Article
Infectious Diseases
Getu Diriba, Ayinalem Alemu, Bazezew Yenew, Habteyes Hailu Tola, Dinka Fikadu Gamtesa, Hilina Mollalign, Kirubel Eshetu, Shewki Moga, Saro Abdella, Getachew Tollera, Abebaw Kebede, Mesay Hailu Dangisso
Summary: This study aimed to estimate the prevalence of extensively drug-resistant tuberculosis (XDR-TB) and pre-extensively drug-resistant tuberculosis (pre-XDR-TB) in patients with multidrug-resistant TB (MDR-TB). A total of 64 studies involving 12,711 patients from 22 countries were included. The pooled proportion of pre-XDR-TB was 26%, while XDR-TB in MDR-TB cases was 9%. These findings highlight the substantial burden of pre-XDR-TB and XDR-TB in MDR-TB patients, emphasizing the importance of strengthening TB programs and drug resistance surveillance.
INTERNATIONAL JOURNAL OF INFECTIOUS DISEASES
(2023)
Article
Infectious Diseases
Li Wang, Ruoyan Ying, Yidian Liu, Qin Sun, Wei Sha
Summary: This study aimed to investigate the metabolic changes in drug-susceptible and multidrug-resistant Mycobacterium tuberculosis and provide insights into the mechanisms of drug resistance based on metabolic pathways.
INFECTION AND DRUG RESISTANCE
(2023)
Review
Microbiology
Francesco Pecora, Giulia Dal Canto, Piero Veronese, Susanna Esposito
Summary: This article discusses the current knowledge of managing MDR-TB and XDR-TB in children, focusing on two promising new drugs: bedaquiline and delamanid. While data on these new anti-TB drugs in pediatric populations are limited, they appear to have good tolerability and efficacy in children with MDR-TB/XDR-TB. More evidence is needed to guide their use in designing effective shorter regimens and reducing adverse effects, drug interactions, and therapeutic failure.
Article
Immunology
Mei-Hua Wu, Hseuh-Chien Hsiao, Po -Wei Chu, Hsin-Hua Chan, Hsiu-Yun Lo, Ruwen Jou
Summary: This retrospective study examined clinical and bacteriological information of 1511 MDR-TB cases in Taiwan from 2008 to 2019, revealing a significant decline in MDR-TB cases. However, the decrease was different for new and previously treated cases. The findings suggest that programmatic management of MDR-TB has been effective in controlling tuberculosis.
JOURNAL OF MICROBIOLOGY IMMUNOLOGY AND INFECTION
(2023)
Article
Computer Science, Interdisciplinary Applications
Ziquan Zhu, Jing Tao, Shuihua Wang, Xin Zhang, Yudong Zhang
Summary: This paper proposes a novel deep-learning model, TBDLNet, for automatically recognizing CT images and classifying multidrug-resistant and drug-sensitive tuberculosis. The model utilizes a pre-trained ResNet50 for feature extraction and employs three randomized neural networks to address overfitting. The ensemble of the three RNNs is applied for boosting robustness through majority voting. The model is evaluated using five-fold cross-validation and achieves high scores on accuracy, sensitivity, precision, F1-score, and specificity. TBDLNet is suitable for classifying multidrug-resistant and drug-sensitive tuberculosis, and allows for early detection of multidrug-resistant pulmonary tuberculosis, enabling timely adjustments to treatment plans and improved treatment outcomes.
ENGINEERING REPORTS
(2023)
Article
Infectious Diseases
Chun-Hua Li, Xiao Fan, Sheng-Xiu Lv, Xue-Yan Liu, Jia-Nan Wang, Yong-Mei Li, Qi Li
Summary: The value of integrating clinical and computed tomography (CT) features to predict multidrug-resistant tuberculosis (MDR-PTB) was explored. Male sex, retreated history, longer duration of previous anti-TB treatment, lower CD4+ T lymphocyte count, thick-walled cavity, centrilobular micronodules and tree-in-bud sign, bronchial stenosis, pleural and pericardial thickening were identified as the most effective variations associated with MDR-PTB. The combined model achieved an accuracy of 78.6% and an external validation cohort obtained an accuracy of 81.7%.
INFECTION AND DRUG RESISTANCE
(2023)
Article
Public, Environmental & Occupational Health
Yang Che, Tianchi Yang, Lv Lin, Yue Xiao, Feng Jiang, Yanfei Chen, Tong Chen, Jifang Zhou
Summary: By analyzing MDR-TB patients with sputum smear testing and genotyping, the study found that latent class analysis based on data-driven genetic determinants could better predict clinical meaningful outcomes, showing promising potential in predicting treatment prognosis for patients.
FRONTIERS IN PUBLIC HEALTH
(2021)
Article
Infectious Diseases
Shynar M. Maretbayeva, Anar S. Rakisheva, Malik M. Adenov, Lyazzat T. Yeraliyeva, Yerkebulan Zh. Algozhin, Assel T. Stambekova, Elmira A. Berikova, Askar Yedilbayev, Michael L. Rich, Kwonjune J. Seung, Assiya M. Issayeva
Summary: Rifampicin-resistant/multidrug-resistant (RR/MDR) and extensively drug-resistant (XDR) strains of M. tuberculosis are a serious public health issue in Kazakhstan. The approval of new TB drugs, bedaquiline and delamanid, offers hope for more effective MDR-TB treatment. A study of patients in Kazakhstan receiving bedaquiline or delamanid-containing regimen found that 89% experienced culture conversion within six months.
INTERNATIONAL JOURNAL OF INFECTIOUS DISEASES
(2021)
Article
Infectious Diseases
Liang Fu, Xilin Zhang, Juan Xiong, Feng Sun, Taoping Weng, Yang Li, Peize Zhang, Hui Li, Qianting Yang, Yi Cai, Hancheng Liang, Qiuqi Chen, Zhaoqing Wang, Lei Liu, Xinchun Chen, Wenhong Zhang, Guofang Deng
Summary: The study evaluated the safety and efficacy of three 9-month, all-oral, 5-drug regimens in the treatment of patients with multidrug-resistant tuberculosis (MDR-TB) and pre-extensive drug-resistant tuberculosis (pre-XDR-TB). The results showed that the primary outcome of these regimens was satisfactory in the majority of patients, with manageable and reversible adverse events.
INTERNATIONAL JOURNAL OF INFECTIOUS DISEASES
(2023)
Article
Infectious Diseases
Hongbing Jia, Yuhui Xu, Zhaogang Sun
Summary: The study found the emergence of multidrug-resistant isolates of Mycobacterium tuberculosis in China, with mutations in six resistant genes showing different characteristics among isolates. Specific mutations in certain genes were found to potentially lead to high levels of drug resistance. Therefore, it is necessary to conduct more epidemiological investigations to support the development of rapid detection technologies for drug-resistant tuberculosis.
Article
Infectious Diseases
Anastasia Ushtanit, Yulia Mikhailova, Alexandra Lyubimova, Marina Makarova, Svetlana Safonova, Alexey Filippov, Sergey Borisov, Danila Zimenkov
Summary: Linezolid resistance mainly emerged during treatment with the latest regimen, and three primary cases with linezolid resistance questioned the possible transmission of totally drug-resistant tuberculosis in the Moscow region, which requires further investigation.
Article
Chemistry, Medicinal
Leonardo Aquino Linhares, Aline dos Santos Peixoto, Luanna de Angelis Correia de Sousa, Joao Paulo Lucena Laet, Aline Caroline da Silva Santos, Valeria Rego Alves Pereira, Maria Madileuza Carneiro Neves, Luiz Felipe Gomes Rebello Ferreira, Marcelo Zaldini Hernandes, Jennifer de la Vega, Antonio Pereira-Neves, Arturo San Feliciano, Esther Del Olmo, Haiana Charifker Schindler, Lilian Maria Lapa Montenegro
Summary: Tuberculosis remains a major public health problem and the increase in multidrug-resistant variants makes it more difficult to treat and control. This study evaluated new compounds related to dihydrosphingosine and ethambutol against sensitive and pre-XDR Mycobacterium strains and characterized their pharmacological activity. Among the compounds analyzed, 11 showed good to moderate activity against sensitive and MDR Mycobacterium tuberculosis, with potential as a prototype substance for further optimization in preclinical studies.
EUROPEAN JOURNAL OF MEDICINAL CHEMISTRY
(2023)
Article
Immunology
Scott K. Heysell, Stellah G. Mpagama, Oleg B. Ogarkov, Mark Conaway, Shahriar Ahmed, Svetlana Zhdanova, Suporn Pholwat, Mohammad H. Alshaer, Anna M. Chongolo, Buliga Mujaga, Margaretha Sariko, Sabrina Saba, S. M. Mazidur Rahman, Mohammad Khaja Mafij Uddin, Alexey Suzdalnitsky, Elena Moiseeva, Elena Zorkaltseva, Mikhail Koshcheyev, Serhiy Vitko, Blandina T. Mmbaga, Gibson S. Kibiki, Jotam G. Pasipanodya, Charles A. Peloquin, Sayera Banu, Eric R. Houpt
Summary: In a multicountry, prospective cohort, serum pharmacokinetics and Mycobacterium tuberculosis minimum inhibitory concentrations were variable, yet certain drugs' parameters were predictive of clinical outcome for rifampin-resistant/multidrug-resistant tuberculosis.
CLINICAL INFECTIOUS DISEASES
(2023)
Article
Microbiology
Martha L. van der Walt, Karen Shean, Piet Becker, Karen H. Keddy, Joey Lancaster
Summary: The study compared treatment outcomes among MDR-TB patients receiving ethambutol, cycloserine, or terizidone as part of a standardized regimen. Results showed higher success rates and lower default rates in patients receiving cycloserine and terizidone, but higher culture conversion rates in those receiving cycloserine. Further investigation is needed to elucidate the differences observed between cycloserine and terizidone.
ANTIMICROBIAL AGENTS AND CHEMOTHERAPY
(2021)
Article
Microbiology
Rong Li, Jin-Bao Ma, Hong Yang, Han Yang, Xin-Jun Yang, Yan-Qin Wu, Fei Ren
Summary: This study compared the effects of bedaquiline alone and combined with other anti-TB drugs on the QT interval in the treatment of MDR-TB patients. It found that the combination of bedaquiline with fluoroquinolones and/or clofazimine significantly increased the incidence of QT prolongation. However, no serious ventricular arrhythmia and permanent drug withdrawal occurred.
MICROBIOLOGY SPECTRUM
(2023)
Article
Respiratory System
Pedro Daibert de Navarro, Isabela Neves de Almeida, Afranio Lineu Kritski, Maria das Gracas Ceccato, Monica Maria Delgado Maciel, Wania da Silva Carvalho, Silvana Spindola de Miranda
JORNAL BRASILEIRO DE PNEUMOLOGIA
(2016)
Article
Computer Science, Interdisciplinary Applications
Fabio S. Aguiar, Rodrigo C. Torres, Joao V. F. Pinto, Afranio L. Kritski, Jose M. Seixas, Fernanda C. Q. Mello
MEDICAL & BIOLOGICAL ENGINEERING & COMPUTING
(2016)
Article
Infectious Diseases
Solange Goncalves David, Kathryn L. Lovero, Maria de Fatima B. Pombo March, Thalita G. Abreu, Antonio Ruffino Netto, Afranio L. Kritski, Clemax C. Sant'Anna
INTERNATIONAL JOURNAL OF INFECTIOUS DISEASES
(2017)
Article
Infectious Diseases
R. I. Calderon, G. E. Velasquez, M. C. Becerra, Z. Zhang, C. C. Contreras, R. M. Yataco, J. T. Galea, L. W. Lecca, A. L. Kritski, M. B. Murray, C. D. Mitnick
INTERNATIONAL JOURNAL OF TUBERCULOSIS AND LUNG DISEASE
(2017)
Article
Infectious Diseases
A. C. Sweetland, A. Kritski, M. A. Oquendo, M. E. Sublette, A. Norcini Pala, L. R. Batista Silva, A. Karpati, E. C. Silva, M. O. Moraes, J. R. Lapa e Silva, M. L. Wainberg
INTERNATIONAL JOURNAL OF TUBERCULOSIS AND LUNG DISEASE
(2017)
Article
Parasitology
Mirela Verza, Karen Barros Schmid, Regina Bones Barcellos, Natali Linck, Graziele Lima Bello, Daniel Scapin, Rosa Dea Sperhacke, Marcia Susana Nunes Silva, Claudia Wollheim, Martha Gabriela Celle Rivero, Afranio Lineu Kritski, Leonides Rezende, Martha Maria Oliveira, Elis Regina Dalla Costa, Maria Lucia Rosa Rossetti
MEMORIAS DO INSTITUTO OSWALDO CRUZ
(2017)
Article
Parasitology
Paula Fernanda Goncalves dos Santos, Elis Regina Dalla Costa, Daniela M. Ramalho, Maria Lucia Rossetti, Regina Bones Barcellos, Luciana de Souza Nunes, Leonardo Souza Esteves, Rodrigo Rodenbusch, Richard M. Anthony, Indra Bergval, Sarah Sengstake, Miguel Viveiros, Afrnio Kritski, Martha M. Oliveira
MEMORIAS DO INSTITUTO OSWALDO CRUZ
(2017)
Article
Parasitology
Silvana Spindola de Miranda, Isabela Neves de Almeida, Maria Luiza Lopes, Jamilly dos Reis de Figueiredo, Lida Jouca de Assis Figueredo, Afranio Lineu Kritski, Wnia da Silva Carvalho, Maria de Fatima Filardi Oliveira Mansur
REVISTA DA SOCIEDADE BRASILEIRA DE MEDICINA TROPICAL
(2017)
Article
Parasitology
Karla Anacleto de Vasconcelos, Silvana Maria Monte Coelho Frota, Antonio Ruffino-Netto, Afranio Lineu Kritski
REVISTA DA SOCIEDADE BRASILEIRA DE MEDICINA TROPICAL
(2017)
Article
Microbiology
Isabela N. de Almeida, Lida J. de Assis Figueredo, Valeria M. Soares, Maria C. Vater, Suely Alves, Wania da Silva Carvalho, Afranio L. Kritski, Silvana S. de Miranda
FRONTIERS IN MICROBIOLOGY
(2017)
Article
Multidisciplinary Sciences
Fabricio M. Almeida, Thatiana L. B. Ventura, Eduardo P. Amaral, Simone C. M. Ribeiro, Sanderson D. Calixto, Marcelle R. Manhaes, Andreza L. Rezende, Giliane S. Souzal, Igor S. de Carvalho, Elisangela C. Silva, Juliana Azevedo da Silva, Eulogio C. Q. Carvalho, Afranio L. Kritski, Elena B. Lasunskaia
Article
Multidisciplinary Sciences
Pryscila Miranda, Leonardo Gil-Santana, Marina G. oliveira, Eliene D. D. Mesquita, Elisangela Silva, Anneloek Rauwerdink, Frank Cobelens, Martha M. Oliveira, Bruno B. Andrade, Afranio Kritski
Article
Infectious Diseases
Mayara Lisboa Bastos, Lorrayne Beliqui Cosme, Geisa Fregona, Thiago Nascimento do Prado, Adelmo Inacio Bertolde, Eliana Zandonade, Mauro N. Sanchez, Margareth Pretti Dalcolmo, Afranio Kritski, Anete Trajman, Ethel Leonor Noia Maciel
BMC INFECTIOUS DISEASES
(2017)
Article
Infectious Diseases
D. M. P. Ramalho, P. F. C. Miranda, M. K. Andrade, T. Brigido, M. P. Dalcolmo, E. Mesquita, C. F. Dias, A. N. Gambirasio, J. Ueleres Braga, A. Detjen, P. P. J. Phillips, I. Langley, P. I. Fujiwara, S. B. Squire, M. M. Oliveira, A. L. Kritski
BMC INFECTIOUS DISEASES
(2017)
Article
Computer Science, Artificial Intelligence
Rui Lv, Dingheng Wang, Jiangbin Zheng, Zhao-Xu Yang
Summary: In this paper, the authors investigate tensor decomposition for neural network compression. They analyze the convergence and precision of tensor mapping theory, validate the rationality of tensor mapping and its superiority over traditional tensor approximation based on the Lottery Ticket Hypothesis. They propose an efficient method called 3D-KCPNet to compress 3D convolutional neural networks using the Kronecker canonical polyadic (KCP) tensor decomposition. Experimental results show that 3D-KCPNet achieves higher accuracy compared to the original baseline model and the corresponding tensor approximation model.
Article
Computer Science, Artificial Intelligence
Xiangkun He, Zhongxu Hu, Haohan Yang, Chen Lv
Summary: In this paper, a novel constrained multi-objective reinforcement learning algorithm is proposed for personalized end-to-end robotic control with continuous actions. The approach trains a single model using constraint design and a comprehensive index to achieve optimal policies based on user-specified preferences.
Article
Computer Science, Artificial Intelligence
Zhijian Zhuo, Bilian Chen, Shenbao Yu, Langcai Cao
Summary: In this paper, a novel method called Expansion with Contraction Method for Overlapping Community Detection (ECOCD) is proposed, which utilizes non-negative matrix factorization to obtain disjoint communities and applies expansion and contraction processes to adjust the degree of overlap. ECOCD is applicable to various networks with different properties and achieves high-quality overlapping community detection.
Article
Computer Science, Artificial Intelligence
Yizhe Zhu, Chunhui Zhang, Jialin Gao, Xin Sun, Zihan Rui, Xi Zhou
Summary: In this work, the authors propose a Contrastive Spatio-Temporal Distilling (CSTD) approach to improve the detection of high-compressed deepfake videos. The approach leverages spatial-frequency cues and temporal-contrastive alignment to fully exploit spatiotemporal inconsistency information.
Review
Computer Science, Artificial Intelligence
Laijin Meng, Xinghao Jiang, Tanfeng Sun
Summary: This paper provides a review of coverless steganographic algorithms, including the development process, known contributions, and general issues in image and video algorithms. It also discusses the security of coverless steganography from theoretical analysis to actual investigation for the first time.
Article
Computer Science, Artificial Intelligence
Yajie Bao, Tianwei Xing, Xun Chen
Summary: Visual question answering requires processing multi-modal information and effective reasoning. Neural-symbolic learning is a promising method, but current approaches lack uncertainty handling and can only provide a single answer. To address this, we propose a confidence based neural-symbolic approach that evaluates NN inferences and conducts reasoning based on confidence.
Article
Computer Science, Artificial Intelligence
Anh H. Vo, Bao T. Nguyen
Summary: Interior style classification is an interesting problem with potential applications in both commercial and academic domains. This project proposes a method named ISC-DeIT, which combines data-efficient image transformer architectures and knowledge distillation, to address the interior style classification problem. Experimental results demonstrate a significant improvement in predictive accuracy compared to other state-of-the-art methods.
Article
Computer Science, Artificial Intelligence
Shashank Kotyan, Danilo Vasconcellos Vargas
Summary: This article introduces a novel augmentation technique called Dynamic Scanning Augmentation to improve the accuracy and robustness of Vision Transformer (ViT). The technique leverages dynamic input sequences to adaptively focus on different patches, resulting in significant changes in ViT's attention mechanism. Experimental results demonstrate that Dynamic Scanning Augmentation outperforms ViT in terms of both robustness to adversarial attacks and accuracy against natural images.
Article
Computer Science, Artificial Intelligence
Hiba Alqasir, Damien Muselet, Christophe Ducottet
Summary: The article proposes a solution to improve the learning process of a classification network by providing shape priors, reducing the need for annotated data. The solution is tested on cross-domain digit classification tasks and a video surveillance application.
Article
Computer Science, Artificial Intelligence
Dexiu Ma, Mei Liu, Mingsheng Shang
Summary: This paper proposes a method using neural dynamics solvers to solve infinity-norm optimization problems. Two improved solvers are constructed and their effectiveness and superiority are demonstrated through theoretical analysis and simulation experiments.
Article
Computer Science, Artificial Intelligence
Francesco Gregoretti, Giovanni Pezzulo, Domenico Maisto
Summary: Active Inference is a computational framework that uses probabilistic inference and variational free energy minimization to describe perception, planning, and action. cpp-AIF is a header-only C++ library that provides a powerful tool for implementing Active Inference for Partially Observable Markov Decision Processes through multi-core computing. It is cross-platform and improves performance, memory management, and usability compared to existing software.
Article
Computer Science, Artificial Intelligence
Zelin Ying, Dawei Cheng, Cen Chen, Xiang Li, Peng Zhu, Yifeng Luo, Yuqi Liang
Summary: This paper proposes a novel stock market trends prediction framework called SMART, which includes a self-supervised stock technical data sequence embedding model S3E. By training with multiple self-supervised auxiliary tasks, the model encodes stock technical data sequences into embeddings and uses the learned sequence embeddings for predicting stock market trends. Extensive experiments on China A-Shares market and NASDAQ market prove the high effectiveness of our model in stock market trends prediction, and its effectiveness is further validated in real-world applications in a leading financial service provider in China.
Article
Computer Science, Artificial Intelligence
Hao Li, Hao Jiang, Dongsheng Ye, Qiang Wang, Liang Du, Yuanyuan Zeng, Liu Yuan, Yingxue Wang, C. Chen
Summary: DHGAT1, a dynamic hyperbolic graph attention network, utilizes hyperbolic metric properties to embed dynamic graphs. It employs a spatiotemporal self-attention mechanism and weighted node representations, resulting in excellent performance in link prediction tasks.
Article
Computer Science, Artificial Intelligence
Jiehui Huang, Zhenchao Tang, Xuedong He, Jun Zhou, Defeng Zhou, Calvin Yu-Chian Chen
Summary: This study proposes a progressive learning multi-scale feature blending model for image deraining tasks. The model utilizes detail dilation and texture extraction to improve the restoration of rainy images. Experimental results show that the model achieves near state-of-the-art performance in rain removal tasks and exhibits better rain removal realism.
Article
Computer Science, Artificial Intelligence
Lizhi Liu, Zilin Gao, Yinhe Wang, Yongfu Li
Summary: This paper proposes a novel discrete-time interconnected model for depicting complex dynamical networks. The model consists of nodes and edges subsystems, which consider the dynamic characteristic of both nodes and edges. By designing control strategies and coupling modes, the stabilization and synchronization of the network are achieved. Simulation results demonstrate the effectiveness of the proposed methods.