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The 2nd CASMS conference was held virtually through Gather. Town platform from October 17 to 21, 2022, with a total of 363 registrants including an outstanding and diverse group of scientists at the forefront of their research fields from both academia and industry worldwide, especially in the United States and China. The conference offered a 5-day agenda with an exciting scientific program consisting of two plenary lectures, 14 parallel symposia, and 4 special sessions in which a total of 97 invited speakers presented technological innovations and their applications in proteomics & biological mass spectrometry and metabo-lipidomics & pharmaceutical mass spectrometry. In addition, 18 invited speakers/panelists presented at 3 research-focused and 2 career development workshops. Moreover, 144 posters, 54 lightning talks, 5 sponsored workshops, and 14 exhibitions were presented, from which 20 posters and 8 lightning talks received presentation awards. Furthermore, the conference featured 1 MCP lectureship and 5 young investigator awardees for the first time to highlight outstanding mid-career and early-career rising stars in mass spectrometry from our society. The conference provided a unique scientific platform for young scientists (i.e., graduate students, postdocs and junior faculty/investigators) to present their research, meet with prominent scientists, and learn about career development and job opportunities (http://casms.org).

Abstract Background Ameloblastoma, a common benign tumor found in the jaw bone, necessitates accurate localization and segmentation for effective diagnosis and treatment. However, the traditional manual segmentation method is plagued with inefficiencies and drawbacks. Hence, the implementation of an AI-based automatic segmentation approach is crucial to enhance clinical diagnosis and treatment procedures. Methods We collected CT images from 79 patients diagnosed with ameloblastoma and employed a deep learning neural network model for training and testing purposes. Specifically, we utilized the Mask R-CNN neural network structure and implemented image preprocessing and enhancement techniques. During the testing phase, cross-validation methods were employed for evaluation, and the experimental results were verified using an external validation set. Finally, we obtained an additional dataset comprising 200 CT images of ameloblastoma from a different dental center to evaluate the model's generalization performance. Results During extensive testing and evaluation, our model successfully demonstrated the capability to automatically segment ameloblastoma. The DICE index achieved an impressive value of 0.874. Moreover, when the IoU threshold ranged from 0.5 to 0.95, the model's AP was 0.741. For a specific IoU threshold of 0.5, the model achieved an AP of 0.914, and for another IoU threshold of 0.75, the AP was 0.826. Our validation using external data confirms the model's strong generalization performance. Conclusion In this study, we successfully applied a neural network model based on deep learning that effectively performs automatic segmentation of ameloblastoma. The proposed method offers notable advantages in terms of efficiency, accuracy, and speed, rendering it a promising tool for clinical diagnosis and treatment.

Background Fatty liver disease is a common frequently-occurring disease in daily life, and the number of non-alcoholic fatty liver disease (NAFLD) patients has been gradually increasing in recent years with an important impact on people's health. The differences in lifestyle and basal metabolism in different gender groups may lead to differences in the prevalence of NAFLD. However, few studies have been conducted to investigate the prevalence of NAFLD in different gender populations. Objective To explore the prevalence and influencing factors of NAFLD in different gender groups, so as to provide reference for the prevention and treatment of NAFLD. Methods A total of 29 271 subjects received physical examination in the Physical Examination Center of Jinshan Hospital of Fudan University were selected in the retrospective case-control study from August 2020 to August 2021. General data, physical examination data, laboratory indexes, combination of underlying diseases, and imaging findings of the subjects were collected. The included subjects were divided into the NAFLD group and control group according to the diagnostic criteria of NAFLD, and the prevalence and characteristics of NAFLD in different gender groups were analyzed. Multivariate Logistic regression analysis was used to explore the influencing factors of NAFLD by gender; the receiver operating characteristic (ROC) curve was used to evaluate the predictive value of related indicators for NAFLD in different gender groups, and the area under the ROC curve (AUC), sensitivity and specificity were calculated. Results There were 10 524 cases of NAFLD among 29 271 subjects with an overall prevalence of 35.95%, including 7 854 NAFLD patients in the 18 322 male subjects with a prevalence of 42.87%, 2 670 NAFLD patients in the 10 949 male subjects with a prevalence of 24.39%. There was a statistically significant difference in the prevalence of NAFLD among subjects with different genders (χ2=1 016.505, P

Proteins are the primary effectors of function in biology, and thus, complete knowledge of their structure and properties is fundamental to deciphering function in basic and translational research. The chemical diversity of proteins is expressed in their many proteoforms, which result from combinations of genetic polymorphisms, RNA splice variants, and posttranslational modifications. This knowledge is foundational for the biological complexes and networks that control biology yet remains largely unknown. We propose here an ambitious initiative to define the human proteome, that is, to generate a definitive reference set of the proteoforms produced from the genome. Several examples of the power and importance of proteoform-level knowledge in disease-based research are presented along with a call for improved technologies in a two-pronged strategy to the Human Proteoform Project.