Research Square (United States)

Analysis of 2023–2024 National Health Interview Survey data suggest that prevalence of cigarette smoking among U.S. adults declined from 10.8% of the population in 2023 to 9.9% in 2024. Use of other tobacco products, including cigars and e-cigarettes, remained unchanged, and 18.8% of adults (47.7 million) used at least one tobacco product.

The COVID-19 pandemic critically emphasized the need for rapid, flexible, and decentralized manufacturing solutions to support the urgent demand for essential medical equipment, such as oximeters. Metal wire directed energy deposition—w-DED, also known as w-LMD (wire laser metal deposition)—combines the benefits of high material utilization, increased printing speed, and reduced waste, making it an attractive alternative to traditional powder-based processes, especially under time-sensitive and resource-constrained conditions. This work presents a case study focusing on the design and fabrication of injection molds for oximeter casings using metal-wire-based DED. Martensitic stainless steel AISI-420 wire was employed as feedstock and processed via laser wire additive manufacturing to produce a robust, near-net-shape mold suitable for plastic injection molding. The material was selected due to good corrosion and wear resistance. However, poor ductility and toughness, together with AM-induced anisotropy, were the main challenges to address. Therefore, a multi-step methodology was defined to study the effect of different process parameters, which was validated through printing trials, and the optimum process parameter set was identified. The process enabled the rapid construction of intricate mold geometries, minimizing lead times and allowing for quick design iterations. Microstructural and physical properties such as microhardness of the as-built molds were thoroughly characterized. This case study not only illustrates the technical feasibility of producing functional injection molds via metal w-DED but also outlines its role as a resilient manufacturing pathway, capable of meeting emergent healthcare needs and supporting broader industrial applications in a post-pandemic context.

BackgroundUsing unguided podcast narratives offers a unique and original opportunity to access patient experiences and understand what patient’s value in their care journey in developing true patient-centered oncology models. Online health communities contain a wealth of information, including unsolicited patient experiences that may go beyond what is captured by guided surveys or patient-reported outcome instruments. This study describes patient experiences reported in a peer support community to gain insight into what patients share amongst each other and what they value.MethodsA purposive sample of 31 unguided, French-speaking self-reported patient narratives were collected from the podcast “Naitre princesse, devenir guerrière” registered between 2021 and 2024. Episodes included patient (n = 30) and tandem patient-professional (n = 1) voices, reflecting diverse cancer types, treatment stages, and lived experiences. A transdisciplinary team, including the podcast founder, conducted the thematic analysis to identify patterns of patient values across narratives.ResultsFrom patient narratives, six core themes of patient value emerged: (peer) support, empowerment, relationship with healthcare professionals, quality of life, cancer as an opportunity, and individuality. Narratives underscored patient value as rooted in reclaiming agency, redefining life priorities, and engaging in meaningful action. Participants revealed the irreplaceable role of peers in providing mutual understanding and solidarity, alongside ambivalent but pivotal relationships with healthcare professionals.ConclusionSelf-reported narratives from peer communities offer empirical evidence into patients’ priorities and lived realities, revealing what patients identify as meaningful in their cancer pathways. These findings accentuate the importance of transdisciplinary analysis of patient experiences beyond clinical outcomes and physician reports. Online health communities represent a promising but underexplored arena for understanding and integrating patient values into care, research, and policy. Future research should further examine their role in shaping participatory and value-driven health systems.

Electrochemical impedance spectroscopy (EIS) is a widely used analytical technique in electrochemistry and physics to study the electrical properties of conductive materials and their interfaces. It investigates ion diffusion, electrochemical reactions, and charge transfer in energy storage systems. The technique involves collecting EIS data with a potentiostat/galvanostat and fitting it to an equivalent electrical circuit to interpret charge and mass transport processes using Nyquist or Bode plots. Randles equivalent circuits are frequently employed to represent these processes. However, many studies do not adequately justify parameter values or achieve precise fittings, resulting in misleading interpretations. This review discusses the principles, applications, and theoretical background of EIS, focusing on layered materials. It highlights the significance of impedance definitions, data validation, and simulation in ensuring accurate analysis. The content aims to assist readers in understanding and applying EIS effectively.

ABSTRACT Objectives this study aimed to develop a fully automated, non-destructive and label-free identification method of bacterial colonies, directly on agar plates, using a combination of digital holography and artificial intelligence and to evaluate its performances. Method high-resolution holographic images of individual colonies on translucent brain-heart agar plates were taken every 30 minutes throughout an 18-hour incubation period (530 MPx for the full plate) using a large field 1x magnification system, a partially coherent LED light source and a high-resolution CMOS sensor. A database containing 49 490 digital holograms of individual colonies from 276 clinical strains belonging to ten of the most prevalent pathogenic bacterial species was used to train the convolutional neural network (CNN). Improvement in the accuracy of the prediction from the CNN algorithms was achieved using the information at different phylogenetic levels. Results the performance of the BAIO-DX solution was assessed on 232 strains belonging to the 10 species used to train the algorithms but also on 64 strains from 8 species not included in the training database. For the species included in the training dataset, this new method identified 86.6% of the strains at the species level with a positive-percent agreement of 96.5%. An additional 48% of the strains not identified at the species level could be identified at the genus level thanks to the phylogenetic interpretation of the results. Conclusions these first results validate this approach as a candidate to obtain a fully automated non-destructive and label-free solution for bacterial identification in clinical microbiology laboratories. IMPORTANCE STATEMENT Identification of pathogenic bacteria by culture-based methods are typically performed using MALDI-TOF mass spectrometry or biochemical systems. While automation and interpretive algorithms based on agar plate imaging and artificial intelligence (AI) has reduced manual steps, bacterial identification is still labor-intensive. Here we developed a fully automated, non-destructive and label-free identification method of bacterial colonies at the species level, directly on agar plates, using a combination of digital holography and convolutional neural network algorithms. After training the system with 276 strains belonging to ten of the most frequent pathogenic bacterial species, the BAIO-DX solution was able to identify 86.6% of new strains from these 10 species with a positive-percent agreement of 96.5%. These thorough proof of concept shows that imaging methods coupled to AI algorithms are promising to reach a fully automated identification of a significant proportion of pathogenic bacteria and has potential to enhance diagnostic workflows in clinical microbiology.

[This corrects the article DOI: 10.2196/65668.].

Abstract - Abstract: This study introduces an efficient super-resolution (SR) method for enhancing low-resolution fingerprint images using an autoencoder combined with spline and bicubic interpolation techniques. The approach upscales images from 129×97 to 258×194 while preserving fine details and minimizing artifacts. Trained on the FVC 2004 dataset and tested on both FVC 2004 and FVC 2002, the model achieves strong performance with SSIM of 0.968, PSNR of 35.14, and MSE of 0.007. It also attains 100% identification accuracy using the SIFT algorithm, demonstrating its effectiveness for biometric and forensic applications. Key Words: Super-Resolution, Fingerprint, Autoencoder, Spline, Bicubic, PSNR, SSIM, SIFT, Biometrics