Amino acid metabolism plays a critical role in the progression and development of breast cancer. Cancer cells, including those in breast cancer, reprogram amino acid metabolism to meet the demands of rapid proliferation, survival, and immune evasion. This includes alterations in the uptake and utilization of amino acids, such as glutamine, serine, glycine, and arginine, which provide essential building blocks for biosynthesis, energy production, and redox homeostasis. Notably, the metabolic phenotypes of breast cancer cells vary across molecular subtypes and disease stages, emphasizing the need for patient stratification and personalized therapeutic strategies. Advances in multi-level diagnostics, including phenotyping and predictive tools, such as AI-based analysis and body fluid profiling, have highlighted the potential for tailoring treatments to individual metabolic profiles. Enzymes, such as glutaminase and serine hydroxymethyltransferase, often upregulated in breast cancer, represent promising therapeutic targets. Understanding the interplay between amino acid metabolism and breast cancer biology, alongside the integration of personalized medicine approaches, can uncover novel insights into tumor progression and guide the development of precision therapies. This review explores the metabolic pathways of amino acids in breast cancer, with a focus on their implications for personalized treatment strategies.
Humans ; Breast Neoplasms/therapy* ; Female ; Amino Acids/metabolism*

Accurate prediction of drug responses in cancer cell lines (CCLs) and transferable prediction of clinical drug responses using CCLs are two major tasks in personalized medicine. Despite the rapid advancements in existing computational methods for preclinical and clinical cancer drug response (CDR) prediction, challenges remain regarding the generalization of new drugs that are unseen in the training set. Herein, we propose a multimodal fusion deep learning (DL) model called drug-target and single-cell language based CDR (DTLCDR) to predict preclinical and clinical CDRs. The model integrates chemical descriptors, molecular graph representations, predicted protein target profiles of drugs, and cell line expression profiles with general knowledge from single cells. Among these features, a well-trained drug-target interaction (DTI) prediction model is used to generate target profiles of drugs, and a pretrained single-cell language model is integrated to provide general genomic knowledge. Comparison experiments on the cell line drug sensitivity dataset demonstrated that DTLCDR exhibited improved generalizability and robustness in predicting unseen drugs compared with previous state-of-the-art baseline methods. Further ablation studies verified the effectiveness of each component of our model, highlighting the significant contribution of target information to generalizability. Subsequently, the ability of DTLCDR to predict novel molecules was validated through in vitro cell experiments, demonstrating its potential for real-world applications. Moreover, DTLCDR was transferred to the clinical datasets, demonstrating satisfactory performance in the clinical data, regardless of whether the drugs were included in the cell line dataset. Overall, our results suggest that the DTLCDR is a promising tool for personalized drug discovery.

OBJECTIVE: To construct a longitudinal trajectory model of arterial oxygen tension (PaO₂) within 24 hours after cardiac arrest (CA). METHODS: A retrospective cohort study was conducted. CA patients admitted to the ICU from 2014 to 2015 were selected from the eICU Collaborative Research Database (eICU-CRD). Data about patients' demographic characteristics, history of comorbidities, laboratory test indicators within 24 hours of intensive care unit (ICU) admission [including all PaO₂ data and arterial carbon dioxide tension (PaCO₂)], vasopressor use, and clinical outcomes were extracted from the database. The primary outcome variable was all-cause in-hospital mortality. Group-based trajectory model (GBTM) were built based on the changes in PaO₂ within 24 hours of ICU admission, and patients were grouped according to their initial static PaO₂ values upon ICU admission. Multivariable adjusted Poisson regression analysis was used to compare the in-hospital mortality risk among patients in different PaO₂ dynamic trajectory groups. Sensitivity analyses were performed using multivariable logistic regression and multivariable adjusted Poisson regression without imputation of missing values. RESULTS: A total of 3 866 CA patients were included. Three GBTM trajectory groups were identified based on PaO₂ changes within 24 hours of ICU admission: Group-1 (low level first increased then decreased, 148 cases), Group-2 (sustained low level, 3 040 cases), and Group-3 (first high level then decreased, 678 cases). Significant differences were found among the three groups in age, body weight, maximum serum potassium, maximum PaCO₂, minimum hemoglobin (Hb), vasopressor use, total hospitalization time, ICU stay, and hospital mortality. After incorporating variables with significant differences into the multivariable adjusted Poisson regression model, results showed that compared to Group-2 patients, patients in Group-1 and Group-3 had an increased risk of all-cause in-hospital mortality [Group-1 adjusted relative risk (aRR) = 1.20, 95% confidence interval (95%CI) was 1.02-1.41; Group-3 aRR = 1.11, 95%CI was 1.01-1.24]. Based on initial static PaO₂ values at ICU admission, patients were divided into four groups: PaO₂ < 100 mmHg (1 mmHg = 0.133 kPa; 1 217 cases), PaO₂ 100-200 mmHg (569 cases), PaO₂ 201-300 mmHg (547 cases), and PaO₂ > 300 mmHg (1 082 cases). Multivariable adjusted Poisson regression analysis indicated a significant upward trend in aRR for the latter three groups compared to the PaO₂ < 100 mmHg group. Sensitivity analyses revealed that compared to Group-2, patients in Group-1 and Group-3 had a significantly increased risk of all-cause in-hospital mortality (both P < 0.05). CONCLUSIONS Within 24 hours after return of spontaneous circulation in CA patients, PaO₂ exhibits different dynamic trajectories, and patients with hyperoxia have an increased risk of in-hospital mortality.
Humans ; Retrospective Studies ; Hospital Mortality ; Heart Arrest/blood* ; Prognosis ; Oxygen/blood* ; Intensive Care Units ; Cardiopulmonary Resuscitation ; Male ; Female ; Middle Aged

OBJECTIVE: Huachansu injection (HCSI), a promising anti-cancer Chinese medicine injection, has been reported to have the potential for reducing the toxicity of chemotherapy and improving the quality of life for colorectal cancer (CRC) patients. The objective of this study is to explore the synergistic and detoxifying effects of HCSI when used in combination with irinotecan (CPT-11). METHODS: To investigate the effect of HCSI on anti-CRC efficacy and intestinal toxicity of CPT-11, we measured changes in the biological behavior of LoVo cells in vitro, and anti-tumor effects in LoVo cell xenograft nude mice models in vivo. Meanwhile, the effect of HCSI on intestinal toxicity and the uridine diphosphate-glucuronosyltransferase 1A1 (UGT1A1) expression was investigated in the CPT-11-induced colitis mouse model. Subsequently, we measured the effect of HCSI and its 13 constituent bufadienolides on the expression of UGT1A1 and organic anion transporting polypeptides 1B3 (OATP1B3) in HepG2 cells. RESULTS: The combination index (CI) results showed that the combination of HCSI and CPT-11 exhibited a synergistic effect (CI < 1), which significantly suppressing the LoVo cell migration, enhancing G2/M and S phase arrest, and inhibiting tumor growth in vivo. Additionally, the damage to intestinal tissues was attenuated by HCSI in CPT-11-induced colitis model, while the increased expression of UGT1A1 in HepG2 cells and in mouse was observed. CONCLUSION The co-therapy with HCSI alleviated the intestinal toxicity induced by CPT-11 and exerted an enhanced anti-CRC effect. The detoxifying mechanism may be related to the increased expression of UGT1A1 and OATP1B3 by HCSI and its bufadienolides components. The findings of this study may serve as a theoretical insights and strategies to improve CRC patient outcomes. Please cite this article as: Jiang B, Meng ZY, Hu YJ, Chen JJ, Zong L, Xu LY, Zhang XQ, Zhang JX, Han YL. Huachansu injection enhances anti-colorectal cancer efficacy of irinotecan and alleviates its induced intestinal toxicity through upregulating UGT1A1-OATP1B3 expression in vitro and in vivo. J Integr Med. 2025; 23(5):576-590.
Irinotecan/therapeutic use* ; Animals ; Glucuronosyltransferase/genetics* ; Humans ; Colorectal Neoplasms/metabolism* ; Drugs, Chinese Herbal/therapeutic use* ; Mice, Nude ; Mice ; Up-Regulation/drug effects* ; Male ; Xenograft Model Antitumor Assays ; Mice, Inbred BALB C ; Hep G2 Cells ; Cell Line, Tumor ; Intestines/drug effects* ; Amphibian Venoms

This study aimed to characterize and identify the non-volatile components in aqueous and ethanolic extracts of the stems and leaves of Rhododendron tomentosum by using sensitive and efficient ultra-performance liquid chromatography-quadrupole-time of flight mass spectrometry(UHPLC-Q-TOF-MS) combined with a self-built information database. By comparing with reference compounds, analyzing fragment ion information, searching relevant literature, and using a self-built information database, 118 compounds were identified from the aqueous and ethanolic extracts of R. tomentosum, including 35 flavonoid glycosides, 15 phenolic glycosides, 12 flavonoids, 7 phenolic acids, 7 phenylethanol glycosides, 6 tannins, 6 phospholipids, 5 coumarins, 5 monoterpene glycosides, 6 triterpenes, 3 fatty acids, and 11 other types of compounds. Among them, 102 compounds were reported in R. tomentosum for the first time, and 36 compounds were identified by comparing them with reference compounds. The chemical components in the ethanolic and aqueous extracts of R. tomentosum leaves and stems showed slight differences, with 84 common chemical components accounting for 71.2% of the total 118 compounds. This study systematically characterized and identified the non-volatile chemical components in the ethanolic and aqueous extracts of R. tomentosum for the first time. The findings provide a reference for active ingredient research, quality control, and product development of R. tomentosum.
Rhododendron/chemistry* ; Chromatography, High Pressure Liquid/methods* ; Drugs, Chinese Herbal/chemistry* ; Mass Spectrometry/methods* ; Plant Leaves/chemistry*

In recent years the detection rate of Elizabethkingia anopheles(EA)in China has shown an upward trend,making it one of the important pathogens causing hospital and community infections.EA accounts for 59.3％to 96.2％of the Elizabethkingia species,and can cause meningitis,pneumonia,bacteremia,etc.There are limited researches on its pathogenic mechanism,which mainly involves various virulence factors.EA is a mul-tidrug-resistant bacterium,with drug resistance mechanisms including biofilm formation,efflux pumps and carry-ing multiple resistance genes(such as GOB,BlaB).Currently,minocycline,piperacillin/tazobactam,trime-thoprim/sulfamethoxazole and rifampin are effective drugs for treating EA infections.Clinically,efforts are be-ing made to find the optimal antibacterial drug combination to achieve ideal therapeutic effects.This paper reviews the epidemiological characteristics,pathogenic mechanisms,drug resistance mechanisms and treatment options of EA,aiming to provide a reference for clinical prevention,control,diagnosis and treatment of EA infections.

Accurate prediction of drug responses in cancer cell lines(CCLs)and transferable prediction of clinical drug responses using CCLs are two major tasks in personalized medicine.Despite the rapid advancements in existing computational methods for preclinical and clinical cancer drug response(CDR)prediction,chal-lenges remain regarding the generalization of new drugs that are unseen in the training set.Herein,we propose a multimodal fusion deep learning(DL)model called drug-target and single-cell language based CDR(DTLCDR)to predict preclinical and clinical CDRs.The model integrates chemical descriptors,mo-lecular graph representations,predicted protein target profiles of drugs,and cell line expression profiles with general knowledge from single cells.Among these features,a well-trained drug-target interaction(DTI)prediction model is used to generate target profiles of drugs,and a pretrained single-cell language model is integrated to provide general genomic knowledge.Comparison experiments on the cell line drug sensitivity dataset demonstrated that DTLCDR exhibited improved generalizability and robustness in predicting unseen drugs compared with previous state-of-the-art baseline methods.Further ablation studies verified the effectiveness of each component of our model,highlighting the significant contribution of target information to generalizability.Subsequently,the ability of DTLCDR to predict novel molecules was validated through in vitro cell experiments,demonstrating its potential for real-world applications.Moreover,DTLCDR was transferred to the clinical datasets,demonstrating satisfactory performance in the clinical data,regardless of whether the drugs were included in the cell line dataset.Overall,our results suggest that the DTLCDR is a promising tool for personalized drug discovery.

ObjectiveTo delineate the species composition structure and seasonal density fluctuations of mosquito populations in Yangpu District, Shanghai, and to provide scientific insights for the prevention and control of vector-borne diseases. MethodsA total of 12 monitoring sites from different habitats were selected to conduct adult mosquito monitoring using the CO₂ trap light method during 2019‒2023, elucidating the temporal and spatial distribution and change patterns of mosquito populations and densities in Yangpu District. ResultsFrom 2019 to 2023, a total of 1 321 female mosquitoes was captured, with an overall density of 0.16 case·(lamp·h)^-1. The species identified were predominantly Culex pipiens pallens (87.28%), followed by Aedes albopictus (12.64%), and Culex tritaeniorhynchus (0.08%). Seasonal distribution showed a typical single-peak pattern, with June‒August being the peak period. In terms of habitat distribution, the highest mosquito population density was found in residential areas [0.18 case·(lamp·h)^-1], followed by hospitals [0.13 case·(lamp·h)^-1] and parks [0.11 case·(lamp·h)^-1]. Culex pipiens pallens was the dominant species in all habitats, with a density of [0.14 case·(lamp·h)^-1]. Notably, there was a statistically significant difference in the composition of Culex pipiens pallens and Aedes albopictus among the three habitats mentioned(χ²=38.396, P<0.001; χ²=36.790, P<0.001). The mosquito density of the 12 streets from high to low is Changhai Road, Pingliang, Yinhang, Xinjiangwan City, Yanji, Daqiao, Wujiaochang, Kongjiang, Changbai New Village, Jiangpu, Siping and Dinghai.The distribution of mosquito density was relatively balanced across different communities (H=1.559, P=1.000). ConclusionThe results of CO₂ trap light monitoring reveals a relatively low density of adult mosquitoes in Yangpu District, with Culex pipiens pallens as the predominant species, and summer being the peak season for mosquitoes. In light of the threat of mosquito-borne diseases such as dengue fever, more effective and evidence-based mosquito control strategies, aligning with the distribution and behavioral patterns of mosquitoes, should be developed.

AIM:To establish a physiological-based pharmacokinetic(PBPK)model of ertapen-em in elderly patients with chronic kidney disease,and to analyze the pharmacokinetic/pharmacody-namic index f％ T＞MIC at different doses.METH-ODS:The physicochemical properties and pharma-cokinetic characteristics of ertapenem were collect-ed by reviewing the literature and databases,and a healthy adult model was established in PKSim? software,and then extrapolated to the PBPK model of the elderly.The clinical pharmacokinetic re-search data were used to optimize and validate the model,and the mean folding error(MFE)was used as the index to evaluate the prediction perfor-mance of the model.The final model was used to simulate the in vivo exposure of elderly patients with chronic kidney disease after administration,and the pharmacokinetic/pharmacodynamic index of commonly used clinical dosing regimens was an-alyzed,and the recommended dosing regimens were given.RESULTS:The MFE of the area under the curve(AUC0-t),peak concentration(Cmax)and peak time(Tmmax)predicted by the established PBPK model of ertapenem in adults were 0.92,0.79 and 1.02,respectively,and the predicted value of the optimized PBPK model of ertapenem in the elderly was also consistent with the observed value of 0.5＜MFE＜2 standards,all of which have good predictive performance.With f％ T＞MIC greater than 40％as the drug efficacy target,the minimum inhibitory concentration(MIC)is 0.5-1 μg/mL for sensitive bacteria,and elderly patients with chronic kidney disease can consider reducing the drug dose as ap-propriate.CONCLUSION:The PBPK model of ertap-enem in elderly patients with renal insufficiency has been successfully established,and the model has good prediction performance and provides a reference for clinical personalized medication in el-derly patients with renal insufficiency.