Onco-critical care has emerged as an important subspecialty at the intersection of critical care medicine and oncology, attracting increasing attention in recent years. With continuous innovations in cancer therapies, patient survival has improved significantly; however, the incidence of associated critical complications has also increased. The reasons for cancer patients requiring intensive care unit admission are diverse and can be broadly categorized into three groups: progression of the underlying malignancy, treatment-related complications, and coexisting classical critical illnesses. Traditional critical care concepts and practices face limitations in addressing the multidimensional and heterogeneous challenges of onco-critical care. Based on the core mechanism of critical illness development—host/organ unregulated response (HOUR)—this article systematically elaborates on how this framework advances understanding and clinical practice into onco-critical care, with emphasis on its manifestations in neuroendocrine, immune-inflammatory, and coagulation-metabolic pathways. The review summarizes recent advances in clinical assessment and phenotyping systems for onco-critical illness and discusses a multidisciplinary, integrated management strategy centered on the "Disease Control, Host Response Modulation, Organ Support" triad. Finally, major challenges and future directions in this field are outlined. By integrating existing evidence and theoretical insights, this review aims to provide new perspectives and a theoretical foundation for the clinical management of onco-critical illness, thereby promoting its evolution toward precision and standardization.

With the rapid advancement of hemodynamic indices and monitoring technologies, their classification methods and application processes have become increasingly complex. Currently, no unified standard hasbeen established, making it difficult to fully meet the clinical requirements for hemodynamic management. To assist in hemodynamic monitoring assessment and therapeutic decision-making in critically ill patients, the Critical Hemodynamic Therapy Collaborative Group, in conjunction with the Critical Ultrasound Study Group, has jointly developed the Standard for the Application of Hemodynamic Monitoring Techniques in Critical Care. The first part of this standard systematically categorizes hemodynamic indicators into flow indicators, pressure and its derivative indicators, and tissue perfusion indicators, while elaborating on the clinical application of each. The second part establishes a standardized clinical implementation pathway for hemodynamic monitoring. It proposes a tiered monitoring strategy-comprising basic, advanced, indication-specific, and special scenario monitoring-tailored to different clinical settings. It emphasizes the central role of critical care ultrasound across all levels of monitoring and establishes hemodynamic assessment standards for organs such as the brain, kidneys, and gastrointestinal tract. This standard aims to provide a unified framework for clinical practice, teaching, training, and research in critical care medicine, thereby promoting standardized development within the discipline.

With the accelerating trend of population aging, the number of elderly patients with lung cancer continues to rise, and the disease burden is becoming increasingly heavy. The clinical management of these patients faces severe challenges due to their decreased physiological reserve, complex comorbidities, and significant individual heterogeneity. Consequently, under traditional diagnosis and treatment models, doctors often struggle to identify the individualized risks of elderly patients in a timely and comprehensive manner, which can easily lead to decision biases such as undertreatment or overtreatment. In view of this, this study advocates for the establishment of an umbrella decision-making model specifically tailored for elderly lung cancer patients. Grounded in a multidisciplinary team (MDT) platform, this model deeply integrates oncological indicators with the comprehensive geriatric assessment (CGA) system. By holistically considering multidimensional variables including tumor burden, organ function, frailty index, cognitive status, and social support, the model establishes an operational mechanism characterized by "single entry, precise stratification, and targeted selection". Accordingly, patients can be scientifically triaged into distinct intervention tiers, such as active surveillance, minimally invasive surgery, drug therapy, radiotherapy, and best supportive care, thereby achieving real-time alignment between treatment intensity and patient fitness. This article elaborates on the construction logic and key operational procedures of this novel decision-making framework, aiming to guide clinical practice beyond the limitations of a tumor-centric perspective toward a holistic, dynamic, whole-course management strategy. This transition seeks to ensure optimal quality of life and clinical net benefit for elderly patients alongside survival prolongation.

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 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 explore the efficiency improvement in segmenting neural network with the application of Transformer + U-Net artificial intelligence (AI) and modeling with the application of Python scripts in three-dimensional (3D) printing brachytherapy. Methods A Transformer + U-Net AI neural network model was constructed, and Adam optimizer was used to ensure rapid gradient descent. Computed tomography or magnetic resonance imaging data of patients were standardized and processed as self-made data sets. The training set was used to train AI and the optimal result weight parameters were saved. The test set was used to evaluate the AI ability. Python programming language was used to write an automated script to obtain the output segmentation image and convert it to the STL file for import. The source applicator and needle could be automatically modeled. The time of automatic segmentation and modeling and the time of manual segmentation and modeling were entered by two people, and the difference was verified by paired t-test. Results Dice similarity coefficient (DSC), mean intersection over union (MIOU), and Hausdorff distance (HD95) were used for evaluation. DSC was 0.9341, MIOU was 0.8762, and mean HD95 was 2.516. The mean time of manual segmentation was 1187 s, and the mean time of AI segmentation was 145 s (P < 0.01). The mean time of manual modeling was 321 s, and the mean time of modeling with Python automated scripts was 18 s (P < 0.01). Conclusion The application of Transformer + U-Net automatic segmentation and Python automated scripts can effectively improve work efficiency in the 3D printing brachytherapy for cervical cancer.

Objective To investigate the diagnostic value of our regression model based on quantitative parameters of dual-energy CT and clinical features for stages T2 and T3 colorectal cancer.Methods A cross-section study was performed on 91 patients with colorectal cancer confirmed by postoperative pathology in our hospital from January 2022 to November 2023.All of them underwent dual-energy CT examination.According to the pathological T staging criteria of Chinese Colorectal Cancer Diagnosis and Treatment Standard(2020 Edition),they were divided into T2 group(n=43)and T3 group(n=48).Univariate analysis was used to compare the differences in quantitative CT parameters and clinical features between the 2 groups,and the obtained significant variables were employed to construct diagnosis models by univariate or multivariate logistic regression analysis.The area under receiver operating characteristic curve(AUC)of the CT parametric model and the model combined with clinical features was compared to evaluate the efficacy of diagnosing T2 and T3 stages.Results Univariate analysis showed that carcinoembryonic antigen(CEA),N stage,tumor location,tumor longest diameter(LD),CT value of virtual noncontrast(CT-VNC),fat fraction,electron density(Rho)and dual energy index(DEI)were significantly different between the T2 and T3 groups(P＜0.05).Multivariate logistic regression analysis found that N stage,tumor location,LD,fat fraction and DEI were independent risk factors for the diagnosis of stage T3.The AUC value of the model of above CT parameters in diagnosing stage T3 colorectal cancer was 0.671(95％CI:0.558～0.783),and the AUC value of the combined model of above CT parameters and clinical features was 0.886(95％CI:0.815～0.957),and statistical difference was observed in the AUC value between the combined model and the CT parametric model(P＜0.01).Conclusion The regression model constructed with dual-energy CT quantitative parameters combined with clinical features has high value in the preoperative diagnosis of stages T2 and T3 colorectal cancer before surgery.

OBJECTIVE: To assess the safety and topical efficacy of prim-O-glucosylcimifugin (POG) and investigate the molecular mechanisms of its therapeutic effects in atopic dermatitis (AD). METHODS: The effects of POG on human keratinocyte cell viability and its anti-inflammatory properties were evaluated using cell counting kit-8 assay and reverse transcription-quantitative polymerase chain reaction (RT-qPCR). Subsequently, the impact of POG on the differentiation of cluster of differentiation (CD) 4⁺ T cell subsets, including T-helper type (Th) 1, Th2, Th17, and regulatory T (Treg), was examined through in vitro experiments. Network pharmacology analysis was used to elucidate POG's therapeutic mechanisms. Furthermore, the therapeutic potential of topically applied POG was further evaluated in a calcipotriol-induced mouse model of AD. The protein and transcript levels of inflammatory markers, including cytokines, lymphocyte-specific protein tyrosine kinase (Lck) mRNA, and LCK phosphorylation (p-LCK), were quantified using immunohistochemistry, RT-qPCR, and Western blot analysis. RESULTS: POG was able to suppress cell proliferation and downregulate the transcription of interleukin 4 (Il4) and Il13 mRNA. In vitro experiments indicated that POG significantly inhibited the differentiation of Th2 cells, whereas it exerted negligible influence on the differentiation of Th1, Th17 and Treg cells. Network pharmacology identified LCK as a key therapeutic target of POG. Moreover, the topical application of POG effectively alleviated skin lesions in the calcipotriol-induced AD mouse models without causing pathological changes in the liver, kidney or spleen tissues. POG significantly reduced the levels of Il4, Il5, Il13, and thymic stromal lymphopoietin (Tslp) mRNA in the AD mice. Concurrently, POG enhanced the expression of p-LCK protein and Lck mRNA. CONCLUSION Our research revealed that POG inhibits Th2 cell differentiation by promoting p-LCK protein expression and hence effectively alleviates AD-related skin inflammation. Please cite this article as: Zhao H, Ma X, Wang H, Ding XJ, Kuai L, Song JK, Zhang Z, Yang D, Gao CJ, Li B, Zhou M. Prim-O-glucosylcimifugin mitigates atopic dermatitis by inhibiting Th2 differentiation through LCK phosphorylation modulation. J Integr Med. 2025; 23(3): 309-319.
Dermatitis, Atopic/drug therapy* ; Animals ; Humans ; Cell Differentiation/drug effects* ; Phosphorylation/drug effects* ; Mice ; Th2 Cells/drug effects* ; Keratinocytes/drug effects* ; Disease Models, Animal ; Mice, Inbred BALB C ; Calcitriol/analogs & derivatives*

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.

Primary liver cancer is one of the most common malignant tumors of the digestive system， and the “inflammation-to-cancer transformation” （ICT） of chronic hepatitis is the core pathological process of the progression of chronic hepatitis to liver cancer. Persistent and uncontrolled liver inflammation in patients with chronic hepatitis often leads to repeated liver tissue damage and repair， which gradually develops into liver fibrosis and cirrhosis， eventually leading to malignant transformation through the mechanisms such as gene mutation and microenvironment imbalance. ICT in chronic hepatitis is the key link between chronic hepatitis and liver cancer， and its dynamic evolution involves various pathogenic factors such as dampness， heat， deficiency， toxin， and stasis； among which damp-heat and vital energy deficiency are the initiating factors for ICT of chronic hepatitis， while intermingled stasis and toxin are the key pathological products that promote malignant transformation. Based on the concept of preventive treatment， traditional Chinese medicine can effectively delay and even block the ICT of chronic hepatitis by regulating inflammation， metabolism， and abnormal cell proliferation through multiple targets， which provides important strategies and research directions for the prevention and treatment of liver cancer.