The detection of minimal residual lesions represented by circulating tumor DNA can detect residual cancer lesions that cannot be displayed by traditional imaging in advance. The positive status and dynamic changes are closely related to the prognosis evaluation of various solid tumors, the effectiveness of neoadjuvant therapy, the prediction of postoperative recurrence risk, and the precise implementation of adjuvant therapy. This article summarizes the advantages and challenges faced by the application of circulating tumor DNA testing in individualized multidisciplinary diagnosis and treatment of esophageal cancer, in order to better carry out precise diagnosis and treatment of esophageal cancer under the guidance of minimal residual lesion detection.

Objective:This study aims to develop a rapid identification technique for various genotypes of extended-spectrum β-lactamase (ESBL) producing Escherichia coli using matrix-assisted laser desorption/ionization-time of flight mass spectrometry (MALDI-TOF MS) in conjunction with machine learning algorithms. Methods:A total of 158 Escherichia coli strains were isolated from the clinical laboratory of the First Hospital of Jilin University from August 2018 to December 2022. Polymerase chain reaction (PCR) was employed to detect the CTX-M-1, CTX-M-8, CTX-M-9, and SHV genes. Mass spectral data of the bacterial strains were acquired by MALDI-TOF MS with a cooperative matrix of (E)-propyl α-cyano-4-hydroxycinnamate (CHCA-C3). Models based on random forest (RF), logistic regression (LR), and support vector machine (SVM) algorithms were constructed. The performance of the constructed models was evaluated using metrics including accuracy, sensitivity, specificity, and the area under the receiver operating characteristic curve (AUC). Mass spectral peaks exhibiting sensitivity and specificity exceeding 80% in the models were designated as characteristic peaks. To validate the efficacy of the cooperative matrix of CHCA-C3, clinical isolates of ESBL-producing Escherichia coli were analyzed by MALDI-TOF MS using the conventional CHCA matrix for comparative purposes. Results:Among the 158 strains of Escherichia coli, 91 strains produced ESBL, all of which were CTX-M genotype. The AUC values for the respective models were as follows: CTX-M-1 genotype exhibited AUC values of 0.98 for LR, 1.00 for RF, and 0.73 for SVM; CTX-M-9 genotype exhibited AUC values of 0.93 for LR, 0.99 for RF, and 0.76 for SVM; for CTX-M-8, all models achieved an AUC of 1.00, indicating excellent classification performance with respect to accuracy, specificity, and sensitivity. The characteristic mass spectral peaks associated with each genotype included: CTX-M-1 genotype at m/z 6 390; CTX-M-8 genotype at m/z 5 224, m/z 5 393, and m/z 9 021; CTX-M-9 genotype at m/z 5 161 and m/z 5 273. In the MALDI-TOF MS analysis conducted with the conventional CHCA matrix, the characteristic peak at m/z 9 021 for CTX-M-8 was the only one detected, with the characteristic peaks for CTX-M-1 and CTX-M-9 remaining undetected. Conclusion:The application of cooperative matrix of CHCA-C3 in conjunction with MALDI-TOF MS and machine learning algorithms facilitates the rapid and precise identification of extended-spectrum β-lactamase (ESBL)-producing Escherichia coli. This approach offers a feasible solution for evidence-based clinical therapy and the control of healthcare-associated infections.

Objective:To investigate the association of blood lipids and body mass index (BMI) with hyperuricemia (HUA) in the elderly.Methods:It was a cross-sectional study. A total of 114 391 elderly individuals received health examinations at primary healthcare institutions in Wujin District from January to December in 2022. The health examination included questionnaire survey, physical examination and laboratory examination. The multivariate logistic regression and restricted cubic spline (RCS) plots were used to analyze the association and dose-response relationship of blood lipid and BMI with HUA. The mediating effect model was used to explore the mediation effect of BMI on the association between blood lipid and HUA.Results:Among the 112 415 subjects, 18 506 (16.46%) were checked with HUA. After adjusting for relevant confounders, total cholesterol (TC) ( OR=1.20, 95% CI: 1.16-1.23), triglyceride (TG) ( OR=1.46, 95% CI: 1.44-1.49), high density lipoprotein cholesterol (HDL-C) ( OR=0.74, 95% CI: 0.73-0.76), low density lipoprotein cholesterol (LDL-C) ( OR=1.14, 95% CI: 1.12-1.15) and BMI ( OR=1.42, 95% CI: 1.39-1.44) were all associated with HUA (all P0.05). The RCS analysis revealed that TG, HDL-C, and LDL-C each exhibited a nonlinear dose-response relationship with HUA, the inflection points was 3.00 mmol/L, 1.57 mmol/L and 2.50 mmol/L, respectively (all P-nonlinear0.001). The results of interaction showed that there were additive interaction between high TC( S=1.27 , 95% CI: 1.17-1.37), high TG( S=1.32 , 95% CI: 1.25-1.40), high LDL-C( S=1.23 , 95% CI: 1.14-1.34) and overweight/obesity with HUA (all P0.05). The results of mediation effect analysis showed that the mediation effect of BMI on the association between blood lipids (HDL-C, LDL-C, TG and TC) and HUA, from high to low, were as follows: 22.5% (95% CI: 20.8%-24.2%), 13.9% (95% CI: 12.0%-16.2%), 13.5% (95% CI: 12.7%-14.4%) and-3.9% (95% CI:-6.6%--1.8%). Conclusion:The blood lipid levels and BMI are positively correlated with HUA in the elderly.

Proton arc therapy (PAT), as an emerging radiation therapy technique, has attracted increasing widespread attention in the field of cancer radiotherapy in recent years. In this review, technological developments, clinical application potential, biological effects, dosimetric optimization, and current challenges of PAT were comprehensively summarized. A systematic analysis of recent studies indicates that PAT offers significant advantages in improving target coverage and sparing normal tissues, particularly in mitigating organ motion-induced uncertainties. However, PAT remains in the clinical validation stage and still faces challenges related to technical optimization and cost control. Further clinical studies are required to confirm its long-term efficacy and safety.

Objective To address the challenges of constructing large language models for traditional Chinese medicine(TCM)classics,which are complex and expensive to fine-tune,this study explores a lightweight fine-tuning method for such models,aiming to develop a question-answering model centered on TCM classics,particularly various editions of Shang Han Lun through the ages.Methods Dataset construction involved designing prompts to guide GPT-4 in generating Q&A pairs based on Shang Han Lun and integrating them with the ShenNong_TCM_Dataset and cMedQA2 datasets.Five general-purpose large models were selected for Lora fine-tuning.The best model was chosen through evaluation,and the performance of multiple quantized versions was validated.Results After fine-tuning,the BLEU,ROUGE-1,ROUGE-2,and ROUGE-L metrics for the Qwen-7B-Chat model improved by 17.61,19.63,14.3,and 21.4,respectively,compared to the base model.Conclusion The selected model in this study is capable of effectively understanding and utilizing professional terms and concepts from TCM classics,such as Shang Han Lun,to provide accurate answers to user queries.Compared to similar models,it requires lower fine-tuning costs and computational power,contributing to the dissemination of TCM knowledge and the development of intelligent systems.

Objective To address the challenges of constructing large language models for traditional Chinese medicine(TCM)classics,which are complex and expensive to fine-tune,this study explores a lightweight fine-tuning method for such models,aiming to develop a question-answering model centered on TCM classics,particularly various editions of Shang Han Lun through the ages.Methods Dataset construction involved designing prompts to guide GPT-4 in generating Q&A pairs based on Shang Han Lun and integrating them with the ShenNong_TCM_Dataset and cMedQA2 datasets.Five general-purpose large models were selected for Lora fine-tuning.The best model was chosen through evaluation,and the performance of multiple quantized versions was validated.Results After fine-tuning,the BLEU,ROUGE-1,ROUGE-2,and ROUGE-L metrics for the Qwen-7B-Chat model improved by 17.61,19.63,14.3,and 21.4,respectively,compared to the base model.Conclusion The selected model in this study is capable of effectively understanding and utilizing professional terms and concepts from TCM classics,such as Shang Han Lun,to provide accurate answers to user queries.Compared to similar models,it requires lower fine-tuning costs and computational power,contributing to the dissemination of TCM knowledge and the development of intelligent systems.

Objective:To investigate the association of blood lipids and body mass index (BMI) with hyperuricemia (HUA) in the elderly.Methods:It was a cross-sectional study. A total of 114 391 elderly individuals received health examinations at primary healthcare institutions in Wujin District from January to December in 2022. The health examination included questionnaire survey, physical examination and laboratory examination. The multivariate logistic regression and restricted cubic spline (RCS) plots were used to analyze the association and dose-response relationship of blood lipid and BMI with HUA. The mediating effect model was used to explore the mediation effect of BMI on the association between blood lipid and HUA.Results:Among the 112 415 subjects, 18 506 (16.46%) were checked with HUA. After adjusting for relevant confounders, total cholesterol (TC) ( OR=1.20, 95% CI: 1.16-1.23), triglyceride (TG) ( OR=1.46, 95% CI: 1.44-1.49), high density lipoprotein cholesterol (HDL-C) ( OR=0.74, 95% CI: 0.73-0.76), low density lipoprotein cholesterol (LDL-C) ( OR=1.14, 95% CI: 1.12-1.15) and BMI ( OR=1.42, 95% CI: 1.39-1.44) were all associated with HUA (all P0.05). The RCS analysis revealed that TG, HDL-C, and LDL-C each exhibited a nonlinear dose-response relationship with HUA, the inflection points was 3.00 mmol/L, 1.57 mmol/L and 2.50 mmol/L, respectively (all P-nonlinear0.001). The results of interaction showed that there were additive interaction between high TC( S=1.27 , 95% CI: 1.17-1.37), high TG( S=1.32 , 95% CI: 1.25-1.40), high LDL-C( S=1.23 , 95% CI: 1.14-1.34) and overweight/obesity with HUA (all P0.05). The results of mediation effect analysis showed that the mediation effect of BMI on the association between blood lipids (HDL-C, LDL-C, TG and TC) and HUA, from high to low, were as follows: 22.5% (95% CI: 20.8%-24.2%), 13.9% (95% CI: 12.0%-16.2%), 13.5% (95% CI: 12.7%-14.4%) and-3.9% (95% CI:-6.6%--1.8%). Conclusion:The blood lipid levels and BMI are positively correlated with HUA in the elderly.

Objective:This study aims to develop a rapid identification technique for various genotypes of extended-spectrum β-lactamase (ESBL) producing Escherichia coli using matrix-assisted laser desorption/ionization-time of flight mass spectrometry (MALDI-TOF MS) in conjunction with machine learning algorithms. Methods:A total of 158 Escherichia coli strains were isolated from the clinical laboratory of the First Hospital of Jilin University from August 2018 to December 2022. Polymerase chain reaction (PCR) was employed to detect the CTX-M-1, CTX-M-8, CTX-M-9, and SHV genes. Mass spectral data of the bacterial strains were acquired by MALDI-TOF MS with a cooperative matrix of (E)-propyl α-cyano-4-hydroxycinnamate (CHCA-C3). Models based on random forest (RF), logistic regression (LR), and support vector machine (SVM) algorithms were constructed. The performance of the constructed models was evaluated using metrics including accuracy, sensitivity, specificity, and the area under the receiver operating characteristic curve (AUC). Mass spectral peaks exhibiting sensitivity and specificity exceeding 80% in the models were designated as characteristic peaks. To validate the efficacy of the cooperative matrix of CHCA-C3, clinical isolates of ESBL-producing Escherichia coli were analyzed by MALDI-TOF MS using the conventional CHCA matrix for comparative purposes. Results:Among the 158 strains of Escherichia coli, 91 strains produced ESBL, all of which were CTX-M genotype. The AUC values for the respective models were as follows: CTX-M-1 genotype exhibited AUC values of 0.98 for LR, 1.00 for RF, and 0.73 for SVM; CTX-M-9 genotype exhibited AUC values of 0.93 for LR, 0.99 for RF, and 0.76 for SVM; for CTX-M-8, all models achieved an AUC of 1.00, indicating excellent classification performance with respect to accuracy, specificity, and sensitivity. The characteristic mass spectral peaks associated with each genotype included: CTX-M-1 genotype at m/z 6 390; CTX-M-8 genotype at m/z 5 224, m/z 5 393, and m/z 9 021; CTX-M-9 genotype at m/z 5 161 and m/z 5 273. In the MALDI-TOF MS analysis conducted with the conventional CHCA matrix, the characteristic peak at m/z 9 021 for CTX-M-8 was the only one detected, with the characteristic peaks for CTX-M-1 and CTX-M-9 remaining undetected. Conclusion:The application of cooperative matrix of CHCA-C3 in conjunction with MALDI-TOF MS and machine learning algorithms facilitates the rapid and precise identification of extended-spectrum β-lactamase (ESBL)-producing Escherichia coli. This approach offers a feasible solution for evidence-based clinical therapy and the control of healthcare-associated infections.

The detection of minimal residual lesions represented by circulating tumor DNA can detect residual cancer lesions that cannot be displayed by traditional imaging in advance. The positive status and dynamic changes are closely related to the prognosis evaluation of various solid tumors, the effectiveness of neoadjuvant therapy, the prediction of postoperative recurrence risk, and the precise implementation of adjuvant therapy. This article summarizes the advantages and challenges faced by the application of circulating tumor DNA testing in individualized multidisciplinary diagnosis and treatment of esophageal cancer, in order to better carry out precise diagnosis and treatment of esophageal cancer under the guidance of minimal residual lesion detection.

Proton arc therapy (PAT), as an emerging radiation therapy technique, has attracted increasing widespread attention in the field of cancer radiotherapy in recent years. In this review, technological developments, clinical application potential, biological effects, dosimetric optimization, and current challenges of PAT were comprehensively summarized. A systematic analysis of recent studies indicates that PAT offers significant advantages in improving target coverage and sparing normal tissues, particularly in mitigating organ motion-induced uncertainties. However, PAT remains in the clinical validation stage and still faces challenges related to technical optimization and cost control. Further clinical studies are required to confirm its long-term efficacy and safety.