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Increased survival of aquatic life through algal pretreatment of nitrate-contaminated runoff

Venkat et al. | Sep 12, 2026

Increased survival of aquatic life through algal pretreatment of nitrate-contaminated runoff
Image credit: Liz Harrell

Here the authors investigated the effectiveness of algal pretreatment in reducing nitrate toxicity and extending the survival of fish in contaminated river water. Their findings demonstrate that treating nitrate-polluted water with algae extended fish survival by up to 50%, suggesting that algae could serve as a valuable tool for mitigating the destructive impacts of agricultural and industrial runoff on aquatic ecosystems.

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It's better than me: Investigating the psychological risks of AI on self-esteem

Kim et al. | Sep 07, 2026

It's better than me: Investigating the psychological risks of AI on self-esteem

Here the authors investigated whether frequent use of generative AI tools triggers upward social comparison and negatively impacts users' performance self-esteem. Based on a survey of 121 adults, they found no significant relationship between AI usage and self-esteem, concluding that current chatbot interactions do not pose the same psychological threat as peer-based comparisons.

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Integrated expression, mutation, and survival analysis of 17 key genes in breast cancer using TCGA-BRCA data

Wang et al. | Aug 06, 2026

Integrated expression, mutation, and survival analysis of 17 key genes in breast cancer using TCGA-BRCA data

This study combines gene expression, mutation profiling, and survival analysis of 17 clinically important genes in breast cancer, utilizing the TCGA-BRCA dataset. Our results show that there are different patterns of oncogene upregulation, different levels of tumor suppressor activity, and complicated survival associations. TP53 was the most frequently mutated gene in this cohort. The results underscore the significance of multidimensional genomic analyses for a comprehensive understanding of breast cancer biology and its therapeutic ramifications.

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Electrocatalytic oxidation of furfural on Co3O4/nickel foam catalyst: performance and mechanistic study

Song et al. | Jul 19, 2026

Electrocatalytic oxidation of furfural on Co<sub>3</sub>O<sub>4</sub>/nickel foam catalyst: performance and mechanistic study
Image credit: Shraga kopstein

In this study, the authors hypothesized that the unique redox properties of cobalt oxide (Co3O4), combined with the conductive nature of the nickel foam (NF) substrate, synergistically enhances the catalytic performance for furfural oxidation. The study showed successful synthesis of Co3O4 nanoflowers directly grown on NF and tested their capacity to serve as a highly efficient electrocatalyst for furfural oxidation. Beyond furfural oxidation, this study also offers broader implications for sustainable chemistry by establishing design principles for efficient nucleophilic oxidation reaction catalysts, demonstrating an energy-saving alternative to conventional oxygen evolution reaction-coupled processes, and showcasing how biomass conversion can be integrated with renewable energy systems.

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Algorithmic barriers: Investigating student perceptions of AI bias in subjective “culture fit” hiring

Mahatara et al. | May 25, 2026

Algorithmic barriers: Investigating student perceptions of AI bias in subjective “culture fit” hiring
Image credit: JonTyson

This study investigated perceptions of the emerging workforce toward the use of artificial intelligence in hiring, specifically for assessing subjective "culture fit." Through a mixed-methods survey of 150 high school and early-college students in Nepal, we found a significant disconnect between organizational adoption of AI and the profound skepticism of young job candidates, who express deep concerns about fairness, transparency, and the potential for AI to perpetuate systemic discrimination.

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Leveraging transfer learning with convolutional neural networks for cardiovascular disease detection

Chen et al. | May 25, 2026

Leveraging transfer learning with convolutional neural networks for cardiovascular disease detection
Image credit: Stephen Andrews

This study shows the efficacy of leveraging transfer learning, specifically from residual networks, to detect CVDs and possible signs of CVDs. The findings indicate that leveraging transfer learning from residual networks alongside medical professionals is a highly promising approach for CVD detection and diagnosis, warranting further investigation.

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Exploring Interactions between PFAS (Per- and Polyfluoroalkyl Substances) and proteins

Lu-Yang et al. | May 16, 2026

Exploring Interactions between PFAS (Per- and Polyfluoroalkyl Substances) and proteins
Image credit: Authors

Here the authors investigated how the "forever chemical" perfluorooctanoic acid binds to bovine serum albumin (BSA) using computational software to simulate its potential impact on essential human plasma proteins. They identify a possible, high-energy binding configuration that could persistently impair protein functions, underscoring the critical need for further research into the long-term health risks of per- and poly-fluoroalkyl substances exposure.

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Rethinking the electric vehicle tax policy: prioritizing affordable solutions for environmental impact

Miao et al. | Jan 26, 2026

Rethinking the electric vehicle tax policy: prioritizing affordable solutions for environmental impact

Car emissions harm both the environment and human health, and current U.S. EV tax credits mainly benefit high-income households because EVs are expensive. This study evaluates U.S. vehicle emissions policies by analyzing 2022 national vehicle data to compare the fuel economy and greenhouse gas impacts of the current EV tax credit with a proposed policy that incentivizes hybrid vehicle purchases.

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