Pathologic myopia(PM)is characterized by progressive axial elongation, posterior staphyloma, and fundus degenerative changes, representing a leading cause of blindness in Asian countries.Posterior scleral reinforcement(PSR)is currently the only surgical intervention to slow axial elongation. Its mechanisms involve mechanical reinforcement, improved blood circulation, and biological stimulation of scleral remodeling. This article reviews recent advances in PSR: surgical techniques have evolved from complex, classic procedures to standardized single-strip methods, incorporating 3D MRI to quantify posterior staphyloma morphology for personalized graft design and facilitating minimally invasive, precision surgery. Among implant materials, biomaterials such as allogeneic dura mater are being continuously optimized, while biomimetic piezoelectric smart materials actively promote scleral collagen synthesis through electrical signals, achieving a paradigm shift from “passive support”to “active reinforcement”. Clinical outcomes indicate advantages in regulating axial length, enhancing visual function and blood circulation, and facilitating early intervention in children. Combined procedures with ICL implantation, cataract surgery or vitrectomy provide comprehensive treatment regimens for individual patients with varied conditions. This article also analyzes long-term issues and core controversies including ectopic posterior staphyloma and reoperation, and prospects future research directions such as artificial intelligence assistance, translation of smart materials and full-cycle prevention and treatment systems, so as to provide references for clinical practice and scientific research.

BACKGROUND: Doxorubicin hydrochloride (DOX) is extensively used in the treatment of various tumors. However, its clinical application is limited due to dose-dependent cardiotoxicity. Currently, few effective strategies exist to mitigate or eliminate DOX-induced cardiomyopathy (DIC). Although ferroptosis is implicated in DIC and its inhibition partially alleviates the condition, the direct targets of DOX in the progression of cardiotoxicity remain unclear. This study aimed to discover the direct targets of DOX in ferroptosis-mediated DIC. METHODS: A DOX pulldown assay was performed to identify proteins specifically binding to DOX in murine hearts, followed by liquid chromatography-tandem mass spectrometry (LC-MS/MS) to identify candidate proteins. A cardiac injury mouse model was established by DOX treatment. Based on this, multiple ferroptosis biomarkers were detected by flow cytometry, quantitative real-time polymerase chain reaction, western blotting, immunochemistry, etc. Besides, specific activator and inhibitor of signaling pathways were applied to illuminate molecular mechanisms. RESULTS: Glutathione S-transferase P1 (GSTP1) was identified as a DOX target. GSTP1 activity was inhibited in DOX-treated cardiomyocytes, while its overexpression significantly alleviated DIC. Moreover, GSTP1 overexpression inhibited acyl-CoA synthetase long-chain family member 4 (ACSL4)-dependent ferroptosis. Mechanistically, GSTP1 overexpression suppressed c-Jun N-terminal kinase (JNK) phosphorylation, thereby reducing reactive oxygen species (ROS) production and inhibiting ferroptosis in DIC. CONCLUSIONS This study identifies the DOX/GSTP1/JNK axis as a critical pathway mediating ACSL4-dependent ferroptosis in DIC. GSTP1 is highlighted as a potential key mediator of ferroptosis and a promising therapeutic target for DIC.

Objective·To develop an early diagnostic model for small cell lung cancer(SCLC)based on differences in serum metabolite expression profiles between patients with SCLC and those with benign pulmonary diseases,using machine learning algorithms.Methods·Serum samples were collected from 29 SCLC patients and 67 patients with benign lung diseases at Shanghai General Hospital,Shanghai Jiao Tong University School of Medicine,as the training cohort.An independent external validation cohort included 20 SCLC patients and 40 patients with benign lung diseases from Gansu Provincial Cancer Hospital.A total of 69 serum metabolites were quantitatively analyzed using liquid chromatography-tandem mass spectrometry(LC-MS/MS).The XGBoost Classifier was employed to rank metabolite importance,and a forward feature selection strategy based on XGBoost was used to identify a subset of key metabolites.Diagnostic models were constructed using AdaBoost,random forest(RF),and light gradient boosting machine(LGBM)algorithms.Model performance was assessed using receiver operating characteristic(ROC)curves and the area under the curve(AUC),and validated on the external test cohort.Results·Principal component analysis(PCA)and orthogonal projections to latent structures-discriminant analysis(OPLS-DA)of the training cohort revealed distinct metabolic profiles between SCLC and benign lung disease patients.Based on feature importance rankings,six key metabolites were selected to construct the MTB-6 diagnostic model.Among the models,AdaBoost achieved the best performance,with an AUC of 0.943,sensitivity of 75.0%,and specificity of 90.9%in the training cohort.In the external test cohort,the model demonstrated robust performance with an AUC of 0.921,sensitivity of 80.0%,and specificity of 87.5%.Conclusion·The MTB-6 model,based on six serum metabolites and the AdaBoost algorithm,exhibits excellent diagnostic performance and holds potential for the differential diagnosis of SCLC and benign pulmonary diseases.

Peritoneal metastasis is a common form of distant metastasis in patients with colorectal cancer, and it is typically associated with a poor prognosis. The development of peritoneal metastasis involves complex molecular mechanisms and multifactorial regulation of the tumor microenvironment. Due to the presence of the blood-peritoneal barrier, only a small amount of systemic medication reaches the peritoneal cavity, resulting in limited efficacy against peritoneal metastasis. Intraperitoneal administration shows significant therapeutic advantages as it can directly target the tumor microenvironment, maintain high local drug concentrations, and reduce systemic toxicity. Intraperitoneal chemotherapy, especially hyperthermic intraperitoneal chemotherapy, has become a cornerstone therapeutic strategy in the clinical treatment of peritoneal metastasis. When selecting chemotherapy drugs and drug combinations, pharmacokinetic properties, efficacy, and safety must be comprehensively considered to optimize the treatment outcomes. In addition, the unique microenvironment of the peritoneal cavity provides new treatment approaches for biological treatment strategies, including antitoxins, vaccines, immune checkpoint inhibitors, etc. Techniques such as pressurized intraperitoneal aerosol chemotherapy and novel drug delivery systems demonstrate potential for enhanced efficacy, offering promising alternatives to improve patient outcomes. This article will review peritoneal barrier characteristics, intraperitoneal drug transport, intraperitoneal chemotherapy, and intraperitoneal biological therapies, thereby establishing a theoretical framework for precision therapy in colorectal cancer peritoneal metastasis.

Objective·To develop an early diagnostic model for small cell lung cancer(SCLC)based on differences in serum metabolite expression profiles between patients with SCLC and those with benign pulmonary diseases,using machine learning algorithms.Methods·Serum samples were collected from 29 SCLC patients and 67 patients with benign lung diseases at Shanghai General Hospital,Shanghai Jiao Tong University School of Medicine,as the training cohort.An independent external validation cohort included 20 SCLC patients and 40 patients with benign lung diseases from Gansu Provincial Cancer Hospital.A total of 69 serum metabolites were quantitatively analyzed using liquid chromatography-tandem mass spectrometry(LC-MS/MS).The XGBoost Classifier was employed to rank metabolite importance,and a forward feature selection strategy based on XGBoost was used to identify a subset of key metabolites.Diagnostic models were constructed using AdaBoost,random forest(RF),and light gradient boosting machine(LGBM)algorithms.Model performance was assessed using receiver operating characteristic(ROC)curves and the area under the curve(AUC),and validated on the external test cohort.Results·Principal component analysis(PCA)and orthogonal projections to latent structures-discriminant analysis(OPLS-DA)of the training cohort revealed distinct metabolic profiles between SCLC and benign lung disease patients.Based on feature importance rankings,six key metabolites were selected to construct the MTB-6 diagnostic model.Among the models,AdaBoost achieved the best performance,with an AUC of 0.943,sensitivity of 75.0%,and specificity of 90.9%in the training cohort.In the external test cohort,the model demonstrated robust performance with an AUC of 0.921,sensitivity of 80.0%,and specificity of 87.5%.Conclusion·The MTB-6 model,based on six serum metabolites and the AdaBoost algorithm,exhibits excellent diagnostic performance and holds potential for the differential diagnosis of SCLC and benign pulmonary diseases.

Peritoneal metastasis is a common form of distant metastasis in patients with colorectal cancer, and it is typically associated with a poor prognosis. The development of peritoneal metastasis involves complex molecular mechanisms and multifactorial regulation of the tumor microenvironment. Due to the presence of the blood-peritoneal barrier, only a small amount of systemic medication reaches the peritoneal cavity, resulting in limited efficacy against peritoneal metastasis. Intraperitoneal administration shows significant therapeutic advantages as it can directly target the tumor microenvironment, maintain high local drug concentrations, and reduce systemic toxicity. Intraperitoneal chemotherapy, especially hyperthermic intraperitoneal chemotherapy, has become a cornerstone therapeutic strategy in the clinical treatment of peritoneal metastasis. When selecting chemotherapy drugs and drug combinations, pharmacokinetic properties, efficacy, and safety must be comprehensively considered to optimize the treatment outcomes. In addition, the unique microenvironment of the peritoneal cavity provides new treatment approaches for biological treatment strategies, including antitoxins, vaccines, immune checkpoint inhibitors, etc. Techniques such as pressurized intraperitoneal aerosol chemotherapy and novel drug delivery systems demonstrate potential for enhanced efficacy, offering promising alternatives to improve patient outcomes. This article will review peritoneal barrier characteristics, intraperitoneal drug transport, intraperitoneal chemotherapy, and intraperitoneal biological therapies, thereby establishing a theoretical framework for precision therapy in colorectal cancer peritoneal metastasis.

Based on the research on the inheritance of clinical experience of traditional Chinese medicine(TCM)master Zhou Zhongying in the diagnosis and treatment of major chronic diseases,a decision support system for learning experience of Zhou Zhongy-ing in diagnosing and treating major chronic diseases was constructed based on intelligent technology.First,the outpatient paper medi-cal records kept by Professor Zhou Zhongying since the 1980s were digitized.Then,data mining was performed on the electronic data-base to form a structured database and knowledge element module network.Finally,the mined results were used to interview famous doctors.The promotion and application of this system in clinical practice has realized a new method of original inheritance and learning of the experience of Zhou Zhongying in diagnosing and treating major chronic diseases.It has greatly improved the diagnosis and treat-ment ideas of middle-aged and young Chinese medicine practitioners in dealing with personalized,complex and changeable major chro-nic diseases,breaking through the traditional one-to-one teaching model of"master-apprentice",and forming a new model of large-scale TCM inheritance education.

Based on the research on the inheritance of clinical experience of traditional Chinese medicine(TCM)master Zhou Zhongying in the diagnosis and treatment of major chronic diseases,a decision support system for learning experience of Zhou Zhongy-ing in diagnosing and treating major chronic diseases was constructed based on intelligent technology.First,the outpatient paper medi-cal records kept by Professor Zhou Zhongying since the 1980s were digitized.Then,data mining was performed on the electronic data-base to form a structured database and knowledge element module network.Finally,the mined results were used to interview famous doctors.The promotion and application of this system in clinical practice has realized a new method of original inheritance and learning of the experience of Zhou Zhongying in diagnosing and treating major chronic diseases.It has greatly improved the diagnosis and treat-ment ideas of middle-aged and young Chinese medicine practitioners in dealing with personalized,complex and changeable major chro-nic diseases,breaking through the traditional one-to-one teaching model of"master-apprentice",and forming a new model of large-scale TCM inheritance education.

Hemorrhage after prostate biopsy is common, but hemorrhagic shock is rare. We reported a case of sudden severe hematuria on the third day after prostate biopsy, which was considered to be bleeding at the puncture site. Conservative treatment was ineffective, and interventional embolization was performed. Bilateral bulbar urethral arteries were embolized, and the bleeding was stopped successfully. On the 4th day after embolization, the patient developed hypovolemic shock. Angiography showed bilateral prostatic artery bleeding, and the bleeding site and its superior branch arteries were embolized immediately. At 4 months after embolization, no bleeding related events occurred.

Objective:To explore the effect of pediatric critical illness score (PCIS), pediatric risk of mortality Ⅲ score (PRISM Ⅲ), pediatric logistic organ dysfunction 2 (PELOD-2), pediatric sequential organ failure assessment (p-SOFA) score and Glasglow coma scale (GCS) in the prognosis evaluation of septic-associated encephalopathy (SAE).Methods:The data of children with SAE admitted to the Pediatric Intensive Care Unit (PICU), Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences from January 2010 to December 2020 were retrospectively analyzed. They were divided into the survival and death groups according to the clinical outcome on the 28th day after admission. The efficiency of PCIS, PRISM Ⅲ, PELOD-2, p-SOFA and GCS scores for predicting death were evaluated by the area under the ROC curve (AUC). The Hosmer-Lemeshow goodness-of-fit test assessed the calibration of each scoring system.Results:Up to 28 d after admission, 72 of 82 children with SAE survived and 10 died, with a mortality rate of 12.20%. Compared with the survival group, the death group had significantly lower GCS [7 (3, 12) vs. 12 (8, 14)] and PCIS scores [76 (64, 82) vs. 82 (78, 88)], and significantly higher PRISM Ⅲ [14 (12, 17) vs. 7 (3, 12)], PELOD-2 [8 (5, 13) vs. 4 (2, 7)] and p-SOFA scores [11 (5, 12) vs. 6 (3, 9)] ( P<0.05). The AUCs of PCIS, PRISM Ⅲ, PELOD-2, p-SOFA and GCS scores for predicting SAE prognosis were 0.773 ( P=0.012, AUC>0.7), 0.832 ( P=0.02, AUC>0.7), 0.767 ( P=0.014, AUC>0.7), 0.688 ( P=0.084, AUC<0.7), and 0.692 ( P=0.077,AUC<0.7), respectively. Hosmer-Lemeshow goodness-of-fit test showed that PCIS ( χ2=5.329, P=0.722) predicted the mortality and the actual mortality in the best fitting effect, while PRISM Ⅲ ( χ2=12.877, P=0.177), PELOD-2 ( χ2=8.487, P=0.205), p-SOFA ( χ2=9.048, P=0.338) and GCS ( χ2=3.780, P=0.848) had poor fitting effect. Conclusions:The PCIS, PRISM Ⅲ and PELOD-2 scores have good predictive ability assessing the prognosis of children with SAE, while the PCIS score can more accurately evaluate the fitting effect of SAE prognosis prediction.