Xiaoji Yinzi is one of the classic prescriptions for treating urinary diseases， originated from the Yan's Prescriptions to Aid the Living （Yan Shi Ji Sheng Fang） written by YAN Yonghe in the Song dynasty. Xiaoji Yinzi is composed of Rehmanniae Radix， Cirsii Herba， Talcum， Akebiae Caulis， Typhae Pollen， Nelumbinis Rhizomatis Nodus， Lophatheri Herba， Angelicae Sinensis Radix， Gardeniae Fructus， and Glycyrrhizae Radix et Rhizoma and has the effects of cooling blood and stopping bleeding， draining water and relieving stranguria. The medical experts of later generations have inherited the original prescription recorded in the Yan's Prescriptions to Aid the Living， while dispute has emerged during the inheritance of this prescription. In this study， the method of bibliometrics was employed to review and analyze the ancient documents and modern clinical studies involving Xiaoji Yinzi. The results showed that Xiaoji Yinzi has two dosage forms： powder and decoction. According to the measurement system in the Song Dynasty， the modern doses of hers in Xiaoji Yinzi were transformed. In the prepration of Xiaoji Yinzi powder， 149.2 g of Rehmanniae Radix and 20.65 g each of Cirsii Herba， Talcum， Akebiae Caulis， stir-fried Typhae Pollen， Nelumbinis Rhizomatis Nodus， Lophatheri Herba， wine-processed Angelicae Sinensis Radix， stir-fried Gardeniae Fructus， and stir-fried Glycyrrhizae Radix et Rhizoma are grounded into fine powder with the particle size of 4-10 meshes and a decocted with 450 mL water to reach a volume of 240 mL. After removal of the residue， the decoction was taken warm before meals， 3 times a day （i.e.， 7.77 g Rehmanniae Radix and 0.97 g each of the other herbs each time）. In the preparation of Xiaoji Yinzi decoction， 20.65 g each of the above 10 herbs are used， with stir-fried Typhae Pollen， wine-processed Angelica Sinensis Radix， stir-fired Gardeniae Fructus， stir-fired Glycyrrhizae Radix et Rhizoma， and raw materials of other herbs. Xiaoji Yinzi is specialized in treating hematuresis and blood stranguria due to heat accumulation in lower energizer， which causes injury of the blood collaterals of gallbladder and dysfunction of Qi transformation. In modern clinical practice， Xiaoji Yinzi is specifically used for treating urinary diseases and can be expanded to treat diseases of the cardiovascular system and other systems according to pathogenesis. The comprehensive research on the key information could provide a scientific reference for the future development of Xiaoji Yinzi.

Sinisan is a classical prescription developed and applied by ancient medical experts and it is first recorded in the Treatise on Cold Damage written by ZHANG Zhongjing in the Eastern Han Dynasty. Later physicians have modified this prescription based on this original one. The bibliometrics methods were used to analyze the key information and research trend of Sinisan. According to the inclusion and exclusion criteria， 69 pieces of effective data were extracted， involving 67 ancient traditional Chinese medicine （TCM） books. The results showed that the name， composition， and decocting methods of Sinisan in later generations were inherited from the original record in the Treatise on Cold Damage. The original plants of medicinal materials used in Sinisan are basically clear. We recommend Bupleuri Radix as the dried root of Bupleurem scorzonerifolium， Paeoniae Radix Alba as the dried root of Paeonia lactiflora， Aurantii Fructus as the dried fruit of Citrus aurantium， Glycyrrhizae Radix et Rhizoma as the dry root and rhizome of Glycyrrhiza uralensis. Raw materials of Bupleuri Radix and Paeoniae Radix Alba， Aurantii Fructus stir-fried with bran， and stir-fried Glycyrrhizae Radix et Rhizoma should be used for preparation of Sinisan. According to measurement system in the Han Dynasty， a bag of Sinisan is composed of 1.25 g Bupleuri Radix， 1.25 g Paeoniae Radix Alba， 1.25 g Aurantii Fructus， and 1.25 g Glycyrrhizae Radix et Rhizoma. The materials should be grounded into coarse powder and taken with a proper amount of rice soup， 3 times a day. Sinisan has the effects of regulating qi movement and harmonizing the liver and spleen. It can be used for treating reversal cold in limbs and cold damage. In modern clinical practice， Sinisan can be used to treat chronic gastritis， irritable bowel syndrome， and dyspepsia. The above research results provide scientific reference for the future research and development of Sinisan.

OBJECTIVES: We propose a novel deep learning segmentation algorithm (DSRF) based on multi-scale supervision and residual feedback strategy for precise segmentation of the optic chiasm and optic nerves in CT images of nasopharyngeal carcinoma (NPC) patients. METHODS: We collected 212 NPC CT images and their ground truth labels from SegRap2023, StructSeg2019 and HaN-Seg2023 datasets. Based on a hybrid pooling strategy, we designed a decoder (HPS) to reduce small organ feature loss during pooling in convolutional neural networks. This decoder uses adaptive and average pooling to refine high-level semantic features, which are integrated with primary semantic features to enable network learning of finer feature details. We employed multi-scale deep supervision layers to learn rich multi-scale and multi-level semantic features under deep supervision, thereby enhancing boundary identification of the optic chiasm and optic nerves. A residual feedback module that enables multiple iterations of the network was designed for contrast enhancement of the optic chiasm and optic nerves in CT images by utilizing information from fuzzy boundaries and easily confused regions to iteratively refine segmentation results under supervision. The entire segmentation framework was optimized with the loss from each iteration to enhance segmentation accuracy and boundary clarity. Ablation experiments and comparative experiments were conducted to evaluate the effectiveness of each component and the performance of the proposed model. RESULTS: The DSRF algorithm could effectively enhance feature representation of small organs to achieve accurate segmentation of the optic chiasm and optic nerves with an average DSC of 0.837 and an ASSD of 0.351. Ablation experiments further verified the contributions of each component in the DSRF method. CONCLUSIONS The proposed deep learning segmentation algorithm can effectively enhance feature representation to achieve accurate segmentation of the optic chiasm and optic nerves in CT images of NPC.
Humans ; Tomography, X-Ray Computed/methods* ; Optic Chiasm/diagnostic imaging* ; Optic Nerve/diagnostic imaging* ; Algorithms ; Nasopharyngeal Carcinoma ; Deep Learning ; Nasopharyngeal Neoplasms/diagnostic imaging* ; Neural Networks, Computer ; Image Processing, Computer-Assisted/methods*

Objective:To evaluate the expression characteristics of the FBJ murine osteosarcoma viral oncogene homolog B(FOSB)gene in immunoglobulin A nephropathy(IgAN)and common chronic kidney diseases(CKDs),and to determine its value as a potential key candidate gene or biomarker.Methods:The RNA sequencing datasets for IgAN,diabetic kidney disease(DKD),membranous nephropathy(MN),and minimal change disease(MCD)glomerular samples were downloaded from the Gene Expression Omnibus(GEO)database.The feature genes for CKD were identified using machine learning methods including least absolute shrinkage and selection operator(LASSO)regression,random forest(RF),and support vector machine(SVM).Gene Ontology(GO)functional enrichment and Kyoto Encyclopedia of Genes and Genomes(KEGG)signaling pathway enrichment analyses were performed on the identified IgAN feature genes.The"pROC"package was used to plot the receiver operating characteristic(ROC)curves to evaluate the diagnostic efficacy of IgAN feature genes.The gene with the highest diagnostic value was selected for Gene Set Enrichment Analysis(GSEA)and correlation analysis with core immune cells in IgAN.Clinical correlation analysis between the FOSB expression level in kidney tissue and renal function was performed using the Nephroseq v5 platform.Kidney tissue samples were collected from 5 cases of IgAN patients,DKD patients,MCD patients,and MN patients,respectively,along with 5 samples of adjacent normal kidney tissues(control group).Immunohistochemistry staining method was used to detect the expression levels of FOSB protein in tissue samples in various groups.Results:A total of 110 differentially expressed genes(DEGs)were identified in IgAN glomeruli,among which FOSB,NR4A2,and DUSP1 were identified as the feature genes.Compared with control group,the expression level of FOSB mRNA in IgAN group was significantly decreased(P＜0.05).The GO fuctional enrichment analysis results revealed that these IgAN feature genes were primarily enriched in biological processes related to dopamine biosynthesis,midbrain dopaminergic neuron differentiation,peptidyl-serine/threonine dephosphorylation,and response to corticosterone.The KEGG signaling pathway enrichment analysis results showed that the DEGs were significantly enriched in cocaine addiction,amphetamine addiction,interleukin 17(IL-17)signaling pathway,aldosterone synthesis and secretion,and serotonergic synapse.The ROC curve analysis results demonstrated that FOSB showed high diagnostic accuracy for IgAN.GSEA analysis revealed that arginine and proline metabolism,butyrate metabolism,erythroblastic leukemia viral oncogene homolog(ERBB)signaling pathway,mitogen-activated protein kinase(MAPK)signaling pathway,and fructose and mannose metabolism pathways were enriched in FOSB high-expression group,while allograft rejection,extracellular matrix receptor interaction,and type 1 diabetes pathways were significantly enriched in FOSB low-expression group.The immune cell infiltration analysis results identified natural killer cells,neutrophils,and M1 macrophages as core immune cells in IgAN,and the expression of FOSB gene was positively correlated with neutrophil infiltration(r=0.42,P＜0.05).The immunohistochemistry analysis results demonstrated that compared with control group,the expression level of FOSB protein in glomeruli of the patients in IgAN,DKD,MN,and MCD groups were significantly decreased(P＜0.05).Conclusion:The expressions of FOSB gene in the glomeruli tissue of IgAN,DKD,MN,and MCD patients ware decreased,suggesting FOSB may represent a potential biomarker for IgAN.

This review systematically summarizes the unique metabolic mechanisms of breast cancer,their interac-tions with the tumor microenvironment(TME),and the latest advances in targeted therapies.The interplay between metabolic reprogramming and the TME underpins malignant progression and therapeutic resistance.Breast cancer cells reshape energy supply through the Warburg effect,aberrant fatty acid synthesis,and amino acid metabolism,while immune cells,fibroblasts,and the acidic milieu within the TME promote immune evasion and drug resistance via metabolic coupling.Although traditional strategies targeting key metabolic enzymes remain valuable,they are often insufficient to overcome metabolic adaptability.In recent years,combined metabolic and immunotherapeutic approaches have emerged as promising strategies:by reducing lactate accumulation,restoring T-cell function,and reprogramming tumor-associated macrophages and cancer-associated fibroblasts,these therapies can remodel the immunosuppressive microenvironment and enhance immunotherapy efficacy.The application of metabolomics and single-cell sequencing further elucidates breast cancer heterogeneity,providing a basis for individualized precision treatment.Future challenges include deciphering resistance mechanisms,developing highly selective metabolic in-hibitors,and designing integrated multi-omics-based therapeutic regimens.

Objective:To study the current status of longitudinal extrauterine growth restriction (EUGR) in extremely preterm infants (EPIs) and to develop a prediction model based on clinical data from multiple NICUs.Methods:From January 2017 to December 2018, EPIs admitted to 32 NICUs in North China were retrospectively studied. Their general conditions, nutritional support, complications during hospitalization and weight changes were reviewed. Weight loss between birth and discharge > 1SD was defined as longitudinal EUGR. The EPIs were assigned into longitudinal EUGR group and non-EUGR group and their nutritional support and weight changes were compared. The EPIs were randomly assigned into the training dataset and the validation dataset with a ratio of 7∶3. Univariate Cox regression analysis and multiple regression analysis were used in the training dataset to select the independent predictive factors. The best-fitting Nomogram model predicting longitudinal EUGR was established based on Akaike Information Criterion. The model was evaluated for discrimination efficacy, calibration and clinical decision curve analysis.Results:A total of 436 EPIs were included in this study, with a mean gestational age of (26.9±0.9) weeks and a birth weight of (989±171) g. The incidence of longitudinal EUGR was 82.3%(359/436). Seven variables (birth weight Z-score, weight loss, weight growth velocity, the proportion of breast milk ≥75% within 3 d before discharge, invasive mechanical ventilation ≥7 d, maternal antenatal corticosteroids use and bronchopulmonary dysplasia) were selected to establish the prediction model. The area under the receiver operating characteristic curve of the training dataset and the validation dataset were 0.870 (95% CI 0.820-0.920) and 0.879 (95% CI 0.815-0.942), suggesting good discrimination efficacy. The calibration curve indicated a good fit of the model ( P>0.05). The decision curve analysis showed positive net benefits at all thresholds. Conclusions:Currently, EPIs have a high incidence of longitudinal EUGR. The prediction model is helpful for early identification and intervention for EPIs with higher risks of longitudinal EUGR. It is necessary to expand the sample size and conduct prospective studies to optimize and validate the prediction model in the future.

Zhigancao Tang （also known as Fumaitang） is a classic formula for treating "intermittent pulse and palpitations" and is widely used in clinical practice. Sanjia Fumaitang， included in the Catalogue of Ancient Classical Formulas （First Batch） published by the National Administration of Traditional Chinese Medicine of China in 2018， is derived from this formula. This paper employed bibliometric methods to comprehensively investigate and summarize the historical evolution， drug composition， herb origins and preparation， prescription meanings， and ancient and modern applications of Zhigancao Tang， analyzed the composition and usage of Zhigancao Tang， and discussed the reasons and applications of the "Fumaitang" variants created by Wu Jutong. A total of 47 valid pieces of data from 38 ancient texts were included. Results showe that Zhigancao Tang originates from the Treatise on Cold Damage （Shang Han Lun）， and the name "Fumaitang" is also recorded in the formula's description. Converted to modern measurements from the Han dynasty system， the recommended preparation for Zhigancao Tang includes 55.2 g of fried Glycyrrhizae Radix et Rhizoma， 41.4 g of Cinnamomi Ramulus， 27.6 g of Ginseng Radix et Rhizoma， 220 g of fresh Rehmannia glutinosa， 27.6 g of Asini Corii Colla， 53 g of Ophiopogonis Radix， 45 g of Cannabis Fructus， and 90 g of Jujubae Fructus. All herbs should be decocted with 1 400 mL of yellow rice wine and 1 600 mL of water until 600 mL. Once the Asini Corii Colla is fully dissolved， the decoction should be taken warm at a dosage of 200 mL， three times a day. Zhigancao Tang is effective for replenishing Qi， warming Yang， nourishing Yin， and nourishing blood and is primarily used to treat “intermittent pulse and palpitations” caused by deficiencies in heart Yin and Yang， as well as malnutrition of the heart meridian and conditions like lung atrophy. Modern applications mainly focus on cardiovascular and cerebrovascular diseases， including arrhythmias， coronary heart disease， and premature ventricular contractions. The findings from this research provide a reference for the further development of Zhigancao Tang.

Intracranial seminoma is a rare malignant tumor originating from the germ cells,usually occurring in the pineal gland or pituitary gland.In June 2020,the Department of Endocrinology at the First Affiliated Hospital of Army Military Medical University admitted a 20-year-old male patient with an intracranial germ cell tumor and spinal metastases.The patient presented with headache,dizziness,and visual impairment.Enhanced magnetic resonance imaging(MRI)of the head indicated thickening of the pituitary stalk.After multidisciplinary consultation,the patient underwent endonasal transsphenoidal resection of the tumor,with the pathological diagnosis confirming germ cell tumor.The patient received regular radiotherapy postoperatively.One year later,the tumor recurred and metastasized,leading to a second surgery for tumor resection in the thoracic spinal canal,followed by continued chemotherapy.The patient's clinical symptoms,such as headache and visual disturbances,improved,but he continued to experience panhypopituitarism and required long-term hormone replacement therapy.Early diagnosis of intracranial germ cell tumors is challenging,and they are prone to metastasis and highly sensitive to radiotherapy and chemotherapy.Early diagnosis and multidisciplinary comprehensive treatment can help improve the quality of life and prognosis for patients.

Objective To establish the method of infrared fingerprint,TLC identification and content determination of phenolic acid components of Yao medicine Calonyction muricatum(Linn)G.Methods The infrared fingerprint of 10 batches of Calonyction muricatum(Linn)G were established by infrared spectroscopy.The spectral datas were analyzed by similarity analysis,infrared spectroscopy(HCA),principal component analysis(PCA)and Partial-least-squares discriminant analysis(PLS-DA).Chlorogenic acid,heterochlorogenic acid A and caffeic acid of Calonyction muricatum(Linn)G were identified by TLC.The contents of neochlorogenic acid,chlorogenic acid,caffeic acid,cryptochlorogenic acid,isochlorogenic acid A and C were determined simultaneously by HPLC method.Results It could be suggested that organic acids,flavonoids and other compounds of Calonyction muricatum(Linn)G by infrared spectroscopy and nine common peaks were calibrated by infrared fingerprint;the similarity evaluation was above 0.999;the results of cluster analysis(CA)and principal component analysis(PCA)showed that it could be clustered into 2 categories,including S1,S2 and S3 were clustered into one categoriy and the rest were one.5 differential components(VIP>1)were selected by Partial-least-squares discriminant analysis;the test and control samples of TLC showed consistent locations the spots were clear,with good separation degree;the six components of Calonyction muricatum(Linn)G showed good linear relationship(r≥0.999 2),average sample recovery rate 97.77%-102.59%,and RSD less than 2.90% .Conclusion The TLC and infrared fingerprint were simple and stable,and the results of the six components were reliable,which can lay a scientific foundation for the quality control of the materials.

Objective To establish a universally applicable logistic risk prediction model for diabetes mellitus type 2(T2DM)in the middle-aged and elderly populations based on the results of a Meta-analysis,and to validate and confirm the efficacy of the model using the follow-up data of medical check-ups of National Basic Public Health Service.Methods Cohort studies evaluating T2DM risks were identified in Chinese and English databases.The logistic model utilized Meta-combined effect values such as the odds ratio(OR)to derive β,the partial regression coefficient,of the logistic model.The Meta-combined incidence rate of T2DM was used to obtain the parameter α of the logistic model.Validation of the predictive performance of the model was conducted with the follow-up data of medical checkups of National Basic Public Health Service.The follow-up data came from a community health center in Chengdu and were collected between 2017 and 2022 from 7 602 individuals who did not have T2DM at their baseline medical checkups done at the community health center.This community health center was located in an urban-rural fringe area with a large population of middle-aged and elderly people.Results A total of 40 cohort studies were included and 10 items covered in the medical checkups of National Basic Public Health Service were identified in the Meta-analysis as statistically significant risk factors for T2DM,including age,central obesity,smoking,physical inactivity,impaired fasting glucose,a reduced level of high-density lipoprotein cholesterol(HDL-C),hypertension,body mass index(BMI),triglyceride glucose(TYG)index,and a family history of diabetes,with the OR values and 95% confidence interval(CI)being 1.04(1.03,1.05),1.55(1.29,1.88),1.36(1.11,1.66),1.26(1.07,1.49),3.93(2.94,5.24),1.14(1.06,1.23),1.47(1.34,1.61),1.11(1.05,1.18),2.15(1.75,2.62),and 1.66(1.55,1.78),respectively,and the combined β values being 0.039,0.438,0.307,0.231,1.369,0.131,0.385,0.104,0.765,and 0.507,respectively.A total of 37 studies reported the incidence rate,with the combined incidence being 0.08(0.07,0.09)and the parameter α being-2.442 for the logistic model.The logistic risk prediction model constructed based on Meta-analysis was externally validated with the data of 7 602 individuals who had medical checkups and were followed up for at least once.External validation results showed that the predictive model had an area under curve(AUC)of 0.794(0.771,0.816),accuracy of 74.5%,sensitivity of 71.0%,and specificity of 74.7% in the 7 602 individuals.Conclusion The T2DM risk prediction model based on Meta-analysis has good predictive performance and can be used as a practical tool for T2DM risk prediction in middle-aged and elderly populations.