This study aimed to determine the healing effect of hydrogen-rich water (HRW) on radiotherapy-induced skin injury. Rats were irradiated with a 6 MeV electron beam from a Varian linear accelerator. After skin wound formation, rats were individually administrated with distilled water, HRW (1.0 ppm) or HRW (2.0 ppm). We measured the healing time and observed the healing rate of the wounded surface. After irradiation, the malondialdehyde (MDA) content and the superoxide dismutase (SOD) activity in the wounded tissues were evaluated, as determined using an MDA and SOD assay kit. Interleukin-6 (IL-6) and epidermal growth factor (EGF) levels were assessed by enzyme-linked immunosorbent assay (ELISA). Models of skin damage were successfully established using a 44 Gy electronic beam. The healing time was shortened in the two HRW-treated groups (P < 0.05). Furthermore, interventions of HRW resulted in a marked reduction in the MDA (P < 0.05) and IL-6 levels (P < 0.01). Additionally, the SOD activity in the two HRW-treated groups was higher than that in the distilled water group at the end of the 1st, 2nd and 3rd weeks (P < 0.001). The EGF level was also significantly increased at the end of the 1st and 2nd weeks (P < 0.05). Compared with the HRW (1.0 ppm) group, the healing rate was higher and the healing time was reduced in the HRW (2.0 ppm) group. A significant decrease was observed in the IL-6 level at the end of the 1st, 3rd and 4th weeks (P < 0.05) and in the EGF content at the end of the 1 week after the HRW administration (P < 0.01). Collectively, our data indicate that HRW accelerates wound healing of radiation-induced skin lesions through anti-oxidative and anti-inflammatory effects, suggesting that HRW has a healing effect on acute radiation-mediated skin injury, and that this is dependent on the concentration of the hydrogen.

Recent research has drawn attention to protective effects of chemopreventive agents that reverse, suppress, or prevent the carcinogenic progression using pharmacological or nutritional agents. Aspirin and celecoxib are the promising preventive agents to effectively reduce the risk of colorectal cancer, but such agents are associated with severe gastrointestinal and cardiovascular side effects in long-term administration at high doses. Recently, the strategy that combinational use with several chemopreventive agents at low doses induces greater inhibition of carcinogenesis has become the focus. The nonsteroidal anti-inflammatory drugs (NSAIDs) may combine with ornithine decarboxylase inhibitors, hydroxymethylglutaryl-CoA reductase inhibitors, epidermal growth factor signaling inhibitors, peroxisome proliferator-activated receptor-γ ligands, and tumor necrosis factor-related apoptosis-inducing ligand, to magnify the chemoprophylactic effect. It is noteworthy that the phase III trial of difluoromethylornithine combination with sulidac has shown greater and effective preventive roles, which pave the way for the use of combinations of other agents. The long-term statins and low-dose NSAIDs have also been associated with risk reduction in vitro, in vivo, and in retrospective studies; however, the data are inconsistent. Epidermal growth factor signaling inhibitors, peroxisome proliferator-activated receptor-γ ligands and tumor necrosis factor-related apoptosis-inducing ligand have been demonstrated to potentiate the preventive effects of NSAIDs in vitro and in vivo, but these combinational regimens have not yet been applied to clinical research. The major goal of this study was to review combination chemoprevention for colorectal cancer by means of combining low doses of potential preventive agents to increase their chemoprophylaxis efficacy and to minimize toxicity.

Background: Oropharyngeal squamous cell carcinoma (OPSCC) accounts for a substantial proportion of head and neck cancers, with a rising incidence largely driven by human papillomavirus (HPV) infection. Despite advances in multimodal treatment, disease recurrence remains common and limits long‐term survival, highlighting the need for reliable biomarkers to guide prognosis and treatment. Methods: This review summarizes recent advances in biomarker development in OPSCC across multiple biological domains. We examined molecular biomarkers, including genomic alterations, DNA methylation, and non‐coding RNAs; protein biomarkers associated with oncogenic signaling, cell‐cycle regulation, apoptosis, inflammation, and angiogenesis; as well as circulating biomarkers such as circulating tumor DNA (ctDNA), circulating tumor cells (CTCs), and exosome‐derived RNA. Results: Among currently available biomarkers, p16, PD‐L1, and circulating HPV DNA demonstrate the strongest clinical applicability, particularly for risk stratification and post‐treatment surveillance. Emerging evidence also supports the use of combined biomarker panels to improve prediction of treatment response to radiotherapy, chemotherapy, and immunotherapy. However, many candidate biomarkers show inconsistent performance due to methodological variability, limited sensitivity and specificity, and insufficient prospective validation. Conclusions: While several biomarkers show promise in OPSCC, further standardization of detection methods and large‐scale prospective studies are required. Integration of multi‐omics data with computational approaches, including artificial intelligence, may facilitate the development of robust and clinically actionable predictive models, ultimately enabling more personalized management and earlier detection of recurrence.

A quick method was developed for diagnosis of nitrogen (N) in apple trees based on multiple linear regressions to establish the relationship between near-infrared reflectance spectra (NIRS) and the N contents of fresh and dry tissue. Spectral pretreatment methods such as derivatives, smoothing, and normalization were used. The derivatives appeared to be the most effective. The best calibration for fresh leaf gave 0.842 for the correlation coefficient of validation (R_v), 1.119 g kg⁻¹ for the root mean square error of prediction (RMSEP), and 8.311 for the ratio of the range in reference data from the validation samples to the root mean square error of prediction (RER). The best calibration for dried ground samples was obtained with R_v = 0.952, RMSEP = 0.633 g kg⁻¹, the ratio performance deviation (RPD) = 3.27, and RER = 13.728. The results showed that calibrations of dry-apple leaf are robust enough for an accurate prediction of N.