthier life by recovering from different diseases. Kattosh (Lasia spinosa L. Thwaites) is a local plant with various traditional uses, especially for arthritis, constipation and coughs. This research investigated the effect of Kattosh stem extract (LSES) on streptozotocin-induced damage to the pancreas, kidney, and liver using in vitro, in vivo and in silico methods. In vitro phytochemical, antioxidative and anti-inflammatory effects of LSES were accomplished by established methods followed by antidiabetic actions in in vivo randomized controlled intervention in STZ-induced animal models for four weeks. In an in silico study, LSES phytocompounds interacted with antidiabetic receptors of peroxisome proliferator-activated receptor-gamma (PPAR, PDB ID: 3G9E), AMP-activated protein kinase (AMPK, PDB ID: 4CFH) and α-amylase enzyme (PDB ID: 1PPI) to verify the in vivo results. In addition, LSES showed promising in vitro antioxidative and anti-inflammatory effects. In contrast, it showed a decrease in weekly blood glucose level, normalized lipid profile, ameliorated liver and cardiac markers, managed serum AST and ALT levels, and increased glucose tolerance ability in the animal model study. Restoration of pancreatic and kidney damage was reflected by improving histopathological images. In ligand-receptor interaction, ethyl α-d-glucopyranoside of Kattosh showed the highest affinity for the α-amylase enzyme, PPAR, and AMPK receptors. Results demonstrate that the affinity of Kattosh phytocompounds potentially attenuates pancreatic and kidney lesions and could be approached as an alternative antidiabetic source with further clarification.

Systemic Delivery of Divalent Europium from Ligand Screening with Implications to Direct Imaging of Hypoxia.

Hypoxia is a hallmark of many diseases, including cancer, arthritis, heart and kidney diseases, and diabetes, and it is often associated with disease aggressiveness and poor prognosis. Consequently, there is a critical need for imaging hypoxia in a noninvasive and direct way to diagnose, stage, and monitor the treatment and development of new therapies for these diseases. Eu-containing contrast agents for magnetic resonance imaging have demonstrated potential for in vivo imaging of hypoxia via changes in metal oxidation state from +2 to +3, but rapid oxidation in blood limits EuII-containing complexes to studies compatible with direct injection to sites. Here, we report a new EuII-containing complex that persists in oxygenated environments and is capable of persisting in blood long enough for imaging by magnetic resonance imaging. We describe the screening of a library of ligands that led to the discovery of the complex as well as a pH-dependent mechanism that hinders oxidation to enable usefulness in vivo. These studies of the first divalent lanthanide complex that persists in oxygenated solutions open the door to the use of EuII-based contrast agents for imaging hypoxia in a wide range of diseases.

Block-HPCT: Blockchain Enabled Digital Health Passports and Contact Tracing of Infectious Diseases like COVID-19.

Due to its significant global impact, both domestic and international efforts are underway to cure the infection and stop the COVID-19 virus from spreading further. In resource-limited environments, overwhelmed healthcare institutions and surveillance systems are struggling to cope with this epidemic, necessitating a specific strategic response. In this study, we looked into the COVID-19 situation and to establish trust, accountability, and transparency, we employed blockchain's immutable and tamper-proof properties. We offered a smart contract (SC)-based solution (Block-HPCT) that has been successfully tested to preserve a digital health passport (DHP) for vaccine recipients; also, for contact tracing (CT) we employed proof of location concept, which aids in a swift and credible response directly from the appropriate healthcare authorities. To connect on-chain and off-chain data, trusted and registered oracles were integrated and to provide a double layer of security along with symmetric key encryption; both Interplanetary File System (IPFS) and Hyperledger Fabric were merged as storage center. We also provided a full description of the suggested solution's system design, implementation, experiment results, and evaluation (privacy and cost analysis). As per the findings, the suggested approach performed satisfactorily across all significant assessment criteria, implying that it can lead the way for practical implementations and also can be used for similar types of situations where contact tracing of infectious can be crucial.

Exploring of bacterial blight resistance in landraces and mining of resistant gene(s) using molecular markers and pathogenicity approach.

Bacterial blight, one of the oldest and most severe diseases of rice poses a major threat to global rice production and food security. Thereafter, sustainable management of this disease has given paramount importance globally. In the current study, we explored 792 landraces to evaluate their disease reaction status against three highly virulent strains (BXo69, BXo87 and BXo93) of Xanthomonas oryzae pv. oryzae (Xoo). Subsequently, we intended to identify the possible candidate resistant (R) genes responsible for the resistant reaction using six STS (Sequence Tagged Site) markers correspond to Xa4, xa5, Xa7, xa13, Xa21 and Xa23 genes and finally, we evaluated morphological variability of the potential bacterial blight resistant germplasm using quantitative traits. Based on pathogenicity test, a single germplasm was found as highly resistant while, 33 germplasm were resistant and 40 were moderately resistant. Further molecular study on these 74 germplasm divulged that 41 germplasm carried Xa4 gene, 15 carried xa5 gene, 62 carried Xa7 gene, 33 carried xa13 gene, and 19 carried Xa23 gene. Only a single germplasm found to carry Xa21 gene. Interestingly, we found a wide range of gene combinations ranged from 2 to 4 genes among the germplasm, where 10 germplasm carried 4 genes, 15 germplasm carried 3 genes and 38 germplasm carried 2 genes of various combinations. Notably, G3 genotype (Acc. No. 4216; highly resistant) having Xa4, Xa7, xa13, Xa21 and G43 genotype (Acc.No. 1523; resistant) having Xa4, xa5, xa13 and Xa23 gene combination being the most effective against all the Xoo strains. Nonetheless, UPGMA dendrogram and heatmap analysis based on quantitative traits identified two clusters viz. cluster-III and cluster-VIII with multiple desired traits. The outcome of this study would enrich and diversify the rice gene pool and would be useful for the development of durable bacterial blight resistant varieties. Supplementary Information: The online version contains supplementary material available at 10.1007/s12298-022-01139-x.

Comparative metabolomics and lipidomics study to evaluate the metabolic differences between first- and second-generation mammalian or mechanistic target of rapamycin inhibitors.

Mammalian or mechanistic target of rapamycin (mTOR) drives its fundamental cellular functions through two distinct catalytic subunits, mTORC1 and mTORC2, and is frequently dysregulated in most cancers. To treat cancers, developed mTOR inhibitors have been classified into first and second generations based on their ability to inhibit single (first-generation) and dual (second-generation) mTOR subunits. However, the underlying metabolic differences due to the effects of first- and second-generation mTOR inhibitors have not been clearly evaluated. In this study, rapamycin (sirolimus) and AZD8055 and PP242 were selected as first- and second-generation mTOR inhibitors, respectively, to evaluate the metabolic differences due to these two generations of mTOR inhibitors after a single oral dose using untargeted metabolomics and lipidomics approaches. The metabolic differences at each time point were compared using multivariate analysis. The multivariate and data analyses showed that metabolic disparity was more prominent within 8 h after drug administration and a broad class of metabolites were affected by the administration of both generations of mTOR inhibitors. Among the metabolite classes, changes in the pattern of fatty acids and glycerophospholipids were opposite, specifically at 4 and 8 h between the two generations of mTOR inhibitors. We speculate that the inhibition of the mTORC2 subunit by the second-generation mTOR inhibitor may have resulted in a distinct metabolic pattern between the first- and second-generation inhibitors. Finally, the findings of this study could assist in a more detailed understanding of the key metabolic differences caused by first- and second-generation mTOR inhibitors.

Recent Development of Machine Learning Methods in Microbial Phosphorylation Sites.

A variety of protein post-translational modifications has been identified that control many cellular functions. Phosphorylation studies in mycobacterial organisms have shown critical importance in diverse biological processes, such as intercellular communication and cell division. Recent technical advances in high-precision mass spectrometry have determined a large number of microbial phosphorylated proteins and phosphorylation sites throughout the proteome analysis. Identification of phosphorylated proteins with specific modified residues through experimentation is often labor-intensive, costly and time-consuming. All these limitations could be overcome through the application of machine learning (ML) approaches. However, only a limited number of computational phosphorylation site prediction tools have been developed so far. This work aims to present a complete survey of the existing ML-predictors for microbial phosphorylation. We cover a variety of important aspects for developing a successful predictor, including operating ML algorithms, feature selection methods, window size, and software utility. Initially, we review the currently available phosphorylation site databases of the microbiome, the state-of-the-art ML approaches, working principles, and their performances. Lastly, we discuss the limitations and future directions of the computational ML methods for the prediction of phosphorylation.

A Secure Occupational Therapy Framework for Monitoring Cancer Patients' Quality of Life.

Once diagnosed with cancer, a patient goes through a series of diagnosis and tests, which are referred to as "after cancer treatment". Due to the nature of the treatment and side effects, maintaining quality of life (QoL) in the home environment is a challenging task. Sometimes, a cancer patient's situation changes abruptly as the functionality of certain organs deteriorates, which affects their QoL. One way of knowing the physiological functional status of a cancer patient is to design an occupational therapy. In this paper, we propose a blockchain and off-chain-based framework, which will allow multiple medical and ambient intelligent Internet of Things sensors to capture the QoL information from one's home environment and securely share it with their community of interest. Using our proposed framework, both transactional records and multimedia big data can be shared with an oncologist or palliative care unit for real-time decision support. We have also developed blockchain-based data analytics, which will allow a clinician to visualize the immutable history of the patient's data available from an in-home secure monitoring system for a better understanding of a patient's current or historical states. Finally, we will present our current implementation status, which provides significant encouragement for further development.

Nonlinear programming technique for analyzing flocculent settling data.

The traditional graphical approach for drawing iso-concentration curves to analyze flocculent settling data and design sedimentation basins poses difficulties for computer-based design methods. Thus, researchers have developed empirical approaches to analyze settling data. In this study, the ability of five empirical approaches to fit flocculent settling test data is compared. Particular emphasis is given to compare rule-based SETTLE and rule-based nonlinear programming (NLP) techniques as a viable alternative to the modeling methods of Berthouex and Stevens (1982), San (1989), and Ozer (1994). Published flocculent settling data are used to test the suitability of these empirical approaches. The primary objective, however, is to determine if the results of a NLP optimization technique are more reliable than those of other approaches. For this, mathematical curve fitting is conducted and the modeled concentration data are graphically compared to the observed data. The design results in terms of average solid removal efficiency as a function of detention times are also compared. Finally, the sum of squared errors values from these approaches are compared. The results indicate a strong correlation between observed and NLP modeled concentration data. The SETTLE and NLP approaches tend to be more conservative at lower retention times and less conservative at longer retention times. The SETTLE approach appears to be the most conservative. In terms of sum of squared errors values, NLP appears to be rank number one (i.e., best model) for eight data sets and number two for six data sets among 15 data sets. Therefore, NLP is recommended for analyzing flocculent settling data as a logical extension of other approaches. The NLP approach is further recommended as it is an optimization technique and uses conventional mathematical algorithms that can be solved using widely available software such as EXCEL and LINGO.

Can digital twin efforts shape microorganism-based alternative food?

With the continuous increment in global population growth, compounded by post-pandemic food security challenges due to labor shortages, effects of climate change, political conflicts, limited land for agriculture, and carbon emissions control, addressing food production in a sustainable manner for future generations is critical. Microorganisms are potential alternative food sources that can help close the gap in food production. For the development of more efficient and yield-enhancing products, it is necessary to have a better understanding on the underlying regulatory molecular pathways of microbial growth. Nevertheless, as microbes are regulated at multiomics scales, current research focusing on single omics (genomics, proteomics, or metabolomics) independently is inadequate for optimizing growth and product output. Here, we discuss digital twin (DT) approaches that integrate systems biology and artificial intelligence in analyzing multiomics datasets to yield a microbial replica model for in silico testing before production. DT models can thus provide a holistic understanding of microbial growth, metabolite biosynthesis mechanisms, as well as identifying crucial production bottlenecks. Our argument, therefore, is to support the development of novel DT models that can potentially revolutionize microorganism-based alternative food production efficiency.

Is gender a factor in socioeconomic disparities in undiagnosed, and untreated hypertension in Bangladesh?

Our objectives were to ascertain the following: (1) the prevalence and socioeconomic distribution of hypertension (HTN), undiagnosed for HTN, and untreated cases of HTN-diagnosed individuals; (2) the relationship between SES and the prevalence of HTN, undiagnosed for HTN, and untreated for HTN; and (3) whether sex moderate this association. Data from the 2017-18 Bangladesh Demographic Health Survey were used. 11,776 participants who were 18 years of age or older responded to our analysis. The age-adjusted prevalence of HTN, undiagnosed for HTN, and untreated cases was 25.1%, 57.2%, and 12.3%. Compared to females, males were less likely to have HTN but more likely to have undiagnosed HTN. People in the rich SES groups had a higher odd of (adjusted odds ratio [aoR] 1.25; 95% confidence interval [CI] 1.08-3.45) of having HTN compared to those in the poor SES group. When compared to individuals in the poor SES group, those in the rich SES group had lower odds of undiagnosed (aoR 0.57; 95% CI 0.44-0.74) and untreated (aoR 0.56; 95% CI 0.31-0.98) for HTN. Sex moderated the association between SES and HTN prevalence, which showed that men from rich SES were more likely to suffer from HTN than men from poor SES. According to this study, the government and other pertinent stakeholders should concentrate more on developing suitable policy measures to reduce the risk of HTN, particularly for men in rich socioeconomic groups. They should also concentrate on screening and diagnosing HTN in socioeconomically disadvantaged populations, regardless of sex.

Network-based identification of diagnosis-specific trans-omic biomarkers via integration of multiple omics data.

The integration of multiple omics data promises to reveal new insights into the pathogenic mechanisms of complex human diseases, with the potential to identify avenues for the development of targeted therapies for disease subtypes. However, the extraction of diagnostic/disease-specific biomarkers from multiple omics data with biological pathway knowledge is a challenging issue in precision medicine. In this paper, we present a novel computational method to identify diagnosis-specific trans-omic biomarkers from multiple omics data. In the algorithm, we integrated multi-class sparse canonical correlation analysis (MSCCA) and molecular pathway analysis in order to derive discriminative molecular features that are correlated across different omics layers. We applied our proposed method to analyzing proteome and metabolome data of heart failure (HF), and extracted trans-omic biomarkers for HF subtypes; specifically, ischemic cardiomyopathy (ICM) and dilated cardiomyopathy (DCM). We were able to detect not only individual proteins that were previously reported from single-omics studies but also correlated protein-metabolite pairs characteristic of HF disease subtypes. For example, we identified hexokinase1(HK1)-d-fructose-6-phosphate as a paired trans-omic biomarker for DCM, which could significantly perturb amino-sugar metabolism. Our proposed method is expected to be useful for various applications in precision medicine.

The main duct of von Ebner's glands is a source of Sox10 + taste bud progenitors and susceptible to pathogen infections.

We have recently demonstrated that Sox10 -expressing ( Sox10 + ) cells give rise to mainly type-III neuronal taste bud cells that are responsible for sour and salt taste. The two tissue compartments containing Sox10 + cells in the surrounding of taste buds include the connective tissue core of taste papillae and von Ebner's glands (vEGs) that are connected to the trench of circumvallate and foliate papillae. In this study, we used inducible Cre mouse models to map the cell lineages of connective tissue (including stromal and Schwann cells) and vEGs and performed single cell RNA-sequencing of the epithelium of Sox10-Cre/tdT mouse circumvallate/vEG complex. In vivo lineage mapping showed that the distribution of traced cells in circumvallate taste buds was closely linked with that in the vEGs, but not in the connective tissue. Sox10 , but not the known stem cells marker Lgr5 , expression was enriched in the cell clusters of main ducts of vEGs that contained abundant proliferating cells, while Sox10-Cre/tdT expression was enriched in type-III taste bud cells and excretory ductal cells. Moreover, multiple genes encoding pathogen receptors are enriched in the vEG main ducts. Our data indicate that the main duct of vEGs is a source of Sox10 + taste bud progenitors and susceptible to pathogen infections.

Ultra-high-performance liquid chromatography-quadrupole time-of-flight-mass spectrometry-characterized extract of Aerides odorata Lour alleviates paracetamol-induced hepatotoxicity in animal model evidenced by biochemical, molecular, and computational studies.

BACKGROUND: Many kinds of orchids have significant health benefits although adequate research on their biological functions is yet to be carried out. This study investigated the paracetamol-induced liver damage-protecting effect of epiphytic Aerides odorata methanol extract (AODE). METHODS: The protective effects of AODE were studied by analyzing its effect on liver function parameters, messenger RNA (mRNA) expression, and tissue histopathological architecture. The results were confirmed by ligand-receptor interaction of molecular docking and multitarget interaction of network pharmacological analyses. RESULTS: AODE significantly (p < 0.05) minimized the dose-dependent increase in acid phosphatase, aspartate aminotransferase, alanine aminotransferase, alkaline phosphatase, γ-glutamyl transferase, lactate dehydrogenase, and total bilirubin compared to the reference drug silymarin. Malondialdehyde level decreased, and the antioxidant genes catalase (CAT), superoxide dismutase (SOD), ß-actin, paraoxonase-1 (PON1), and phosphofructokinase-1 (PFK-1) were upregulated in AODE-treated paracetamol-intoxicated rats. A total of 376 compounds comprising phenols and flavonoids were identified using ultra-high-performance liquid chromatography-quadrupole time-of-flight-mass spectrometry (UPLC-qTOF-MS). The online toxicity assessment using SwissADME and admetSAR exhibited drug-like, nontoxic, and potential pharmacological properties. Additionally, in silico analysis showed that isoacteoside, one of the identified compounds, exhibited the best docking score (-11.42) with the liver protein human pituitary adenylate cyclase-1 (Protein Data Bank ID: 3N94). Furthermore, network pharmacology analysis identified the top 10 hub genes, namely AKT1 (protein kinase B), CTNNB1 (catenin beta-1), SRC (proto-oncogene c-Src), TNF (tumor necrosis factor), EGFR (epidermal growth factor receptor), HSP90AA1 (heat shock protein 90α), MAPK3 (mitogen-activated protein kinase 3), STAT3 (signal transducer and activator of transcription 3), CASP3 (caspase protein), and ESR1 (estrogen receptor 1), which are responsible for hepatoprotective activity. CONCLUSION: The findings demonstrate that AODE could be a novel hepatoprotective target in drug-induced liver damage with a further single compound-based animal study.

Biomarker Discovery for Hepatocellular Carcinoma in Patients with Liver Cirrhosis Using Untargeted Metabolomics and Lipidomics Studies.

Hepatocellular carcinoma (HCC), the most prevalent form of liver cancer, is the third leading cause of mortality globally. Patients with HCC have a poor prognosis due to the fact that the emergence of symptoms typically occurs at a late stage of the disease. In addition, conventional biomarkers perform suboptimally when identifying HCC in its early stages, heightening the need for the identification of new and more effective biomarkers. Using metabolomics and lipidomics approaches, this study aims to identify serum biomarkers for identification of HCC in patients with liver cirrhosis (LC). Serum samples from 20 HCC cases and 20 patients with LC were analyzed using ultra-high-performance liquid chromatography-Q Exactive mass spectrometry (UHPLC-Q-Exactive-MS). Metabolites and lipids that are significantly altered between HCC cases and patients with LC were identified. These include organic acids, amino acids, TCA cycle intermediates, fatty acids, bile acids, glycerophospholipids, sphingolipids, and glycerolipids. The most significant variability was observed in the concentrations of bile acids, fatty acids, and glycerophospholipids. In the context of HCC cases, there was a notable increase in the levels of phosphatidylethanolamine and triglycerides, but the levels of fatty acids and phosphatidylcholine exhibited a substantial decrease. In addition, it was observed that all of the identified metabolites exhibited a superior area under the receiver operating characteristic (ROC) curve in comparison to alpha-fetoprotein (AFP). The pathway analysis of these metabolites revealed fatty acid, lipid, and energy metabolism as the most impacted pathways. Putative biomarkers identified in this study will be validated in future studies via targeted quantification.

Nano-crystallite bones of Oreochromis niloticus and Katsuwonus pelamis for the photocatalytic degradation of Congo red dye.

The bones of two fish species, Oreochromis niloticus and Katsuwonus pelamis, were chosen in this research for evaluating their photocatalytic efficacy under solar radiation. The fish bones were isolated and conditioned before analyzing crystallographic parameters. The samples were characterized by using different instrumental techniques such as Fourier Transform Infrared (FTIR), X-ray diffraction (XRD), Energy Dispersive X-ray (EDX), Field Emission Scanning Electronic Microscopy (FESEM), and optical bandgap. From the XRD data, various types of crystallographic information such as crystallite size, microstrain, lattice parameters, dislocation density, degree of crystallinity, crystallinity index, Hydroxylapatite (HAp), the volume fraction of ß-TCP, ß-Tricalcium phosphate (ß-TCP) percentage, and specific surface area were evaluated. Different model equations such as the Sahadat-Scherrer model, Linear Straight-line model, Monshi-Scherrer's method, and Williamson-Hall plot were employed to justify the nano-crystallite size. The photocatalytic efficacy of the two types of samples was explored by changing the catalyst concentration, dye concentration, interaction time, pH of the solution, etc. under solar irradiation.

Correction: Rashid et al. Natural Compounds of Lasia spinosa (L.) Stem Potentiate Antidiabetic Actions by Regulating Diabetes and Diabetes-Related Biochemical and Cellular Indexes. Pharmaceuticals 2022, 15, 1466.

In the published publication [...].

Appearance of tolerance-induction and non-inflammatory SARS-CoV-2 spike-specific IgG4 antibodies after COVID-19 booster vaccinations.

Background: Understanding the characteristics of the humoral immune responses following COVID-19 vaccinations is crucial for refining vaccination strategies and predicting immune responses to emerging SARS-CoV-2 variants. Methods: A longitudinal analysis of SARS-CoV-2 spike receptor binding domain (RBD) specific IgG antibody responses, encompassing IgG subclasses IgG1, IgG2, IgG3, and IgG4 was performed. Participants received four mRNA vaccine doses (group 1; n=10) or two ChAdOx1 nCoV-19 and two mRNA booster doses (group 2; n=19) in Bangladesh over two years. Results: Findings demonstrate robust IgG responses after primary Covishield or mRNA doses; declining to baseline within six months. First mRNA booster restored and surpassed primary IgG responses but waned after six months. Surprisingly, a second mRNA booster did not increase IgG levels further. Comprehensive IgG subclass analysis showed primary Covishield/mRNA vaccination generated predominantly IgG1 responses with limited IgG2/IgG3, Remarkably, IgG4 responses exhibited a distinct pattern. IgG4 remained undetectable initially but increased extensively six months after the second mRNA dose, eventually replacing IgG1 after the 3rd/4th mRNA doses. Conversely, initial Covishield recipients lack IgG4, surged post-second mRNA booster. Notably, mRNA-vaccinated individuals displayed earlier, robust IgG4 levels post first mRNA booster versus Covishield counterparts. IgG1 to IgG4 ratios decreased with increasing doses, most pronounced with four mRNA doses. This study highlights IgG response kinetics, influenced by vaccine type and doses, impacting immunological tolerance and IgG4 induction, shaping future vaccination strategies. Conclusions: This study highlights the dynamics of IgG responses dependent on vaccine type and number of doses, leading to immunological tolerance and IgG4 induction, and shaping future vaccination strategies.

Long-term trends in storm surge climate derived from an ensemble of global surge reconstructions.

We address the challenge, due to sparse observational records, of investigating long-term changes in the storm surge climate globally. We use two centennial and three satellite-era daily storm surge time series from the Global Storm Surge Reconstructions (GSSR) database and assess trends in the magnitude and frequency of extreme storm surge events at 320 tide gauges across the globe from 1930, 1950, and 1980 to present. Before calculating trends, we perform change point analysis to identify and remove data where inhomogeneities in atmospheric reanalysis products could lead to spurious trends in the storm surge data. Even after removing unreliable data, the database still extends existing storm surge records by several decades for most of the tide gauges. Storm surges derived from the centennial 20CR and ERA-20C atmospheric reanalyses show consistently significant positive trends along the southern North Sea and the Kattegat Bay regions during the periods from 1930 and 1950 onwards and negative trends since 1980 period. When comparing all five storm surge reconstructions and observations for the overlapping 1980-2010 period we find overall good agreement, but distinct differences along some coastlines, such as the Bay of Biscay and Australia. We also assess changes in the frequency of extreme surges and find that the number of annual exceedances above the 95th percentile has increased since 1930 and 1950 in several regions such as Western Europe, Kattegat Bay, and the US East Coast.

p53 Modulation of Autophagy Signaling in Cancer Therapies: Perspectives Mechanism and Therapeutic Targets.

The key tumor suppressor protein p53, additionally known as p53, represents an attractive target for the development and management of anti-cancer therapies. p53 has been implicated as a tumor suppressor protein that has multiple aspects of biological function comprising energy metabolism, cell cycle arrest, apoptosis, growth and differentiation, senescence, oxidative stress, angiogenesis, and cancer biology. Autophagy, a cellular self-defense system, is an evolutionarily conserved catabolic process involved in various physiological processes that maintain cellular homeostasis. Numerous studies have found that p53 modulates autophagy, although the relationship between p53 and autophagy is relatively complex and not well understood. Recently, several experimental studies have been reported that p53 can act both an inhibitor and an activator of autophagy which depend on its cellular localization as well as its mode of action. Emerging evidences have been suggested that the dual role of p53 which suppresses and stimulates autophagy in various cencer cells. It has been found that p53 suppression and activation are important to modulate autophagy for tumor promotion and cancer treatment. On the other hand, activation of autophagy by p53 has been recommended as a protective function of p53. Therefore, elucidation of the new functions of p53 and autophagy could contribute to the development of novel therapeutic approaches in cancer biology. However, the underlying molecular mechanisms of p53 and autophagy shows reciprocal functional interaction that is a major importance for cancer treatment and manegement. Additionally, several synthetic drugs and phytochemicals have been targeted to modulate p53 signaling via regulation of autophagy pathway in cancer cells. This review emphasizes the current perspectives and the role of p53 as the main regulator of autophagy-mediated novel therapeutic approaches against cancer treatment and managements.