Anticipating the transmissibility of the 2022 mpox outbreak.

An ongoing outbreak of monkeypox virus (MPXV) was first reported in the United Kingdom on 6 May 2022. As of 17 November, there had been a total of 80 221 confirmed MPXV cases in over 110 countries. Based on data reported between 6 May and 30 June 2022 in the United Kingdom, Spain, and Germany, we applied a deep learning approach using convolutional neural networks to evaluate the parameters of the 2022 MPXV outbreak. The basic reproduction number (R0 ) of MPXV was estimated to be 2.32 in the United Kingdom, which indicates the active diffusion of MPXV since the beginning of the outbreak. The data from Spain and Germany produced higher median R0 values of 2.42 and 2.88, respectively. Importantly, the estimated R0 of MPXV in the three countries tends to the previously calculated R0 of smallpox (3.50 to 6.00). Furthermore, the incubation (1/ε) and infectious (1/γ) period was predicted between 9 and 10 days and 4-5 days, respectively. The R0 value derived from MPXV is consistent with the significantly increasing number of cases, indicating the risk of a rapid spread of MPXV worldwide, which would provide important insights for the prevention and control of MPXV epidemic.

AIDMAN: An AI-based object detection system for malaria diagnosis from smartphone thin-blood-smear images.

Malaria is a significant public health concern, with ∼95% of cases occurring in Africa, but accurate and timely diagnosis is problematic in remote and low-income areas. Here, we developed an artificial intelligence-based object detection system for malaria diagnosis (AIDMAN). In this system, the YOLOv5 model is used to detect cells in a thin blood smear. An attentional aligner model (AAM) is then applied for cellular classification that consists of multi-scale features, a local context aligner, and multi-scale attention. Finally, a convolutional neural network classifier is applied for diagnosis using blood-smear images, reducing interference caused by false positive cells. The results demonstrate that AIDMAN handles interference well, with a diagnostic accuracy of 98.62% for cells and 97% for blood-smear images. The prospective clinical validation accuracy of 98.44% is comparable to that of microscopists. AIDMAN shows clinically acceptable detection of malaria parasites and could aid malaria diagnosis, especially in areas lacking experienced parasitologists and equipment.

Computer-Aided Rational Engineering of Signal Sensitivity of Quorum Sensing Protein LuxR in a Whole-Cell Biosensor.

LuxR, a bacterial quorum sensing-related transcription factor that responds to the signaling molecule 3-oxo-hexanoyl-homoserine lactone (3OC6-HSL). In this study, we employed molecular dynamics simulation and the Molecular Mechanics Generalized Born Surface Area (MM-GB/SA) method to rationally identify residues in Vibrio fischeri LuxR that are important for its interaction with 3OC6-HSL. Isoleucine-46 was selected for engineering as the key residue for interaction with 3OC6-HSL-LuxR-I46F would have the strongest binding energy to 3OC6-HSL and LuxR-I46R the weakest binding energy. Stable wild-type (WT) LuxR, I46F and I46R variants were produced in Escherichia coli (E. coli) in the absence of 3OC6-HSL by fusion with maltose-binding protein (MBP). Dissociation constants for 3OC6-HSL from MBP-fusions of WT-, I46F- and I46R-LuxR determined by surface plasmon resonance confirmed the binding affinity. We designed and constructed a novel whole-cell biosensor on the basis of LuxR-I46F in E. coli host cells with a reporting module that expressed green fluorescent protein. The biosensor had high sensitivity in response to the signaling molecule 3OC6-HSL produced by the target bacterial pathogen Yersinia pestis. Our work demonstrates a practical, generalizable framework for the rational design and adjustment of LuxR-family proteins for use in bioengineering and bioelectronics applications.