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Objectives: To investigate the impact of patient characteristics and treatment factors on excessive respiratory drive, effort, and lung-distending pressure during transitioning from controlled to spontaneous assisted ventilation in patients with acute respiratory distress syndrome (ARDS). Methods: Multicenter cohort observational study of patients with ARDS at four academic intensive care units. Respiratory drive (P0.1), diaphragm electrical activity (EAdi), inspiratory effort derived from EAdi (∆PmusEAdi) and from occlusion of airway pressure (∆Pocc) (PmusΔPocc), and dynamic transpulmonary driving pressure (ΔPL,dyn) were measured at the first transition to assisted spontaneous breathing. Results: A total of 4171 breaths were analyzed in 48 patients. P0.1 was >3.5 cmH2O in 10%, EAdiPEAK > 15 µV in 29%, ∆PmusEAdi > 15 cmH2O in 28%, and ΔPL,dyn > 15 cmH2O in 60% of the studied breaths. COVID-19 etiology of ARDS was the strongest independent risk factor for a higher proportion of breaths with excessive respiratory drive (RR 3.00 [2.43-3.71], p < 0.0001), inspiratory effort (RR 1.84 [1.58-2.15], p < 0.0001), and transpulmonary driving pressure (RR 1.48 [1.36-1.62], p < 0.0001). The P/F ratio at ICU admission, days of deep sedation, and dose of steroids were additional risk factors for vigorous inspiratory effort. Age and dose of steroids were risk factors for high transpulmonary driving pressure. Days of deep sedation (aHR 1.15 [1.07-1.24], p = 0.0002) and COVID-19 diagnosis (aHR 6.96 [1-48.5], p = 0.05) of ARDS were independently associated with composite outcome of transitioning from light to deep sedation (RASS from 0/-3 to -4/-5) or return to controlled ventilation within 48 h of spontaneous assisted breathing. Conclusions: This study identified that specific patient characteristics, including age, COVID-19-related ARDS, and P/F ratio, along with treatment factors such as the duration of deep sedation and the dosage of steroids, are independently associated with an increased likelihood of assisted breaths reaching potentially harmful thresholds of drive, effort, and lung-distending pressure during the initial transition to spontaneous assisted breathing. It is noteworthy that patients who were subjected to prolonged deep sedation under controlled mechanical ventilation, as well as those with COVID-19, were more susceptible to failing the transition from controlled to assisted breathing.

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The development of multifunctional nanofibrous membranes (NFMs) that enable anti-viral protection during air purification and respiratory disease diagnosis for health management is of increasing importance. Herein, we unraveled a heterostructure-enhanced electro-induced stereocomplexation (HEIS) strategy to fabrication of poly(lactic acid) (PLA) NFMs enabling a combination of efficient PM removal, respiratory monitoring and self-sterilization. The strategy involved an electro-induced stereocomplexation (EIS) approach to trigger the generation of hydrogen bonds between enantiomeric poly(L-lactic acid) (PLLA) and poly(D-lactic acid) (PDLA) chains, promoting CO dipole alignment and molecular polarization during electrospinning. This was further enhanced by incorporation of Ag-doped TiO2 (Ag-TIO) nanodielectrics to promote the electroactivity and surface activity, conferring profound refinement of PLA nanofibers (from 460 nm to an ultralow level of 168 nm) and high porosities of over 91 %. Arising from the sustainable generation of plentiful charges based on triboelectric nanogenerator (TENG) mechanisms, the electroactive PLA NFMs exhibited remarkable triboelectric properties even in high-humidity environments (80 %RH), excellent PM0.3 filtration efficiency with an ultralow pressure drop (93.1 %, 31.8 Pa, 32 L/min), and 100 % antimicrobial efficiency against both E. coli and S. aureus. Moreover, a deep-learning algorithm based on convolutional neural network (CNN) was proposed to recognize various respiratory patterns. The proposed strategy confers the biodegradable NFMs an unusual combination of ultralow-resistance air purification and machine learning-assisted health management, signifying promising prospects in environmental protection and personal healthcare.

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Direct and consistent monitoring of respiratory patterns is crucial for disease prognostication. Although the wired clinical respiratory monitoring apparatus can operate accurately, the existing defects are evident, such as the indispensability of an external power supply, low mobility, poor comfort, and limited monitoring timeframes. Here, we present a self-powered in-nostril hydrogel sensor for long-term non-irritant anti-interference respiratory monitoring, which is developed from a dual-network binary-solvent thermogalvanic polyvinyl alcohol hydrogel fiber (d = 500 µm, L=30 mm) with Fe2+/Fe3+ ions serving as a redox couple, which can generate a thermoelectrical signal in the nasal cavity based on the temperature difference between the exhaled gas and skin as well as avoid interference from the external environment. Due to strong hydrogen bonding between solvent molecules, the sensor retains over 90 % of its moisture after 14 days, exhibiting great potential in wearable respiratory surveillance. With the assistance of deep learning, the hydrogel fiber-based respiration monitoring strategy can actively recognize seven typical breathing patterns with an accuracy of 97.1 % by extracting the time sequence and dynamic parameters of the thermoelectric signals generated by respiration, providing an alert for high-risk respiratory symptoms. This work demonstrates the significant potential of thermogalvanic gels for next-generation wearable bioelectronics for early screening of respiratory diseases.

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The advancement of intelligent and biodegradable respiratory protection equipment is pivotal in the realm of human health engineering. Despite significant progress, achieving a balance between efficient filtration and intelligent monitoring remains a great challenge, especially under conditions of high relative humidity (RH) and high airflow rate (AR). Herein, we proposed an interfacial stereocomplexation (ISC) strategy to facilitate intensive interfacial polarization for poly(lactic acid) (PLA) nanofibrous membranes, which were customized for machine learning-assisted respiratory diagnosis. Theoretical principles underlying the facilitated formation of the electroactive phase and aligned PLA chains were quantitatively depicted in the ISC-PLA nanofibers, contributing to the increased dielectric constant and surface potential (as high as 2.2 and 5.1 kV, respectively). Benefiting from the respiration-driven triboelectric mechanisms, the ISC-PLA demonstrated a high PM0.3 filtration efficiency of over 99% with an ultralow pressure drop (75 Pa), even in challenging circumstances (95 ± 5% RH, AR of 85 L/min). Furthermore, we implemented the ISC-PLA with multifunction respiratory monitoring (response time of 0.56 s and recovery time of 0.25 s) and wireless transmission technology, yielding a high recognition rate of 83% for personal breath states. This innovation has practical implications for health management and theoretical advancements in respiratory protection equipment.

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Humidity-sensor-based fully contactless respiratory monitoring can eliminate the discomfort and infection risks associated with any wearable device. However, challenges in the facile fabrication of highly sensitive humidity sensors continue to hinder their widespread application for fully contactless respiratory monitoring. In this study, we introduce a simple method to fabricate highly sensitive humidity sensors. Our method employs laser-induced graphene (LIG) on an ethanol-soaked polyimide (PI) film as the electrode of the humidity sensor. The ethanol-soaked PI between adjacent LIG electrodes functions as the sensing material, enabling ion-conductive humidity sensing. Compared to the LIG humidity sensors fabricated on untreated PI films, LIG humidity sensors fabricated on ethanol-soaked PI films exhibit superior performance with higher linearity (R2 = 0.9936), reduced hysteresis (ΔH = 5.1% RH), and increased sensitivity (0.65%/RH). Notably, the LIG humidity sensor fabricated on the ethanol-soaked PI film can detect a person's breathing from a distance of 30 cm, a capability not achieved by sensors fabricated on untreated PI films. Moreover, incorporating these LIG humidity sensors into an array further enhances both the detection distance and the sensitivity for respiratory monitoring. Experimental results demonstrate that the LIG humidity sensor array can be employed for fully contactless on-bed respiration monitoring and for continuous, fully contactless monitoring of the respiratory rate during treadmill exercise. These results highlight the great potential of our LIG humidity sensors for various practical applications in medicine and sports.

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The concept of bio-inspired gradient hierarchies, in which the well-defined MOF nanocrystals serve as active nanodielectrics to create electroactive shell at poly(lactic acid) (PLA) nanofibers, is introduced to promote the surface activity and electroactivity of PLA nanofibrous membranes (NFMs). The strategy enabled significant refinement of PLA nanofibers during coaxial electrospinning (∼40 % decline of fiber diameter), accompanied by remarkable increase of specific surface area (nearly 1.5 m2/g), porosity (approximately 85 %) and dielectric constants for the bio-inspired gradient PLA (BG-PLA) NFMs. It largely boosted initial electret properties and electrostatic adsorption capability of BG-PLA NFMs, as well as charge regeneration by TENG mechanisms even under high-humidity environment. The BG-PLA NFMs thus featured exceptionally high PM0.3 filtration efficiencies with well-controlled air resistance (94.3 %, 163.4 Pa, 85 L/min), in contrast to the relatively low efficiency of only 80.0 % for normal PLA. During the application evaluation of outdoor air purification, excellent long-term filtering performance was demonstrated for the BG-PLA for up to 4 h (nearly 98.0 %, 53 Pa), whereas normal PLA exhibited a gradually declined filtration efficiency and an increased pressure drop. Moreover, the BG-PLA NFMs of increased electroactivity were ready to generate tribo-output currents as driven by respiratory vibrations, which enabled real-time monitoring of electrophysiological signals. This bio-inspired gradient strategy opens up promising pathways to engender biodegradable nanofibers of high surface activity and electroactivity, which has significant implications for intelligent protective membranes.

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BACKGROUND: Modern radiotherapeutic techniques, such as intensity-modulated radiation therapy or stereotactic body radiotherapy, require high-dose delivery precision. However, the precise localization of tumors during patient respiration remains a challenge. Therefore, it is essential to investigate effective methods for monitoring respiration to minimize potential complications. Despite several systems currently in clinical use, there are drawbacks, including the complexity of the setup, the discomfort to the patient, and the high cost. PURPOSE: This study investigated the feasibility of using a novel pressure sensor array (PSA) as a tool to monitor respiration during radiotherapy treatments. The PSA was positioned between the treatment couch and the back of the patient lying on it and was intended to overcome some limitations of current methods. The main objectives included assessing the PSA's capability in monitoring respiratory behavior and to investigate prospective applications that extend beyond respiratory monitoring. METHODS: A PSA with 31 pressure-sensing elements was used in 12 volunteers. The participants were instructed to breathe naturally while lying on a couch without any audio or visual guidance. The performance of the PSA was compared to that of a camera-based respiratory monitoring system (RPM, Varian, USA), which served as a reference. Several metrics, including pressure distribution, weight sensitivity, and correlations between PSA and RPM signals, were analyzed. The PSA's capacity to provide information on potential applications related to patient stability was also investigated. RESULTS: The linear relationship between the weight applied to the PSA and its output was demonstrated in this study, confirming its sensitivity to pressure changes. A comparison of PSA and RPM curves revealed a high correlation coefficient of 0.9391 on average, indicating consistent respiratory cycles. The PSA also effectively measured the weight distribution at the volunteer's back in real-time, which allows for monitoring the patient's movements during the radiotherapy. CONCLUSION: PSA is a promising candidate for effective respiratory monitoring during radiotherapy treatments. Its performance is comparable to the established RPM system, and its additional capabilities suggest its multifaceted utility. This paper shows the potential use of PSA for patient monitoring in radiotherapy and suggests possibilities for further research, including performance comparisons with other existing systems and real-patient applications with respiratory training.

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Monitoring the patient's physiological functions is critical in clinical anesthesia. The latest version of the Japanese Society of Anesthesiologists' Guidelines for Safe Anesthesia Monitoring, revised in 2019, covers various factors, including electroencephalogram monitoring, oxygenation, ventilation, circulation, and muscle relaxation. However, with recent advances in monitoring technologies, the information provided has become more detailed, requiring practitioners to update their knowledge. At a symposium organized by the Journal of Anesthesia in 2023, experts across five fields discussed their respective topics: anesthesiologists need to interpret not only the values displayed on processed electroencephalogram monitors but also raw electroencephalogram data in the foreseeable future. In addition to the traditional concern of preventing hypoxemia, monitoring for potential hyperoxemia and the effects of mechanical ventilation itself will become increasingly important. The importance of using AI analytics to predict hypotension, assess nociception, and evaluate microcirculation may increase. With the recent increase in the availability of neuromuscular monitoring devices in Japan, it is important for anesthesiologists to become thoroughly familiar with the features of each device to ensure its effective use. There is a growing desire to develop and introduce a well-organized, integrated "single screen" monitor.

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OBJECTIVE: Today, respiratory movement can be monitored and recorded with different methods during a simulation on a four-dimensional (4D) computed tomography (CT) device to be used in radiotherapy planning. A synchronized respiratory monitoring system (RPM) with an externally equipped device is one of these methods. Another method is to create 4D images of the patient's breathing phases without the need for extra equipment, with an anatomy-based software program integrated into the CT device. Our aim is to compare the RPM system and the software system (Deviceless) which are two different respiratory monitoring methods used in tracking moving targets during 4D-CT imaging and to assess their clinical usability. METHODS: Ten patients who underwent paraaortic nodal irradiation were enrolled. The simulation was performed using intravenous contrast material on a 4D-CT device with both respiratory monitoring methods. The right/left kidneys and renal arteries were chosen as references to evaluate abdominal organ movement. It was then manually contoured one by one on both sets of images. The images were compared volumetrically and geometrically after rigid reconstruction. The similarity between the contours was determined by the Dice index. Wilcoxon test was used for statistical comparisons. RESULTS: The motion of the kidneys in all three directions was found to be 0.0 cm in both methods. The shifts in the right/left renal arteries were submillimetric. The Dice index showed a high similarity in both kidney and renal artery contours. CONCLUSION: In our study, no difference was found between RPM and Deviceless systems used for tracking and detection of moving targets during simulation in 4D-CT. Both methods can be used safely for radiotherapy planning according to the available possibilities in the clinic.

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Advanced respiratory monitoring encompasses a diverse range of mini- or noninvasive tools used to evaluate various aspects of respiratory function in patients experiencing acute respiratory failure, including those requiring extracorporeal membrane oxygenation (ECMO) support. Among these techniques, key modalities include esophageal pressure measurement (including derived pressures), lung and respiratory muscle ultrasounds, electrical impedance tomography, the monitoring of diaphragm electrical activity, and assessment of flow index. These tools play a critical role in assessing essential parameters such as lung recruitment and overdistention, lung aeration and morphology, ventilation/perfusion distribution, inspiratory effort, respiratory drive, respiratory muscle contraction, and patient-ventilator synchrony. In contrast to conventional methods, advanced respiratory monitoring offers a deeper understanding of pathological changes in lung aeration caused by underlying diseases. Moreover, it allows for meticulous tracking of responses to therapeutic interventions, aiding in the development of personalized respiratory support strategies aimed at preserving lung function and respiratory muscle integrity. The integration of advanced respiratory monitoring represents a significant advancement in the clinical management of acute respiratory failure. It serves as a cornerstone in scenarios where treatment strategies rely on tailored approaches, empowering clinicians to make informed decisions about intervention selection and adjustment. By enabling real-time assessment and modification of respiratory support, advanced monitoring not only optimizes care for patients with acute respiratory distress syndrome but also contributes to improved outcomes and enhanced patient safety.

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The rapid growth of the Internet of Things and wearable sensors has led to advancements in monitoring technology in the field of health. One such advancement is the development of wearable respiratory sensors, which offer a new approach to real-time respiratory monitoring compared to traditional methods. However, the energy consumption of these sensors raises concerns about environmental pollution. To address the issue, this study proposes the use of a triboelectric nanogenerator (TENG) as a sustainable energy source. The electrical conductivity of the TENG is improved by incorporating chitosan and carbon nanotubes, with the added benefit of chitosan's biodegradability reducing negative environmental impact. A wireless intelligent respiratory monitoring system (WIRMS) is then introduced, which utilizes a degradable triboelectric nanogenerator for real-time respiratory monitoring, diagnosis, and prevention of obstructive respiratory diseases. WIRMS offers stable and highly accurate respiratory information monitoring, while enabling real-time and nondestructive transmission of information. In addition, machine learning technology is used for sleep respiration state analysis. The potential applications of WIRMS extend to wearables, medical monitoring and sports monitoring, thereby presenting innovative ideas for modern medical and sports monitoring.

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Breathing is an important physiological activity of human body, which not only reflects the state of human movement, but also is one of the important health indicators. Breathing can change the concentration of water molecules, so monitoring humidity has gradually become a hot topic in modern research. In this study, a humidity sensing composite film with high sensitivity and short response time was made by using the mixture of graphene oxide (GO) and bacterial cellulose (BC) with simple dry film-forming method. L-ascorbic acid was used as reducing agent to reduce GO and improve the conductivity of GO/BC composite film (BG). The influence of different BC contents and the different reduction degree on the resistance change rate of composite film was investigated in details. The maximum resistance change rate of partially reduced BG humidity sensitive composite film reached up to 94%, and the response and recovery time were 13 s and 47 s respectively. Furthermore, the sensor shows obvious resistance change in noncontact sensing test and different breathing states. This kind of humidity sensitive film with fast response and high sensitivity has great potential in human health monitoring and noncontact sensing, and is of great significance in promoting health detection and intelligent life.

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RATIONALE: End-expiratory lung volume (EELV) is reduced in mechanically ventilated patients, especially in pathologic conditions. The resulting heterogeneous distribution of ventilation increases the risk for ventilation induced lung injury. Clinical measurement of EELV however, remains difficult. OBJECTIVE: Validation of a novel continuous capnodynamic method based on expired carbon dioxide (CO2) kinetics for measuring EELV in mechanically ventilated critically-ill patients. METHODS: Prospective study of mechanically ventilated patients scheduled for a diagnostic computed tomography exploration. Comparisons were made between absolute and corrected EELVCO2 values, the latter accounting for the amount of CO2 dissolved in lung tissue, with the reference EELV measured by computed tomography (EELVCT). Uncorrected and corrected EELVCO2 was compared with total CT volume (density compartments between - 1000 and 0 Hounsfield units (HU) and functional CT volume, including density compartments of - 1000 to - 200HU eliminating regions of increased shunt. We used comparative statistics including correlations and measurement of accuracy and precision by the Bland Altman method. MEASUREMENTS AND MAIN RESULTS: Of the 46 patients included in the final analysis, 25 had a diagnosis of ARDS (24 of which COVID-19). Both EELVCT and EELVCO2 were significantly reduced (39 and 40% respectively) when compared with theoretical values of functional residual capacity (p < 0.0001). Uncorrected EELVCO2 tended to overestimate EELVCT with a correlation r2 0.58; Bias - 285 and limits of agreement (LoA) (+ 513 to - 1083; 95% CI) ml. Agreement improved for the corrected EELVCO2 to a Bias of - 23 and LoA of (+ 763 to - 716; 95% CI) ml. The best agreement of the method was obtained by comparison of corrected EELVCO2 with functional EELVCT with a r2 of 0.59; Bias - 2.75 (+ 755 to - 761; 95% CI) ml. We did not observe major differences in the performance of the method between ARDS (most of them COVID related) and non-ARDS patients. CONCLUSION: In this first validation in critically ill patients, the capnodynamic method provided good estimates of both total and functional EELV. Bias improved after correcting EELVCO2 for extra-alveolar CO2 content when compared with CT estimated volume. If confirmed in further validations EELVCO2 may become an attractive monitoring option for continuously monitor EELV in critically ill mechanically ventilated patients. TRIAL REGISTRATION: clinicaltrials.gov (NCT04045262).

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PURPOSE: The Prone positioning in addition to non invasive respiratory support is commonly used in patients with acute respiratory failure. The aim of this study was to assess the accuracy of an impedance-based non-invasive respiratory volume monitor (RVM) in supine and in prone position. METHODS: In sedated, paralyzed and mechanically ventilated patients in volume-controlled mode with acute respiratory distress syndrome scheduled for prone positioning it was measured and compared non-invasively tidal volume and respiratory rate provided by the RVM in supine and, subsequently, in prone position, by maintaining unchanged the ventilatory setting. RESULTS: Forty patients were enrolled. No significant difference was found between measurements in supine and in prone position either for tidal volume (p = 0.795; p = 0.302) nor for respiratory rate (p = 0.181; p = 0.604). Comparing supine vs. prone position, the bias and limits of agreements for respiratory rate were 0.12 bpm (-1.4 to 1.6) and 20 mL (-80 to 120) for tidal volume. CONCLUSIONS: The RVM is accurate in assessing tidal volume and respiratory rate in prone compared to supine position. Therefore, the RVM could be applied in non-intubated patients with acute respiratory failure receiving prone positioning to monitor respiratory function.

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As one of the most important human health indicators, respiratory status is an important basis for the diagnosis of many diseases. However, the high cost of respiratory monitoring makes its use uncommon. This study introduces a low-cost, wearable, flexible humidity sensor for respiratory monitoring. Solution-processed chitosan (CS) placed on a polyethylene terephthalate substrate was used as the sensing layer. An Arduino circuit board was used to read humidity-sensitive voltage changes. The CS-based sensor demonstrated capacitive humidity sensitivity, whereby the capacitance instantly increased from 10-2 to 30 nF when the environmental humidity changed from 43% to 97%. The capacitance logarithm sensitivity and response voltage change was 35.9 pF/%RH and 0.8 V in the RH range from 56% to 97%. And the voltage variation between inhalation and exhalation was ~0.5 V during normal breathing. A rapid response time of ~0.7 s and a recovery time of ~2 s were achieved during respiration testing. Breathing modes (i.e., normal breathing, rest breathing, deep breathing, and fast breathing) and tonal changes during speech could be clearly distinguished. Therefore, such sensors provide a means for economical and convenient wearable respiratory monitoring, and they have the potential to be used for daily health examinations and professional medical diagnoses.

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This review comprehensively explores pediatric capnography, a vital tool in contemporary respiratory monitoring. The overview encompasses the foundational principles of capnography, elucidating its real-time measurement of carbon dioxide (CO2) in respiratory gases. The review emphasizes its paramount role in pediatric care and underscores capnography's significance in detecting respiratory abnormalities and guiding timely interventions. The distinctions between mainstream and sidestream capnography, the key to understanding their applications, are meticulously outlined. Addressing the importance of ongoing research and education, the review advocates for a dynamic approach to refine guidelines and optimize capnography utilization in pediatric settings. The conclusion reflects on the scope and limitations of pediatric capnography, acknowledging its transformative impact while advocating for a judicious recognition of constraints. As we navigate the future of pediatric respiratory care, the synergy of research, education, and clinical application emerges as the cornerstone for advancing pediatric capnography to new horizons.

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Respiratory signals are critical clinical diagnostic criteria for respiratory diseases and health conditions, and respiratory sensors play a crucial role in achieving the desired respiratory monitoring effect. High sensitivity to a single factor can improve the reliability of respiratory monitoring, and maintaining the hygiene of the sensors is also important for daily health monitoring. Herein, we propose a flexible Au-modified anatase titanium dioxide resistive respiratory sensor, which can be mechanically compliantly attached to curved surfaces for respiratory monitoring in different modalities (i.e., respiratory intensity, frequency, and rate). The uniform and preferentially oriented anatase titanium dioxide films gained by the polymer-assisted deposition technique can be fabricated on flexible substrates through a liquid-assisted transferring process. The Au modification can enhance surface plasmon resonance to facilitate the photocatalytic activity of titanium dioxide, and the optimized distribution of Au on the surface of titanium dioxide film made the sensor have an excellent antibacterial effect. The uniquely designed encapsulation can effectively control the contact between the surface of titanium dioxide films and electrodes, allowing the flexible sensor to exhibit fast response time (0.71 s) and recovery time (1.06 s) to respiratory as well as insensitivity or low sensitivity to other factors (i.e., gas composition, humidity, temperature, stress, and strain). This work provided an effective strategy for flexible wearable respiratory sensors and has great potential in daily respiratory monitoring for health management and pandemic control.

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Spirometry is a lung function test involving deep inhalation and forceful deep exhalation. It is widely used to obtain objective information about airflow limitation and to diagnose lung diseases. In contrast, tidal spirometry is based on normal breathing and therefore much more convenient, but it is hardly used in medical care and its relation with conventional (forced) spirometry is largely unknown. Therefore, the objective of this work is to reveal the relation between tidal and forced spirometry. Employing the strong correspondence between the forced flow-volume curves and the Tiffeneau-Pinelli (TP) index, we present a method to obtain (a) the expected tidal flow-volume curve for a given TP-index, and (b) the expected TP-index for a given tidal curve. For patients with similar values of the TP-index, the tidal curves show a larger spread than the forced curves, but their average shape varies in a characteristic way with varying index. Therefore, just as with forced curves, the TP-index provides a useful objective ranking of the average of tidal curves: upon decreasing TP-index the expiratory flow rate changes in that its peak shifts towards smaller expiratory volumes, and its post-peak part becomes dented.

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Flexible hydroelectric generators (HEGs) are promising self-powered devices that spontaneously derive electrical power from moisture. However, achieving the desired compatibility between a continuous operating voltage and superior current density remains a significant challenge. Herein, a textile-based van der Waals heterostructure is rationally designed between conductive 1T phase tungsten disulfide@carbonized silk (1T-WS2@CSilk) and carbon black@cotton (CB@Cotton) fabrics with an asymmetric distribution of oxygen-containing functional groups, which enhances the proton concentration gradients toward high-performance wearable HEGs. The vertically staggered 1T-WS2 nanosheet arrays on the CSilk fabric provide abundant hydrophilic nanochannels for rapid carrier transport. Furthermore, the moisture-induced primary battery formed between the active aluminum (Al) electrode and the conductive textiles introduces the desired electric field to facilitate charge separation and compensate for the decreased streaming potential. These devices exhibit a power density of 21.6 µW cm-2, an open-circuit voltage (Voc) of 0.65 V sustained for over 10 000 s, and a current density of 0.17 mA cm-2. This performance makes them capable of supplying power to commercial electronics and human respiratory monitoring. This study presents a promising strategy for the refined design of wearable electronics.