BackgroundMedical staff fighting the COVID-19 pandemic are experiencing stress from high occupational risk, panic in the community and the extreme workload. Maintaining the psychological health of a medical team is essential for efficient functioning, but psychological intervention models for emergency medical teams are rare.AimsTo design a systematic, full-coverage psychological health support scheme for medical teams serving large-scale emergent situations, and demonstrate its effectiveness in a real-world study in Leishenshan Hospital during the COVID-19 epidemic in Wuhan, China.MethodsThe scheme integrates onsite and online mental health resources and features team-based psychosocial support and evidence-based interventions. It contained five modules, including a daily measurement of mood, a daily mood broadcast that promotes positive affirmation, a daily online peer-group activity with themes based on the challenges reported by the team, Balint groups and an after-work support team. The daily mood measurement provides information to the other modules. The scheme also respects the special psychological characteristics of medical staff by promoting their strengths.ResultsThe scheme economically supported a special medical team of 156 members with only one onsite psychiatrist. Our data reflected that the entire medical team maintained an overall positive outlook (7–9 out of 10 in a Daily Mood Index, DMI) for nearly 6 weeks of continuous working. Since the scheme promoted self-strengths and positive self-affirmation, the number of self-reports of life-related gains were high and played a significant effect on the DMI. Our follow-up investigations also revealed that multiple modules of the scheme received high attention and evaluation levels.ConclusionOur quantitative data from Leishenshan hospital, Wuhan, China, show that the programme is adequate to support the continuous high workload of medical teams. This scheme could be applied to medical teams dealing with emergent situations.

Incorporation of a reporter peptide in solutions submitted to fast photochemical oxidation of proteins (FPOP) allows for the correction of adventitious scavengers and enables the normalization and comparison of time-dependent results. Reporters will also be useful in differential experiments to control for the inclusion of a radical-reactive species. This incorporation provides a simple and quick check of radical dosage and allows comparison of FPOP results from day-to-day and lab-to-lab. Use of a reporter peptide in the FPOP workflow requires no additional measurements or spectrometers while building a more quantitative FPOP platform. It requires only measurement of the extent of reporter-peptide modification in a LC/MS/MS run, which is performed by using either data-dependent scanning or an inclusion list.

Text clustering is a critical step in text data analysis and has been extensively studied by the text mining community. Most existing text clustering algorithms are based on the bag-of-words model, which faces the high-dimensional and sparsity problems and ignores text structural and sequence information. Deep learning-based models such as convolutional neural networks and recurrent neural networks regard texts as sequences but lack supervised signals and explainable results. In this paper, we propose a deep feature-based text clustering (DFTC) framework that incorporates pretrained text encoders into text clustering tasks. This model, which is based on sequence representations, breaks the dependency on supervision. The experimental results show that our model outperforms classic text clustering algorithms and the state-of-the-art pretrained language model, i.e., BERT, on almost all the considered datasets. In addition, the explanation of the clustering results is significant for understanding the principles of the deep learning approach. Our proposed clustering framework includes an explanation module that can help users understand the meaning and quality of the clustering results.

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