Machine Learning Predictions of Recovery in Bilingual Poststroke Aphasia: Aligning Insights With Clinical Evidence.

Background: Predicting treated language improvement (TLI) and transfer to the untreated language (cross-language generalization, CLG) after speech-language therapy in bilingual individuals with poststroke aphasia is crucial for personalized treatment planning. This study evaluated machine learning models to predict TLI and CLG and identified the key predictive features (eg, patient severity, demographics, and treatment variables) aligning with clinical evidence.

Methods: Forty-eight Spanish-English bilingual individuals with poststroke aphasia received 20 sessions of semantic feature-based naming treatment in either their first or second language.

Olaparib and Radiotherapy Induce Type I Interferon- and CD8+ T Cell-Dependent Sensitization to Immunotherapy in Pancreatic Cancer.

PARP inhibitors sensitize pancreatic ductal adenocarcinoma (PDAC) to radiation by inducing DNA damage and replication stress. These mechanisms also have the potential to enhance radiation-induced type I interferon (T1IFN)-mediated antitumoral immune responses. We hypothesized that the PARP inhibitor olaparib would also potentiate radiation-induced T1IFN to promote antitumor immune responses and sensitization of otherwise resistant PDAC to immunotherapy.

Structures of TASK-1 and TASK-3 K2P channels provide insight into their gating and dysfunction in disease.

TASK-1 and TASK-3 are pH-sensitive two-pore domain (K2P/KCNK) K channels. Their functional roles make them promising targets for treatment of multiple disorders including sleep apnea, pain, and atrial fibrillation. Mutations in these channels are also associated with neurodevelopmental and hypertensive disorders.

Quantifying the Impact of Hair and Skin Characteristics on Signal Quality with Practical Recommendations for Improvement.

Functional Near-Infrared Spectroscopy (fNIRS) holds transformative potential for research and clinical applications in neuroscience due to its non-invasive nature and adaptability to real-world settings. However, despite its promise, fNIRS signal quality is sensitive to individual differences in biophysical factors such as hair and skin characteristics, which can significantly impact the absorption and scattering of near-infrared light. If not properly addressed, these factors risk biasing fNIRS research by disproportionately affecting signal quality across diverse populations.