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.

Protein acylation is a group of recently identified chemical modification closely linked to me-tabolism with the metabolic intermediate acyl-coenzyme A(acyl-coA)as the substrate.Acylation posses-ses similar chemical structures to acetylation,with differences in carbon chain length,hydrophobicity,and charge.Most acyl-CoAs are intermediate metabolites,the level of which are influenced by the intra-cellular metabolic state.Therefore,protein acylation is greatly affected by cellular metabolism.Metabolic reprogramming is an important feature of tumor cells.In addition to the classic"Warburg effect",cancer cells exhibit abnormal regulation in lipid metabolism,amino acid metabolism and biological oxidation.Acylation on histones can impact chromatin structure,regulating gene expression and DNA repair critical-ly involved in cancer progression.On the other hand,acylation on non-histones can regulate signal trans-duction,enzymatic activity,or protein-protein interactions to affect cancer cell behaviors such as prolifer-ation,invasion,immune evasion,and vascular remodeling.Focusing on three types of acylation closely related to metabolism:lactylation,succinylation,and crotonylation,this article introduces the produc-tion,raw materials,regulatory mechanism and factors of protein acylation.We then review representative studies to show how cancer cell metabolic reprogramming can regulate these processes and histone/non-histone acylation levels,which subsequently affect the expression and function metabolism-related genes/proteins to form a bidirectional dialogue and accelerate cancer progression.In addition,we present sever-al prospects for potential research directions and translational applications in the field.

The development status of the key technologies of first-aid robotics was introduced in autonomous system,embo-died intelligence,digital twins,large artificial intelligence model and autonomous unmanned medical treatment.The present situation in first-aid robotics equipment was reviewed for first-aid diagnosis,treatment,assistance and transportation.The development trends of the key technology and equipment of first-aid robotics were analyzed.It was pointed out the involve-ment of big model-based embodied intelligence technology and digital twins technology in first aid might provide new pers-pectives for the application and advancement of specialized first-aid robotics.[Chinese Medical Equipment Journal,2025,46(3):96-114]

Objective To propose a brain electrical phase prediction method based on long short-term memory network(LSTM)to improve the accuracy and robustness of phase synchronization prediction in transcranial magnetic stimulation(TMS).Methods First,an LSTM consisting of an input layer,an LSTM layer,an ReLU activation layer,a fully connected layer and a regression layer was constructed to capture the EEG signal features through the synergistic action of input gates,forgetting gates and output gates.Second,eye-open resting-state EEG data from 30 healthy subjects were trained using the LSTM to obtain a predictive model for EEG signal and EEG phase prediction.Finally,the LSTM method and the traditional autoregressive(AR)method were compared in terms of the phase prediction errors at the overall and individual levels and the prediction performance for peaks and troughs.A regression model was used to explore the relationships between instantaneous EEG amplitude,signal-to-noise ratio and phase prediction error with the LSTM method.Results The LSTM method achieved a total phase prediction error of 0.04°±5.69°,which was lower than that of the traditional AR method(-3.36°±51.13°).For each subject,the LSTM method demonstrated superior phase prediction accuracy compared to the traditional AR method(P＜0.001).The accuracy for predicting peaks(troughs)by the LSTM method(about 89％)was higher than that by the traditional AR method(about 10％).Unlike the traditional AR method,the LSTM method didnot result in linear relationships between instantaneous EEG amplitude,signal-to-noise ratio and phase prediction error,with Pvalues being 0.58 and 0.18,respectively.Conclusion The LSTM-based brain electrical phase prediction method shows high accuracy and robustness when used for EEG phase-synchronized TMS.[Chinese Medical Equipment Journal,2025,46(3):1-8]

The detection rate of thyroid nodules has gradually increased in recent years.Comprehensive and accurate preoperative evaluation and early identification of risk factors help doctors to choose treatment options and improve prognosis.Radiomics extracts quantitative features from medical images for evaluation of thyroid nodules through high-throughput mining of invisible image features,which has been widely used and has excellent performance in the identification of benign and malignant thyroid nodules,lymph node metastasis,extrathyroidal extension,molecular biological changes,recurrence and prognosis of thyroid cancer.However,there are also shortcomings such as large differences in performance among models from different institutions.This article reviews the application value,limitations and future development prospects of radiomics in the thyroid nodules,so as to provide new ideas for clinical practice and research.

The development status of the key technologies of first-aid robotics was introduced in autonomous system,embo-died intelligence,digital twins,large artificial intelligence model and autonomous unmanned medical treatment.The present situation in first-aid robotics equipment was reviewed for first-aid diagnosis,treatment,assistance and transportation.The development trends of the key technology and equipment of first-aid robotics were analyzed.It was pointed out the involve-ment of big model-based embodied intelligence technology and digital twins technology in first aid might provide new pers-pectives for the application and advancement of specialized first-aid robotics.[Chinese Medical Equipment Journal,2025,46(3):96-114]

Objective To propose a brain electrical phase prediction method based on long short-term memory network(LSTM)to improve the accuracy and robustness of phase synchronization prediction in transcranial magnetic stimulation(TMS).Methods First,an LSTM consisting of an input layer,an LSTM layer,an ReLU activation layer,a fully connected layer and a regression layer was constructed to capture the EEG signal features through the synergistic action of input gates,forgetting gates and output gates.Second,eye-open resting-state EEG data from 30 healthy subjects were trained using the LSTM to obtain a predictive model for EEG signal and EEG phase prediction.Finally,the LSTM method and the traditional autoregressive(AR)method were compared in terms of the phase prediction errors at the overall and individual levels and the prediction performance for peaks and troughs.A regression model was used to explore the relationships between instantaneous EEG amplitude,signal-to-noise ratio and phase prediction error with the LSTM method.Results The LSTM method achieved a total phase prediction error of 0.04°±5.69°,which was lower than that of the traditional AR method(-3.36°±51.13°).For each subject,the LSTM method demonstrated superior phase prediction accuracy compared to the traditional AR method(P＜0.001).The accuracy for predicting peaks(troughs)by the LSTM method(about 89％)was higher than that by the traditional AR method(about 10％).Unlike the traditional AR method,the LSTM method didnot result in linear relationships between instantaneous EEG amplitude,signal-to-noise ratio and phase prediction error,with Pvalues being 0.58 and 0.18,respectively.Conclusion The LSTM-based brain electrical phase prediction method shows high accuracy and robustness when used for EEG phase-synchronized TMS.[Chinese Medical Equipment Journal,2025,46(3):1-8]

The detection rate of thyroid nodules has gradually increased in recent years.Comprehensive and accurate preoperative evaluation and early identification of risk factors help doctors to choose treatment options and improve prognosis.Radiomics extracts quantitative features from medical images for evaluation of thyroid nodules through high-throughput mining of invisible image features,which has been widely used and has excellent performance in the identification of benign and malignant thyroid nodules,lymph node metastasis,extrathyroidal extension,molecular biological changes,recurrence and prognosis of thyroid cancer.However,there are also shortcomings such as large differences in performance among models from different institutions.This article reviews the application value,limitations and future development prospects of radiomics in the thyroid nodules,so as to provide new ideas for clinical practice and research.

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.

Protein acylation is a group of recently identified chemical modification closely linked to me-tabolism with the metabolic intermediate acyl-coenzyme A(acyl-coA)as the substrate.Acylation posses-ses similar chemical structures to acetylation,with differences in carbon chain length,hydrophobicity,and charge.Most acyl-CoAs are intermediate metabolites,the level of which are influenced by the intra-cellular metabolic state.Therefore,protein acylation is greatly affected by cellular metabolism.Metabolic reprogramming is an important feature of tumor cells.In addition to the classic"Warburg effect",cancer cells exhibit abnormal regulation in lipid metabolism,amino acid metabolism and biological oxidation.Acylation on histones can impact chromatin structure,regulating gene expression and DNA repair critical-ly involved in cancer progression.On the other hand,acylation on non-histones can regulate signal trans-duction,enzymatic activity,or protein-protein interactions to affect cancer cell behaviors such as prolifer-ation,invasion,immune evasion,and vascular remodeling.Focusing on three types of acylation closely related to metabolism:lactylation,succinylation,and crotonylation,this article introduces the produc-tion,raw materials,regulatory mechanism and factors of protein acylation.We then review representative studies to show how cancer cell metabolic reprogramming can regulate these processes and histone/non-histone acylation levels,which subsequently affect the expression and function metabolism-related genes/proteins to form a bidirectional dialogue and accelerate cancer progression.In addition,we present sever-al prospects for potential research directions and translational applications in the field.