The DDNN employed fewer neurons due to the induced feedback characteristic.

This work introduces an integrated system incorporated seamlessly with a commercial Foley urinary catheter for bacterial growth sensing and biofilm treatment.

The system is comprised of flexible, interdigitated electrodes incorporated with a urinary catheter via a 3D-printed insert for impedance sensing and bioelectric effect-based treatment. Each of the functions were wirelessly controlled using a custom application that provides a user-friendly interface for communicating with a custom PCB via Bluetooth to facilitate implementation in practice.

The integrated catheter system maintains the primary functions of indwelling catheters – urine drainage, balloon inflation – while being capable of detecting the growth of Escherichia coli, with an average decrease in impedance of 13.0% after 24 hours, tested in a newly-developed simulated bladder environment. Furthermore, the system enables bioelectric effect-based biofilm reduction, which is performed by applying a low-intensity electric field that increases the susceptibility of biofilm bacteria to antimicrobials, ultimately reducing the required antibiotic dosage.

Overall, this modified catheter system represents a significant step forward for catheter-associated urinary tract infection (CAUTI) management using device-based approaches, integrating flexible electrodes with an actual Foley catheter along with the control electronics and mobile application.

CAUTIs, exacerbated by the emergence of antibiotic-resistant pathogens, represent a significant challenge as one of the most prevalent healthcare-acquired infections. These infections are driven by the colonization of indwelling catheters by bacterial biofilms.

CAUTIs, exacerbated by the emergence of antibiotic-resistant pathogens, represent a significant challenge as one of the most prevalent healthcare-acquired infections. These infections are driven by the colonization of indwelling catheters by bacterial biofilms.Health data that are publicly available are valuable resources for digital health research. BAY2416964 Several public datasets containing ophthalmological imaging have been frequently used in machine learning research; however, the total number of datasets containing ophthalmological health information and their respective content is unclear. This Review aimed to identify all publicly available ophthalmological imaging datasets, detail their accessibility, describe which diseases and populations are represented, and report on the completeness of the associated metadata. With the use of MEDLINE, Google’s search engine, and Google Dataset Search, we identified 94 open access datasets containing 507 724 images and 125 videos from 122 364 patients. Most datasets originated from Asia, North America, and Europe. Disease populations were unevenly represented, with glaucoma, diabetic retinopathy, and age-related macular degeneration disproportionately overrepresented in comparison with other eye diseases. The reporting of basic demographic characteristics such as age, sex, and ethnicity was poor, even at the aggregate level. This Review provides greater visibility for ophthalmological datasets that are publicly available as powerful resources for research. Our paper also exposes an increasing divide in the representation of different population and disease groups in health data repositories. The improved reporting of metadata would enable researchers to access the most appropriate datasets for their needs and maximise the potential of such resources.The current COVID-19 pandemic has resulted in the unprecedented development and integration of infectious disease dynamic transmission models into policy making and public health practice. Models offer a systematic way to investigate transmission dynamics and produce short-term and long-term predictions that explicitly integrate assumptions about biological, behavioural, and epidemiological processes that affect disease transmission, burden, and surveillance. Models have been valuable tools during the COVID-19 pandemic and other infectious disease outbreaks, able to generate possible trajectories of disease burden, evaluate the effectiveness of intervention strategies, and estimate key transmission variables. Particularly given the rapid pace of model development, evaluation, and integration with decision making in emergency situations, it is necessary to understand the benefits and pitfalls of transmission models. We review and highlight key aspects of the history of infectious disease dynamic models, the role of rigorous testing and evaluation, the integration with data, and the successful application of models to guide public health. Rather than being an expansive history of infectious disease models, this Review focuses on how the integration of modelling can continue to be advanced through policy and practice in appropriate and conscientious ways to support the current pandemic response.

In current approaches to vision screening in the community, a simple and efficient process is needed to identify individuals who should be referred to tertiary eye care centres for vision loss related to eye diseases. The emergence of deep learning technology offers new opportunities to revolutionise this clinical referral pathway. We aimed to assess the performance of a newly developed deep learning algorithm for detection of disease-related visual impairment.

In this proof-of-concept study, using retinal fundus images from 15 175 eyes with complete data related to best-corrected visual acuity or pinhole visual acuity from the Singapore Epidemiology of Eye Diseases Study, we first developed a single-modality deep learning algorithm based on retinal photographs alone for detection of any disease-related visual impairment (defined as eyes from patients with major eye diseases and best-corrected visual acuity of <20/40), and moderate or worse disease-related visual impairment (eyes with disease and best-. Across the five external test datasets (16 993 eyes), the algorithm achieved AUCs ranging between 86·6% (83·4-89·7; sensitivity 87·5% [80·7-92·5]; specificity 70·0% [66·7-73·1]) and 93·6% (92·4-94·8; sensitivity 87·8% [84·1-90·9]; specificity 87·1% [86·2-88·0]) for any disease-related visual impairment, and the AUCs for moderate or worse disease-related visual impairment ranged between 85·9% (81·8-90·1; sensitivity 84·7% [73·0-92·8]; specificity 74·4% [71·4-77·2]) and 93·5% (91·7-95·3; sensitivity 90·3% [84·2-94·6]; specificity 84·2% [83·2-85·1]).

This proof-of-concept study shows the potential of a single-modality, function-focused tool in identifying visual impairment related to major eye diseases, providing more timely and pinpointed referral of patients with disease-related visual impairment from the community to tertiary eye hospitals.

National Medical Research Council, Singapore.

National Medical Research Council, Singapore.