ObjectiveGastrodia elata has evolved ecological types with shortened rhizome internodes and diversified flower and fruit coloration in response to different altitudes. Studying the genetic mechanisms of different ecotype germplasm is significant for guiding variety breeding in different cultivation areas. MethodsThe bHLH gene family was identified based on the whole-genome datasets of G. elata f. elata and G. elata f. glauca. Subsequently， the gene family members were subject to analysis， including gene structure， chromosomal localization， cis-acting elements， gene synteny， and phylogeny. Combined with transcriptome data and quantitative Real-time PCR， the expression patterns of bHLH genes in the stems of the different G. elata ecotype germplasm were analyzed. Finally， correlation analysis was conducted between gene expression patterns and color to obtain the key bHLH genes regulating the color formation of stem. ResultsA total of 63 bHLH genes were identified in both G elata f. elata and G. elata f. glauca， unevenly distributed across 17 chromosomes and clustered into 16 subfamilies， with significant expansion in some family members. Obvious inversions of bHLH genes on the same chromosome and interchromosomal translocations were detected in the two ecotype germplasm. Among these genes， 12 bHLH genes （such as bHLH62-3 and bHLH74） were associated with the bright yellow color of G elata f. elata stem， while 9 bHLH genes （such as PIL13， UNE12， and bHLH130） were correlated with the red color of G. elata f. glauca stem. Compared to G. elata f. glauca， the bHLH48 expression level was significantly higher in flowers and scale leaves of G elata f. elata， and the bHLH62-3 expression level was significantly higher in all organs of G elata f. elata. ConclusionsFunctional pathway divergence of the bHLH family members has occurred across different chromosomes in G elata f. elata and G. elata f. glauca. Through synergism or antagonism with other genes， 21 bHLH genes participate in the coloration metabolic pathway regulation of stems， flowers， and fruits. Specifically， bHLH62-3 is involved in regulating stem color differentiation in the anthocyanin biosynthesis pathway of G. elata， thus relevant to the color formation of stem. Additionally， GebHLH48 positively regulates flowering-related pathways to promote the early-flowering phenotype of G. elata f. elata. These findings have laid the foundation for analyzing the genetic regulatory mechanisms underlying the color formation of the G. elata stem.

ObjectiveGastrodia elata has evolved ecological types with shortened rhizome internodes and diversified flower and fruit coloration in response to different altitudes. Studying the genetic mechanisms of different ecotype germplasm is significant for guiding variety breeding in different cultivation areas. MethodsThe bHLH gene family was identified based on the whole-genome datasets of G. elata f. elata and G. elata f. glauca. Subsequently， the gene family members were subject to analysis， including gene structure， chromosomal localization， cis-acting elements， gene synteny， and phylogeny. Combined with transcriptome data and quantitative Real-time PCR， the expression patterns of bHLH genes in the stems of the different G. elata ecotype germplasm were analyzed. Finally， correlation analysis was conducted between gene expression patterns and color to obtain the key bHLH genes regulating the color formation of stem. ResultsA total of 63 bHLH genes were identified in both G elata f. elata and G. elata f. glauca， unevenly distributed across 17 chromosomes and clustered into 16 subfamilies， with significant expansion in some family members. Obvious inversions of bHLH genes on the same chromosome and interchromosomal translocations were detected in the two ecotype germplasm. Among these genes， 12 bHLH genes （such as bHLH62-3 and bHLH74） were associated with the bright yellow color of G elata f. elata stem， while 9 bHLH genes （such as PIL13， UNE12， and bHLH130） were correlated with the red color of G. elata f. glauca stem. Compared to G. elata f. glauca， the bHLH48 expression level was significantly higher in flowers and scale leaves of G elata f. elata， and the bHLH62-3 expression level was significantly higher in all organs of G elata f. elata. ConclusionsFunctional pathway divergence of the bHLH family members has occurred across different chromosomes in G elata f. elata and G. elata f. glauca. Through synergism or antagonism with other genes， 21 bHLH genes participate in the coloration metabolic pathway regulation of stems， flowers， and fruits. Specifically， bHLH62-3 is involved in regulating stem color differentiation in the anthocyanin biosynthesis pathway of G. elata， thus relevant to the color formation of stem. Additionally， GebHLH48 positively regulates flowering-related pathways to promote the early-flowering phenotype of G. elata f. elata. These findings have laid the foundation for analyzing the genetic regulatory mechanisms underlying the color formation of the G. elata stem.

The combination of radiotherapy and immunotherapy is not merely a superposition of therapeutic effects but rather a dynamic competition involving radiation-induced damage, tissue repair programs, and antitumor immune amplification. Accumulating evidence indicates that heme metabolism does more than supply prosthetic groups. It acts as a critical interface connecting radiation stress, mitochondrial homeostasis, redox imbalance, iron metabolism, and tumor immune remodeling. Focusing on the "radiation-heme-immunotherapy" axis, this review systematically examines how radiation reshapes the heme synthesis-degradation network via reactive oxygen species (ROS) bursts, Nrf2/BACH1 responses, mitochondrial damage, and disruption of iron homeostasis. In turn, these changes reprogram the cGAS-STING-type I interferon axis, myeloid cell polarization, T-cell function, and the hypoxic/stromal state of the tumor microenvironment. We pay special attention to the functional paradox of heme oxygenase-1 (HO-1): it protects normal tissues from radiation injury yet promotes tumor tolerance. We propose that HO-1 outcomes are not determined solely by its expression level but are collectively shaped by the expressing cell type, the intensity and duration of induction, subcellular localization, and the microenvironmental context. Accordingly, HMOX1/HO-1 and related heme metabolic features may serve as novel biomarkers for patient stratification and sensitization of radioimmunotherapy combinations. The key to its clinical translation lies in establishing detection systems with spatial and temporal resolution and developing localized, temporally sequenced, and population-selective targeted therapeutic strategies.

ObjectiveTo evaluate the clinical efficacy and safety of Xiaozhen Formula （消疹方） in preventing skin toxicity induced by epidermal growth factor receptor inhibitors （EGFRIs） in patients with EGFR-mutant non-small cell lung cancer （NSCLC）. MethodsA randomized， double-blind， placebo-controlled， multicenter clinical study was conducted. A total of 120 patients with EGFR-mutant NSCLC from seven centers were enrolled and randomly assigned to a treatment group （60 cases） or a control group （60 cases）. On the basis of EGFRI-targeted therapy， patients in the treatment group received Xiaozhen Formula granules， whereas those in the control group received Xiaozhen Formula placebo granules， both at 10 g twice daily for 4 consecutive weeks. The primary outcomes were the grading of skin toxicity evaluated using the National Cancer Institute Common Terminology Criteria for Adverse Events （NCI-CTCAE） version 5.0 and the incidence of rash. Secondary outcomes included the time to onset and time to resolution of the highest-grade rash， the European Organisation for Research and Treatment of Cancer Quality of Life Questionnaire-Core 30 （EORTC QLQ-C30） scores， the Hospital Anxiety and Depression Scale （HADS） scores， and disease control rate， together with safety assessments. ResultsBased on the full analysis set （FAS）， the incidence of skin toxicity in the treatment group was 18.33% （11/60）， significantly lower than 45.00% （27/60） in the control group （P＜0.01）， with the highest severity of skin toxicity in both groups was grade 2. Among patients who developed rash， the resolution rate in the treatment group was 90.91% （10/11）， higher than that 33.33% （9/27） in the control group. The median time to resolution was 15 days （95%CI： 8-17 days） in the treatment group and it was not reached in the control group； the distribution of time to resolution differed significantly between groups （P＜0.001）. After treatment， the treatment group had higher EORTC QLQ-C30 functional scale and global health status/quality-of-life scores， and lower symptom scale， single-item scores， as well as HADS-A， and HADS-D scores than the control group （P＜0.05）. In the FAS analysis， the disease control rate （DCR） was 85.0% （51/60） in the treatment group， lower than 100.0% （60/60） in the control group （P=0.003）， whereas no significant between-group difference was observed in the per-protocol set （PPS） analysis （P=0.464）. The incidence of adverse events did not differ significantly between the two groups （P＞0.05）， and no definite safety signals related to the investigational drug were observed. ConclusionXiaozhen Formula can reduce the incidence of EGFRI-induced skin toxicity， and improve the outcome of rash regression， the patients' quality of life and psychological status in EGFR-mutant NSCLC patients.

Objective:To analyze risk factors for early trauma-induced coagulopathy (TIC) in pediatric patients with moderate-to-severe traumatic brain injury (msTBI), develop a predictive model and evaluate its predictive performance.Methods:A retrospective cohort study was conducted to analyze the clinical data of 290 pediatric patients with msTBI who were admitted to Children′s Hospital of Soochow University between January 2016 and December 2024, including 188 boys and 102 girls, aged 0.2-15.7 years [5.2(2.8, 9.3)years]. Based on the coagulation test results at admission, the patients were divided into TIC group ( n=162) and non-TIC group ( n=128). The patients were randomly allocated into training set ( n=203) and validation set ( n=87) at a ratio of 7∶3. Demographic characteristics, clinical data, vital signs, imaging findings, arterial blood gas analysis results, and coagulation profiles of the patients were collected. Univariate analysis and Lasso regression analysis were used to identify risk factors associated with early TIC in children with msTBI and multivariate Logistic regression analysis was performed to determine independent risk factors and construct a predictive model. The model′s discrimination and calibration were evaluated using the area under the receiver operating characteristic (ROC) curve (AUC), Hosmer-Lemeshow (H-L) test, and calibration curve. Its clinical utility was assessed through decision curve analysis (DCA). Results:Significant differences were observed between the TIC group and non-TIC group in terms of age, weight, time from injury to admission, child′s Glasgow coma scale (CGCS) score, pediatric trauma score (PTS), shock index, heart rate, respiratory rate, systolic blood pressure, Rotterdam CT score, intraventricular hemorrhage, cerebral contusion, brain herniation, long bone fracture, pelvic fracture, hemopneumothorax, pulmonary contusion, intra-abdominal organ injury, actual bicarbonate, base excess, base excess in the extracellular fluid, blood glucose, hemoglobin (Hb), osmolarity, blood calcium, anion gap, blood lactate, prothrombin time, activated partial thromboplastin time, international normalized ratio, and platelet count ( P<0.05). With coagulation-related variables excluded, the following features were identified with Lasso regression including CGCS score, PTS, heart rate, systolic blood pressure, long bone fracture, blood glucose, and Hb. Multivariate Logistic regression analysis revealed that CGCS score≤8 points ( OR=3.05, 95% CI 1.65, 5.63), PTS>5 points ( OR=0.45, 95% CI 0.23, 0.89), systolic blood pressure ( OR=0.98, 95% CI 0.97, 0.99), blood glucose ( OR=1.09, 95% CI 1.01, 1.17), and long bone fracture ( OR=2.47, 95% CI 1.13, 5.42) were influencing factors for early TIC in children with msTBI ( P<0.05). The regression equation of the predictive model was established as follows: Logit[ P/(1- P)]=1.01×"CGCS score≤8 points"-0.69×"PTS>5 points"- 0.02×"systolic blood pressure"+0.89×"long bone fracture"+0.08×"blood glucose"+1.32. The ROC curve analysis showed that the training set had an AUC of 0.86 (95% CI 0.78, 0.94), with sensitivity and specificity of 76.6% and 92.5%, while the AUC was 0.80 (95% CI 0.74, 0.86), with sensitivity and specificity of 75.7% and 79.6% in the validation set. H-L test results showed a χ2 value of 8.18 ( P=0.416) in the training set and 5.30 ( P=0.216) in the validation set. The calibration curves for both sets demonstrated good agreement with the actual curves, indicating that the predicted probabilities closely matched the observed probabilities. The DCA results indicated that both the training set and validation set demonstrated positive net benefits within threshold probabilities ranges of 10%-100% and 15%-96%. Conclusions:Independent risk factors for early TIC in pediatric msTBI patients include CGCS score≤ 8 points, PTS≤5 points, low systolic blood pressure, long bone fracture, and high blood glucose. The predictive model constructed based on these factors demonstrates favorable predictive performance and clinical application value.

With the widespread application of nuclear technology,the prevention and treatment of radiation damage have become significant issues in the fields of public health and medicine.This article reviews the adverse drug reactions and their monitoring for radioprotective drugs,and discusses their post-marketing re-evaluation in terms of safety,efficacy,economics,and quality standards.Research indicates that while there is a wide variety of radioprotective drugs,safety concerns are prevalent.Future efforts should focus on strengthening the monitoring and management of adverse drug reactions,optimizing drug safety,and exploring the potential of combined drug applications through multidisciplinary collaboration to enhance anti-radiation efficacy.

With the continuous development of society, a large number of new chemicals are continuously emerging, which presents a challenge to current risk assessment and safety management of chemicals. Traditional toxicology research methods have certain limitations in quickly, efficiently, and accurately assessing the toxicity of many chemicals, and cannot meet the actual needs. In response to this challenge, computational toxicology that use mathematical and computer models to achieve the prediction of chemical toxicity has emerged. In the meantime, as researchers increasingly pay attention to understanding the interaction mechanisms between exogenous chemical substances and the body from the system level, and multiomics technologies develop rapidly such as genomics, transcriptomics, proteomics, and metabolomics, huge amounts of data have been generated, providing rich information resources for studying the interactions between chemical substances and biological molecules. System toxicology and network toxicology have also developed accordingly. Of these, network toxicology can integrate these multiomics data to construct biomolecular networks, and then quickly predict the key toxicological targets and pathways of chemicals at the molecular level. This paper outlined the concept and development of network toxicology, summarized the main methods and supporting tools of network toxicology research, expounded the application status of network toxicology in studying potential toxicity of exogenous chemicals such as agricultural chemicals, environmental pollutants, industrial chemicals, and foodborne chemicals, and analyzed the development prospects and limitations of network toxicology research. This paper aimed to provide a reference for the application of network toxicology in other fields.

OBJECTIVE: To explore the potential role of mRNA m7G modification in the pathogenesis of human adenocarcinoma of esophagogastric junction (AEG). METHODS: Pathological tissue specimens from four AEG patients who underwent surgical treatment at the People's Hospital Affiliated to Jiangsu University between 2018 and 2019 were selected. Tumor tissues and adjacent normal tissues were collected from these patients. RNA was extracted from both tissue types and subjected to m7G methylated RNA immunoprecipitation sequencing (m7G-MeRIP-seq) to analyze the patterns of m7G modification, the characteristics of differential m7G modification sites, the differentially expressed mRNA, and the correlation between m7G modification and mRNA expression levels. Differential m7G-modified genes (MSH6, BRCA1, and SOX9) were further validated using methylated RNA immunoprecipitation quantitative PCR (MeRIP-qPCR), while the expression of METTL1 and WDR4 genes was examined by real-time quantitative PCR (RT-qPCR). This study was approved by the Medical Ethics Committee of the People's Hospital Affiliated to Jiangsu University (Ethics No. 20150083). RESULTS: m7G-MeRIP-seq analysis revealed that m7G modifications in both AEG and adjacent normal tissues were predominantly located in the GC-rich region surrounding the internal start codon of mRNA. Differential m7G modification sites between the two groups were closely associated with cancer-related genes. mRNA library analysis showed that differentially expressed mRNA were predominantly upregulated in AEG tissues and downregulated in adjacent normal tissues. Cross-analysis indicated that genes with hypermethylation tended to exhibit upregulated expression, while genes with hypomethylation were typically downregulated in AEG tissues. MeRIP-qPCR validation confirmed that the mRNA expression of MSH6, BRCA1, and SOX9 were significantly upregulated in AEG tissues compared to adjacent normal tissues (AEG vs. normal, P < 0.05). RT-qPCR results demonstrated that the mRNA expression levels of METTL1 and WDR4 were also upregulated in AEG tissues (AEG vs. normal, P < 0.000 5). CONCLUSION These findings suggest that mRNA m7G modification plays a significant role in the development of AEG. Furthermore, proteins as METTL1 and WDR4 may facilitate AEG progression by regulating mRNA m7G modification. These results provide valuable insights into the molecular mechanisms underlying AEG and may inform future therapeutic strategies for this malignancy.
Humans ; RNA, Messenger/metabolism* ; Adenocarcinoma/pathology* ; Esophagogastric Junction/metabolism* ; Esophageal Neoplasms/metabolism* ; Gene Expression Regulation, Neoplastic ; Female ; Male ; Middle Aged ; DNA Methylation ; Methyltransferases/metabolism* ; Stomach Neoplasms/genetics*

OBJECTIVE: To explore the association between single nucleotide polymorphism (SNP) rs2073618 and rs3102735 of the TNFRSF11B gene and the susceptibility to gastric cancer. METHODS: A case-control study was conducted. A total of 577 patients with primary gastric cancer treated at Zhenjiang First People's Hospital from May 2013 to June 2017 were selected as the case group, and 678 healthy individuals who underwent physical examinations at the same hospital during the same period were enrolled as the control group. Blood samples were collected from both groups, and genomic DNA was extracted. The target gene fragments were amplified using PCR, and genotyping was performed using the Snapshot technique. Statistical analysis was conducted using SPSS v2.0 software. This study was approved by the Medical Ethics Committee of the Zhenjiang First People's Hospital (Ethics No. 20150083). RESULTS: The smoking rate was significantly higher in the case group than in the control group (P = 0.006). The T>C polymorphism at the rs3102735 locus of the TNFRSF11B gene was significantly associated with an increased risk of gastric cancer (CC vs. TT: OR = 2.164, 95%CI = 1.063~4.406, P = 0.030). In contrast, the rs2073618 polymorphism did not show a significant association with gastric cancer susceptibility (P > 0.05). Stratified analysis by age, gender, smoking status, and drinking status revealed no significant association between the rs2073618 polymorphism and gastric cancer susceptibility (P > 0.05). However, the rs3102735 polymorphism showed a significant association with gastric cancer risk in individuals over 62 years of age (CC vs. TT: OR = 5.44, 95%CI = 1.54~19.21, P = 0.003). CONCLUSION The rs3102735 polymorphism of the TNFRSF11B gene may be associated with susceptibility to gastric cancer, particularly in older populations. This polymorphism could serve as a potential indicator for identifying high-risk groups for gastric cancer.
Humans ; Stomach Neoplasms/genetics* ; Polymorphism, Single Nucleotide ; Male ; Female ; Genetic Predisposition to Disease ; Middle Aged ; Case-Control Studies ; Osteoprotegerin/genetics* ; Aged ; Adult ; Genotype

Objective:To analyze risk factors for early trauma-induced coagulopathy (TIC) in pediatric patients with moderate-to-severe traumatic brain injury (msTBI), develop a predictive model and evaluate its predictive performance.Methods:A retrospective cohort study was conducted to analyze the clinical data of 290 pediatric patients with msTBI who were admitted to Children′s Hospital of Soochow University between January 2016 and December 2024, including 188 boys and 102 girls, aged 0.2-15.7 years [5.2(2.8, 9.3)years]. Based on the coagulation test results at admission, the patients were divided into TIC group ( n=162) and non-TIC group ( n=128). The patients were randomly allocated into training set ( n=203) and validation set ( n=87) at a ratio of 7∶3. Demographic characteristics, clinical data, vital signs, imaging findings, arterial blood gas analysis results, and coagulation profiles of the patients were collected. Univariate analysis and Lasso regression analysis were used to identify risk factors associated with early TIC in children with msTBI and multivariate Logistic regression analysis was performed to determine independent risk factors and construct a predictive model. The model′s discrimination and calibration were evaluated using the area under the receiver operating characteristic (ROC) curve (AUC), Hosmer-Lemeshow (H-L) test, and calibration curve. Its clinical utility was assessed through decision curve analysis (DCA). Results:Significant differences were observed between the TIC group and non-TIC group in terms of age, weight, time from injury to admission, child′s Glasgow coma scale (CGCS) score, pediatric trauma score (PTS), shock index, heart rate, respiratory rate, systolic blood pressure, Rotterdam CT score, intraventricular hemorrhage, cerebral contusion, brain herniation, long bone fracture, pelvic fracture, hemopneumothorax, pulmonary contusion, intra-abdominal organ injury, actual bicarbonate, base excess, base excess in the extracellular fluid, blood glucose, hemoglobin (Hb), osmolarity, blood calcium, anion gap, blood lactate, prothrombin time, activated partial thromboplastin time, international normalized ratio, and platelet count ( P<0.05). With coagulation-related variables excluded, the following features were identified with Lasso regression including CGCS score, PTS, heart rate, systolic blood pressure, long bone fracture, blood glucose, and Hb. Multivariate Logistic regression analysis revealed that CGCS score≤8 points ( OR=3.05, 95% CI 1.65, 5.63), PTS>5 points ( OR=0.45, 95% CI 0.23, 0.89), systolic blood pressure ( OR=0.98, 95% CI 0.97, 0.99), blood glucose ( OR=1.09, 95% CI 1.01, 1.17), and long bone fracture ( OR=2.47, 95% CI 1.13, 5.42) were influencing factors for early TIC in children with msTBI ( P<0.05). The regression equation of the predictive model was established as follows: Logit[ P/(1- P)]=1.01×"CGCS score≤8 points"-0.69×"PTS>5 points"- 0.02×"systolic blood pressure"+0.89×"long bone fracture"+0.08×"blood glucose"+1.32. The ROC curve analysis showed that the training set had an AUC of 0.86 (95% CI 0.78, 0.94), with sensitivity and specificity of 76.6% and 92.5%, while the AUC was 0.80 (95% CI 0.74, 0.86), with sensitivity and specificity of 75.7% and 79.6% in the validation set. H-L test results showed a χ2 value of 8.18 ( P=0.416) in the training set and 5.30 ( P=0.216) in the validation set. The calibration curves for both sets demonstrated good agreement with the actual curves, indicating that the predicted probabilities closely matched the observed probabilities. The DCA results indicated that both the training set and validation set demonstrated positive net benefits within threshold probabilities ranges of 10%-100% and 15%-96%. Conclusions:Independent risk factors for early TIC in pediatric msTBI patients include CGCS score≤ 8 points, PTS≤5 points, low systolic blood pressure, long bone fracture, and high blood glucose. The predictive model constructed based on these factors demonstrates favorable predictive performance and clinical application value.