The recent global increase in the prevalence of antibiotic-resistant bacteria and lack of development of new therapeutic agents emphasize the importance of selecting appropriate antimicrobials for the treatment of infections. However, to date, the development of completely accelerated drug susceptibility testing methods has not been achieved despite the availability of a rapid identification method. We proposed an innovative rapid method for drug susceptibility testing for Pseudomonas aeruginosa that provides results within 3 h. The drug susceptibility testing microfluidic (DSTM) device was prepared using soft lithography. It consisted of five sets of four microfluidic channels sharing one inlet slot, and the four channels are gathered in a small area, permitting simultaneous microscopic observation. Antimicrobials were pre-introduced into each channel and dried before use. Bacterial suspensions in cation-adjusted Mueller–Hinton broth were introduced from the inlet slot and incubated for 3 h. Susceptibilities were microscopically evaluated on the basis of differences in cell numbers and shapes between drug-treated and control cells, using dedicated software. The results of 101 clinically isolated strains of P. aeruginosa obtained using the DSTM method strongly correlated with results obtained using the ordinary microbroth dilution method. Ciprofloxacin, meropenem, ceftazidime, and piperacillin caused elongation in susceptible cells, while meropenem also induced spheroplast and bulge formation. Morphological observation could alternatively be used to determine the susceptibility of P. aeruginosa to these drugs, although amikacin had little effect on cell shape. The rapid determination of bacterial drug susceptibility using the DSTM method could also be applicable to other pathogenic species, and it could easily be introduced into clinical laboratories without the need for expensive instrumentation.

Traditional approaches for the screening of cognitive function are often based on paper tests, such as Mini-Mental State Examination (MMSE), that evaluate the degree of cognitive impairment and provide a score of patient’s mental ability. Procedures for conducting paper tests require time investment involving a questioner and not suitable to be carried out frequently. Previous studies showed that dementia impaired patients are not capable of multi-tasking efficiently. Based on this observation an automated system utilizing Kinect device for collecting primarily patient’s gait data who carry out locomotion and calculus tasks individually (i.e., single-tasks) and then simultaneously (i.e., dual-task) was introduced. We installed this system in three elderly facilities and collected 10,833 behavior data from 90 subjects. We conducted analyses of the acquired information extracting 12 features of single- and dual-task performance developed a method for automatic dementia score estimation to investigate determined which characteristics are the most important. In result, a machine learning algorithm using single and dual-task performance classified subjects with an MMSE score of 23 or lower with a recall 0.753 and a specificity 0.799. We found the gait characteristics were important features in the score estimation, and referring to both single and dual-task features was effective.

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In recent years, a rapid increase in bacterial strains resistant to modern antibiotics has been observed. This alarming rise in drug-resistant organisms has emphasized the importance of identifying new effective antimicrobial agents. Since traditional approaches for drug susceptibility testing are time-consuming and labor-intensive, more ef cient methods are urgently needed. Here, we report an automatic image analysis system for drug susceptibility testing that provides results within 3 hours using a drug susceptibility testing micro uidic (DSTM) device. The device consists of ve sets of four micro uidic channels prepared by soft lithography. The channels are in close proximity to allow simultaneous observations. The antimicrobial agent and bacterial suspension to be tested are added to the channel and incubated for 3 hours. Previously, microscopic images of the DSTM device were analyzed manually by an expert to evaluate the susceptibility of a strain. In this work, we present an automatic computer vision algorithm for processing images and performing analysis. The algorithm enhances the quality of the input image, detects cells in each channel, extracts a variety of cell-related characteristics, and estimates drug susceptibility using a pre-trained support vector machine. We address the issue of overlapping cells by incorporating a graph-based cell separation algorithm. The minimum concentration of a drug for which the proposed method predicted susceptibility represents the minimum inhibitory concentration (MIC). The novel method was implemented as a standalone application and tested on a dataset containing images of 101 clinically isolated strains of Pseudomonas aeruginosa incubated in the presence of ve different drugs. The estimated MICs correlated well with the results obtained using the conventional broth microdilution method.