Laparoscopic pancreaticoduodenectomy(LPD) poses a high risk of intraoperative bleeding due to the complex anatomy and rich blood supply in the pancreatic head region. This paper innovatively proposes a blood flow control technique system for LPD, adopting a strategy of “priority devascularization and pre-blocking”.By first addressing the peripheral collateral blood supply and the gastroduodenal artery, and then performing dual-system pre-blocking, the dorsal pancreatic artery and the inferior pancreaticoduodenal artery are treated in situ through a combined middle and left posterior approach. This progressive blood flow control method enhances surgical safety and oncological radicality, offering a new paradigm for the development of minimally invasive pancreatic surgery.

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A retrospective analysis was conducted on 26 patients with relapsed and refractory multiple myeloma (RRMM) who were treated with carfilzomib-based regimens at Beijing Chaoyang Integrative Medicine Rescue and First Aid Hospital from July 2021 to May 2024. The median number of treatment cycles was 5 (range, 2-8). The overall response rate was 53.8% (14/26). The median follow-up duration was 9.5 months, and the median progression-free survival (PFS) was 8.6 months (range, 1.7-25.2 months). Patients without plasmacytoma recurrence ( n=17) had a PFS of 12.3 months. In contrast, patients with plasmacytoma recurrence ( n=9) had a PFS of 6.2 months. The difference in PFS between these two groups was statistically significant ( P=0.013). Age (over 65 vs. 65 or younger), and treatment line (more than three vs. three or fewer) did not significantly affect treatment efficacy (all P>0.05). Common adverse reactions of grade 3 or higher included hematologic adverse reactions in 10 cases, which improved with symptomatic treatment. Non-hematologic adverse reactions included pulmonary infection (2 cases), renal failure (1 case), and heart failure (1 case). In conclusion, carfilzomib-based regimens demonstrate good clinical efficacy and manageable safety in the treatment of RRMM.

With the continuous development of genomics and precision medicine,targeted therapy and immunotherapy targeting biomarkers have ush-ered in a new era of anti-tumor therapy.However,due to the heterogeneity of tumor cells and the variability of tumor microenvironment,there are still significant differences in response to the same drug even in patient populations with the same bio-marker enrichment.By combining omics data with drug sensitivity algorithms,the response of anti-tu-mor drugs can be predicted and transformed into personalized diagnosis and treatment strategies re-quired for precision medicine,which is expected to improve the effectiveness of anti-tumor drugs in clinical treatment.Currently,machine learning is one of the commonly used modeling algorithms for predicting the response of anti-tumor drugs.How-ever,due to differences in input data and algorithm construction methods,there is currently a lack of comprehensive literature review in this field.There-fore,this article provides a review of machine learning algorithms for predicting anti-tumor drug responses,summarizing publicly available cell ge-nome characterization datasets,machine learning algorithms,and evaluation indicators in drug re-sponse prediction,as well as the current situation and challenges faced in clinical applications,in or-der to provide methodological references for the main research problems and potential solutions of machine learning algorithms in the field of drug re-sponse prediction.

Objective To investigate the value of constructing a risk prediction model of brain metastasis in lung cancer based on clinical-CT imaging.Methods The clinical and CT imaging data of 208 patients with lung cancer confirmed by surgical pathology or puncture biopsy were analyzed retrospectively,including 98 patients in the metastasis group and 110 patients in the non-metastasis group.Univariable and binary logistic regression analyses were performed between the two groups,and the clinical,CT imaging,and clinical-CT imaging models were constructed according to the selected independent risk factors.Prediction model performance was eval-uated with receiver operating characteristic(ROC)curve,calibration curve and decision curve analysis(DCA).Results Multivariate analysis showed that T stage,pathological type,radiotherapy and chemotherapy,surgery,long diameter(LD),short diameter(SD),minimum CT value(CTmin)were the independent risk factors for predicting brain metastasis in lung cancer(P<0.05).The area under the curve(AUC)of clinical,CT imaging and clinical-CT imaging models were 0.925,0.764,0.941,respectively.DeLong test analysis showed that the AUC of clinical-CT imaging model,clinical model and CT imaging model was statistical difference(Z=2.093,5.777,all P<0.05).The calibration curve suggested a good fit of the clinical-CT imaging model.The DCA suggested that the clinical-CT imaging model demonstrates good clinical benefits.Conclusion The clinical-CT imaging model can effectively predict the occurrence of brain metastasis in lung cancer,which is helpful to guide the development of accurate diagnosis and treatment plan.

The study and control of elemental impurities are crucial for ensuring the quality and safety of drugs.The ICH has published the Q3D guideline as a globally harmonised approach for the research and control of elemental impurities in drugs.In accordance with the requirements of the ICH Q3D guideline for risk assessment and control of elemental impurities,how to carry out the development and validation of detecting methods for elemental impurities is important to analysts.In this research,the key points of ICP-AES and ICP-MS method development are summarized,including the determination of the types and limits of the elements to be measured,the selection of pretreatment methods,interferences and corrections;the validation requirements for the two methods in ICH Q2(R2)and different pharmacopoeial general rules are analyzed,and the evaluation methods of each validation experiments are compared in detail.This paper can provide a reference for the development and validation of detecting methods for elemental impurities,and research ideas for related research workers.

With the continuous development of genomics and precision medicine,targeted therapy and immunotherapy targeting biomarkers have ush-ered in a new era of anti-tumor therapy.However,due to the heterogeneity of tumor cells and the variability of tumor microenvironment,there are still significant differences in response to the same drug even in patient populations with the same bio-marker enrichment.By combining omics data with drug sensitivity algorithms,the response of anti-tu-mor drugs can be predicted and transformed into personalized diagnosis and treatment strategies re-quired for precision medicine,which is expected to improve the effectiveness of anti-tumor drugs in clinical treatment.Currently,machine learning is one of the commonly used modeling algorithms for predicting the response of anti-tumor drugs.How-ever,due to differences in input data and algorithm construction methods,there is currently a lack of comprehensive literature review in this field.There-fore,this article provides a review of machine learning algorithms for predicting anti-tumor drug responses,summarizing publicly available cell ge-nome characterization datasets,machine learning algorithms,and evaluation indicators in drug re-sponse prediction,as well as the current situation and challenges faced in clinical applications,in or-der to provide methodological references for the main research problems and potential solutions of machine learning algorithms in the field of drug re-sponse prediction.

Objective To investigate the value of constructing a risk prediction model of brain metastasis in lung cancer based on clinical-CT imaging.Methods The clinical and CT imaging data of 208 patients with lung cancer confirmed by surgical pathology or puncture biopsy were analyzed retrospectively,including 98 patients in the metastasis group and 110 patients in the non-metastasis group.Univariable and binary logistic regression analyses were performed between the two groups,and the clinical,CT imaging,and clinical-CT imaging models were constructed according to the selected independent risk factors.Prediction model performance was eval-uated with receiver operating characteristic(ROC)curve,calibration curve and decision curve analysis(DCA).Results Multivariate analysis showed that T stage,pathological type,radiotherapy and chemotherapy,surgery,long diameter(LD),short diameter(SD),minimum CT value(CTmin)were the independent risk factors for predicting brain metastasis in lung cancer(P<0.05).The area under the curve(AUC)of clinical,CT imaging and clinical-CT imaging models were 0.925,0.764,0.941,respectively.DeLong test analysis showed that the AUC of clinical-CT imaging model,clinical model and CT imaging model was statistical difference(Z=2.093,5.777,all P<0.05).The calibration curve suggested a good fit of the clinical-CT imaging model.The DCA suggested that the clinical-CT imaging model demonstrates good clinical benefits.Conclusion The clinical-CT imaging model can effectively predict the occurrence of brain metastasis in lung cancer,which is helpful to guide the development of accurate diagnosis and treatment plan.

The study and control of elemental impurities are crucial for ensuring the quality and safety of drugs.The ICH has published the Q3D guideline as a globally harmonised approach for the research and control of elemental impurities in drugs.In accordance with the requirements of the ICH Q3D guideline for risk assessment and control of elemental impurities,how to carry out the development and validation of detecting methods for elemental impurities is important to analysts.In this research,the key points of ICP-AES and ICP-MS method development are summarized,including the determination of the types and limits of the elements to be measured,the selection of pretreatment methods,interferences and corrections;the validation requirements for the two methods in ICH Q2(R2)and different pharmacopoeial general rules are analyzed,and the evaluation methods of each validation experiments are compared in detail.This paper can provide a reference for the development and validation of detecting methods for elemental impurities,and research ideas for related research workers.

A retrospective analysis was conducted on 26 patients with relapsed and refractory multiple myeloma (RRMM) who were treated with carfilzomib-based regimens at Beijing Chaoyang Integrative Medicine Rescue and First Aid Hospital from July 2021 to May 2024. The median number of treatment cycles was 5 (range, 2-8). The overall response rate was 53.8% (14/26). The median follow-up duration was 9.5 months, and the median progression-free survival (PFS) was 8.6 months (range, 1.7-25.2 months). Patients without plasmacytoma recurrence ( n=17) had a PFS of 12.3 months. In contrast, patients with plasmacytoma recurrence ( n=9) had a PFS of 6.2 months. The difference in PFS between these two groups was statistically significant ( P=0.013). Age (over 65 vs. 65 or younger), and treatment line (more than three vs. three or fewer) did not significantly affect treatment efficacy (all P>0.05). Common adverse reactions of grade 3 or higher included hematologic adverse reactions in 10 cases, which improved with symptomatic treatment. Non-hematologic adverse reactions included pulmonary infection (2 cases), renal failure (1 case), and heart failure (1 case). In conclusion, carfilzomib-based regimens demonstrate good clinical efficacy and manageable safety in the treatment of RRMM.