Nevin Manimala

Proc Biol Sci. 2024 Apr 10;291(2020):20240295. doi: 10.1098/rspb.2024.0295. Epub 2024 Apr 10.

ABSTRACT

Interdependence occurs when individuals have a stake in the success or failure of others, such that the outcomes experienced by one individual also generate costs or benefits for others. Discussion on this topic has typically focused on positive interdependence (where gains for one individual result in gains for another) and on the consequences for cooperation. However, interdependence can also be negative (where gains for one individual result in losses for another), which can spark conflict. In this article, we explain when negative interdependence is likely to arise and, crucially, the role played by (mis)perception in shaping an individual’s understanding of their interdependent relationships. We argue that, owing to the difficulty in accurately perceiving interdependence with others, individuals might often be mistaken about the stake they hold in each other’s outcomes, which can spark needless, resolvable forms of conflict. We then discuss when and how reducing misperceptions can help to resolve such conflicts. We argue that a key mechanism for resolving interdependent conflict, along with better sources of exogenous information, is to reduce reliance on heuristics such as stereotypes when assessing the nature of our interdependent relationships.

PMID:38593846 | DOI:10.1098/rspb.2024.0295

Am J Vet Res. 2024 Apr 13:1-10. doi: 10.2460/ajvr.23.12.0285. Online ahead of print.

ABSTRACT

OBJECTIVE: To investigate associations between hepatic fat accumulation, fibrosis, and plasma values of primary metabolites, biochemical measurands, insulin, and lipoproteins in bearded dragons.

ANIMALS: 48 adult central bearded dragons (Pogona vitticeps).

METHODS: Dragons were sedated with alfaxalone, and a blood sample was collected. Plasma was submitted for untargeted primary metabolomics using gas chromatography time-of-flight mass spectrometry, a biochemistry panel, and a lipoprotein panel determined by PAGE. Hepatic lipid content was quantified by liver attenuation measurements from CT images and digital image analysis of standardized histologic sections of the liver. Fibrosis was quantified by digital image analysis on Masson’s trichrome-stained histologic sections. Severity was determined from pathologic review of liver sections according to a standardized grading system. Statistical associations were investigated using serial linear models adjusted for false discovery rate and multivariate statistics.

RESULTS: Both hepatic fat and fibrosis had a significant effect on CT liver attenuation values. Several oligosaccharides (maltotriose, maltose, ribose, trehalose) and alkaline phosphatase were significantly and linearly increased with hepatic lipid content (all q < .05). On partial least square-discriminant analysis, β-hydroxybutyric acid was the most important discriminatory variable between fatty liver severity grades on histology. No significant associations were found with insulin, lipoproteins, and succinic acid.

CLINICAL RELEVANCE: Bearded dragons with hepatic lipid accumulation experienced multiple metabolic pathway disruptions, some being compatible with mitochondrial dysfunction. No evidence of insulin resistance or dyslipidemia was found. Hepatic biopsy and histopathology remain recommended for reliably diagnosing and staging fatty liver disease in bearded dragons.

PMID:38593838 | DOI:10.2460/ajvr.23.12.0285

Cell Chem Biol. 2024 Apr 5:S2451-9456(24)00091-6. doi: 10.1016/j.chembiol.2024.03.002. Online ahead of print.

ABSTRACT

Identification of new druggable protein targets remains the key challenge in the current antimalarial development efforts. Here we used mass-spectrometry-based cellular thermal shift assay (MS-CETSA) to identify potential targets of several antimalarials and drug candidates. We found that falcilysin (FLN) is a common binding partner for several drug candidates such as MK-4815, MMV000848, and MMV665806 but also interacts with quinoline drugs such as chloroquine and mefloquine. Enzymatic assays showed that these compounds can inhibit FLN proteolytic activity. Their interaction with FLN was explored systematically by isothermal titration calorimetry and X-ray crystallography, revealing a shared hydrophobic pocket in the catalytic chamber of the enzyme. Characterization of transgenic cell lines with lowered FLN expression demonstrated statistically significant increases in susceptibility toward MK-4815, MMV000848, and several quinolines. Importantly, the hydrophobic pocket of FLN appears amenable to inhibition and the structures reported here can guide the development of novel drugs against malaria.

PMID:38593807 | DOI:10.1016/j.chembiol.2024.03.002

Cell Stem Cell. 2024 Apr 2:S1934-5909(24)00090-0. doi: 10.1016/j.stem.2024.03.008. Online ahead of print.

ABSTRACT

Many patient-derived tumor models have emerged recently. However, their potential to guide personalized drug selection remains unclear. Here, we report patient-derived tumor-like cell clusters (PTCs) for non-small cell lung cancer (NSCLC), capable of conducting 100-5,000 drug tests within 10 days. We have established 283 PTC models with an 81% success rate. PTCs contain primary tumor epithelium self-assembled with endogenous stromal and immune cells and show a high degree of similarity to the original tumors in phenotypic and genotypic features. Utilizing standardized culture and drug-response assessment protocols, PTC drug-testing assays reveal 89% overall consistency in prospectively predicting clinical outcomes, with 98.1% accuracy distinguishing complete/partial response from progressive disease. Notably, PTCs enable accurate prediction of clinical outcomes for patients undergoing anti-PD1 therapy by combining cell viability and IFN-γ value assessments. These findings suggest that PTCs could serve as a valuable preclinical model for personalized medicine and basic research in NSCLC.

PMID:38593797 | DOI:10.1016/j.stem.2024.03.008

J Environ Manage. 2024 Apr 8;358:120815. doi: 10.1016/j.jenvman.2024.120815. Online ahead of print.

ABSTRACT

The present research study investigates the performance of pyrolysis oils recycled from waste tires as a collector in coal flotation. Three different types of pyrolysis oils (namely, POT₁, POT₂, and POT₃) were produced through a two-step pressure pyrolysis method followed by an oil rolling process. The characteristics of POTs were adjusted using various oil-modifying additives such as mineral salts and organic solvents. The chemical structure of POTs was explored by employing necessary instrumental analysis techniques, including microwave-assisted acid digestion (MAD), inductively coupled plasma atomic emission spectroscopy (ICP-AES), Fourier-transform infrared spectroscopy (FT-IR), and gas chromatography-mass spectrometry (GC-MS). The collecting performance of POTs in coal flotation was evaluated using an experimental design based on Response Surface Methodology (RSM), considering the ash content and yield of the final concentrate. The effect of the type and dosage of POTs was evaluated in conjunction with other important operating variables, including the dosage of frother, dosage of depressant, and the type of coal. Results of POTs characterization revealed that the pyrolysis oils were a complex composition of light and heavy hydrocarbon molecules, including naphthalene, biphenyl, acenaphthylene, fluorene, and pyrene. Statistical analysis of experimental results showed that among different POTs, POT₁ exhibited remarkable superiority, achieving not only a 15% higher coal recovery but also a 12% lower ash content. The outstanding performance of POT₁ was attributed to its unique composition, which includes a concentrated presence of carbon chains within the optimal range for efficient flotation. Additionally, the FT-IR spectra of POT₁ reveal specific functional groups, including aromatic and aliphatic compounds, greatly enhancing its interaction with coal surfaces, as confirmed by contact angle measurement. This research provides valuable insights into the specific carbon chains and functional groups that contribute to the effectiveness of POT as a collector, facilitating the optimization of coal flotation processes and underscoring the environmental advantages of employing pyrolysis oils as sustainable alternatives in the mining industry.

PMID:38593739 | DOI:10.1016/j.jenvman.2024.120815

J Environ Manage. 2024 Apr 8;358:120810. doi: 10.1016/j.jenvman.2024.120810. Online ahead of print.

ABSTRACT

The rise in oil trade and transportation has led to a continuous increase in the risk of oil spills, posing a serious worldwide concern. However, there is a lack of numerical models for predicting oil spill transport in freshwater, especially under icy conditions. To tackle this challenge, we developed a prediction system for oil with ice modeling by coupling the General NOAA Operational Modeling Environment (GNOME) model with the Great Lakes Operational Forecast System (GLOFS) model. Taking Lake Erie as a pilot study, we used observed drifter data to evaluate the performance of the coupled model. Additionally, we developed six hypothetical oil spill cases in Lake Erie, considering both with and without ice conditions during the freezing, stable, and melting seasons spanning from 2018 to 2022, to investigate the impacts of ice cover on oil spill processes. The results showed the effective performance of the coupled model system in capturing the movements of a deployed drifter. Through ensemble simulations, it was observed that the stable season with high-concentration ice had the most significant impact on limiting oil transport compared to the freezing and melting seasons, resulting in an oil-affected open water area of 49 km² on day 5 with ice cover, while without ice cover it reached 183 km². The stable season with high-concentration ice showed a notable reduction in the probability of oil presence in the risk map, whereas this reduction effect was less prominent during the freezing and melting seasons. Moreover, negative correlations between initial ice concentration and oil-affected open water area were consistent, especially on day 1 with a linear regression R-squared value of 0.94, potentially enabling rapid prediction. Overall, the coupled model system serves as a useful tool for simulating oil spills in the world’s largest freshwater system, particularly under icy conditions, thus enhancing the formulation of effective emergency response strategies.

PMID:38593738 | DOI:10.1016/j.jenvman.2024.120810

Immunobiology. 2024 Apr 7;229(3):152801. doi: 10.1016/j.imbio.2024.152801. Online ahead of print.

ABSTRACT

BACKGROUND: Trauma is statistically a significant cause of mortality among patients across countries. Nevertheless, the precise correlation between genetic diagnostic markers and the intricate mechanism of trauma remains indistinct.

METHODS: Our study exclusively centered on trauma patients and selected three trauma-related datasets from the Gene Expression Omnibus (GEO) database, all of which had blood samples collected within post-traumatic 12 h. Differential gene screening, the WGCNA and Cytoscape software were employed to analyze the two datasets, with a particular emphasis on the top 100 genes selected based on MCC algorithm scores. A logistic diagnostic model was constructed by analyzing the intersection genes in the third dataset, leading to the identification of diagnostic biomarkers with high efficiency. The global immune landscape of these patients was extensively investigated using a multidimensional approach. Meanwhile, the underlying pathological and physiological mechanisms associated with early trauma status are summarized by integrating existing literature.

RESULTS: Out of these two GEO datasets, 21 overlapping genes were identified and incorporated into in the logistic diagnostic model constructed in the GSE36809 dataset. A panel of 9 genes was uncovered as a diagnostic biomarker, and their expression and correlation were subsequently verified. Additionally, by virtue of various algorithms, the findings revealed an upregulation of neutrophil expression and a downregulation of CD8+ T cell expression, indicating characteristic early trauma-induced inflammation activation and immune suppression. The correlation observed between the feature genes and immune cells serves to validate the exceptional diagnostic capability of these 9 genes in identifying trauma status and their promising potential for patients who could benefit from targeted immune interventions. Drawing from these findings, the discussion section offers insights into the underlying pathological and physiological mechanisms at play.

CONCLUSION: Our research has discovered a novel diagnostic biomarker and unveiled its association with post-traumatic immune alterations. This breakthrough enables accurate and timely diagnosis of early trauma, facilitating the implementation of appropriate healthcare interventions.

PMID:38593729 | DOI:10.1016/j.imbio.2024.152801

Epidemics. 2024 Feb 20;47:100745. doi: 10.1016/j.epidem.2024.100745. Online ahead of print.

ABSTRACT

We analyse infectious disease case surveillance data to estimate COVID-19 spread and gain an understanding of the impact of introducing vaccines to counter the disease in Switzerland. The data used in this work is extensive and detailed and includes information on weekly number of cases and vaccination rates by age and region. Our approach takes into account waning immunity. The statistical analysis allows us to determine the effects of choosing alternative vaccination strategies. Our results indicate greater uptake of vaccine would have led to fewer cases with a particularly large effect on undervaccinated regions. An alternative distribution scheme not targeting specific age groups also leads to fewer cases overall but could lead to more cases among the elderly (a potentially vulnerable population) during the early stage of prophylaxis rollout.

PMID:38593727 | DOI:10.1016/j.epidem.2024.100745

Public Health. 2024 Apr 8;231:15-22. doi: 10.1016/j.puhe.2024.03.009. Online ahead of print.

ABSTRACT

OBJECTIVE: This study comprehensively analyzed the temporal and spatial dynamics of COVID-19 cases and deaths within the obstetric population in Brazil, comparing the periods before and during mass COVID-19 vaccination. We explored the trends and geographical patterns of COVID-19 cases and maternal deaths over time. We also examined their correlation with the SARS-CoV-2 variant circulating and the social determinants of health.

STUDY DESIGN: This is a nationwide population-based ecological study.

METHODS: We obtained data on COVID-19 cases, deaths, socioeconomic status, and vulnerability information for Brazil’s 5570 municipalities for both the pre-COVID-19 vaccination and COVID-19 vaccination periods. A Bayesian model was used to mitigate indicator fluctuations. The spatial correlation of maternal cases and fatalities with socioeconomic and vulnerability indicators was assessed using bivariate Moran.

RESULTS: From March 2020 to June 2023, a total of 23,823 cases and 1991 maternal fatalities were recorded among pregnant and postpartum women. The temporal trends in maternal incidence and mortality rates fluctuated over the study period, largely influenced by widespread COVID-19 vaccination and the dominant SARS-CoV-2 variant. There was a significant reduction in maternal mortality due to COVID-19 following the introduction of vaccination. The geographical distribution of COVID-19 cases and maternal deaths exhibited marked heterogeneity in both periods, with distinct spatial clusters predominantly observed in the North, Northeast, and Central West regions. Municipalities with the highest Human Development Index reported the highest incidence rates, while those with the highest levels of social vulnerability exhibited elevated mortality and fatality rates.

CONCLUSION: Despite the circulation of highly transmissible variants of concern, maternal mortality due to COVID-19 was significantly reduced following the mass vaccination. There was a heterogeneous distribution of cases and fatalities in both periods (before and during mass vaccination). Smaller municipalities and those grappling with social vulnerability issues experienced the highest rates of maternal mortality and fatalities.

PMID:38593681 | DOI:10.1016/j.puhe.2024.03.009

J Hazard Mater. 2024 Apr 3;470:134208. doi: 10.1016/j.jhazmat.2024.134208. Online ahead of print.

ABSTRACT

This study introduces an innovative strategy for the rapid and accurate identification of pesticide residues in agricultural products by combining surface-enhanced Raman spectroscopy (SERS) with a state-of-the-art transformer model, termed SERSFormer. Gold-silver core-shell nanoparticles were synthesized and served as high-performance SERS substrates, which possess well-defined structures, uniform dispersion, and a core-shell composition with an average diameter of 21.44 ± 4.02 nm, as characterized by TEM-EDS. SERSFormer employs sophisticated, task-specific data processing techniques and CNN embedders, powered by an architecture features weight-shared multi-head self-attention transformer encoder layers. The SERSFormer model demonstrated exceptional proficiency in qualitative analysis, successfully classifying six categories, including five pesticides (coumaphos, oxamyl, carbophenothion, thiabendazole, and phosmet) and a control group of spinach data, with 98.4% accuracy. For quantitative analysis, the model accurately predicted pesticide concentrations with a mean absolute error of 0.966, a mean squared error of 1.826, and an R² score of 0.849. This novel approach, which combines SERS with machine learning and is supported by robust transformer models, showcases the potential for real-time pesticide detection to improve food safety in the agricultural and food industries.

PMID:38593663 | DOI:10.1016/j.jhazmat.2024.134208