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Aims: Information on breastfeeding and safety of biologics in infants is lacking due to difficulties in case collection. We evaluated methods for determining the concentration of biologics in breast milk using a dry filter method that can simplify the collection, storage and transport of breast milk. Methods: To generate dried filter paper (DFP) samples, approximately 30 μL of breast milk was placed onto a Whatman 903 card and punched out. After extraction, the supernatant was measured using an enzyme‐linked immunosorbent assay. Three concentrations of each drug were prepared in liquid breast milk (LBM) and DFP samples to determine their stability up to 28 days after storage at 2–8°C or −20°C for LBM and 25 ± 5°C for DFP. LBM and DFP samples were also provided by nursing mothers using biologics during lactation, and drug concentrations in both samples were compared. The agreement between the two measurement methods was confirmed by Bland–Altman analysis. Results: Breast milk was provided by 12 mothers who used biologics (tocilizumab, abatacept, etanercept, golimumab, sarilumab and belimumab). The coefficients of variation for within‐run and between‐run precision for the six drugs were within 15% for both LBM and DFP, and accuracy was within 90%–110% of the quality controls. After 28 days, concentrations remained at more than 90%. The difference between the values obtained by each method was within the acceptable range of error (−12.1 to +16.6 ng/mL). Conclusions: A method for determining the concentration of biologics using DFP is expected to help improve pharmacotherapy for lactating women. [ABSTRACT FROM AUTHOR]

To compare the urine Congo‐red dot paper test (CRD) with dipstick urinalysis to screen preeclampsia (PE). A total of 409 paired spot urine samples were obtained prospectively from women with suspected pre‐eclampsia attending for routine hospital visits. Congo‐red dot paper test and dipstick urinalysis were examined and compared to screen pre‐eclampsia. The agreement between the two urinary test is modest (kappa coefficient = 0.28, 95% CI 0.14–0.42). The specificity of CRD was higher than urinalysis (97.4% vs. 90.4%, p <.001). Urinalysis performed better in sensitivity (77.3% vs. 40.9%, p =.04) and the area under the receiver operating characteristic curves (AUC) (0.84 [95% CI 0.74–0.94] vs. 0.69 [95% CI 0.55–0.83], p =.04) than CRD, respectively. The sensitivity, specificity, AUC of the parallel test of them is 86.4% (64.0%–96.4%), 89.1% (85.5%–92.0%), and 0.88 (95% CI 0.79–0.96). And the serial test is 31.8% (14.7%–54.9%), 98.7% (96.8%–99.5%), 0.65 (95% CI 0.51–0.79), accordingly. The urinalysis is a better diagnosing test for preeclampsia. CRD could aid in the diagnosis of patients with preeclampsia. Combined the two tests in suspected patients may further improve the performance in the diagnosis of preeclampsia. Further study need to be made for its potential clinical practice. [ABSTRACT FROM AUTHOR]

Paper, an inexpensive material with natural biocompatibility, non‐toxicity, and biodegradability, allows for affordable and cost‐effective substrates for unconventional advanced electronics, often called papertronics. On the other hand, polymeric elastomers have shown to be an excellent success for substrates of soft bioelectronics, providing stretchability in skin wearable technology for continuous sensing applications. Although both materials hold their unique advantageous characteristics, merging both material properties into a single electronic substrate reimagines paper‐based bioelectronics for wearable and patchable applications in biosensing, energy generation and storage, soft actuators, and more. Here, a breathable, light‐weighted, biocompatible engineered stretchable paper is reported via coaxial nonwoven microfibers for unconventional bioelectronic substrates. The stretchable papers allow intimate bioconformability without adhesive through coaxial electrospinning of a cellulose acetate polymer (sheath) and a silicone elastomer (core). The fabricated cellulose‐silicone fibers exhibit a greater percent strain than commercially available paper while retaining hydrophilicity, biocompatibility, combustibility, disposable, and other natural characteristics of paper. Moreover, the nonwoven stretchable cellulose‐silicone fibrous mat can adapt conventional printing and fabrication process for paper‐based electronics, an essential aspect of advanced bioelectronic manufacturing. [ABSTRACT FROM AUTHOR]

Key points: Academia is already witnessing the abuse of authorship in papers with text generated by large language models (LLMs) such as ChatGPT.LLM‐generated text is testing the limits of publishing ethics as we traditionally know it.We alert the community to imminent risks of LLM technologies, like ChatGPT, for amplifying the predatory publishing 'industry'.The abuse of ChatGPT for the paper mill industry cannot be over‐emphasized.Detection of LLM‐generated text is the responsibility of editors and journals/publishers. [ABSTRACT FROM AUTHOR]

The erratic, intermittent, and unpredictable nature of sweat production, resulting from physiological or psychological fluctuations, poses intricacies to consistently and accurately sample and evaluate sweat biomarkers. Skin‐interfaced microfluidic devices that rely on colorimetric mechanisms for semi‐quantitative detection are particularly susceptible to these inaccuracies due to variations in sweat secretion rate or instantaneous volume. This work introduces a skin‐interfaced colorimetric bifluidic sweat device with two synchronous channels to quantify sweat rate and biomarkers in real‐time, even during uncertain sweat activities. In the proposed bifluidic‐distance metric approach, with one channel to measure sweat rate and quantify collected sweat volume, the other channel can provide an accurate analysis of the biomarkers based on the collected sweat volume. The closed channel design also reduces evaporation and resists contamination from the external environment. The feasibility of the device is highlighted in a proof‐of‐the‐concept demonstration to analyze sweat chloride for evaluating hydration status and sweat glucose for assessing glucose levels. The low‐cost yet highly accurate device provides opportunities for clinical sweat analysis and disease screening in remote and low‐resource settings. The developed device platform can be facilely adapted for the other biomarkers when corresponding colorimetric reagents are exploited. [ABSTRACT FROM AUTHOR]

Personalized healthcare management is an emerging field that requires the development of environment‐friendly, integrated, and electrochemical multimodal devices. In this study, the concept of integrated paper‐based biosensors (IFP−Multi) for personalized healthcare management is introduced. By leveraging ink printing technology and a ChatGPT–bioelectronic interface, these biosensors offer ultrahigh areal‐specific capacitance (74633 mF cm−2), excellent mechanical properties, and multifunctional sensing and humidity power generation capabilities. More importantly, the IFP−Multi devices have the potential to simulate deaf‐mute vocalization and can be integrated into wearable sensors to detect muscle contractions and bending motions. Moreover, they also enable monitoring of physiological signals from various body parts, such as the throat, nape, elbow, wrist, and knee, and successfully record sharp and repeatable signals generated by muscle contractions. In addition, the IFP−Multi devices demonstrate self‐powered handwriting sensing and moisture power generation for sweat‐sensing applications. As a proof‐of‐concept, a GPT 3.5 model‐based fine‐tuning and prediction pipeline that utilizes recorded physiological signals through IFP−Multi is showcased, enabling artificial intelligence with multimodal sensing capabilities for personalized healthcare management. This work presents a promising and ecofriendly approach to developing paper‐based electrochemical multimodal devices, paving the way for a new era of healthcare advancements. [ABSTRACT FROM AUTHOR]

Avoiding financial losses requires preventing catastrophic oil‐filled power transformer breakdowns. Continuous online transformer monitoring is needed. The authors use paper insulation to evaluate transformer health for continuous online transformer monitoring. The study suggests a new artificial intelligence method for estimating paper insulation residual life in oil‐immersed power transformers. The four artificial intelligence models use backpropagation‐based neural networks to predict paper insulation lifespan. Four primary transformer insulating paper failure indices—degree of polymerisation, 2‐furfuraldehyde, carbon monoxide, and carbon dioxide—form the basis of these models. Each model, including the backpropagation‐based neural networks, estimates paper insulation life using one failure index, along with moisture and temperature data. Optimisation techniques enhance hidden layer neurons and epoch count for improved performance. Results are validated against literature‐based life models, establishing a precise input–output correlation. This method accurately predicts the remaining useable life of power transformer paper insulation, enabling utilities to take proactive measures for safe and efficient transformer operation. [ABSTRACT FROM AUTHOR]