Effectiveness of a novel cellular therapy to treat multidrug-resistant tuberculosis

AbstractBackground/objectiveWe urgently need novel treatments for multidrug-resistant tuberculosis (MDR-TB). Autologous mesenchymal stromal cell (MSC) infusion is one such possibility due to its potential to repair damaged lung tissue and boost immune responses. We aimed to assess the safety and effectiveness of MSC to improve treatment outcomes among MDR-TB patients.MethodsWe analyzed treatment outcomes for 108 Belarusian MDR-TB patients receiving chemotherapy. Thirty-six patients (cases) also had MSCs collected, extracted, cultured, and reinfused (average time from chemotherapy start to infusion was 49days) in optimal dose; another 36 patients (without MSC treatment) were “study controls”. We identified another control group: 36 patients from the Belarusian national surveillance database (surveillance controls) 1:1 matched to cases.ResultsSuccessful outcomes were observed in 81% of cases, 42% of surveillance controls, and 39% of study controls. After adjusting for age, odds of a successful outcome were 6.5 (95% confidence interval, 1.2–36.2, p=0.032) times greater for cases than surveillance controls. Adjusting for other potential confounders increased the effect estimate while maintaining statistical significance. Cases were less likely (p=0.01) to be culture negative at 2months than surveillance controls, indicating a poorer initial prognosis in cases before (or shortly after) infusion. Radiological improvement was more likely in cases than in study controls.ConclusionMSC treatment could vastly improve treatment outcomes for MDR-TB patients. Our findings could revolutionize therapy options and have strong implications for future directions of MDR-TB therapy researc

ImageCLEF 2019: Multimedia Retrieval in Medicine, Lifelogging, Security and Nature

This paper presents an overview of the ImageCLEF 2019 lab, organized as part of the Conference and Labs of the Evaluation Forum - CLEF Labs 2019. ImageCLEF is an ongoing evaluation initiative (started in 2003) that promotes the evaluation of technologies for annotation, indexing and retrieval of visual data with the aim of providing information access to large collections of images in various usage scenarios and domains. In 2019, the 17th edition of ImageCLEF runs four main tasks: (i) a medical task that groups three previous tasks (caption analysis, tuberculosis prediction, and medical visual question answering) with new data, (ii) a lifelog task (videos, images and other sources) about daily activities understanding, retrieval and summarization, (iii) a new security task addressing the problems of automatically identifying forged content and retrieve hidden information, and (iv) a new coral task about segmenting and labeling collections of coral images for 3D modeling. The strong participation, with 235 research groups registering, and 63 submitting over 359 runs, shows an important interest in this benchmark campaign

Overview of ImageCLEFtuberculosis 2019 ::automatic CT-based report generation and tuberculosis severity assessment

ImageCLEF is the image retrieval task of the Conference and Labs of the Evaluation Forum (CLEF). ImageCLEF has historically focused on the multimodal and language-independent retrieval of images. Many tasks are related to image classification and the annotation of image data as well as the retrieval of images. Since 2017, when the tuberculosis task started in ImageCLEF, the number of participants has kept growing. In 2019, 13 groups from 11 countries participated in at least one of the two subtasks proposed: (1) SVR subtask: the assessment of a tuberculosis severity score and (2) CTR subtask: the automatic generation of a CT report based on six relevant CT findings. In this second edition of the SVR subtask the results support the assessment of a severity score based on the CT scan with up to 0.79 area under the curve (AUC) and 74% accuracy, so very good results. In addition, in the first edition of the CTR subtask, impressive results were obtained with 0.80 average AUC and 0.69 minimum AUC for the six CT findings proposed