Detection of Extended-Spectrum ß-Lactamase-Producing Escherichia coli Using Infrared Microscopy and Machine-Learning Algorithms.

The spread of multidrug resistant bacteria has become a global concern. One of the most important and emergent classes of multidrug-resistant bacteria is extended-spectrum ß-lactamase-producing bacteria (ESBL-positive = ESBL+). Due to widespread and continuous evolution of ESBL-producing bacteria, they become increasingly resistant to many of the commonly used antibiotics, leading to an increase in the mortality associated with resulting infections. Timely detection of ESBL-producing bacteria and rapid determination of their susceptibility to appropriate antibiotics can reduce the spread of these bacteria and the consequent complications. Routine methods used for the detection of ESBL-producing bacteria are time-consuming, requiring at least 48 h to obtain results. In this study, we evaluated the potential of infrared spectroscopic microscopy, combined with multivariate analysis for rapid detection of ESBL-producing Escherichia coli ( E. coli) isolated from urinary-tract infection (UTI) samples. Our measurements were conducted on 837 samples of uropathogenic E. coli (UPEC), including 268 ESBL+ and 569 ESBL-negative (ESBL-) samples. All samples were obtained from bacterial colonies after 24 h culture (first culture) from midstream patients' urine. Our results revealed that it is possible to detect ESBL-producing bacteria, with a 97% success rate, 99% sensitivity, and 94% specificity for the tested samples, in a time span of few minutes following the first culture.

Using EfficientNet-B7 (CNN), Variational Auto Encoder (VAE) and Siamese Twins' Networks to Evaluate Human Exercises as Super Objects in a TSSCI Images.

In this article, we introduce a new approach to human movement by defining the movement as a static super object represented by a single two-dimensional image. The described method is applicable in remote healthcare applications, such as physiotherapeutic exercises. It allows researchers to label and describe the entire exercise as a standalone object, isolated from the reference video. This approach allows us to perform various tasks, including detecting similar movements in a video, measuring and comparing movements, generating new similar movements, and defining choreography by controlling specific parameters in the human body skeleton. As a result of the presented approach, we can eliminate the need to label images manually, disregard the problem of finding the start and the end of an exercise, overcome synchronization issues between movements, and perform any deep learning network-based operation that processes super objects in images in general. As part of this article, we will demonstrate two application use cases: one illustrates how to verify and score a fitness exercise. In contrast, the other illustrates how to generate similar movements in the human skeleton space by addressing the challenge of supplying sufficient training data for deep learning applications (DL). A variational auto encoder (VAE) simulator and an EfficientNet-B7 classifier architecture embedded within a Siamese twin neural network are presented in this paper in order to demonstrate the two use cases. These use cases demonstrate the versatility of our innovative concept in measuring, categorizing, inferring human behavior, and generating gestures for other researchers.

Evaluation of functional tests performance using a camera-based and machine learning approach.

The objective of this study is to evaluate the performance of functional tests using a camera-based system and machine learning techniques. Specifically, we investigate whether OpenPose and any standard camera can be used to assess the quality of the Single Leg Squat Test and Step Down Test functional tests. We recorded these exercises performed by forty-six healthy subjects, extract motion data, and classify them to expert assessments by three independent physiotherapists using 15 binary parameters. We calculated ranges of movement in Keypoint-pair orientations, joint angles, and relative distances of the monitored segments and used machine learning algorithms to predict the physiotherapists' assessments. Our results show that the AdaBoost classifier achieved a specificity of 0.8, a sensitivity of 0.68, and an accuracy of 0.7. Our findings suggest that a camera-based system combined with machine learning algorithms can be a simple and inexpensive tool to assess the performance quality of functional tests.

Assessing Human Mobility by Constructing a Skeletal Database and Augmenting it Using a Generative Adversarial Network (GAN) Simulator.

This paper presents a neural network simulator based on anonymized patient motions that measures, categorizes, and infers human gestures based on a library of anonymized patient motions. There is a need for a sufficient training set for deep learning applications (DL). Our proposal is to extend a database that includes a limited number of videos of human physiotherapy activities with synthetic data. As a result of our posture generator, we are able to generate skeletal vectors that depict human movement. A human skeletal model is generated by using OpenPose (OP) from multiple-person videos and photographs. In every video frame, OP represents each human skeletal position as a vector in Euclidean space. The GAN is used to generate new samples and control the parameters of the motion. The joints in our skeletal model have been restructured to emphasize their linkages using depth-first search (DFS), a method for searching tree structures. Additionally, this work explores solutions to common problems associated with the acquisition of human gesture data, such as synchronizing activities and linking them to time and space. A new simulator is proposed that generates a sequence of virtual coordinated human movements based upon a script.

Adapting to change: Clalit's response to the COVID-19 pandemic.

The COVID-19 pandemic is the most significant global health event of the past century. The profound and unexpected changes that it brought about have forced healthcare organizations to make far-reaching adjustments to accommodate the new reality. With the outbreak of the pandemic in Israel and the understanding of its consequences, Clalit Health Services (Clalit), the largest healthcare organization in Israel, rapidly mobilized in order to provide the best response possible from the perspective of both its patients and its employees. In the short term, four designated workgroups were established just days into the pandemic. Their task was to prepare operational work plans to achieve the following goals: providing the best possible treatment for COVID patients; maintaining the level of care for non-COVID patients; protecting healthcare personnel without compromising their competence and level of functioning; and beginning the process of post-crisis planning. In the context of the long term, and with the understanding that the changes in healthcare brought about by the COVID-19 pandemic would be long-lasting and irreversible, and would act as a catalyst in Clalit's preparations for the future, Clalit has carried out the called-for modifications in its organizational strategy. This was based on the need to shift service and treatment foci from the hospitals to the community and the patient's home and his cellular device, by means of strengthening Clalit's strategic abilities to become more proactive, more digital and more home-based. In this article, we present a survey of Clalit's preparations for the new reality in the short and medium terms, as well as the leveraging of insights gained during the first wave of the pandemic, with goal of revising Clalit's long-term strategic plan. We conclude and point out the organizational abilities required for optimal response to future large-scale emergencies: The ability to quickly identify the need for change, respond quickly while harnessing the various parts of the organization in order to provide an agile and adaptive response, and facilitate long-term planning activity in parallel to providing an operational response in the short and medium terms.