Novel perfluorocarbon-based oxygenation therapy alleviates Post-SAH hypoxic brain injury by inhibiting HIF-1α.

In comparison to other stroke types, subarachnoid hemorrhage (SAH) is characterized by an early age of onset and often results in poor prognosis. The inadequate blood flow at the site of the lesion leads to localized oxygen deprivation, increased level of hypoxia-inducible factor-1α (HIF-1α), and triggers inflammatory responses and oxidative stress, ultimately causing hypoxic brain damage. Despite the potential benefits of oxygen (O2) administration, there is currently a lack of efficient focal site O2 delivery following SAH. Conventional clinical O2 supply methods, such as transnasal oxygenation and hyperbaric oxygen therapy, do not show the ideal therapeutic effect in severe SAH patients. The perfluorocarbon oxygen carrier (PFOC) demonstrates efficacy in transporting O2 and responding to elevated levels of CO2 at the lesion site. Through cellular experiments, we determined that PFOC oxygenation serves as an effective therapeutic approach in inhibiting hypoxia. Furthermore, our animal experiments showed that PFOC oxygenation outperforms O2 breathing, leading to microglia phenotypic switching and the suppression of inflammatory response via the inhibition of HIF-1α. Therefore, as a new type of O2 therapy after SAH, PFOC oxygenation can effectively reduce hypoxic brain injury and improve neurological function.

Multifunctional hydrogels based on photothermal therapy: A prospective platform for the postoperative management of melanoma.

Preventing the recurrence of melanoma after surgery and accelerating wound healing are among the most challenging aspects of melanoma management. Photothermal therapy has been widely used to treat tumors and bacterial infections and promote wound healing. Owing to its efficacy and specificity, it may be used for postoperative management of tumors. However, its use is limited by the uncontrollable distribution of photosensitizers and the likelihood of damage to the surrounding normal tissue. Hydrogels provide a moist environment with strong biocompatibility and adhesion for wound healing owing to their highly hydrophilic three-dimensional network structure. In addition, these materials serve as excellent drug carriers for tumor treatment and wound healing. It is possible to combine the advantages of both of these agents through different loading modalities to provide a powerful platform for the prevention of tumor recurrence and wound healing. This review summarizes the design strategies, research progress and mechanism of action of hydrogels used in photothermal therapy and discusses their role in preventing tumor recurrence and accelerating wound healing. These findings provide valuable insights into the postoperative management of melanoma and may guide the development of promising multifunctional hydrogels for photothermal therapy.

Deep learning model for the prediction of all-cause mortality among long term care people in China: a prospective cohort study.

This study aimed to develop a deep learning model to predict the risk stratification of all-cause death for older people with disability, providing guidance for long-term care plans. Based on the government-led long-term care insurance program in a pilot city of China from 2017 and followed up to 2021, the study included 42,353 disabled adults aged over 65, with 25,071 assigned to the training set and 17,282 to the validation set. The administrative data (including baseline characteristics, underlying medical conditions, and all-cause mortality) were collected to develop a deep learning model by least absolute shrinkage and selection operator. After a median follow-up time of 14 months, 17,565 (41.5%) deaths were recorded. Thirty predictors were identified and included in the final models for disability-related deaths. Physical disability (mobility, incontinence, feeding), adverse events (pressure ulcers and falls from bed), and cancer were related to poor prognosis. A total of 10,127, 25,140 and 7086 individuals were classified into low-, medium-, and high-risk groups, with actual risk probabilities of death of 9.5%, 45.8%, and 85.5%, respectively. This deep learning model could facilitate the prevention of risk factors and provide guidance for long-term care model planning based on risk stratification.