Discovery of Q203, a potent clinical candidate for the treatment of tuberculosis

New therapeutic strategies are needed to combat the tuberculosis pandemic and the spread of multidrug-resistant (MDR) and extensively drug-resistant (XDR) forms of the disease, which remain a serious public health challenge worldwide1, 2. The most urgent clinical need is to discover potent agents capable of reducing the duration of MDR and XDR tuberculosis therapy with a success rate comparable to that of current therapies for drug-susceptible tuberculosis. The last decade has seen the discovery of new agent classes for the management of tuberculosis3, 4, 5, several of which are currently in clinical trials6, 7, 8. However, given the high attrition rate of drug candidates during clinical development and the emergence of drug resistance, the discovery of additional clinical candidates is clearly needed. Here, we report on a promising class of imidazopyridine amide (IPA) compounds that block Mycobacterium tuberculosis growth by targeting the respiratory cytochrome bc1 complex. The optimized IPA compound Q203 inhibited the growth of MDR and XDR M. tuberculosis clinical isolates in culture broth medium in the low nanomolar range and was efficacious in a mouse model of tuberculosis at a dose less than 1 mg per kg body weight, which highlights the potency of this compound. In addition, Q203 displays pharmacokinetic and safety profiles compatible with once-daily dosing. Together, our data indicate that Q203 is a promising new clinical candidate for the treatment of tuberculosis

Analysis of the Relationship between Patent Litigation and Citation: Subdivision of Citations

Recent examples of patent litigation show the evidence of firms strategic patent use. Thus forecasting patent litigation becomes a greater priority. Patent citations have been prevalent in its usage in analyzing business environment as diverse patent indicators or a tool to predict patent litigation. However, most previous research has considered only direct patent citations. In order to overcome the limitation, this study analyzes patent litigation quantitatively through three kinds of patent citations: direct, indirect and latent citation, and empirically analyzed the relationship between these citations and patent litigation between plaintiff and defendant firms based on U.S. patent documents and patent litigation information. Consequently, this study found that the indirect citation is more by 7% than direct citations to patent litigation. In addition, latent citation is 8% higher in frequency compared with the number of litigations in in/direct citation relationship. Therefore, these results indicate that various approach for patent citation can provide more information for forecasting patent litigation

Sensor Data Acquisition and Multimodal Sensor Fusion for Human Activity Recognition Using Deep Learning

In this paper, we perform a systematic study about the on-body sensor positioning and data acquisition details for Human Activity Recognition (HAR) systems. We build a testbed that consists of eight body-worn Inertial Measurement Units (IMU) sensors and an Android mobile device for activity data collection. We develop a Long Short-Term Memory (LSTM) network framework to support training of a deep learning model on human activity data, which is acquired in both real-world and controlled environments. From the experiment results, we identify that activity data with sampling rate as low as 10 Hz from four sensors at both sides of wrists, right ankle, and waist is sufficient in recognizing Activities of Daily Living (ADLs) including eating and driving activity. We adopt a two-level ensemble model to combine class-probabilities of multiple sensor modalities, and demonstrate that a classifier-level sensor fusion technique can improve the classification performance. By analyzing the accuracy of each sensor on different types of activity, we elaborate custom weights for multimodal sensor fusion that reflect the characteristic of individual activities

Multi-Path and Group-Loss-Based Network for Speech Emotion Recognition in Multi-Domain Datasets

Speech emotion recognition (SER) is a natural method of recognizing individual emotions in everyday life. To distribute SER models to real-world applications, some key challenges must be overcome, such as the lack of datasets tagged with emotion labels and the weak generalization of the SER model for an unseen target domain. This study proposes a multi-path and group-loss-based network (MPGLN) for SER to support multi-domain adaptation. The proposed model includes a bidirectional long short-term memory-based temporal feature generator and a transferred feature extractor from the pre-trained VGG-like audio classification model (VGGish), and it learns simultaneously based on multiple losses according to the association of emotion labels in the discrete and dimensional models. For the evaluation of the MPGLN SER as applied to multi-cultural domain datasets, the Korean Emotional Speech Database (KESD), including KESDy18 and KESDy19, is constructed, and the English-speaking Interactive Emotional Dyadic Motion Capture database (IEMOCAP) is used. The evaluation of multi-domain adaptation and domain generalization showed 3.7% and 3.5% improvements, respectively, of the F1 score when comparing the performance of MPGLN SER with a baseline SER model that uses a temporal feature generator. We show that the MPGLN SER efficiently supports multi-domain adaptation and reinforces model generalization

A New Approach to Accuracy Evaluation of Single-Tooth Abutment Using Two-Dimensional Analysis in Two Intraoral Scanners

The aim of this study was to two-dimensionally evaluate deviation errors at five digital cross-sections of single-tooth abutment in regards to data obtained from two intraoral scanners, and to evaluate accuracy of individual scanners. Two intraoral scanners, the Trios 3® (3 Shape, Copenhagen, Denmark) and EzScan® (Vatech, Hwaseong, Korea), were evaluated by utilizing 13 stone models. The superimposed 3D data files were sectioned into five different planes: buccal-lingual section (BL), mesial-distal section (MD), transverse high section (TH), transverse middle section (TM), and transverse low section (TL). Accuracy comparison between the two scanners in 5 groups was performed. BL vs. MD of each scanner, and three transverse groups (TH, TM, TL) of each scanner were analyzed for accuracy comparison. In comparison of 2-D analyses for two intraoral scanners, Trios 3® showed statistically significant higher accuracy in root mean square (RMS) at BL, TH, and TL (p < 0.05). For each scanner, RMS value showed that mesial-distal sections were more prone to error than buccal-lingual section, which exhibited statistically significant errors (p < 0.05) while the transverse groups did not. Two-dimensional analysis is more insightful than three-dimensional analysis on single-tooth abutment. In mesiodistal areas, rough prepped areas, and sharp edges where scanner accessibility is difficult, high deviation errors are shown

Effectiveness of group cognitive behavioral therapy with mindfulness in end-stage renal disease hemodialysis patients

Background : Many patients with end-stage renal disease (ESRD) undergoing hemodialysis (HD) experience depression. Depression influences patient quality of life (QOL), dialysis compliance, and medical comorbidity. We developed and applied a group cognitive behavioral therapy (CBT) program including mindfulness meditation for ESRD patients undergoing HD, and measured changes in QOL, mood, anxiety, perceived stress, and biochemical markers. Methods : We conducted group CBT over a 12-week period with seven ESRD patients undergoing HD and suffering from depression. QOL, mood, anxiety, and perceived stress were measured at baseline and at weeks 8 and 12 using the World Health Organization Quality of Life scale, abbreviated version (WHOQOL-BREF), the Beck Depression Inventory II (BDI-II), the Hamilton Rating Scale for Depression (HAM-D), the Beck Anxiety Inventory (BAI), and the Perceived Stress Scale (PSS). Biochemical markers were measured at baseline and after 12 weeks. The Temperament and Character Inventory was performed to assess patient characteristics before starting group CBT. Results : The seven patients showed significant improvement in QOL, mood, anxiety, and perceived stress after 12 weeks of group CBT. WHOQOL-BREF and the self-rating scales, BDI-II and BAI, showed continuous improvement across the 12-week period. HAM-D scores showed significant improvement by week 8; PSS showed significant improvement after week 8. Serum creatinine levels also improved significantly following the 12 week period. Conclusion : In this pilot study, a CBT program which included mindfulness meditation enhanced overall mental health and biochemical marker levels in ESRD patients undergoing HD