Combinatorial chemistry, high-throughput and virtual screening technologies have been extensively used for discovering agrochemical leads from chemical libraries. The knowledge of the physicochemical properties of the marketed agrochemicals is useful for guiding the design and selection of such libraries. Since the earlier profiling of marketed agrochemicals, the number and types of marketed agrochemicals have significantly increased. Recent studies have shown the change of some physicochemical properties of oral drugs with time. There is a need to also profile the physicochemical properties of the marketed agrochemicals. In this work, we analyzed the key physicochemical properties of 1751 marketed agrochemicals in comparison with the previously-analyzed herbicides and insecticides, 106 391 natural products and 57 548 diverse synthetic libraries compounds. Our study revealed the distribution profiles and evolution trend of different types of agrochemicals that in many respects are broadly similar to the reported profiles for oral drugs, with the most marked difference being that agrochemicals have a lower number of hydrogen bond donors. The derived distribution patterns provided the rule of thumb guidelines for selecting potential agrochemical leads and also provided clues for further improving the libraries for agrochemical lead discovery.

BackgroundMost studies of molecular subtype prediction in breast cancer were mainly based on two-dimensional MRI images, the predictive value of three-dimensional volumetric features from dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) for predicting breast cancer molecular subtypes has not been thoroughly investigated. This study aimed to look into the role of features derived from DCE-MRI and how they could be combined with clinical data to predict invasive ductal breast cancer molecular subtypes.MethodsFrom January 2019 to December 2021, 190 Chinese women with invasive ductal breast cancer were studied (32 triple-negative, 59 HER2-enriched, and 99 luminal lesions) in this institutional review board-approved retrospective cohort study. The image processing software extracted 1130 quantitative radiomic features from the segmented lesion area, including shape-based, first-order statistical, texture, and wavelet features. Three binary classifications of the subtypes were performed: triple-negative vs. non-triple-negative, HER2-overexpressed vs. non-HER2-overexpressed, and luminal (A + B) vs. non-luminal. For the classification, five machine learning methods (random forest, logistic regression, support vector machine, naïve Bayes, and eXtreme Gradient Boosting) were employed. The classifiers were chosen using the least absolute shrinkage and selection operator method. The area evaluated classification performance under the receiver operating characteristic curve, sensitivity, specificity, accuracy, F1-Score, false positive rate, precision, and geometric mean.ResultsEXtreme Gradient Boosting model showed the best performance in luminal and non-luminal groups, with AUC, sensitivity, specificity, accuracy, F1-Score, false positive rate, precision, and geometric mean of 0.8282, 0.7524, 0.6542, 0.6964, 0.6086, 0.3458, 0.8524 and 0.7016, respectively. Meanwhile, the random forest model showed the best performance in HER2-overexpressed and non-HER2-overexpressed groups, with AUC, sensitivity, specificity, accuracy, F1-Score, false positive rate, precision, and geometric mean of 0.8054, 0.2941, 0.9744, 0.7679, 0.4348, 0.0256, 0.8333 and 0.5353, respectively. Furthermore, eXtreme Gradient Boosting model showed the best performance in the triple-negative and non-triple-negative groups, with AUC, sensitivity, specificity, accuracy, F1-Score, false positive rate, precision, and geometric mean of 0.9031, 0.9362, 0.4444, 0.8571, 0.9167, 0.5556, 0.8980 and 0.6450.ConclusionClinical data and three-dimension imaging features from DCE-MRI were identified as potential biomarkers for distinguishing between three molecular subtypes of invasive ductal carcinomas breast cancer. In the future, more extensive studies will be required to evaluate the findings.

p53 is a common tumor suppressor, and its mutation drives tumorigenesis. What is more, p53 mutations have also been reported to be indicative of poor prognosis in lung cancer, but the detailed mechanism has not been elucidated. In this study, we found that DNA primase subunit 2 (PRIM2) had a high expression level and associated with poor prognosis in lung cancer. Furthermore, we found that PRIM2 expression was abnormally increased in lung cancer cells with p53 mutation or altered the p53/RB pathway based on database. We also verified that PRIM2 expression was elevated by mutation or deletion of p53 in lung cancer cell lines. Lastly, silence p53 increased the expression of RPIM2. Thus, these data suggest that PRIM2 is a cancer-promoting factor which is regulated by the p53/RB pathway. The p53 tumor-suppressor gene integrates numerous signals that control cell proliferation, cell cycle, and cell death; and the p53/RB pathway determines the cellular localization of transcription factor E2F, which regulates the expression of downstream targets. Next, we explored the role of PRIM2 in lung cancer and found that knockdown of PRIM2 induced cell cycle arrest, increased DNA damage, and increased cell senescence, leading to decreased lung cancer cell proliferation. Lastly, the positive correlation between PRIM2 and E2F/CDK also indicated that PRIM2 was involved in promoting cell cycle mediated by p53/RB pathway. These results confirmed that the expression of PRIM2 is regulated by the p53/RB pathway in lung cancer cells, promotes DNA replication and mismatch repair, and activates the cell cycle. Overall, we found that frequent p53 mutations increased PRIM2 expression, activated the cell cycle, and promoted lung cancer progression.

Objective: To investigate the effect of aortic esophageal fistula treatment after thoracic aortic endovascular repair (TEVAR) with artificial vessel bypass. Methods: The clinical data of 6 consecutive patients who received surgical treatment at Shanghai Deda Hospital from September 2019 to June 2021 due to aortic esophageal fistula after TEVAR were retrospectively analyzed. There were 6 males, aged (47.7±8.2) years old (range: 35-56 years old). All patients had recurrent fever, and 4 patients had positive blood cultures. According to the specific conditions of the patients, all patients underwent artificial blood vessel bypass and jejunostomy under general anesthesia without extracorporeal circulation. One case underwent artificially infected vascular segment resection and esophageal repair at the same time. 5 cases underwent artificial infection vascular resection, 4 of them underwent esophageal repair, and 1 case had a large intraoperative fistula and local resection of the esophagus. Sensitive antibacterial drugs were continued after the operation for 6 to 8 weeks. Results: There were 2 deaths in hospital, 1 case of large cerebral infarction early postoperatively, and 1 case of septic shock. The remaining 4 patients recovered well after the operation and were discharged. The follow-up period was 2 to 23 months. During the follow-up period, the remaining patients had no recurrence of infection and esophageal fistula. Conclusion ：In patients with aortic esophageal fistula after TEVAR, the establishment of artificial vascular bypass, the resection of the infected vascular segment, contemporaneous or staged esophageal repair, regular anti-infective treatment can obtain a good prognosis.