ObjectiveThis paper aims to explore the risk factors for chronic atrophic gastritis （CAG） with turbidity toxin accumulation syndrome and establish a prediction model. MethodsClinical data of 180 patients with CAG who participated in the "clinical study of Xianglian Huazhuo Particles blocking CAG cancer transformation" of Hebei Sheng Zhong Yi Yuan from July 2021 to March 2022 were collected. After confounding factors were controlled by propensity score matching， patients were divided into a training set （namely dev） and a validation set （namely vad） in a seven to three ratio. The risk factors for CAG with turbidity toxin accumulation syndrome in the training set were investigated by using univariate Logistic regression analysis and least absolute shrinkage and selection operator （namely Lasso） regression algorithms. Subsequently， a model， named model 1se， was developed by using the training set data to predict the risk factors for CAG with turbidity toxin accumulation syndrome. The accuracy of the prediction model was assessed by using various methods， including the receiver operating characteristic （ROC） curve， Hosmer-Lemeshow test （H-L）， calibration plot， and decision curve analysis （DCA）. ResultsAge， body mass index （BMI）， family history of cancer， job and life satisfaction， yellow and greasy fur with slippery pulse， and heavy body sensation were independent risk factors of the model. The prediction model showed excellent predictive value for both the training and validation sets. ConclusionThe established prediction model for CAG with turbidity toxin accumulation syndrome has high discrimination and excellent calibration， which could provide an excellent clinical basis for disease diagnosis and individualized treatment of patients.

ObjectiveThis paper aims to explore the risk factors for chronic atrophic gastritis （CAG） with turbidity toxin accumulation syndrome and establish a prediction model. MethodsClinical data of 180 patients with CAG who participated in the "clinical study of Xianglian Huazhuo Particles blocking CAG cancer transformation" of Hebei Sheng Zhong Yi Yuan from July 2021 to March 2022 were collected. After confounding factors were controlled by propensity score matching， patients were divided into a training set （namely dev） and a validation set （namely vad） in a seven to three ratio. The risk factors for CAG with turbidity toxin accumulation syndrome in the training set were investigated by using univariate Logistic regression analysis and least absolute shrinkage and selection operator （namely Lasso） regression algorithms. Subsequently， a model， named model 1se， was developed by using the training set data to predict the risk factors for CAG with turbidity toxin accumulation syndrome. The accuracy of the prediction model was assessed by using various methods， including the receiver operating characteristic （ROC） curve， Hosmer-Lemeshow test （H-L）， calibration plot， and decision curve analysis （DCA）. ResultsAge， body mass index （BMI）， family history of cancer， job and life satisfaction， yellow and greasy fur with slippery pulse， and heavy body sensation were independent risk factors of the model. The prediction model showed excellent predictive value for both the training and validation sets. ConclusionThe established prediction model for CAG with turbidity toxin accumulation syndrome has high discrimination and excellent calibration， which could provide an excellent clinical basis for disease diagnosis and individualized treatment of patients.

Objective: To evaluate the feasibility of dynamic contrast-enhanced MRI (DCE-MRI) in differentiating tumor deposits (TDs) from metastatic lymph nodes (MLNs) in rectal cancer. Materials and Methods: A retrospective analysis was conducted on 70 patients with rectal cancer, including 168 lesions (70 TDs and 98 MLNs confirmed by histopathology), who underwent pretreatment MRI and subsequent surgery between March 2019 and December 2022. The morphological characteristics of TDs and MLNs, along with quantitative parameters derived from DCE-MRI (K trans , kep, and v e) and DWI (ADCmin, ADCmax, and ADCmean), were analyzed and compared between the two groups.Multivariable binary logistic regression and receiver operating characteristic (ROC) curve analyses were performed to assess the diagnostic performance of significant individual quantitative parameters and combined parameters in distinguishing TDs from MLNs. Results: All morphological features, including size, shape, border, and signal intensity, as well as all DCE-MRI parameters showed significant differences between TDs and MLNs (all P < 0.05). However, ADC values did not demonstrate significant differences (all P > 0.05). Among the single quantitative parameters, v e had the highest diagnostic accuracy, with an area under the ROC curve (AUC) of 0.772 for distinguishing TDs from MLNs. A multivariable logistic regression model incorporating short axis, border, v e, and ADC mean improved diagnostic performance, achieving an AUC of 0.833 (P = 0.027). Conclusion The combination of morphological features, DCE-MRI parameters, and ADC values can effectively aid in the preoperative differentiation of TDs from MLNs in rectal cancer.

This study aimed to localize the workplace readiness questionnaire (WRQ) and validate its applicability for assessing readiness for implementation of evidence-based practices (EBP) in primary care settings in China. The localization of the instrument will provide a practical instrument for assessing organizational readiness for change (ORC).

In this study, serum metabolomics techniques and molecular biology methods were used to investigate the intervention effect of kaji-ichigoside F1 on chronic unpredictable mild stress(CUMS) depression mouse model and its mechanism. The CUMS depression mouse model was constructed, and the mice were divided into blank group, model group, escitalopram(ESC, 10 mg·kg~(-1)) group, and low-dose, medium-dose, and high-dose kaji-ichigoside F1 groups(1, 2, and 4 mg·kg~(-1)). CUMS modeling was performed on all mice except the blank group, and the cycle was four weeks. At the end of modelling, ESC and kaji-ichigoside F1 were administered by gavage once a day for 28 days. After the end of the administration, behavioral testing(sucrose preference test, open field test, forced swimming test, and tail suspension test) was conducted to evaluate the improvement of depression symptoms of different doses of kaji-ichigoside F1 on CUMS depression mouse model. The morphology of neurons and the number of Nissl bodies in the hippocampus were observed by Nissl staining. Metabolomics technique was used to analyze the changes in serum differential metabolites in mice. Protein expression levels of P2X7 purinergic receptor(P2X7R), adenosine A1 receptor(A1R), and adenosine receptor A2A(A2AR) in mouse hippocampus were detected by Western blot. The results showed that compared with that in the blank group, the body weight of mice in the model group was significantly decreased, and the sucrose preference rate was significantly decreased. The immobility time was significantly increased in the forced swimming and tail suspension tests, and the total moving distance was significantly decreased in the open field test. The number of Nissl bodies was significantly decreased, and the depression-like behavior and the number of Nissl bodies in the hippocampus of mice were significantly improved after administration of kaji-ichigoside F1. In the metabonomics analysis, the purine metabolism of serum after kaji-ichigoside F1 administration was involved in the metabolic passage of depression, and Western blot analysis verified the expression of P2X7R, A1R, and A2AR proteins in purine metabolic pathways. The results show that kaji-ichigoside F1 significantly decreases the expression of P2X7R and A2AR proteins in the hippocampus of CUMS model mice and increases the expression level of A1R proteins. It is suggested that kaji-ichigoside F1 may play an antidepressant role by regulating the expression of P2X7R, A1R, and A2AR proteins in the purine metabolism pathway.
Animals ; Mice ; Antidepressive Agents/administration & dosage* ; Metabolomics ; Depression/genetics* ; Male ; Drugs, Chinese Herbal/administration & dosage* ; Disease Models, Animal ; Hippocampus/metabolism* ; Behavior, Animal/drug effects* ; Humans

Objective: To evaluate the feasibility of dynamic contrast-enhanced MRI (DCE-MRI) in differentiating tumor deposits (TDs) from metastatic lymph nodes (MLNs) in rectal cancer. Materials and Methods: A retrospective analysis was conducted on 70 patients with rectal cancer, including 168 lesions (70 TDs and 98 MLNs confirmed by histopathology), who underwent pretreatment MRI and subsequent surgery between March 2019 and December 2022. The morphological characteristics of TDs and MLNs, along with quantitative parameters derived from DCE-MRI (K trans , kep, and v e) and DWI (ADCmin, ADCmax, and ADCmean), were analyzed and compared between the two groups.Multivariable binary logistic regression and receiver operating characteristic (ROC) curve analyses were performed to assess the diagnostic performance of significant individual quantitative parameters and combined parameters in distinguishing TDs from MLNs. Results: All morphological features, including size, shape, border, and signal intensity, as well as all DCE-MRI parameters showed significant differences between TDs and MLNs (all P < 0.05). However, ADC values did not demonstrate significant differences (all P > 0.05). Among the single quantitative parameters, v e had the highest diagnostic accuracy, with an area under the ROC curve (AUC) of 0.772 for distinguishing TDs from MLNs. A multivariable logistic regression model incorporating short axis, border, v e, and ADC mean improved diagnostic performance, achieving an AUC of 0.833 (P = 0.027). Conclusion The combination of morphological features, DCE-MRI parameters, and ADC values can effectively aid in the preoperative differentiation of TDs from MLNs in rectal cancer.

OBJECTIVE: To analyze the distribution characteristics of glucose-6-phosphate-dehydrogenase (G6PD) mutations and their enzyme activity in newborns patients with G6PD deficiency in Guilin region. METHODS: From July 2022 to July 2024, umbilical cord blood samples from 4 554 newborns in Guilin were analyzed for G6PD mutations using fluorescence PCR melting curve analysis. Enzyme activity was detected in 4 467 cases using the rate assay. RESULTS: Among 4 467 newborns who underwent G6PD activity testing, 162 newborns (3.63%) were identified as G6PD-deficient, including 142 males (6.04%) and 20 females (0.94%), the prevalence of G6PD deficiency was significantly higher in males than in females (P < 0.001). Genetic analysis of 4 554 newborns detected G6PD mutations in 410 cases (9%), including 171 males (7.13%) and 239 females (11.09%), with a significantly higher mutation detection rate in females than in males (P < 0.001). A total of nine single mutations and four compound heterozygous mutations were identified. The most common mutations were c.1388G>A (33.66%), c.1376G>T (23.66%) and c.95A>G (16.34%). Among newborns who underwent both enzyme activity and genetic mutation testing, males with G6PD mutations had significantly lower enzyme activity than that of females with G6PD mutations(P < 0.001). Specifically, among newborns carrying the mutations c.1388G>A, c.1376G>T, c.95A>G, c.1024C>T or c.871G>A, males consistently exhibited lower enzymatic activity than females with the same mutations (P < 0.001). Furthermore, in male G6PD-deficient newborns, the enzyme activity levels in those carrying c.1388G>A, c.1376G>T, c.95A>G, c.1024C>T, or c.871G>A were lower than those in both the control group and the c.519C>T group (P < 0.05). CONCLUSION This study provides a comprehensive profile of G6PD deficiency incidence and mutation spectrum in the Guilin region. By analyzing enzyme activity and genetic mutation results, this study provides insights into potential intervention strategies and personalized management approaches for the prevention and treatment of neonatal G6PD deficiency in the region.
Humans ; Infant, Newborn ; Glucosephosphate Dehydrogenase Deficiency/epidemiology* ; Glucosephosphate Dehydrogenase/genetics* ; Female ; Male ; Mutation ; China/epidemiology*

Traditional Chinese medicine (TCM) represents a paradigmatic approach to personalized medicine, developed through the systematic accumulation and refinement of clinical empirical data over more than 2000 years, and now encompasses large-scale electronic medical records (EMR) and experimental molecular data. Artificial intelligence (AI) has demonstrated its utility in medicine through the development of various expert systems (e.g., MYCIN) since the 1970s. With the emergence of deep learning and large language models (LLMs), AI's potential in medicine shows considerable promise. Consequently, the integration of AI and TCM from both clinical and scientific perspectives presents a fundamental and promising research direction. This survey provides an insightful overview of TCM AI research, summarizing related research tasks from three perspectives: systems-level biological mechanism elucidation, real-world clinical evidence inference, and personalized clinical decision support. The review highlights representative AI methodologies alongside their applications in both TCM scientific inquiry and clinical practice. To critically assess the current state of the field, this work identifies major challenges and opportunities that constrain the development of robust research capabilities-particularly in the mechanistic understanding of TCM syndromes and herbal formulations, novel drug discovery, and the delivery of high-quality, patient-centered clinical care. The findings underscore that future advancements in AI-driven TCM research will rely on the development of high-quality, large-scale data repositories; the construction of comprehensive and domain-specific knowledge graphs (KGs); deeper insights into the biological mechanisms underpinning clinical efficacy; rigorous causal inference frameworks; and intelligent, personalized decision support systems.
Medicine, Chinese Traditional/methods* ; Artificial Intelligence ; Humans ; Precision Medicine ; Decision Support Systems, Clinical

The prefrontal cortex (PFC) plays a pivotal role in orchestrating higher-order emotional and cognitive processes, a function that depends on the precise modulation of synaptic activity. Although pharmacological studies have demonstrated that dopamine signaling through dopamine D1 receptor (DRD1) in the PFC is essential for these functions, the cell-type-specific and molecular mechanisms underlying the neuromodulatory effects remain elusive. Using cell-type-specific knockout mice and patch-clamp recordings, we investigated the regulatory role of DRD1 on neurons and astrocytes in synaptic transmission and plasticity. Furthermore, we explored the mechanisms by which DRD1 on astrocytes regulate synaptic transmission and plasticity at the cellular level, as well as emotional and cognitive functions at the behavioral level, through two-photon imaging, microdialysis, high-performance liquid chromatography, transcriptome sequencing, and behavioral testing. We found that conditional knockout of the Drd1 in astrocytes (CKO^AST) increased glutamatergic synaptic transmission and long-term potentiation (LTP) in the medial prefrontal cortex (mPFC), whereas Drd1 deletion in pyramidal neurons did not affect synaptic transmission. The elevated level of d-serine in the mPFC of CKO^AST mice increased glutamatergic transmission and LTP through NMDA receptors. In addition, CKO^AST mice exhibited abnormal emotional and cognitive function. Notably, these behavioral changes in CKO^AST mice could be reversed through the administration of d-serine degrease to the mPFC. These results highlight the critical role of the astrocytic DRD1 in modulating mPFC synaptic transmission and plasticity, as well as higher brain functions through d-serine, and may shed light on the treatment of mental disorders.

Attention is the cornerstone of effective functioning in a complex and information-rich world. While the neural activity of attention has been extensively studied in the cortex, the brain-wide neural activity patterns are largely unknown. In this study, we conducted a comprehensive analysis of neural activity across the mouse brain during attentional processing using EEG and c-Fos staining, utilizing hierarchical clustering and c-Fos-based functional network analysis to evaluate the c-Fos activation patterns. Our findings reveal that a wide range of brain regions are activated, notably in the high-order cortex, thalamus, and brain stem regions involved in advanced cognition and arousal regulation, with the central lateral nucleus of the thalamus as a strong hub, suggesting the crucial role of the thalamus in attention control. These results provide valuable insights into the neural network mechanisms underlying attention, offering a foundation for formulating functional hypotheses and conducting circuit-level testing.
Animals ; Attention/physiology* ; Mice ; Brain/physiology* ; Male ; Electroencephalography ; Reaction Time/physiology* ; Brain Mapping ; Mice, Inbred C57BL ; Choice Behavior/physiology* ; Proto-Oncogene Proteins c-fos/metabolism*