Acute respiratory distress syndrome (ARDS) is a fatal inflammatory lung disorder with few effective treatments. Hyaluronan (HA), a major extracellular matrix component, exhibits diverse biological activities depending on its molecular weight. This study aimed to evaluate the therapeutic potential of HA of various molecular weights in a rat model of ARDS. ARDS was induced in rats via the intratracheal instillation of 5 mg of bleomycin. Seven days later, when ARDS symptoms developed, low (LHA), medium (MHA), high (HHA), and mixed (MIX HA) hyaluronan were intratracheally administered seven times from Days 7 to 28. On Day 7, arterial oxygen saturation (SpO₂) and the partial pressure of oxygen (PaO₂) decreased, carbon dioxide levels increased, the respiratory rate increased, and extensive lung cell infiltration was observed, confirming successful ARDS induction. LHA and MIX HA improved the SpO₂ and PaO₂, and the latter increased lung and alveolar volume, reduced infiltration, and normalized breathing. All HA types attenuated collagen deposition and M1 macrophage activity, while MIX HA enhanced M2 polarization and upregulated MMP-2, MMP-9, and TLR-4. LHA increased VEGF and EGF expression. These findings demonstrate that different-weight HAs provide partial ARDS protection via distinct mechanisms. MIX HA shows synergistic effects, restoring and improving lung structure and function, respectively, representing a promising ARDS therapy.

The clinical treatments of pelvic cancers, such as cervical cancer, adenocarcinoma, and prostate cancer, include not only surgical resection but also radiotherapy. Radiotherapy uses radiation to target and control the development of the disease. Before radiotherapy, precise marking and delineation are essential for radiation dose planning. The delineated areas include the lesion site and clinically determined potential areas, such as lymph nodes and surrounding organs at risk (OAR), including the bladder, uterus, large intestine, and small intestine. Currently, the clinical labeling of these lesions and OAR areas is done manually by experienced radiation oncologists and medical physicists. The pelvic cavity's anatomical structure is complex, and the lymph node area in the pelvic cavity is recognized as a high-risk area, thus included in the irradiation area. However, lymph nodes on CT images have fewer clear borders than other OAR areas, making it difficult to delineate and increasing variability between different markers. This study employs artificial intelligence algorithms to automatically delineate clinical target volumes (CTV), especially pelvic lymph nodes, and attempts to incorporate anatomical information to facilitate more accurate annotation. This approach aims to reduce the burden of clinical treatment planning and improve precision in radiotherapy.