Collective sensemaking within institutions: Control of the COVID-19 epidemic in Vietnam.

How people make initial and collective sense under crises remains unanswered. This paper addresses this question using the control of COVID-19 in Vietnam as a case study. Our results suggest that sensemaking under crises is influenced by an institutional propensity for prevention that has developed gradually over time. Local governments play a vital role in fostering collective sensemaking which enables concerted actions in epidemic control. However, biases are inherent in sensemaking, including a delay in access to vaccine and a violation of privacy. For policy makers, this study suggests that developing specific prevention policies and programs, building large-scale coordination capacity, and promoting local initiatives are necessary for coping with epidemics. For theory development, the study explores how institutions condition sensemaking and specifies several mechanisms in which local authorities could facilitate collective sensemaking in crises.

Regulation of SOX11 expression through CCND1 and STAT3 in mantle cell lymphoma.

The neural transcription factor SOX11 is usually highly expressed in typical mantle cell lymphoma (MCL), but it is absent in the more indolent form of MCL. Despite being an important diagnostic marker for this hard-to-treat malignancy, the mechanisms of aberrant SOX11 expression are largely unknown. Herein, we describe 2 modes of SOX11 regulation by the cell-cycle regulator cyclin D1 (CCND1) and the signal transducer and activator of transcription 3 (STAT3). We found that ectopic expression of CCND1 in multiple human MCL cell lines resulted in increased SOX11 transcription, which correlated with increased acetylated histones H3K9 and H3K14 (H3K9/14Ac). Increased H3K9/14Ac and SOX11 expression was also observed after histone deacetylase 1 (HDAC1) or HDAC2 was depleted by RNA interference or inhibited by the HDAC inhibitor vorinostat. Mechanistically, we showed that CCND1 interacted with and sequestered HDAC1 and HDAC2 from the SOX11 locus, leading to SOX11 upregulation. Interestingly, our data revealed a potential inverse relationship between phosphorylated Y705 STAT3 and SOX11 expression in MCL cell lines, primary tumors, and patient-derived xenografts. Functionally, inactivation of STAT3 by inhibiting the upstream Janus kinase (JAK) 1 or JAK2 or by STAT3 knockdown was found to increase SOX11 expression, whereas interleukin-21 (IL-21)-induced STAT3 activation or overexpression of the constitutively active form of STAT3 decreased SOX11 expression. In addition, targeting SOX11 directly by RNA interference or indirectly by IL-21 treatment induced toxicity in SOX11+ MCL cells. Collectively, we demonstrate the involvement of CCND1 and STAT3 in the regulation of SOX11 expression, providing new insights and therapeutic implications in MCL.

Phase recognition in contrast-enhanced CT scans based on deep learning and random sampling.

PURPOSE: A fully automated system for interpreting abdominal computed tomography (CT) scans with multiple phases of contrast enhancement requires an accurate classification of the phases. Current approaches to classify the CT phases are commonly based on three-dimensional (3D) convolutional neural network (CNN) approaches with high computational complexity and high latency. This work aims at developing and validating a precise, fast multiphase classifier to recognize three main types of contrast phases in abdominal CT scans. METHODS: We propose in this study a novel method that uses a random sampling mechanism on top of deep CNNs for the phase recognition of abdominal CT scans of four different phases: noncontrast, arterial, venous, and others. The CNNs work as a slicewise phase prediction, while random sampling selects input slices for the CNN models. Afterward, majority voting synthesizes the slicewise results of the CNNs to provide the final prediction at the scan level. RESULTS: Our classifier was trained on 271 426 slices from 830 phase-annotated CT scans, and when combined with majority voting on 30% of slices randomly chosen from each scan, achieved a mean F1 score of 92.09% on our internal test set of 358 scans. The proposed method was also evaluated on two external test sets: CTPAC-CCRCC (N = 242) and LiTS (N = 131), which were annotated by our experts. Although a drop in performance was observed, the model performance remained at a high level of accuracy with a mean F1 scores of 76.79% and 86.94% on CTPAC-CCRCC and LiTS datasets, respectively. Our experimental results also showed that the proposed method significantly outperformed the state-of-the-art 3D approaches while requiring less computation time for inference. CONCLUSIONS: In comparison to state-of-the-art classification methods, the proposed approach shows better accuracy with significantly reduced latency. Our study demonstrates the potential of a precise, fast multiphase classifier based on a two-dimensional deep learning approach combined with a random sampling method for contrast phase recognition, providing a valuable tool for extracting multiphase abdomen studies from low veracity, real-world data.

Learning to diagnose common thorax diseases on chest radiographs from radiology reports in Vietnamese.

Deep learning, in recent times, has made remarkable strides when it comes to impressive performance for many tasks, including medical image processing. One of the contributing factors to these advancements is the emergence of large medical image datasets. However, it is exceedingly expensive and time-consuming to construct a large and trustworthy medical dataset; hence, there has been multiple research leveraging medical reports to automatically extract labels for data. The majority of this labor, however, is performed in English. In this work, we propose a data collecting and annotation pipeline that extracts information from Vietnamese radiology reports to provide accurate labels for chest X-ray (CXR) images. This can benefit Vietnamese radiologists and clinicians by annotating data that closely match their endemic diagnosis categories which may vary from country to country. To assess the efficacy of the proposed labeling technique, we built a CXR dataset containing 9,752 studies and evaluated our pipeline using a subset of this dataset. With an F1-score of at least 0.9923, the evaluation demonstrates that our labeling tool performs precisely and consistently across all classes. After building the dataset, we train deep learning models that leverage knowledge transferred from large public CXR datasets. We employ a variety of loss functions to overcome the curse of imbalanced multi-label datasets and conduct experiments with various model architectures to select the one that delivers the best performance. Our best model (CheXpert-pretrained EfficientNet-B2) yields an F1-score of 0.6989 (95% CI 0.6740, 0.7240), AUC of 0.7912, sensitivity of 0.7064 and specificity of 0.8760 for the abnormal diagnosis in general. Finally, we demonstrate that our coarse classification (based on five specific locations of abnormalities) yields comparable results to fine classification (twelve pathologies) on the benchmark CheXpert dataset for general anomaly detection while delivering better performance in terms of the average performance of all classes.

Artemisinin or chloroquine for blood stage Plasmodium vivax malaria in Vietnam.

Chloroquine-resistant Plasmodium vivax has not yet occurred in Vietnam. The efficacy of artemisinin for P. vivax was not established. We conducted a double-blind randomized study involving 240 inpatients with P. vivax malaria who received artemisinin (40 mg/kg over 3 days) plus placebo chloroquine (Art) or chloroquine (25 mg/kg over 3 days) plus placebo artemisinin (Chl). Patients were followed up with weekly blood smears for 28 days. In each group 113 cases were analysed. All patients recovered rapidly. The median (range) parasite clearance time of regimen Art was 24 h (8-72) and of Chl 24 h (8-64; P = 0.3). Parasites reappeared in two cases in each group on day 14, in eight cases in each group (7%) on day 16 and in 25 (23%) and 18 (16%) cases, respectively, at the end of 4-week follow-up (P = 0.3). The population parasite clearance curve followed a mono-exponential decline. The parasite reduction ratio per 48 h reproduction cycle was 2.3 x 104 for both regimens. We conclude that artemisinin and chloroquine are equally effective in the treatment of P. vivax infections in Vietnam. Reappearance of parasites before day 16 (7%) suggests the emergence of chloroquine resistance. Three days of artemisinin monotherapy does not prevent recrudescence.