Ferroptosis is a programmed death mode that regulates redox homeostasis in cells, and recent studies suggest that it is a promising mode of tumor cell death. Ferroptosis is regulated by iron metabolism, lipid metabolism, and intracellular reducing substances, which is the mechanism basis of its combination with photodynamic therapy (PDT). PDT generates reactive oxygen species (ROS) and ¹O₂ through type I and type II photochemical reactions, and subsequently induces ferroptosis through the Fenton reaction and the peroxidation of cell membrane lipids. PDT kills tumor cells by generating excessive cytotoxic ROS. Due to the limited laser depth and photosensitizer enrichment, the systemic treatment effect of PDT is not good. Combining PDT with ferroptosis can compensate for these shortcomings. Nanoparticles constructed by photosensitizers and ferroptosis agonists are widely used in the field of combination therapy, and their targeting and biological safety can be improved through modification. These nanoparticles not only directly kill tumor cells but also further exert the synergistic effect of PDT and ferroptosis by activating antitumor immunity, improving the hypoxia microenvironment, and inhibiting the tumor angiogenesis. Ferroptosis-agonist-induced chemotherapy and PDT-induced ablation also have good clinical application prospects. In this review, we summarize the current research progress on PDT and ferroptosis and how PDT and ferroptosis promote each other.

Pancreatitis is a digestive system disease that imposes a significant burden on society. However, there is a lack of comprehensive research on the incidence, prevalence, mortality, years of life lost (YLLs), years lived with disability (YLDs), and disability-adjusted life years (DALYs) of pancreatitis, as well as on health inequalities and future trends. Pancreatitis burden data, including the number and age-standardized rates (ASR) of incidence, prevalence, deaths, years of life lost (YLLs), years lived with disability (YLDs), and disability-adjusted life years (DALYs), were collected from the Global Burden of Diseases 2019 (GBD 2019). SDI and HDI were used to analyze the influence of societal development on the burden of pancreatitis in the population. Additionally, the Gini coefficient and the Concentration index were used to assess health inequalities in the burden of pancreatitis. Global population data from 1990 to 2034 was obtained from WHO. Based on the population data and pancreatitis burden data, a prediction model of the burden was constructed to calculate the number and ASR of incidence, prevalence, deaths, YLLs, YLDs, and DALYs from 2019 to 2034 using the BAPC package and the Nordpred package. From 1990 to 2019, there has been a decreasing trend in the ASR of incidence, prevalence, deaths, YLLs, YLDs, and DALYs in pancreatitis. However, despite this decline, the number of cases has been on the rise. Furthermore, pancreatitis imposes a higher burden on males in comparison to females, and there exists a negative correlation between pancreatitis burden and both the Social Development Index (SDI) and the Human Development Index (HDI). Additionally, health inequalities have progressively worsened globally between 1990 and 2019, particularly concerning the burden of pancreatitis in countries with low Social Development Index (SDI). Looking to the future, it is projected that the number of deaths and new cases will continue to increase from 2020 to 2034. Pancreatitis remains a mounting worldwide burden. In order to alleviate this challenge, preventive strategies should focus on males and middle-aged or older individuals, specifically in countries with a low SDI. Pancreatitis is expected to predominantly impact Eastern Europe, characterized by a high ASR of incidence, and Asia, boasting a substantial population.

Background: Insomnia affects the quality of life of a significant number of individuals worldwide. Despite the fact that pharmaceutical sleep treatments have shown brief enhancements in sleep quality, these are still not recommended for the long-term management of sleep issues. To deal with this problem, our study aims to assess the effectiveness of auricular acupressure for treating insomnia by conducting a systematic review and meta-analysis.Method: Data from randomized controlled trials (RCTs) of auricular acupressure for insomnia was collected from five English-language databases (Cochrane Central Register of Controlled Trials, MEDLINE, EMBASE, AMED, and CINAHL) and four Chinese databases (CBM, CNKI, CQVIP, and Wanfang). Relevant data were extracted by two reviewers. I² statistics were adopted to appraise heterogeneity. A network meta-analysis was applied to compare the effect of auricular acupressure with other methods.Result: In all, 23 RCTs involving a total of 1,689 patients were included. The results demonstrated a significant decrease in the Pittsburgh Sleep Quality Index (PSQI) score for the intervention group compared to the control group [SMD = −1.30, 95% CI (−1.65, −0.96), I² = 90%]. Furthermore, the group receiving auricular acupressure in addition to usual care showed a lower PSQI score compared to the usual care group [SMD = −1.13, 95% CI (−1.33, −0.93), I² = 23%]. Auricular acupressure was found to enhance the effectiveness of estazolam in improving PSQI score, with the combination of auricular acupressure and estazolam resulting in a lower PSQI score [MD = −4.8, 95% CI (−7.4, −2.1)]. Importantly, no serious adverse events were reported. In patients with insomnia following stroke, the intervention group (which received auricular acupressure) exhibited a lower PSQI score compared to the control group [SMD = −0.74, 95% CI (−1.03, −0.46), I² = 0%]. Similarly, in patients with insomnia related to cancer, the intervention group (receiving auricular acupressure) demonstrated a lower PSQI score compared to the control group [SMD = −0.99, 95% CI (−1.37, −0.61), I² = 0%].Conclusion: The effects of auricular acupressure on insomnia are comparable to those of estazolam. Furthermore, auricular acupressure can serve as a complementary treatment to estazolam or other interventions, effectively improving symptoms of insomnia.

Non-invasive phototherapy includes modalities such as photodynamic therapy (PDT) and photothermal therapy (PTT). When combined with tumor immunotherapy, these therapeutic approaches have demonstrated significant efficacy in treating advanced malignancies, thus attracting considerable attention from the scientific community. However, the progress of these therapies is hindered by inherent limitations and potential adverse effects. Recent findings indicate that certain therapeutic strategies, including phototherapy, can induce immunogenic cell death (ICD), thereby opening new avenues for the integration of phototherapy with tumor immunotherapy. Currently, the development of biofilm nanomaterial-encapsulated drug delivery systems has reached a mature stage. Immune cell membrane-encapsulated nano-photosensitizers hold great promise, as they can enhance the tumor immune microenvironment. Based on bioengineering technology, immune cell membranes can be designed according to the tumor immune microenvironment, thereby enhancing the targeting and immune properties of nano-photosensitizers. Additionally, the space provided by the immune cell membrane allows for the co-encapsulation of immunotherapeutic agents and chemotherapy drugs, achieving a synergistic therapeutic effect. At the same time, the timing of photodynamic therapy (PDT) can be precisely controlled to regulate the action timing of both immunotherapeutic and chemotherapy drugs. This article summarizes and analyzes current research based on the aforementioned advancements.

Postoperative admission to the ICU for surgical patients is a significant burden in nursing care, and there is currently a lack of corresponding assessment tools. Clinical information of patients was extracted from the VitalDB database. LASSO regression and random forest algorithms were used to screen clinical variables related to postoperative ICU admission. Subsequently, the effectiveness of logistic regression, random forest, support vector machine, and multi-layer perceptron algorithms was compared using ROC curves. After selecting the best algorithm, postoperative ICU admission probability prediction nomogram was constructed. This study identified 18 clinical factors that influence postoperative ICU admission. The factors influencing patient outcomes include three physiological characteristics: age, weight, and gender; five preoperative laboratory tests:platelet count, prothrombin time(%),activated partial thromboplastin time, albumin, and blood urea nitrogen; and seven intraoperative anesthesia details: anesthesia duration, propofol dosing during surgery, midazolam dosing during surgery, phenylephrine dosing during surgery, calcium chloride dosing during surgery, American Society of Anesthesiologists (ASA) classification, and anesthesia method. Additionally, three other factors are considered: whether the surgery is classified as an emergency, the department category, and the type of surgery. The logistic regression model developed using these 18 variables was identified as the most effective predictive model for postoperative ICU admission, achieving an ROC AUC of 0.925. The postoperative admission warning model constructed in this study can effectively predict the probability of patients being admitted to the ICU after surgery, providing a corresponding management tool for postoperative care in surgical patients.

Malignant pleural mesothelioma (MPM) is a rare but aggressive thoracic malignancy primarily linked to asbestos exposure. Despite advances in research, the prognosis of MPM remains poor, and there is a lack of efficient and precise prognostic assessment tools. Its insidious onset, limited treatment options, and high resistance to therapy contribute to poor clinical outcomes and underscore the urgent need for reliable prognostic biomarkers. To address this gap, we integrated multi-omics data to identify a prognostic gene set for MPM and subsequently developed a prognostic model aiming to improve the clinical outcomes of this disease. Transcriptomic data from TCGA were used to identify genes related to prognosis and tumor stage in malignant pleural mesothelioma (MPM), followed by construction of a prognostic model via LASSO-Cox regression and external validation using a GEO dataset. The model was then integrated with DNA methylation and SNP data for GO enrichment analysis. Chemotherapy drug IC50 data from MPM cell lines were correlated with model gene expression to evaluate associations between risk scores and drug sensitivity, which were further validated using TCGA clinical response data. Finally, the model’s impact on the immune microenvironment was assessed using single-cell RNA-seq data, and its predictive value for immunotherapy response was validated in an independent MPM immunotherapy cohort. Our novel prognostic model demonstrated consistent performance in both the training and validation cohorts. Patients with high-risk scores had poorer outcomes, with AUC values exceeding 0.8 and reaching 0.9 for 3 year survival prediction. The risk score accurately reflected biological processes such as tumor proliferation and metastasis in mesothelioma. It was also closely associated with clinical responses to radiotherapy and chemotherapy, with low-risk patients showing greater sensitivity and lower drug IC50 values. Additionally, the risk score correlated positively with tumor immune activity and was predictive of immunotherapy response. In conclusion, our prognostic model shows strong and reliable predictive power for patient survival and treatment response in malignant pleural mesothelioma. It not only reflects key tumor biological processes but also serves as a potential tool for guiding personalized therapy, including chemotherapy, radiotherapy, and immunotherapy.