Functional polarization of tumour-associated macrophages by tumour-derived lactic acid.

Macrophages have an important role in the maintenance of tissue homeostasis. To perform this function, macrophages must have the capacity to monitor the functional states of their 'client cells': namely, the parenchymal cells in the various tissues in which macrophages reside. Tumours exhibit many features of abnormally developed organs, including tissue architecture and cellular composition. Similarly to macrophages in normal tissues and organs, macrophages in tumours (tumour-associated macrophages) perform some key homeostatic functions that allow tumour maintenance and growth. However, the signals involved in communication between tumours and macrophages are poorly defined. Here we show that lactic acid produced by tumour cells, as a by-product of aerobic or anaerobic glycolysis, has a critical function in signalling, through inducing the expression of vascular endothelial growth factor and the M2-like polarization of tumour-associated macrophages. Furthermore, we demonstrate that this effect of lactic acid is mediated by hypoxia-inducible factor 1α (HIF1α). Finally, we show that the lactate-induced expression of arginase 1 by macrophages has an important role in tumour growth. Collectively, these findings identify a mechanism of communication between macrophages and their client cells, including tumour cells. This communication most probably evolved to promote homeostasis in normal tissues but can also be engaged in tumours to promote their growth.

Wireless wearable sensors can facilitate rapid detection of sleep apnea in hospitalized stroke patients.

STUDY OBJECTIVES: To evaluate wearable devices and machine learning for detecting sleep apnea in patients with stroke at an acute inpatient rehabilitation facility (IRF). METHODS: A total of 76 individuals with stroke wore a standard home sleep apnea test (ApneaLink Air), a multimodal, wireless wearable sensor system (ANNE), and a research-grade actigraphy device (ActiWatch) for at least one night during their first week after IRF admission as part of a larger clinical trial. Logistic regression algorithms were trained to detect sleep apnea using biometric features obtained from the ANNE sensors and ground truth apnea rating from the ApneaLink Air. Multiple algorithms were evaluated using different sensor combinations and different apnea detection criteria based on the Apnea-Hypopnea Index (AHI≥5, AHI≥15). RESULTS: Seventy-one (96%) participants wore the ANNE sensors for multiple nights. In contrast, only forty-eight participants (63%) could be successfully assessed for OSA by ApneaLink; 28 (37%) refused testing. The best-performing model utilized photoplethysmography (PPG) and finger temperature features to detect moderate-severe sleep apnea (AHI≥15), with 88% sensitivity and a positive likelihood ratio (LR+) of 44.00. This model was tested on additional nights of ANNE data achieving 71% sensitivity (10.14 LR+) when considering each night independently and 86% accuracy when averaging multi-night predictions. CONCLUSIONS: This research demonstrates the feasibility of accurately detecting moderate-severe sleep apnea early in the stroke recovery process using wearable sensors and machine learning techniques. These findings can inform future efforts to improve early detection for post-stroke sleep disorders, thereby enhancing patient recovery and long-term outcomes.