OBJECTIVE To explore the effects of Polygala tenuifolia compatibility on toxicity， anti-inflammatory and analgesic efficacy of sand-ironing Strychnos nux-vomica （SS）. METHODS The preparation of SS single decoction， SS-P. tenuifolia core-removed （PC） （1∶2.5） or （1∶5） combined decoction， and SS-PC （1∶5） mixture were carried out to investigate their median lethal dose （LD50）. Using aspirin as positive control， the number of writhing movements， analgesic rate， pain latency， ear swelling degree and inflammation inhibition rate induced by the above-mentioned medicinal liquids in mice were compared. The contents of the active and toxic components， strychnine and brucine， in the above-mentioned medicinal liquids were also determined. RESULTS The LD50 values of SS single decoction， SS-PC （1∶2.5） combined decoction， SS-PC （1∶5） combined decoction and SS- PC （1∶5） mixture were 302.00， 614.47， 1 445.44 and 1 778.28 mg/kg， respectively. Compared with control group， the number of writhing movements and ear swelling degree in the mice of the above-mentioned medicinal liquid groups were reduced or decreased significantly （P＜0.05 or P＜0.01）； pain latency [at 90 and 120 minutes in the SS single decoction group， at 60 and 90 minutes in the SS-PC （1∶2.5） combined decoction group， and at 60，90， 120 minutes in the SS-PC （1∶5） combined decoction group and SS-PC （1∶5） mixture group] was significantly prolonged （P＜0.05 or P＜0.01）； analgesic rates of the respective medicinal liquids were 39.30%， 70.87%， 80.00% and 82.46%， and inflammation inhibition rates were 38.08%，TD 57.89%， 76.47% and 50.46%； analgesic and anti-inflammatory effects of combined decoction and mixture were generally better than those of the single decoction （P＜0.05 or P＜0.01）. In the above-mentioned four medicinal liquids， the total contents of strychnine were 0.71%， 0.42%， 0.47% and 0.64%， and the total contents of brucine were 0.88%， 0.63%， 0.57% and 0.88%， respectively. CONCLUSIONS The combination of P. tenuifolia can reduce the toxicity of SS and enhance its anti-inflammatory and analgesic effects. Moreover， there is a tendency for the toxicity-reducing and efficacy-enhancing effects to increase with the increasing dosage of P. tenuifolia. Additionally， the combined decoction of SS and P. tenuifolia can reduce the contents of the active and toxic components， strychnine and brucine， in SS.

Objective To construct a key technical indicator system for in-hospital treatment and nursing of patients with nuclear radiation injury, and provide a basis for the implementation of such treatment and nursing. Methods The draft of the key technical indicator system for in-hospital treatment and nursing of patients with nuclear radiation injury was determined by literature review, case study, and field investigation. The indicators of the system were determined through two rounds of Delphi consultation and using the precedence chart method. According to the criteria of indicator evaluation, the reliability of expert opinions, and the opinions of the research group, the indicators were refined and evaluated. Results Twenty experts were included for two rounds of consultation via mailed inquiries, with a 100% effective response rate in both rounds. The expert authority coefficients were both 0.945, and the Kendall’s W values were 0.347 and 0.448, respectively (P < 0.05). Following the expert consultations, 1 indicator was deleted, 12 indicators were added, and 6 indicators were modified. The key technical indicator system for in-hospital treatment and nursing of patients with nuclear radiation injury established in this study included 4 first-level indicators, 17 second-level indicators, and 73 third-level indicators. The means of importance assignment for all indicators were > 4.00, and the coefficients of variation were < 0.25. Conclusion The key technical indicator system for in-hospital treatment and nursing of patients with nuclear radiation injury established in this study is scientifically rigorous and practically grounded. The indicators demonstrate strong professional relevance and provide important guidance for in-hospital treatment and nursing of patients with nuclear radiation injury.

Abstract Modern western medicine typically focuses on treating specific symptoms or diseases, and traditional Chinese medicine (TCM) emphasizes the interconnections of the body’s various systems under external environment and takes a holistic approach to preventing and treating diseases. Phenomics was initially introduced to the field of TCM in 2008 as a new discipline that studies the laws of integrated and dynamic changes of human clinical phenomes under the scope of the theories and practices of TCM based on phenomics. While TCM Phenomics 1.0 has initially established a clinical phenomic system centered on Zhenghou (a TCM definition of clinical phenome), bottlenecks remain in data standardization, mechanistic interpretation, and precision intervention. Here, we systematically elaborates on the theoretical foundations, technical pathways, and future challenges of integrating digital medicine with TCM phenomics under the framework of “TCM phenomics 2.0”, which is supported by digital medicine technologies such as artificial intelligence, wearable devices, medical digital twins, and multi-omics integration. This framework aims to construct a closed-loop system of “Zhenghou–Phenome–Mechanism–Intervention” and to enable the digitization, standardization, and precision of disease diagnosis and treatment. The integration of digital medicine and TCM phenomics not only promotes the modernization and scientific transformation of TCM theory and practice but also offers new paradigms for precision medicine. In practice, digital tools facilitate multi-source clinical data acquisition and standardization, while AI and big data algorithms help reveal the correlations between clinical Zhenghou phenomes and molecular mechanisms, thereby improving scientific rigor in diagnosis, efficacy evaluation, and personalized intervention. Nevertheless, challenges persist, including data quality and standardization issues, shortage of interdisciplinary talents, and insufficiency of ethical and legal regulations. Future development requires establishing national data-sharing platforms, strengthening international collaboration, fostering interdisciplinary professionals, and improving ethical and legal frameworks. Ultimately, this approach seeks to build a new disease identification and classification system centered on phenomes and to achieve the inheritance, innovation, and modernization of TCM diagnostic and therapeutic patterns.

In recent years, the study of single-cell transcriptome sequencing technology in the heterogeneity of astrocytes (astrocytes) after spinal cord injury (SCI) has provided new perspectives on post-traumatic nerve regeneration and repair. To provide a review on the research progress of single-cell sequencing technology in astrocytes after spinal cord injury (SCI), and to more comprehensively and deeply elaborate the application of single-cell sequencing technology in the field of astrocytes after SCI. Single-cell sequencing technology can analyse the transcriptomes of individual cells in a high-throughput manner, thus revealing fine differences in cell types and states. By using single-cell sequencing technology, the heterogeneity of astrocytes after SCI and their association with nerve regeneration and repair were revealed. In conclusion, the application of single-cell sequencing technology provides an important tool to reveal the heterogeneity of astrocytes after SCI, to further explore the mechanisms of astrocytes in SCI, and to develop intervention strategies targeting their regulatory mechanisms in order to improve the therapeutic efficacy of SCI. The discovery of changes in astrocyte transcriptome dynamics has improved researchers' understanding of spinal cord injury lesion progression and provided new insights into the treatment of spinal cord injury at different time points. To date, all of these findings need to be validated by more basic research and sufficient clinical trials. In the future, single-cell sequencing technology, through interdisciplinary collaboration with bioinformatics, computer science, tissue engineering, and clinical medicine, is expected to open a new window for the treatment of spinal cord injury.
Spinal Cord Injuries/metabolism* ; Astrocytes/cytology* ; Single-Cell Analysis/methods* ; Humans ; Animals ; Transcriptome ; Nerve Regeneration

OBJECTIVES: To investigate the molecular epidemiology of children with phenylketonuria (PKU) in Gansu, China, providing foundational data for intervention strategies. METHODS: A retrospective analysis was conducted on 1 159 PKU families who attended Gansu Provincial Maternity and Child Care Hospital from January 2012 to December 2024. Sanger sequencing, multiplex ligation-dependent probe amplification, whole exome sequencing, and deep intronic variant analysis were used to analyze the PAH gene. RESULTS: For the 1 159 children with PKU, 2 295 variants were identified in 2 318 alleles, resulting in a detection rate of 99.01%. The detection rates were 100% (914/914) in 457 classic PKU families, 99.45% (907/912) in 456 mild PKU families, and 96.34% (474/492) in 246 mild hyperphenylalaninemia families. The 2 295 variants detected comprised 208 distinct mutation types, among which c.728G>A (14.95%, 343/2 295) had the highest frequency, followed by c.611A>G (4.88%, 112/2 295) and c.721C>T (4.79%, 110/2 295). The cumulative frequency of the top 23 hotspot variants reached 70.28% (1 613/2 295), and most variant alleles were detected in exon 7 (29.19%, 670/2 295). CONCLUSIONS Deep intronic variant analysis of the PAH gene can improve the genetic diagnostic rate of PKU. The development of targeted detection kits for PAH hotspot variants may enable precision screening programs and enhance preventive strategies for PKU.
Humans ; Phenylketonurias/epidemiology* ; Female ; Male ; Retrospective Studies ; Phenylalanine Hydroxylase/genetics* ; Mutation ; Child, Preschool ; China/epidemiology* ; Child ; Infant

OBJECTIVES: To investigate the current status and influencing factors of quality of life in children and adolescents with type 1 diabetes (T1DM) in Xinjiang. METHODS: A convenience sampling method was used to select 259 children with T1DM and their primary caregivers who attended three tertiary hospitals in Xinjiang from January 2023 to February 2024. The Pediatric Quality of Life Inventory^TM Version 4.0 Generic Core Scales (PedsQL^TM4.0) and Pediatric Quality of Life Inventory^TM Version 3.2 Diabetes Module (PedsQL^TM3.2-DM) were used to assess the quality of life of the children. Information on family demographics, caregiver burden, and caregiving ability was also collected. Multiple linear regression analysis was employed to identify factors associated with the quality of life of the children. RESULTS: The scores for PedsQL^TM4.0 and PedsQL^TM3.2-DM were 77±16 and 71±16, respectively. Both were negatively correlated with caregiver burden (P<0.05) and positively correlated with caregiving ability (P<0.05). Multiple linear regression analysis indicated that caregiver burden, caregiving ability, family income, and parent-child relationship were significantly associated with generic quality of life (P<0.05), whereas caregiver burden, caregiving ability, disease duration, place of residence, and glycated hemoglobin level were significantly associated with diabetes-specific quality of life (P<0.05). CONCLUSIONS The overall quality of life of children and adolescents with T1DM in Xinjiang is relatively low. The quality of life is influenced by a combination of factors including family caregiver burden, caregiving ability, family income, parent-child relationship, disease duration, place of residence, and glycated hemoglobin level. Strategies to improve quality of life should consider the combined impact of individual disease characteristics and family factors.
Humans ; Quality of Life ; Diabetes Mellitus, Type 1/psychology* ; Adolescent ; Child ; Male ; Female ; Caregivers/psychology* ; Child, Preschool ; Linear Models

OBJECTIVE: To retrospectively analyze the clinical characteristics, co-mutated genes in newly diagnosed acute myeloid leukemia (AML) patients with NRAS and KRAS gene mutations, and the impact of NRAS and KRAS mutations on prognosis. METHODS: The clinical data and next-generation sequencing results of 80 newly diagnosed AML patients treated at our hospital from December 2018 to December 2023 were collected. The clinical characteristics, co-mutated genes of NRAS and KRAS , and the impact of NRAS and KRAS mutations on prognosis in newly diagnosed AML patients were analyzed. RESULTS: Among 80 newly diagnosed AML patients, NRAS mutations were detected in 20 cases(25.0%), and KRAS mutations were detected in 9 cases(11.3%). NRAS mutations predominantly occurred at codons 12 and 13 of exon 2, as well as codon 61 of exon 3, while KRAS mutations were most commonly occurred at codons 12 and 13 of exon 2, all of which were missense mutations. There were no statistically significant differences observed in terms of age, sex, white blood cell count(WBC), hemoglobin(Hb), platelet count(PLT), bone marrow blasts, first induction chemotherapy regimen, CR1/CRi1 rates, chromosome karyotype, 2022 ELN risk classification and allogeneic hematopoietic stem cell transplantation(allo-HSCT) among the NRAS mutation group, KRAS mutation group and NRAS/KRAS wild-type group (P >0.05). KRAS mutations were significantly correlated with PTPN11 mutations (r =0.344), whereas no genes significantly associated with NRAS mutations were found. Survival analysis showed that compared to the NRAS/KRAS wild-type group, patients with NRAS mutation had a relatively higher 5-year overall survival (OS) rate and relapse-free survival (RFS) rate, though the differences were not statistically significant (P =0.097, P =0.249). Compared to the NRAS/KRAS wild-type group, patients with KRAS mutation had a lower 5-year OS rate and RFS rate, with no significant differences observed (P =0.275, P =0.442). There was no significant difference in the 5-year RFS rate between the KRAS mutation group and NRAS mutation group (P =0.157), but the 5-year OS rate of patients with KRAS mutation was significantly lower than that of patients with NRAS mutation (P =0.037). CONCLUSION In newly diagnosed AML patients, KRAS mutation was significantly correlated with PTPN11 mutation. Compared to patients with NRAS/KRAS wild-type, those with NRAS mutation showed a more favorable prognosis, while patients with KRAS mutation showed a poorer prognosis; however, these differences did not reach statistical significance. Notably, the prognosis of AML patients with KRAS mutation was significantly inferior compared to those with NRAS mutation.
Humans ; Leukemia, Myeloid, Acute/diagnosis* ; Mutation ; Prognosis ; Proto-Oncogene Proteins p21(ras)/genetics* ; GTP Phosphohydrolases/genetics* ; Retrospective Studies ; Membrane Proteins/genetics* ; Female ; Male ; Middle Aged ; Adult ; Aged

BACKGROUND: Primary pulmonary mucoepidermoid carcinoma (PMEC) is an exceedingly rare malignancy originating from bronchial submucosal glands, accounting for <0.2% of lung cancers. Histologically characterized by a triphasic composition of mucinous, epidermoid, and intermediate cells, PMEC is classified into low-grade (favorable prognosis) and high-grade (aggressive behavior) subtypes. This study aimed to investigate the clinicopathological characteristics and prognostic indicators of PMEC. METHODS: Clinicopathological, radiological, molecular, and survival data from 26 PMEC patients were retrospectively analyzed, including immunohistochemical profiles and MAML2 rearrangement status, supplemented by literature review. RESULTS: The cohort comprised 14 males and 12 females (mean age: 55.6 years). Eight patients (30.8%) were smokers, and 19 (73.1%) presented with symptoms. Central tumors predominated (n=19, 73.1%) versus peripheral lesions (n=7, 26.9%). Computed tomography (CT) imaging consistently revealed hypo-to-isodense masses/nodules. Pathologically, 19 cases were low-grade and 7 high-grade. Immunohistochemically, the tumor cells were positive for CK7, P40, P63 and CK5/6, and the Ki-67 index ranged from 2% to 70%. MAML2 rearrangement was detected in 52.4% (11/21) of tested cases. Clinical staging distribution: stage I (n=14), stage II (n=8), stage III (n=3), stage IV (n=1). Treatment modalities: radical surgery alone (n=13), surgery with adjuvant chemotherapy (n=11), chemoradiotherapy (n=1), and conservative management (n=1). With a median follow-up of 57 months, 6 patients (23.1%) died. Prognostic analysis demonstrated: (1) Significantly inferior survival in high-grade versus low-grade groups (P<0.05); (2) Lymph node metastasis, advanced stage, Ki-67>20%, and high-grade histology significantly correlated with reduced overall survival (P<0.05); (3) Lymph node metastasis constituted an independent poor prognostic factor (HR=12.73, 95%CI: 1.22-132.96). CONCLUSIONS PMEC exhibits distinct clinicopathological features, with MAML2 rearrangement present in approximately half of cases. Lymph node metastasis, advanced stage, high Ki-67 proliferation index, and high-grade histology are key determinants of poor prognosis, with lymph node metastasis serving as an independent risk factor.
Humans ; Male ; Female ; Middle Aged ; Carcinoma, Mucoepidermoid/mortality* ; Lung Neoplasms/mortality* ; Trans-Activators/genetics* ; Prognosis ; Adult ; Gene Rearrangement ; Aged ; Retrospective Studies ; Transcription Factors/genetics* ; DNA-Binding Proteins/genetics*

OBJECTIVES: Cardiovascular disease (CVD) poses a major threat to global health. Evaluating atherosclerosis in asymptomatic individuals can help identify those at high risk of CVD. This study aims to establish an individualized nomogram prediction model to estimate the risk of vascular plaque formation in asymptomatic individuals. METHODS: A total of 5 655 participants who underwent CVD screening at the Health Management Center of The Third Xiangya Hospital, Central South University, between January 2022 and June 2024 we retrospectively enrolled. Using simple random sampling, participants were divided into a training set (n=4 524) and a validation set (n=1 131) in an 8꞉2 ratio. Demographic and clinical data were collected and compared between groups. Multivariate logistic regression analysis was used to identify independent factors associated with vascular plaques and to construct a nomogram prediction model. The predictive performance and clinical utility of the model were evaluated using receiver operating characteristic (ROC) curves, the Hosmer-Lemeshow goodness-of-fit test, calibration plots, and decision curve analysis (DCA). RESULTS: The mean age of participants was 52 years old. There were 3 400 males (60.12%). The overall detection rate of vascular plaque in the screening population was 49.87% (2 820/5 655). No statistically significant differences were observed in clinical indicators between the training and validation sets (all P>0.05). Multivariate Logistic regression analysis identified age, systolic blood pressure, high-density lipoprotein (HDL), low-density lipoprotein (LDL), lipoprotein(a), male sex, smoking history, hypertension history, and diabetes history as independent risk factors for vascular plaque in asymptomatic individuals (all P<0.05). The area under the curve (AUC) of the nomogram model for predicting vascular plaque risk were 0.778 (95% CI 0.765 to 0.791, P<0.001) in the training set and 0.760 (95% CI 0.732 to 0.787, P<0.001) in the validation set. The Hosmer-Lemeshow goodness-of-fit test indicated good model calibration (training set: P=0.628; validation set: P=0.561). The calibration curve plotted using the Bootstrap method demonstrated good agreement between predicted probabilities and actual probabilities. DCA showed that the nomogram provided a clinical net benefit for predicting vascular plaque risk when the threshold probability ranged from 0.02 to 0.99. CONCLUSIONS The nomogram prediction model for vascular plaque risk, constructed using readily available and cost-effective physical examination indicators, exhibited good predictive performance. This model can assist in the early identification and intervention of asymptomatic individuals at high risk for cardiovascular disease.
Humans ; Male ; Middle Aged ; Female ; Nomograms ; Retrospective Studies ; Risk Factors ; Plaque, Atherosclerotic/diagnosis* ; Aged ; Adult ; Physical Examination ; Logistic Models ; Cardiovascular Diseases/epidemiology* ; ROC Curve