Formal Synthesis of (±)-Guanacastepene A

A 17 step synthesis of 55, a late intermediate in Danishefsky's guanacastepene A synthesis, has been completed in 4% overall yield. Key features include the use of vinylmagnesium bromide in the Pd-catalyzed coupling with triflate 13 to give triene 16 without the formation of Heck products, a novel extension of the Stork−Jung vinylsilane Robinson annulation that provides tricyclic 2-hydroxymethylcyclohexenone 42 from 23b in four steps and 51% yield, the ability to obtain almost exclusively α‘-alkylation of 35ba by the proper choice of protecting groups, and the ability to obtain the desired β-alcohol selectively by reduction of keto alcohol 42 rather than keto ester 53

Formal Synthesis of (±)-Guanacastepene A

A 17 step synthesis of 55, a late intermediate in Danishefsky's guanacastepene A synthesis, has been completed in 4% overall yield. Key features include the use of vinylmagnesium bromide in the Pd-catalyzed coupling with triflate 13 to give triene 16 without the formation of Heck products, a novel extension of the Stork−Jung vinylsilane Robinson annulation that provides tricyclic 2-hydroxymethylcyclohexenone 42 from 23b in four steps and 51% yield, the ability to obtain almost exclusively α‘-alkylation of 35ba by the proper choice of protecting groups, and the ability to obtain the desired β-alcohol selectively by reduction of keto alcohol 42 rather than keto ester 53

DataSheet_3_Economic Evaluation of Sacituzumab Govitecan for the Treatment of Metastatic Triple-Negative Breast Cancer in China and the US.docx

BackgroundThe effectiveness of Sacituzumab Govitecan (SG) for metastatic triple-negative breast cancer (mTNBC) has been demonstrated. We aimed to evaluate its cost-effectiveness on mTNBC from the Chinese and United States (US) perspective.MethodsA partitioned survival model was developed to compare the cost and effectiveness of SG versus single-agent chemotherapy based on clinical data from the ASCENT phase 3 randomized trial. Cost and utility data were obtained from the literature. The incremental cost-effectiveness ratio (ICER) was measured, and one-way and probabilistic sensitivity analyses (PSA) were performed to observe model stability. A Markov model was constructed to validate the results.ResultsIn China, SG yielded an additional 0.35 quality-adjusted life-year (QALY) at an additional cost of Chinese Renminbi ¥2257842. The ICER was ¥6375856 ($924037)/QALY. In the US, SG yielded the same additional QALY at an extra cost of$175393 and the ICER was $494479/QALY. Similar results were obtained from the Markov model. One-way sensitivity analyses showed that SG price had the greatest impact on the ICER. PSA showed the probability of SG to be cost-effective when compared with chemotherapy was zero at the current willing-to-pay threshold of ¥217341/QALY and$150000/QALY in China and the US, respectively. The probability of cost-effectiveness of SG would approximate 50% if its price was reduced to ¥10.44/mg in China and $3.65/mg in the US.ConclusionSG is unlikely to be a cost-effective treatment of mTNBC at the current price both in China and the US.</p

DataSheet_1_Economic Evaluation of Sacituzumab Govitecan for the Treatment of Metastatic Triple-Negative Breast Cancer in China and the US.docx

BackgroundThe effectiveness of Sacituzumab Govitecan (SG) for metastatic triple-negative breast cancer (mTNBC) has been demonstrated. We aimed to evaluate its cost-effectiveness on mTNBC from the Chinese and United States (US) perspective.MethodsA partitioned survival model was developed to compare the cost and effectiveness of SG versus single-agent chemotherapy based on clinical data from the ASCENT phase 3 randomized trial. Cost and utility data were obtained from the literature. The incremental cost-effectiveness ratio (ICER) was measured, and one-way and probabilistic sensitivity analyses (PSA) were performed to observe model stability. A Markov model was constructed to validate the results.ResultsIn China, SG yielded an additional 0.35 quality-adjusted life-year (QALY) at an additional cost of Chinese Renminbi ¥2257842. The ICER was ¥6375856 ($924037)/QALY. In the US, SG yielded the same additional QALY at an extra cost of$175393 and the ICER was $494479/QALY. Similar results were obtained from the Markov model. One-way sensitivity analyses showed that SG price had the greatest impact on the ICER. PSA showed the probability of SG to be cost-effective when compared with chemotherapy was zero at the current willing-to-pay threshold of ¥217341/QALY and$150000/QALY in China and the US, respectively. The probability of cost-effectiveness of SG would approximate 50% if its price was reduced to ¥10.44/mg in China and $3.65/mg in the US.ConclusionSG is unlikely to be a cost-effective treatment of mTNBC at the current price both in China and the US.</p

DataSheet_2_Economic Evaluation of Sacituzumab Govitecan for the Treatment of Metastatic Triple-Negative Breast Cancer in China and the US.xlsx

BackgroundThe effectiveness of Sacituzumab Govitecan (SG) for metastatic triple-negative breast cancer (mTNBC) has been demonstrated. We aimed to evaluate its cost-effectiveness on mTNBC from the Chinese and United States (US) perspective.MethodsA partitioned survival model was developed to compare the cost and effectiveness of SG versus single-agent chemotherapy based on clinical data from the ASCENT phase 3 randomized trial. Cost and utility data were obtained from the literature. The incremental cost-effectiveness ratio (ICER) was measured, and one-way and probabilistic sensitivity analyses (PSA) were performed to observe model stability. A Markov model was constructed to validate the results.ResultsIn China, SG yielded an additional 0.35 quality-adjusted life-year (QALY) at an additional cost of Chinese Renminbi ¥2257842. The ICER was ¥6375856 ($924037)/QALY. In the US, SG yielded the same additional QALY at an extra cost of$175393 and the ICER was $494479/QALY. Similar results were obtained from the Markov model. One-way sensitivity analyses showed that SG price had the greatest impact on the ICER. PSA showed the probability of SG to be cost-effective when compared with chemotherapy was zero at the current willing-to-pay threshold of ¥217341/QALY and$150000/QALY in China and the US, respectively. The probability of cost-effectiveness of SG would approximate 50% if its price was reduced to ¥10.44/mg in China and $3.65/mg in the US.ConclusionSG is unlikely to be a cost-effective treatment of mTNBC at the current price both in China and the US.</p

Image1_Expression profiles and functions of ferroptosis-related genes in intimal hyperplasia induced by carotid artery ligation in mice.jpg

Intimal hyperplasia (IH) is a prominent pathological event that occurs during in-stent restenosis and atherosclerosis. Ferroptosis, characterized by iron-dependent and lipid peroxidation, has become the recent focus of studies on the occurrence and progress of cardiovascular diseases. However, there are few studies on ferroptosis and IH. Therefore, we aimed to identify and validate ferroptosis-related markers in IH to explore new possibilities for IH diagnosis and treatment. The IH microarray dataset (GSE182291) was downloaded from the Gene Expression Omnibus (GEO) database and ferroptosis-related genes (FRGs) were obtained from the FerrDb databases. The differentially expressed genes (DEGs) were analyzed using the GEO2R. Overlapping was performed to identify the ferroptosis-related DEGs among the DEGs and FRGs. Then, clustering, Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment, and protein–protein interaction (PPI) analyses were performed. Subsequently, the hub genes were identified using Cytoscape and hub gene–transcription factors and hub gene–microRNA networks were constructed. Finally, real-time qPCR (RT-qPCR) and immunohistochemistry (IHC) were used to verify the mRNA and protein levels of the hub FRGs in IH. Thirty-four FRGs showing significantly different expression were identified from a total of 1,197 DEGs 2 days after ligation; 31 FRGs were selected from a total of 1,556 DEGs 14 days after ligation. The GO and KEGG analyses revealed that these 34 ferroptosis-related DEGs identified 2 days after ligation were mainly enriched in the basolateral plasma membrane, ferroptosis, lipid and atherosclerosis, and IL-17 signaling pathways. The 31 ferroptosis-related DEGs in endometrial hyperplasia identified 14 days after ligation were mainly enriched in response to oxidative stress, ferroptosis, tumor necrosis factor signaling pathway, and lipid and atherosclerosis. Five hub FRGs (Il1b, Ptgs2, Cybb, Cd44, and Tfrc) were identified using PPI networks; four hub FRGs (Il1b, Ptgs2, Cybb, and Cd44) were validated to be upregulated 2 and 14 days after ligation using RT-qPCR and show significantly different expression 14 days after ligation via IHC. Our findings verify the expression of hub DEGs related to ferroptosis in IH and elucidate the potential relationship between ferroptosis and IH, providing more evidence about the vital role of ferroptosis in IH.</p

Table_1_Interpretable machine learning model to predict surgical difficulty in laparoscopic resection for rectal cancer.docx

BackgroundLaparoscopic total mesorectal excision (LaTME) is standard surgical methods for rectal cancer, and LaTME operation is a challenging procedure. This study is intended to use machine learning to develop and validate prediction models for surgical difficulty of LaTME in patients with rectal cancer and compare these models’ performance.MethodsWe retrospectively collected the preoperative clinical and MRI pelvimetry parameter of rectal cancer patients who underwent laparoscopic total mesorectal resection from 2017 to 2022. The difficulty of LaTME was defined according to the scoring criteria reported by Escal. Patients were randomly divided into training group (80%) and test group (20%). We selected independent influencing features using the least absolute shrinkage and selection operator (LASSO) and multivariate logistic regression method. Adopt synthetic minority oversampling technique (SMOTE) to alleviate the class imbalance problem. Six machine learning model were developed: light gradient boosting machine (LGBM); categorical boosting (CatBoost); extreme gradient boost (XGBoost), logistic regression (LR); random forests (RF); multilayer perceptron (MLP). The area under receiver operating characteristic curve (AUROC), accuracy, sensitivity, specificity and F1 score were used to evaluate the performance of the model. The Shapley Additive Explanations (SHAP) analysis provided interpretation for the best machine learning model. Further decision curve analysis (DCA) was used to evaluate the clinical manifestations of the model.ResultsA total of 626 patients were included. LASSO regression analysis shows that tumor height, prognostic nutrition index (PNI), pelvic inlet, pelvic outlet, sacrococcygeal distance, mesorectal fat area and angle 5 (the angle between the apex of the sacral angle and the lower edge of the pubic bone) are the predictor variables of the machine learning model. In addition, the correlation heatmap shows that there is no significant correlation between these seven variables. When predicting the difficulty of LaTME surgery, the XGBoost model performed best among the six machine learning models (AUROC=0.855). Based on the decision curve analysis (DCA) results, the XGBoost model is also superior, and feature importance analysis shows that tumor height is the most important variable among the seven factors.ConclusionsThis study developed an XGBoost model to predict the difficulty of LaTME surgery. This model can help clinicians quickly and accurately predict the difficulty of surgery and adopt individualized surgical methods.</p

Table_3_Interpretable machine learning model to predict surgical difficulty in laparoscopic resection for rectal cancer.docx

BackgroundLaparoscopic total mesorectal excision (LaTME) is standard surgical methods for rectal cancer, and LaTME operation is a challenging procedure. This study is intended to use machine learning to develop and validate prediction models for surgical difficulty of LaTME in patients with rectal cancer and compare these models’ performance.MethodsWe retrospectively collected the preoperative clinical and MRI pelvimetry parameter of rectal cancer patients who underwent laparoscopic total mesorectal resection from 2017 to 2022. The difficulty of LaTME was defined according to the scoring criteria reported by Escal. Patients were randomly divided into training group (80%) and test group (20%). We selected independent influencing features using the least absolute shrinkage and selection operator (LASSO) and multivariate logistic regression method. Adopt synthetic minority oversampling technique (SMOTE) to alleviate the class imbalance problem. Six machine learning model were developed: light gradient boosting machine (LGBM); categorical boosting (CatBoost); extreme gradient boost (XGBoost), logistic regression (LR); random forests (RF); multilayer perceptron (MLP). The area under receiver operating characteristic curve (AUROC), accuracy, sensitivity, specificity and F1 score were used to evaluate the performance of the model. The Shapley Additive Explanations (SHAP) analysis provided interpretation for the best machine learning model. Further decision curve analysis (DCA) was used to evaluate the clinical manifestations of the model.ResultsA total of 626 patients were included. LASSO regression analysis shows that tumor height, prognostic nutrition index (PNI), pelvic inlet, pelvic outlet, sacrococcygeal distance, mesorectal fat area and angle 5 (the angle between the apex of the sacral angle and the lower edge of the pubic bone) are the predictor variables of the machine learning model. In addition, the correlation heatmap shows that there is no significant correlation between these seven variables. When predicting the difficulty of LaTME surgery, the XGBoost model performed best among the six machine learning models (AUROC=0.855). Based on the decision curve analysis (DCA) results, the XGBoost model is also superior, and feature importance analysis shows that tumor height is the most important variable among the seven factors.ConclusionsThis study developed an XGBoost model to predict the difficulty of LaTME surgery. This model can help clinicians quickly and accurately predict the difficulty of surgery and adopt individualized surgical methods.</p

Hollow-core fiber Characterization with Correlation-Optical Time Domain Reflectometry

Using a Correlation-OTDR, we characterized the temperature-induced group delay variations of two nested antiresonant nodeless hollow core fibers. The temperature sensitivity of both is substantially less than for SSMF with some dependency on coating type

Table_2_Interpretable machine learning model to predict surgical difficulty in laparoscopic resection for rectal cancer.docx

BackgroundLaparoscopic total mesorectal excision (LaTME) is standard surgical methods for rectal cancer, and LaTME operation is a challenging procedure. This study is intended to use machine learning to develop and validate prediction models for surgical difficulty of LaTME in patients with rectal cancer and compare these models’ performance.MethodsWe retrospectively collected the preoperative clinical and MRI pelvimetry parameter of rectal cancer patients who underwent laparoscopic total mesorectal resection from 2017 to 2022. The difficulty of LaTME was defined according to the scoring criteria reported by Escal. Patients were randomly divided into training group (80%) and test group (20%). We selected independent influencing features using the least absolute shrinkage and selection operator (LASSO) and multivariate logistic regression method. Adopt synthetic minority oversampling technique (SMOTE) to alleviate the class imbalance problem. Six machine learning model were developed: light gradient boosting machine (LGBM); categorical boosting (CatBoost); extreme gradient boost (XGBoost), logistic regression (LR); random forests (RF); multilayer perceptron (MLP). The area under receiver operating characteristic curve (AUROC), accuracy, sensitivity, specificity and F1 score were used to evaluate the performance of the model. The Shapley Additive Explanations (SHAP) analysis provided interpretation for the best machine learning model. Further decision curve analysis (DCA) was used to evaluate the clinical manifestations of the model.ResultsA total of 626 patients were included. LASSO regression analysis shows that tumor height, prognostic nutrition index (PNI), pelvic inlet, pelvic outlet, sacrococcygeal distance, mesorectal fat area and angle 5 (the angle between the apex of the sacral angle and the lower edge of the pubic bone) are the predictor variables of the machine learning model. In addition, the correlation heatmap shows that there is no significant correlation between these seven variables. When predicting the difficulty of LaTME surgery, the XGBoost model performed best among the six machine learning models (AUROC=0.855). Based on the decision curve analysis (DCA) results, the XGBoost model is also superior, and feature importance analysis shows that tumor height is the most important variable among the seven factors.ConclusionsThis study developed an XGBoost model to predict the difficulty of LaTME surgery. This model can help clinicians quickly and accurately predict the difficulty of surgery and adopt individualized surgical methods.</p