Prediction of post-treatment recurrence in early-stage breast cancer using deep-learning with mid-infrared chemical histopathological imaging.

Predicting long-term recurrence of disease in breast cancer (BC) patients remains a significant challenge for patients with early stage disease who are at low to intermediate risk of relapse as determined using current clinical tools. Prognostic assays which utilize bulk transcriptomics ignore the spatial context of the cellular material and are, therefore, of limited value in the development of mechanistic models. In this study, Fourier-transform infrared (FTIR) chemical images of BC tissue were used to train deep learning models to predict future disease recurrence.

From policy to progress: Environmental taxation to mitigate air pollution in OECD countries.

Environmental taxes play a critical role in mitigating air pollution and fostering sustainability by internalizing the social costs of environmental damage. By imposing financial disincentives on polluters, these taxes encourage cleaner practices and technological innovation. Using panel ARDL models, this study examines the impact of environmental taxes on CO₂ emissions across 38 OECD countries, accounting for cross-sectional dependence, non-stationarity, and heterogeneity.

Residual antibiotics in milk samples: Assessing the risk and prevalence in Bangladesh.

The study aimed to analyze five commonly used veterinary antibiotics: tetracycline (TC), oxytetracycline (OTC), doxycycline (DOX), chlortetracycline (CTC), and enrofloxacin (ENR) in different types of milk samples, risk estimation, and to investigate the correlation between the presence of multiple antibiotic residues. About 27 milk samples, such as raw milk from collection centers, processed milk from processing plants, pasteurized, UHT, and flavored milk from retail stores, were examined using RP-HPLC against five veterinary antibiotics in Dhaka, Bangladesh. The correlation between antibiotics was analyzed using Pearson's correlation test.

In silico analysis of -derived peptides and plasmid construction for recombinant anti-aging therapies.

Skin aging is one of the degenerative processes influenced by tyrosinase, elastase, collagenase, hyaluronidase, and matrix metalloproteinase-9 (MMP9) activity. One promising avenue for discovering antiaging therapeutics is the peptides from the spine. The aim of this study was to explore the potential of peptides from spine as a multitarget inhibitor for recombinant antiaging therapies through in silico approaches.