Triple-negative breast cancer (TNBC) is the most challenging breast cancer subtype. Molecular stratification and target therapy bring clinical benefit for TNBC patients, but it is difficult to implement comprehensive molecular testing in clinical practice. Here, using our multi-omics TNBC cohort (N = 425), a deep learning-based framework was devised and validated for comprehensive predictions of molecular features, subtypes and prognosis from pathological whole slide images. The framework first incorporated a neural network to decompose the tissue on WSIs, followed by a second one which was trained based on certain tissue types for predicting different targets. Multi-omics molecular features were analyzed including somatic mutations, copy number alterations, germline mutations, biological pathway activities, metabolomics features and immunotherapy biomarkers. It was shown that the molecular features with therapeutic implications can be predicted including the somatic PIK3CA mutation, germline BRCA2 mutation and PD-L1 protein expression (area under the curve [AUC]: 0.78, 0.79 and 0.74 respectively). The molecular subtypes of TNBC can be identified (AUC: 0.84, 0.85, 0.93 and 0.73 for the basal-like immune-suppressed, immunomodulatory, luminal androgen receptor, and mesenchymal-like subtypes respectively) and their distinctive morphological patterns were revealed, which provided novel insights into the heterogeneity of TNBC. A neural network integrating image features and clinical covariates stratified patients into groups with different survival outcomes (log-rank P < 0.001). Our prediction framework and neural network models were externally validated on the TNBC cases from TCGA (N = 143) and appeared robust to the changes in patient population. For potential clinical translation, we built a novel online platform, where we modularized and deployed our framework along with the validated models. It can realize real-time one-stop prediction for new cases. In summary, using only pathological WSIs, our proposed framework can enable comprehensive stratifications of TNBC patients and provide valuable information for therapeutic decision-making. It had the potential to be clinically implemented and promote the personalized management of TNBC.

BACKGROUND: Accurately identifying drug-target interaction (DTI), affinity (DTA), and binding sites (DTS) is crucial for drug screening, repositioning, and design, as well as for understanding the functions of target. Although there are a few online platforms based on deep learning for drug-target interaction, affinity, and binding sites identification, there is currently no integrated online platforms for all three aspects.
RESULTS: Our solution, the novel integrated online platform Drug-Online, has been developed to facilitate drug screening, target identification, and understanding the functions of target in a progressive manner of \"interaction-affinity-binding sites\". Drug-Online platform consists of three parts: the first part uses the drug-target interaction identification method MGraphDTA, based on graph neural networks (GNN) and convolutional neural networks (CNN), to identify whether there is a drug-target interaction. If an interaction is identified, the second part employs the drug-target affinity identification method MMDTA, also based on GNN and CNN, to calculate the strength of drug-target interaction, i.e., affinity. Finally, the third part identifies drug-target binding sites, i.e., pockets. The method pt-lm-gnn used in this part is also based on GNN.
CONCLUSIONS: Drug-Online is a reliable online platform that integrates drug-target interaction, affinity, and binding sites identification. It is freely available via the Internet at http://39.106.7.26:8000/Drug-Online/ .

OBJECTIVE: Clinical pharmacological modeling and statistical analysis software is an essential basic tool for drug development and personalized drug therapy. The learning curve of current basic tools is steep and unfriendly to beginners. The curve is even more challenging in cases of significant individual differences or measurement errors in data, resulting in difficulties in accurately estimating pharmacokinetic parameters by existing fitting algorithms. Hence, this study aims to explore a new optimized parameter fitting algorithm that reduces the sensitivity of the model to initial values and integrate it into the CPhaMAS platform, a user-friendly online application for pharmacokinetic data analysis.
METHODS: In this study, we proposed an optimized Nelder-Mead method that reinitializes simplex vertices when trapped in local solutions and integrated it into the CPhaMAS platform. The CPhaMAS, an online platform for pharmacokinetic data analysis, includes three modules: compartment model analysis, non-compartment analysis (NCA) and bioequivalence/bioavailability (BE/BA) analysis. Our proposed CPhaMAS platform was evaluated and compared with existing WinNonlin.
RESULTS: The platform was easy to learn and did not require code programming. The accuracy investigation found that the optimized Nelder-Mead method of the CPhaMAS platform showed better accuracy (smaller mean relative error and higher R2) in two-compartment and extravascular administration models when the initial value was set to true and abnormal values (10 times larger or smaller than the true value) compared with the WinNonlin. The mean relative error of the NCA calculation parameters of CPhaMAS and WinNonlin was <0.0001 %. When calculating BE for conventional, high-variability and narrow-therapeutic drugs. The main statistical parameters of the parameters Cmax, AUCt, and AUCinf in CPhaMAS have a mean relative error of <0.01% compared to WinNonLin.
CONCLUSIONS: In summary, CPhaMAS is a user-friendly platform with relatively accurate algorithms. It is a powerful tool for analysing pharmacokinetic data for new drug development and precision medicine.

UNASSIGNED: The prevalence of food allergy (FA) has risen in recent decades, yet there is limited data on the cognition and beliefs of FA among the parents of FA children.
UNASSIGNED: To investigate the prevalence of FA and assess the knowledge and perception of FA among parents of FA children in Wuhan, China.
UNASSIGNED: Online questionnaires were conducted for the parents of 3- to 16-year-old children. They reported symptoms of suspected FA in the screening questionnaire were interviewed for further diagnostic evaluation. All the parents of the suspected FA children completed the subsequent assessments of the knowledge and perception on FA as well as their attitude towards the current online platforms.
UNASSIGNED: A total of 1963 children were recruited. The prevalence of self-reported FA was 10.2% (95% CI: 8.1-12.4%) and the physician-diagnosed FA was 6.2% (95% CI: 5.1-7.2%) in 3- to 16-year-olds in Wuhan. And the children with family history (57.9%) were predisposed to developing FA (P＜0.001). The total Brief Illness Perception Questionnaire (B-IPQ) score was 41.3 ± 10.0 among the parents. The B-IPQ scores correlated with symptom onset, but not with family history or other atopic comorbidities. The parents who never sought treatments obtained lower B-IPQ scores on most items compared to those who received treatments. The accuracy rate of the FA knowledge questionnaire was 56.7%. 11.6% of participants reported that children\'s FA had an impact on their lives. 67.2% of participants had searched information of FA online, among whom 80% expected to obtain professional suggestions on management and prevention strategies of FA from online platform.
UNASSIGNED: In 3- to 16-year-old children in Wuhan, the prevalence of self-reported and physician-diagnosed FA was 10.2% and 6.2% respectively. Parents\' knowledge of FA was insufficient and only a small proportion of parents perceived that their lives and careers have been affected considerably by FA of their children. Patient education and current online platforms should be improved among parents of FA children.

Hypertriglyceridemia is a risk factor for cardiovascular diseases. Internet usage in China is increasing, giving rise to large-scale data sources, especially to access, disseminate, and discuss medical information. Social media listening (SML) is a new approach to analyze and monitor online discussions related to various health-related topics in diverse diseases, which can generate insights into users\' experiences and expectations. However, to date, no studies have evaluated the utility of SML to understand patients\' cognizance and expectations pertaining to the management of hypertriglyceridemia.
The aim of this study was to utilize SML to explore the disease cognition level of patients with hypertriglyceridemia, choice of intervention measures, and the status quo of online consultations and question-and-answer (Q&A) search platforms.
An infosurveillance study was conducted wherein a disease-specific comprehensive search was performed between 2004 and 2020 in Q&A search and online consultation platforms. Predefined single and combined keywords related to hypertriglyceridemia were used in the search, including disease, symptoms, diagnosis, and treatment indicators; lifestyle interventions; and therapeutic agents. The search output was aggregated using an aggregator tool and evaluated.
Disease-specific consultation data (n=69,845) and corresponding response data (n=111,763) were analyzed from 20 data sources (6 Q&A search platforms and 14 online consultation platforms). Doctors from inland areas had relatively high voice volumes and appear to exert a substantial influence on these platforms. Patients with hypertriglyceridemia engaging on the internet have an average level of cognition about the disease and its intervention measures. However, a strong demand for the concept of the disease and \"how to treat it\" was observed. More emphasis on the persistence of the disease and the safety of medications was observed. Young patients have a lower willingness for drug interventions, whereas patients with severe hypertriglyceridemia have a clearer intention to use drug intervention and few patients have a strong willingness for the use of traditional Chinese medicine.
Findings from this disease-specific SML study revealed that patients with hypertriglyceridemia in China actively seek information from both online Q&A search and consultation platforms. However, the integrity of internet doctors\' suggestions on lifestyle interventions and the accuracy of drug intervention recommendations still need to be improved. Further, a combined prospective qualitative study with SML is required for added rigor and confirmation of the relevance of the findings.

Recent advances in sequencing technologies lead to the generation of an enormous amount of regulome and epigenome data in a variety of plant species. However, a comprehensive standardized resource is so far not available. In this chapter, we present ChIP-Hub, an integrative platform that has been developed based on the ENCODE standards by collecting and reanalyzing regulatory genomic datasets from 41 plant species. The ChIP-hub website is introduced in this chapter, including information on detailed steps of searching, data download, and online analyses, which facilitates users to explore ChIP-Hub. We also provide a cross-species comparison of chromatin accessibility information that gives a thorough view of evolutionary regulatory networks in plants.

The study aimed to evaluate the effectiveness of learning blood cell morphology by learning on our Artificial intelligence (AI)-based online platform.
Our study is based on mixed-methods sequential explanatory design and crossover design. Thirty-one third-year medical students were randomly divided into two groups. The two groups had platform learning and microscopy learning in diferent sequences with pretests and posttests, respectively. Students were interviewed, and the records were coded and analyzed by NVivo 12.0.
For both groups, test scores increased significantly after online-platform learning. Feasibility was the most mentioned advantage of the platform. The AI system could inspire the students to compare the similarities and differences between cells and help them understand the cells better. Students had positive perspectives on the online-learning platform.
The AI-based online platform could assist medical students in blood cell morphology learning. The AI system could function as a more knowledgeable other (MKO) and guide the students through their zone of proximal development (ZPD) to achieve mastery. It could be an effective and beneficial complement to microscopy learning. Students had very positive perspectives on the AI-based online learning platform. It should be integrated into the course and curriculum to facilitate the students.[Box: see text].

Biochemistry and Molecular Biology are the cornerstone courses of talent training in the field of life science. Taking these course as an example, this study explored reconstructing the knowledge framework, developing teaching cases, sharing teaching resources, innovating teaching means and establishing ideological education patterns. Supported by the scientific research achievements with discipline characteristics and online teaching platform, this research explored and practiced an integrated curriculum reform mode. This mode is guided by scientific research and education, based on the course development, and driven by communication and cooperation. A shared space of \"exchange, practice, openness and informatization\" was developed to achieve free and independent integration of undergraduate and graduate teaching motivated by learning knowledge, resulting in an effective student training.

The study of contemporary art is one of the main means of implementing aesthetic education in China. This discipline allows a student to develop a system of spiritual values, reveal talents and acquire skills needed for professional growth. Chinese art is an integral part of the global cultural space, and Chinese contemporary art performs the functions of cultural reflection, occupying an important place in developing the state\'s cultural identity. In the process of globalization, it is extremely important to study not only Chinese, but also world contemporary art, as well as the peculiarities of its teaching in the educational institutions of the PRC within the framework of the multiculturalism movement. The study objectives are to develop a unique online platform for teaching and learning contemporary art in China, to identify the features of art teacher training, to identify the methods of and trends in teaching contemporary art in the Chinese system of secondary and higher education, as well as to determine the most effective method of teaching contemporary art in China based on the experiment. In the course of the study, a plan and structure of the website containing audio and video materials, graphic editors and a conference system for students and teachers were developed. An innovative classification of the contemporary art movements was made in accordance with the tools and methods of creating art works. The styles of contemporary art were divided into groups: painting, sculpture/architecture, photography, cybernetic art, literature, optical art, theatrical art, mass culture, and hybrid art. The database was compiled by the masters of Art from China and Ireland, which emphasizes the internationality and multiculturalism of the project. An experiment was conducted to determine the level of effectiveness of different methods of teaching contemporary art in China. In the course of the experiment, the Spark platform developed for the study purpose was recognized as the most effective (56.1%). The effectiveness of the active method and passive method of teaching was 27.2% and 16.7%, respectively. The study results can be used in the study of various trends in contemporary art in the process of modernization and within the framework of pedagogical and commercial activities related to the work of exhibitions and galleries. In addition, they can be used to improve the methods of teaching contemporary art online and offline to the broad public audience and improve the professional skills of graphic or UI/UX designers, 3D modeling experts, engineers, editors, and IT developers. The practical significance of the developed online platform lies in the possibility of database use, practical application of the knowledge gained, and taking online lessons with Chinese and foreign teachers as part of a multicultural experience exchange.

Online learning has received extensive attention in the field of education in the recent decade, especially after COVID-19 swept the globe in 2020. Online learning satisfaction (OLS) has become the focal point of the research, since it is of vital significance to enhance online learning efficiency. This paper reviews the research on OLS from the dimensions of online learners, online instructors, online platforms and online instructional design to have a clear picture of factors affecting OLS. Based on the review of previous studies, this mini review presents the prospect of future research on OLS and believes that breakthroughs on OLS research can be achieved by innovating research methods, expanding research subjects, and enriching research topics. OLS is a complicated dynamic system influenced by a diversity of factors, and it is worth more in-depth research by scholars and educators in future.