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Same-day discharge (SDD) after Laparoscopic Roux-En-Y Gastric Bypass (LRYGB) faces resistance due to possible undetected postoperative complications. These present with changes in vital signs, which continuous remote monitoring devices can detect. This study compared continuous vital signs monitoring using the Isansys Patient Status Engine™ with standard nursing vital signs measurements to assess the device's reliability in postoperative surveillance of patients undergoing LRYGB. We conducted a pilot study including patients who underwent LRYGB. During their hospital stay, patients were continuously monitored using the Isansys Patient Status Engine™ with Lifetouch™, Lifetemp™, and Nonin Pulse Oximeter™ sensors. The heart rate (HR), body temperature, and oxygen saturation (SpO2) collected by the device were compared with standard nursing assessments. Thirteen patients with a mean body mass index of 41.5 ± 4.4 kg/m2 were included. No major complications occurred. The median HR assessed by standard and continuous monitoring did not significantly differ (75.5 [69-88] vs. 77 [66-91] bpm, p = 0.995), nor did the mean values of SpO2 (94.7 ± 2.0 vs. 93.7 ± 1.8%, p = 0,057). A significant difference was observed in median body temperature between the nursing staff and the monitoring device (36.3 [36.1-36.7] vs. 36.1 [34.5-36.6] degrees Celsius, p = 0.012), with a tendency for lower temperature measurements by the device. In conclusion, this is the first study on continuous postoperative surveillance using the Isansys Patient Status Engine™ monitoring device for LRYGB patients. Our results introduce a novel tool for more efficient surgery. Prospective randomized experimental studies are warranted to evaluate this method's efficacy and safety.

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Tissue hysteresivity is an important marker for determining the onset and progression of respiratory diseases, calculated from forced oscillation lung function test data. This study aims to reduce the number and duration of required measurements by combining multivariate data from various sensing devices. We propose using the Forced Oscillation Technique (FOT) lung function test in both a low-frequency prototype and the commercial RESMON device, combined with continuous monitoring from the Equivital (EQV) LifeMonitor and processed by artificial intelligence (AI) algorithms. While AI and deep learning have been employed in various aspects of respiratory system analysis, such as predicting lung tissue displacement and respiratory failure, the prediction or forecasting of tissue hysteresivity remains largely unexplored in the literature. In this work, the Long Short-Term Memory (LSTM) model is used in two ways: (1) to estimate the hysteresivity coefficient η using heart rate (HR) data collected continuously by the EQV sensor, and (2) to forecast η values by first predicting the heart rate from electrocardiogram (ECG) data. Our methodology involves a rigorous two-hour measurement protocol, with synchronized data collection from the EQV, FOT, and RESMON devices. Our results demonstrate that LSTM networks can accurately estimate the tissue hysteresivity parameter η, achieving an R2 of 0.851 and a mean squared error (MSE) of 0.296 for estimation, and forecast η with an R2 of 0.883 and an MSE of 0.528, while significantly reducing the number of required measurements by a factor of three (i.e., from ten to three) for the patient. We conclude that our novel approach minimizes patient effort by reducing the measurement time and the overall ambulatory time and costs while highlighting the potential of artificial intelligence methods in respiratory monitoring.

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Cardiovascular diseases (CVDs) are a predominant global health concern, accounting for over 17.9 million deaths in 2019, representing approximately 32% of all global fatalities. In North America and Europe, over a million adults undergo cardiac surgeries annually. Despite the benefits, such surgeries pose risks and require precise postsurgery monitoring. However, during the postdischarge period, where monitoring infrastructures are limited, continuous monitoring of vital signals is hindered. In this area, the introduction of implantable electronics is altering medical practices by enabling real-time and out-of-hospital monitoring of physiological signals and biological information postsurgery. The multimodal implantable bioelectronic platforms have the capability of continuous heart sensing and stimulation, in both postsurgery and out-of-hospital settings. Furthermore, with the emergence of machine learning algorithms into healthcare devices, next-generation implantables will benefit artificial intelligence (AI) and connectivity with skin-interfaced electronics to provide more precise and user-specific results. This Review outlines recent advancements in implantable bioelectronics and their utilization in cardiovascular health monitoring, highlighting their transformative deployment in sensing and stimulation to the heart toward reaching truly personalized healthcare platforms compatible with the Sustainable Development Goal 3.4 of the WHO 2030 observatory roadmap. This Review also discusses the challenges and future prospects of these devices.

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Continuous biosensors measure concentration-time profiles of biomolecular substances in order to allow for comparisons of measurement data over long periods of time. To make meaningful comparisons of time-dependent data, it is essential to understand how measurement imprecision depends on the time interval between two evaluation points, as the applicable imprecision determines the significance of measured concentration differences. Here, we define a set of measurement imprecisions that relate to different sources of variation and different time scales, ranging from minutes to weeks, and study these using statistical analyses of measurement data. The methodology is exemplified for Biosensing by Particle Motion (BPM), a continuous, affinity-based sensing technology with single-particle and single-molecule resolution. The studied BPM sensor measures specific small molecules (glycoalkaloids) in an industrial food matrix (potato fruit juice). Measurements were performed over several months at two different locations, on nearly 50 sensor cartridges with in total more than 1000 fluid injections. Statistical analyses of the measured signals and concentrations show that the relative residuals are normally distributed, allowing extraction and comparisons of the proposed imprecision parameters. The results indicate that sensor noise is the most important source of variation followed by sample pretreatment. Variations caused by fluidic transport, changes of the sensor during use (drift), and variations due to different sensor cartridges and cartridge replacements appear to be small. The imprecision due to sensor noise is recorded over few-minute time scales and is attributed to stochastic fluctuations of the single-molecule measurement principle, false-positive signals in the signal processing, and nonspecific interactions. The developed methodology elucidates both time-dependent and time-independent factors in the measurement imprecision, providing essential knowledge for interpreting concentration-time profiles as well as for further development of continuous biosensing technologies.

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This study introduces a lightweight storage system for wearable devices, aiming to optimize energy efficiency in long-term and continuous monitoring applications. Utilizing Direct Memory Access and the Serial Peripheral Interface protocol, the system ensures efficient data transfer, significantly reduces energy consumption, and enhances the device autonomy. Data organization into Time Block Data (TBD) units, rather than files, significantly diminishes control overhead, facilitating the streamlined management of periodic data recordings in wearable devices. A comparative analysis revealed marked improvements in energy efficiency and write speed over existing file systems, validating the proposed system as an effective solution for boosting wearable device performance in health monitoring and various long-term data acquisition scenarios.

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A LOx-based electrochemical biosensor for high-level lactate determination was developed. For the construction of the biosensor, chitosan and Nafion layers were integrated by using a spin coating procedure, leading to less porous surfaces in comparison with those recorded after a drop casting procedure. The analytical performance of the resulting biosensor for lactate determination was evaluated in batch and flow regime, displaying satisfactory results in both modes ranging from 0.5 to 20 mM concentration range for assessing the lactic acidosis. Finally, the lactate levels in raw serum samples were estimated using the biosensor developed and verified with a blood gas analyzer. Based on these results, the biosensor developed is promising for its use in healthcare environment, after its proper miniaturization. A pH probe based on common polyaniline-based electrochemical sensor was also developed to assist the biosensor for the lactic acidosis monitoring, leading to excellent results in stock solutions ranging from 6.0 to 8.0 mM and raw plasma samples. The results were confirmed by using two different approaches, blood gas analyzer and pH-meter. Consequently, the lactic acidosis monitoring could be achieved in continuous flow regime using both (bio)sensors.

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BACKGROUND: Conventional daytime monitoring in a single day may be influenced by factors such as motion artifacts and emotions, and continuous monitoring of nighttime heart rate variability (HRV) and respiration to assist in chronic obstructive pulmonary disease (COPD) diagnosis has not been reported yet. OBJECTIVE: The aim of this study was to explore and compare the effects of continuously monitored HRV, heart rate (HR), and respiration during night sleep on the remote diagnosis of COPD. METHODS: We recruited patients with different severities of COPD and healthy controls between January 2021 and November 2022. Vital signs such as HRV, HR, and respiration were recorded using noncontact bed sensors from 10 PM to 8 AM of the following day, and the recordings of each patient lasted for at least 30 days. We obtained statistical means of HRV, HR, and respiration over time periods of 7, 14, and 30 days by continuous monitoring. Additionally, the effects that the statistical means of HRV, HR, and respiration had on COPD diagnosis were evaluated at different times of recordings. RESULTS: In this study, 146 individuals were enrolled: 37 patients with COPD in the case group and 109 participants in the control group. The median number of continuous night-sleep monitoring days per person was 56.5 (IQR 32.0-113.0) days. Using the features regarding the statistical means of HRV, HR, and respiration over 1, 7, 14, and 30 days, binary logistic regression classification of COPD yielded an accuracy, Youden index, and area under the receiver operating characteristic curve of 0.958, 0.904, and 0.989, respectively. The classification performance for COPD diagnosis was directionally proportional to the monitoring duration of vital signs at night. The importance of the features for diagnosis was determined by the statistical means of respiration, HRV, and HR, which followed the order of respiration > HRV > HR. Specifically, the statistical means of the duration of respiration rate faster than 21 times/min (RRF), high frequency band power of 0.15-0.40 Hz (HF), and respiration rate (RR) were identified as the top 3 most significant features for classification, corresponding to cutoff values of 0.1 minute, 1316.3 nU, and 16.3 times/min, respectively. CONCLUSIONS: Continuous monitoring of nocturnal vital signs has significant potential for the remote diagnosis of COPD. As the duration of night-sleep monitoring increased from 1 to 30 days, the statistical means of HRV, HR, and respiration showed a better reflection of an individual's health condition compared to monitoring the vital signs in a single day or night, and better was the classification performance for COPD diagnosis. Further, the statistical means of RRF, HF, and RR are crucial features for diagnosing COPD, demonstrating the importance of monitoring HRV and respiration during night sleep.

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Wearable sensors, specifically microneedle sensors based on electrochemical methods, have expanded extensively with recent technological advances. Today's wearable electrochemical sensors present specific challenges: they show significant modulus disparities with skin tissue, implying possible discomfort in vivo, especially over extended wear periods or on sensitive skin areas. The sensors, primarily based on polyethylene terephthalate (PET) or polyimide (PI) substrates, might also cause pressure or unease during insertion due to the skin's irregular deformation. To address these constraints, we developed an innovative, wearable, all-fiber-structured electrochemical sensor. Our composite sensor incorporates polyurethane (PU) fibers prepared via electrospinning as electrode substrates to achieve excellent adaptability. Electrospun PU nanofiber films with gold layers shaped via thermal evaporation are used as base electrodes with exemplary conductivity and electrochemical catalytic attributes. To achieve glucose monitoring, gold nanofibers functionalized by gold nanoflakes (AuNFs) and glucose oxidase (GOx) serve as the working electrode, while Pt nanofibers and Ag/AgCl nanofibers serve as the counter and reference electrode. The acrylamide-sodium alginate double-network hydrogel synthesized on electrospun PU fibers serves as the adhesive and substance-transferring layer between the electrodes. The all-fiber electrochemical sensor is assembled layer-by-layer to form a robust structure. Given the stretchability of PU nanofibers coupled with a high specific surface area, the manufactured porous microneedle glucose sensor exhibits enhanced stretchability, superior sensitivity at 31.94 µA (lg(mM))-1 cm-2, a broad detection range (1-30 mM), and a significantly low detection limit (1 mM, S/N = 3), as well as satisfactory biocompatibility. Therefore, the novel electrochemical microneedle design is well-suited for wearable or even implantable continuous monitoring applications, thereby showing promising significant potential within the global arena of wearable medical technology.

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To enhance personalized diabetes management, there is a critical need for non-invasive wearable electrochemical sensors made from flexible materials to enable continuous monitoring of sweat glucose levels. The main challenge lies in developing glucose sensors with superior electrochemical characteristics and high adaptability. Herein, we present a wearable sensor for non-enzymatic electrochemical glucose analysis. The sensor was synthesized using hydrothermal and one-pot preparation methods, incorporating gold nanoparticles (AuNPs) functionalized onto aminated multi-walled carbon nanotubes (AMWCNTs) as an efficient catalyst, and crosslinked with carboxylated styrene butadiene rubber (XSBR) and PEDOT:PSS. The sensors were then integrated onto screen-printed electrodes (SPEs) to create flexible glucose sensors (XSBR-PEDOT:PSS-AMWCNTs/AuNPs/SPE). Operating under neutral conditions, the sensor exhibits a linear range of 50 µmol/L to 600 µmol/L, with a limit of detection limit of 3.2 µmol/L (S/N = 3), enabling the detection of minute glucose concentrations. The flexible glucose sensor maintains functionality after 500 repetitions of bending at a 180° angle, without significant degradation in performance. Furthermore, the sensor exhibits exceptional stability, repeatability, and resistance to interference. Importantly, we successfully monitored changes in sweat glucose levels by applying screen-printed electrodes to human skin, with results consistent with normal physiological blood glucose fluctuations. This study details the fabrication of a wearable sensor characterized by ease of manufacture, remarkable flexibility, high sensitivity, and adaptability for non-invasive blood glucose monitoring through non-enzymatic electrochemical analysis. Thus, this streamlined fabrication process presents a novel approach for non-invasive, real-time blood glucose level monitoring.

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BACKGROUND: Paroxysmal atrial fibrillation (pAF) occurs sporadically and can impair athletic performance. Gold standard for diagnosis is surface electrocardiography (ECG), however, this requires AF to be sustained. Implantable loop recorders (ILRs) are routinely used for AF detection in human medicine. While ILR placement has been studied in horses, its AF detection performance is unknown. OBJECTIVES: (I) Validation of ILRs for AF detection in horses. (II) Determining pAF incidence using ILRs and estimate the positive predictive value (PPV). STUDY DESIGN: (I) Experimental study; (II) Longitudinal observational study. METHODS: (I) Implantation of ILRs in 15 horses with AF and 13 horses in sinus rhythm. Holter ECGs were recorded at: 1, 4, 8, 12 and 16 weeks of AF. The ILR ECGs were compared with surface ECGs to assess diagnostic sensitivity and specificity. (II) Eighty horses (43 Warmbloods, 37 Standardbreds) with ILRs were monitored for 367 days [IQR 208-621]. RESULTS: (I) ILRs detected AF on all recording days, in horses with AF, with a sensitivity of 66.1% (95% CI: 65.8-66.5) and a specificity of 99.99% (95% CI: 99.97-99.99). The sensitivity remained consistent across all time points. (II) The incidence of pAF was 6.3% (5/80). In horses with pAF, the PPV ranged from 8% to 87%. Increased body condition score (BCS > 6/9) was associated with an increased number of false positive episodes (p = 0.005). MAIN LIMITATIONS: (I) Horses were stabled during the ECG recordings, and AF was induced, rather than naturally occurring pAF. (II) Integrated algorithm in this ILR is optimised for AF detection in humans using remote monitors. Additionally, sensing is affected by motion artefacts. CONCLUSION: The ILR reliably detected AF in resting horses, particularly in horses with normal BCS (6/9). The ILR proved useful to detect pAF and is recommended alongside Holter monitoring for diagnostic workup of horses with suspected pAF.

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The Sudokwon landfill (SL) in the Seoul metropolitan area, South Korea, is among the world's largest landfills, striving to curtail landfill gas (LFG) emissions and achieve carbon neutrality by 2050. Since 2005, the SL Management Corporation (SLC) has measured LFG emissions (i.e., methane (CH4) and carbon dioxide (CO2)) using a dynamic flux chamber proposed by the US EPA. However, uncertainty prevails in validating the reduction of LFG emissions due to the limited spatiotemporal data coverage. In 2020, an eddy covariance (EC) system was installed to enhance measurements, revealing highly fluctuating LFG emissions driven by waste layer LFG production, LFG collection, and atmospheric pressure changes. During the study period, the annual CH4 emission increased slightly from 465.0 ± 4.2 to 485.5 ± 6.4 g C m-2, while that of CO2 decreased by 2/3 (from 408.7 ± 16.5 to 270.6 ± 18.8 g C m-2), primarily due to the doubled CO2 uptake by the vegetated topsoil. Our first long-term (March 2020 to February 2022) quasi-continuous monitoring using EC (with a gap-filling and partitioning technique based on Random Forest) emphasizes the difficulty of temporal upscaling of discontinuously observed surface emissions to quantify the LFG inventory and the need for continuous observations or suitable proxies (e.g., atmospheric CH4 concentration).

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The capability to record data in passive, image-based wearable sensors can simplify data readouts and eliminate the requirement for the integration of electronic components on the skin. Here, we developed a skin-strain-actuated microfluidic pump (SAMP) that utilizes asymmetric aspect ratio channels for the recording of human activity in the fluidic domain. An analytical model describing the SAMP's operation mechanism as a wearable microfluidic device was established. Fabrication of the SAMP was achieved using soft lithography from polydimethylsiloxane (PDMS). Benchtop experimental results and theoretical predictions were shown to be in good agreement. The SAMP was mounted on human skin and experiments conducted on volunteer subjects demonstrated the SAMP's capability to record human activity for hundreds of cycles in the fluidic domain through the observation of a stable liquid meniscus. Proof-of-concept experiments further revealed that the SAMP could quantify a single wrist activity repetition or distinguish between three different shoulder activities.

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Microneedles, the miniaturized needles, which can pierce the skin with minimal invasiveness open up new possibilities for constructing personalized Point-of-Care (POC) diagnostic platforms. Recent advances in microneedle-based POC diagnostic systems, especially their successful implementation with wearable technologies, enable biochemical detection and physiological recordings in a user-friendly manner. This review presents an overview of the current advances in microneedle-based sensor devices, with emphasis on the biological basis of transdermal sensing, fabrication, and application of different types of microneedles, and a summary of microneedle devices based on various sensing strategies. It concludes with the challenges and future prospects of this swiftly growing field. The aim is to present a critical and thorough analysis of the state-of-the-art development of transdermal diagnostics and sensing devices based on microneedles, and to bridge the gap between microneedle technology and pragmatic applications.

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BACKGROUND: Patients undergoing major oncological abdominal surgery are prone to postoperative complications, making early recognition crucial. Clinical deterioration is often preceded by changes in vital signs, which are typically measured thrice a day by a nurse. However, intermittent measurements may delay recognizing clinical deterioration. Continuous vital parameter monitoring may lead to earlier recognition and management of complications and reduce nursing workload. OBJECTIVE: To compare vital parameter measurements between ward nurses and a wireless continuous monitoring system (Sensium® wireless patch) and assess whether this patch can detect clinical deterioration earlier in patients with complications in the first postoperative week. METHODS: Vital parameters (heart rate, respiratory rate, and temperature) were collected in patients undergoing an oncological resection of the liver, colorectal, or pancreas. Sensium® patch measurements were compared to nurses' measurements to assess the percentages of discordant measurements. In patients with complications in the first postoperative week, time discrepancies between nurses and Sensium® patch measurements were identified in cases of clinical deterioration (respiratory rate ≥15/min, heart rate ≥100/min, and temperature ≥38°C). RESULTS: Among 227 patients, 22% of the patients experienced complications. Nurse and Sensium® measurements were discrepant in 586/2272 measurements (26%). In 506/586 discrepancies (86%), this was due to the respiratory rate (difference ≥4/min). Compared to nurses, the Sensium® patch detected an elevated respiratory rate 14 h earlier and heart rate 2 h earlier within complications in the first postoperative week. For temperature, no difference was observed. CONCLUSION: Continuous monitoring with the Sensium® wireless patch holds promise for earlier recognition of complications in patients who underwent major oncological abdominal surgery.

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An increase in respiratory rate (RR) can be an early indicator of clinical deterioration, yet it remains an often-neglected vital sign. The most common way of measuring RR is by manually counting chest-wall movements, a time-consuming and error-prone process. Staffing and funding shortages, particularly in post-acute and long-term care, mean these RR measurements are often infrequent, potentially leading to missed diagnoses and preventable readmissions. Here we present a case series from skilled nursing facilities, highlighting how continuous respiratory monitoring using a contactless remote patient monitoring (RPM) system can support clinicians in initiating timely interventions, potentially reducing preventable hospitalizations, mortality, and associated financial implications.

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BACKGROUND: Obstructive sleep apnea/hypopnea syndrome (OSAHS) is a prevalent condition affecting a substantial portion of the global population, with its prevalence increasing over the past 2 decades. OSAHS is characterized by recurrent upper airway (UA) closure during sleep, leading to significant impacts on quality of life and heightened cardiovascular and metabolic morbidity. Despite continuous positive airway pressure (CPAP) being the gold standard treatment, patient adherence remains suboptimal due to various factors, such as discomfort, side effects, and treatment unacceptability. OBJECTIVE: Considering the challenges associated with CPAP adherence, an alternative approach targeting the UA muscles through myofunctional therapy was explored. This noninvasive intervention involves exercises of the lips, tongue, or both to improve oropharyngeal functions and mitigate the severity of OSAHS. With the goal of developing a portable device for home-based myofunctional therapy with continuous monitoring of exercise performance and adherence, the primary outcome of this study was the degree of completion and adherence to a 4-week training session. METHODS: This proof-of-concept study focused on a portable device that was designed to facilitate tongue and lip myofunctional therapy and enable precise monitoring of exercise performance and adherence. A clinical study was conducted to assess the effectiveness of this program in improving sleep-disordered breathing. Participants were instructed to perform tongue protrusion, lip pressure, and controlled breathing as part of various tasks 6 times a week for 4 weeks, with each session lasting approximately 35 minutes. RESULTS: Ten participants were enrolled in the study (n=8 male; mean age 48, SD 22 years; mean BMI 29.3, SD 3.5 kg/m2; mean apnea-hypopnea index [AHI] 20.7, SD 17.8/hour). Among the 8 participants who completed the 4-week program, the overall compliance rate was 91% (175/192 sessions). For the tongue exercise, the success rate increased from 66% (211/320 exercises; SD 18%) on the first day to 85% (272/320 exercises; SD 17%) on the last day (P=.05). AHI did not change significantly after completion of training but a noteworthy correlation between successful lip exercise improvement and AHI reduction in the supine position was observed (Rs=-0.76; P=.03). These findings demonstrate the potential of the device for accurately monitoring participants' performance in lip and tongue pressure exercises during myofunctional therapy. The diversity of the training program (it mixed exercises mixed training games), its ability to provide direct feedback for each exercise to the participants, and the easy measurement of treatment adherence are major strengths of our training program. CONCLUSIONS: The study's portable device for home-based myofunctional therapy shows promise as a noninvasive alternative for reducing the severity of OSAHS, with a notable correlation between successful lip exercise improvement and AHI reduction, warranting further development and investigation.

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Waste treatment plants (WTPs) often generate odours that may cause nuisance to citizens living nearby. In general, people are becoming more sensitive to environmental issues, and particularly to odour pollution. Instrumental Odour Monitoring Systems (IOMSs) represent an emerging tool for continuous odour measurement and real-time identification of odour peaks, which can provide useful information about the process operation and indicate the occurrence of anomalous conditions likely to cause odour events in the surrounding territories. This paper describes the implementation of two IOMSs at the fenceline of a WTP, focusing on the definition of a specific experimental protocol and data processing procedure for dealing with the interferences of humidity and temperature affecting sensors' responses. Different approaches for data processing were compared and the optimal one was selected based on field performance testing. The humidity compensation model developed proved to be effective, bringing the IOMS classification accuracy above 95%. Also, the adoption of a class-specific regression model compared to a global regression model resulted in an odour quantification capability comparable with those of the reference method (i.e., dynamic olfactometry). Lastly, the validated models were used to process the monitoring data over a period of about one year.

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Aims: Elevated blood pressure (BP) is a key risk factor in cardiovascular diseases. However, obtaining reliable and reproducible BP remains a challenge. This study, therefore, aimed to evaluate a novel cuffless wristband, based on photoplethysmography (PPG), for continuous BP monitoring. Methods and results: Predictions by a PPG-guided algorithm were compared to arterial BP measurements (in the sub-clavian artery), obtained during cardiac catheterization. Eligible patients were included and screened based on AAMI/European Society of Hypertension (ESH)/ISO Universal Standard requirements. The machine learning-based BP algorithm required three cuff-based initialization measurements in combination with ∼100 features (signal-derived and patient demographic-based). Ninety-seven patients and 420 samples were included. Mean age, weight, and height were 67.1 years (SD 11.1), 83.4 kg (SD 16.1), and 174 cm (SD 10), respectively. Systolic BP was ≤100 mmHg in 48 samples (11%) and ≥160 mmHg in 106 samples (25%). Diastolic BP was ≤70 mmHg in 222 samples (53%) and ≥85 mmHg in 99 samples (24%). The algorithm showed mean errors of ±3.7 mmHg (SD 4.4 mmHg) and ±2.5 mmHg (SD 3.7 mmHg) for systolic and diastolic BP, respectively. Similar results were observed across all genders and skin colours (Fitzpatrick I-VI). Conclusion: This study provides initial evidence for the accuracy of a PPG-based BP algorithm in combination with a cuffless wristband across a range of BP distributions. This research complies with the AAMI/ESH/ISO Universal Standard, however, further research is required to evaluate the algorithms performance in light of the remaining European Society of Hypertension recommendations. Clinical trial registration: www.clinicaltrials.gov, NCT05566886.

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The N2O emissions resulting from sludge incineration are estimated using the default values published by the Intergovernmental Panel on Climate Change (IPCC), which may differ significantly from the actual emissions. In this investigation, N2O emissions from four sludge incineration lines in two plants were monitored for varying durations. The variation in N2O emission factors (EFs) between incineration lines of the same plant was much smaller than the difference between different plants. Data on N2O EFs obtained from brief monitoring may contain variabilities of up to 30%. N2O EFs were more sensitive to temperature changes at low temperatures, necessitating extended monitoring periods to improve the reliability of N2O monitoring outcomes in cases of low furnace temperatures. Excessive use of the SNCR system to reduce NOx emissions resulted in concentrations of N2O and NH3 in the exhaust gases exceeding NOx levels. In the case of furnace temperature control and advanced reburning technology, it is advisable to utilize actual monitoring data or the smaller default values provided by the IPCC in China. Otherwise, the estimated N2O emissions may exceed the actual emissions.

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Recent advancements in polymer-assisted layer-by-layer (LbL) fabrication have revolutionized the development of wearable sensors for health monitoring. LbL self-assembly has emerged as a powerful and versatile technique for creating conformal, flexible, and multi-functional films on various substrates, making it particularly suitable for fabricating wearable sensors. The incorporation of polymers, both natural and synthetic, has played a crucial role in enhancing the performance, stability, and biocompatibility of these sensors. This review provides a comprehensive overview of the principles of LbL self-assembly, the role of polymers in sensor fabrication, and the various types of LbL-fabricated wearable sensors for physical, chemical, and biological sensing. The applications of these sensors in continuous health monitoring, disease diagnosis, and management are discussed in detail, highlighting their potential to revolutionize personalized healthcare. Despite significant progress, challenges related to long-term stability, biocompatibility, data acquisition, and large-scale manufacturing are still to be addressed, providing insights into future research directions. With continued advancements in polymer-assisted LbL fabrication and related fields, wearable sensors are poised to improve the quality of life for individuals worldwide.

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