OBJECTIVE To investigate the mechanism of Xihuang pill （XHP） against diffuse large B-cell lymphoma （DLBCL）. METHODS The active ingredients of XHP and potential therapeutic targets for DLBCL were identified using TCMSP， GeneCards and DisGeNET databases. Protein-protein interaction networks were constructed using the String database and Cytoscape software to screen core components and core targets. Gene ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses were then performed. The clinical relevance of core targets was analyzed using the GEPIA and PanCanSurvPlot databases. Molecular docking and molecular dynamics （MD） simulation were conducted to verify the interactions between core components and core targets， and the binding free energy was calculated using the molecular mechanics Poisson-Boltzmann surface area （MM-PBSA） method. The effects of XHP on DLBCL and the related molecular mechanisms were validated using CCK-8 assay， flow cytometry and Western blot. RESULTS Network pharmacology analysis identified 108 active ingredients of XHP and 410 potential therapeutic targets for DLBCL. Six core components （e.g.， 17 beta-estradiol， quercetin） and ten core targets [e.g.， tumor protein 53 （TP53）， proto-oncogene tyrosine-protein kinase Src （SRC）] were obtained. Enrichment analysis indicated that the anti-DLBCL effects of XHP were primarily associated with the apoptotic signaling pathway， the phosphoinositide 3-kinase （PI3K）/protein kinase B （Akt） signaling pathway and so on. Clinical correlation analysis revealed that TP53 and SRC expression were significantly up-regulated in DLBCL tissues and associated with poor patient prognosis （P＜0.05）. Molecular docking， MD simulations and MM-PBSA calculations confirmed that the SRC-quercetin complex had a mail：stronger and more stable binding affinity. In vitro experiments demonstrated that XHP concentration-dependently inhibited the proliferation of DLBCL cells； compared with control group， XHP medium- and high-dose groups could significantly induce the apoptosis of SU-DHL2 and SU-DHL4 cells， and significantly down- regulated the expressions of SRC protein， phosphorylated （p）-PI3K/PI3K and p-Akt/Akt in SU-DHL4 cells （P＜0.05）. CONCLUSIONS XHP may inhibit the proliferation and induce the apoptosis of DLBCL cells by regulating the SRC/PI3K/Akt signaling pathway.

BACKGROUND:Type 2 diabetes mellitus is a secondary causative factor for osteoporosis.As highly heterogeneous innate immune cells,macrophages may be polarized in a hyperglycemic environment,which affects osteogenesis-angiogenesis coupling.This may be a research target for improving bone quality in patients with type 2 diabetic osteoporosis.OBJECTIVE:To explore the role of modulating macrophage M1/M2 polarization to influence osteogenesis-angiogenesis coupling in type 2 diabetic osteoporosis and to summarize the effects of commonly used anti-glucose and anti-osteoporosis drugs and bone biorepair materials on bone osteogenesis-angiogenesis coupling by regulating macrophage M1/M2 polarization.METHODS:The keywords of"macrophage polarization,type 2 diabetes,osteoporosis,osteogenesis-angiogenesis coupling"in Chinese and"macrophages,macrophage polarization,osteogenesis-angiogenesis coupling"in English were used to search for relevant literature in CNKI and PubMed,respectively.Seventy-nine pieces of literature were screened and analyzed.RESULTS AND CONCLUSION:(1)Type 2 diabetes mellitus causes the body to be in a hyperglycemic environment and increases the secretion of inflammatory-related factors in the body,which promotes macrophage polarization towards M1 and decreases the number of M2 macrophages.(2)In type 2 diabetes,promoting M2 macrophage polarization is beneficial for osteogenesis-angiogenesis coupling.(3)Some anti-glycemic drugs,active ingredients in traditional Chinese medicine and bone biorepair materials can improve type 2 diabetic osteoporosis by regulating macrophage M1/M2 polarization,reducing M1/M2 ratio,and promoting osteogenesis-angiogenesis coupling.

BACKGROUND:Type 2 diabetes mellitus is a secondary causative factor for osteoporosis.As highly heterogeneous innate immune cells,macrophages may be polarized in a hyperglycemic environment,which affects osteogenesis-angiogenesis coupling.This may be a research target for improving bone quality in patients with type 2 diabetic osteoporosis.OBJECTIVE:To explore the role of modulating macrophage M1/M2 polarization to influence osteogenesis-angiogenesis coupling in type 2 diabetic osteoporosis and to summarize the effects of commonly used anti-glucose and anti-osteoporosis drugs and bone biorepair materials on bone osteogenesis-angiogenesis coupling by regulating macrophage M1/M2 polarization.METHODS:The keywords of"macrophage polarization,type 2 diabetes,osteoporosis,osteogenesis-angiogenesis coupling"in Chinese and"macrophages,macrophage polarization,osteogenesis-angiogenesis coupling"in English were used to search for relevant literature in CNKI and PubMed,respectively.Seventy-nine pieces of literature were screened and analyzed.RESULTS AND CONCLUSION:(1)Type 2 diabetes mellitus causes the body to be in a hyperglycemic environment and increases the secretion of inflammatory-related factors in the body,which promotes macrophage polarization towards M1 and decreases the number of M2 macrophages.(2)In type 2 diabetes,promoting M2 macrophage polarization is beneficial for osteogenesis-angiogenesis coupling.(3)Some anti-glycemic drugs,active ingredients in traditional Chinese medicine and bone biorepair materials can improve type 2 diabetic osteoporosis by regulating macrophage M1/M2 polarization,reducing M1/M2 ratio,and promoting osteogenesis-angiogenesis coupling.

Based on the theory of traditional Chinese medicine （TCM） chronomedicine， this paper explores the pathogenesis and treatment strategies of colorectal cancer mediated by circadian rhythm disorders. It is believed that the imbalance of yin and yang is isomorphic to the disorder of biological clock genes， which is the root cause of the disease. The disharmony of ying and wei （营卫） corresponds to the imbalance of immunity and metabolism， which is the pivot of the disease. The disorder of qi movement is related to the imbalance of intestinal barrier， which is the manifestation of the disease. Accordingly， relevant strategies are proposed， including chronochemotherapy regimen that aligns with biological rhythms to enhance efficacy and reduce toxicity， acupuncture based on the ebb and flow of midnight and midday which selects acupoints along meridians to harmonize qi and blood， and a timed medication strategy that matches the disease mechanism by administering drugs at optimal times. These strategies aim to deepen the understanding of the pathogenesis of colorectal cancer mediated by circadian rhythm disorders from the perspective of TCM chronomedicine， thereby providing new ideas for clinical practice.

OBJECTIVE: To propose a machine learning-based method for predicting the trajectories during manual acupuncture manipulation (MAM), aiming to improve the precision and consistency of acupuncture practitioner' operation and provide the real-time suggestions on MAM error correction. METHODS: Computer vision technology was used to analyze the hand micromotion when holding needle during acupuncture, and provide a three-dimensional coordinate description method of the index finger joints of the holding hand. Focusing on the 4 typical motions of MAM, a machine learning-based MAM trajectory predictive model was designed. By integrating the changes of phalangeal joint angle and hand skeletal information of acupuncture practitioner, the motion trajectory of the index finger joint was predicted accurately. Besides, the roles of machine learning-based MAM trajectory predictive model in the skill transmission of acupuncture manipulation were verified by stratified randomized controlled trial. RESULTS: The performance of MAM trajectory predictive model, based on the long short-term memory network (LSTM), obtained the highest stability and precision, up to 98%. The learning effect was improved when the model applied to the skill transmission of acupuncture manipulation. CONCLUSION The machine learning-based MAM predictive model provides acupuncture practitioner with precise action prediction and feedback. It is valuable and significant for the inheritance and error correction of manual operation of acupuncture.
Humans ; Acupuncture Therapy/instrumentation* ; Machine Learning ; Adult ; Male ; Female

OBJECTIVE: To investigate the effect of manual acupuncture manipulations (MAMs) on subcutaneous muscle tissue, by developing quantitative models of "lifting and thrusting" and "twisting and rotating", based on machine learning techniques. METHODS: A depth camera was used to capture the acupuncture operator's hand movements during "lifting and thrusting" and "twisting and rotating" of needle. Simultaneously, the ultrasound imaging was employed to record the muscle tissue responses of the participants. Amplitude and angular features were extracted from the movement data of operators, and muscle fascicle slope features were derived from the data of ultrasound images. The dynamic time warping barycenter averaging algorithm was adopted to align the dual-source data. Various machine learning techniques were applied to build quantitative models, and the performance of each model was compared. The most optimal model was further analyzed for its interpretability. RESULTS: Among the quantitative models built for the two types of MAMs, the random forest model demonstrated the best performance. For the quantitative model of the "lifting and thrusting" technique, the coefficient of determination (R²) was 0.825. For the "twisting and rotating" technique, R² reached 0.872. CONCLUSION Machine learning can be used to effectively develop the models and quantify the effects of MAMs on subcutaneous muscle tissue. It provides a new perspective to understand the mechanism of acupuncture therapy and lays a foundation for optimizing acupuncture technology and designing personalized treatment regimen in the future.
Humans ; Acupuncture Therapy/methods* ; Machine Learning ; Male ; Adult ; Female ; Subcutaneous Tissue/diagnostic imaging* ; Young Adult

Objective:To explore the predictive potential of dual-energy CT (DECT) quantitative parameters in identifying breast cancer molecular subtypes and the expression of the epidermal growth factor receptor (EGFR) .Methods:A cohort of 97 breast cancer patients, treated between Jun. 2022 and Jun. 2024 were selected. The study compared DECT parameters-such as iodine concentration (IC) , normalized iodine concentration (NIC) , spectral curve slope (λ HU) , and effective atomic number (Z eff) in both arterial and venous phases across different molecular subtypes. A multiclass logistic regression model was employed to assess the parameters' value in predicting molecular subtypes, while a binary logistic regression model was used to evaluate their predictive value for EGFR expression. Results:Multiple Logistic regression analysis showed that after adjusting for confounder age and family history, IC ( OR=1.72, 2.78, 3.05) , NIC ( OR=2.52, 1.94, 2.93) , λ HU ( OR=2.08, 2.54, 3.17) and Z eff ( OR=2.03, 2.30, 2.37) at arterial stage were independently correlated with the molecular subtypes of breast cancer ( P<0.05) . Binary logistic regression analysis, adjusted for tumor size and lymph node metastasis, identified arterial phase IC ( OR=3.45) , NIC ( OR=2.73) , λ HU ( OR=2.59) , and Z eff ( OR=1.76) as independent risk factors for EGFR-positive breast cancer ( P<0.05) . Conclusion:DECT quantitative parameters, particularly arterial phase IC, NIC, λ HU, and Zeff, offer valuable insights into the molecular subtyping of breast cancer and EGFR expression, thereby assisting in the development of personalized treatment strategies.

Objective:To investigate the spatial distribution patterns of local tumor progression（LTP）after microwave ablation（MWA）for colorectal liver metastases（CRLMs）and identify high-risk progression zones.Methods:A total of 471 CRLM lesions from 246 patients treated with MWA at the Chinese PLA General Hospital between September 2009 and March 2022 were retrospectively analyzed. Three-dimensional visualized MRI image fusion technology was employed to evaluate the spatial relationship between ablation margins（AM）and LTP. The liver was partitioned into nine specific anatomical regions. Machine learning（Boruta algorithm）was used to assess the importance of these regions on LTP risk. Multivariate analysis of LTP was performed at the tumor level and at the patient level using the Cox mixed effects model and the Cox regression model，respectively.Results:LTP occurred in 115 lesions，with an LTP rate of 40.0%（80/200）in ablated lesions which were located in the high-risk progression area，and 12.9%（35/271）in low-risk progression area. Multivariate analysis identified AM < 5 mm，tumor size ≥3 cm and location in high-risk zones as independent risk factors for LTP. Notably，LTP in high-risk zones predominantly clustered around the ablation needle tip.Conclusions:Post-MWA LTP in CRLMs exhibits distinct spatial clustering，particularly at the needle tip within high-risk progression zones. These findings provide critical insights for optimizing ablation strategies and improving clinical outcomes.

Phospholipid pharmaceutical excipients are primarily composed of a glycerol backbone,fatty acid chains,and phosphate groups.Due to their amphiphilic structure and unique physicochemical properties,they are commonly used as emulsifiers and carrier materials in pharmaceutical formulations.The pharmacopoeias of various countries have included phospholipid based pharmaceutical excipients and specified the testing methods and limits for their critical quality attributes.This article focuses on the current inclusion status,characteristic indicators,component content determination indicators,and functionality-related indicators determination methods and limits of phospholipid pharmaceutical excipients of various countries,in order to provide references and a basis for the rational applicatione of phospholipid pharmaceutical excipients.