Shared Latent Membership Enables Joint Shape Abstraction and Segmentation With Deformable Superquadrics.

Part-level 3D shape representations are crucial to shape reasoning and understanding. Two key sub-tasks are: 1) shape abstraction, creating primitive-based object parts; and 2) shape segmentation, finding partition-based object parts. However, for 3D object point clouds, most advanced methods produce parts relying on task-specific priors, such as similarity metrics and primitive geometries, resulting in misleading parts that deviate from semantics. To address prior limitations, we establish a foundation for joint shape abstraction and shape segmentation as formal linear transformations within a shared latent space, encapsulating essential dual-purpose membership information linking points and object parts for mutual reinforcement. We demonstrate that the transformations are underpinned by a derivation based on k-means, Non-negative Matrix Factorization (NMF), and the attention mechanism. As a result, we introduce Latent Membership Pursuit (LMP) for joint optimization of shape abstraction and segmentation. LMP utilizes a shared latent representation of object part membership to autonomously identify common object parts in both tasks without any supervision and priors. Furthermore, we adapt deformable superquadrics (DSQs) for primitives to capture variable part-level geometric and semantic information. Experiments on benchmark datasets validate that our approach enables mutual learning of shape abstraction and segmentation, and promotes consistent interpretations of 3D object shapes across instances and even categories in a fully unsupervised manner.

Relationship of Glycated Hemoglobin A1c with All-Cause and Cardiovascular Mortality among Patients with Hypertension.

Both low and high glycated hemoglobin A1c (HbA1c) levels are well-established causal risk factors for all-cause and cardiovascular mortality in the general population and diabetic patients. However, the relationship between HbA1c with all-cause and cardiovascular mortality among patients with hypertension is unclear. We used NHANES data from 1999 to 2014 as the basis for this population-based cohort study. Based on HbA1c levels (HbA1c > 5, HbA1c > 5.5, HbA1c > 6, HbA1c > 6.5, HbA1c > 7%), hypertensive patients were divided into five groups. An analysis of multivariable Cox proportional hazards was conducted based on hazard ratios (HRs) and respective 95% confidence intervals (CIs). The relationship between HbA1c and mortality was further explored using Kaplan-Meier survival curves, restricted cubic spline curves, and subgroup analyses. In addition, 13,508 patients with hypertension (average age 58.55 ± 15.56 years) were included in the present analysis, with 3760 (27.84%) all-cause deaths during a follow-up of 127.69 ± 57.9 months. A U-shaped relationship was found between HbA1c and all-cause and cardiovascular mortality (all p for likelihood ratio tests were 0.0001). The threshold value of HbA1c related to the lowest risk for all-cause and cardiovascular mortality was 5.3% and 5.7%, respectively. Below the threshold value, increased HbA1c levels reduced the risk of all-cause mortality (HR 0.68, 95% CI 0.51-0.90, p = 0.0078) and cardiovascular mortality (HR 0.77, 95% CI 0.57-1.05, p = 0.0969). Inversely, above the threshold value, increased HbA1c levels accelerated the risk of all-cause mortality (HR 1.14, 95% CI 1.11-1.18, p < 0.0001) and cardiovascular mortality (HR 1.22, 95% CI 1.16-1.29, p < 0.0001). In conclusion, A U-shape relationship was observed between HbA1c and all-cause and cardiovascular mortality among hypertensive patients.

Personalised recommendations for hospitalised patients with Acute Kidney Injury using a Kidney Action Team (KAT-AKI): protocol and early data of a randomised controlled trial.

INTRODUCTION: Although studies have examined the utility of clinical decision support tools in improving acute kidney injury (AKI) outcomes, no study has evaluated the effect of real-time, personalised AKI recommendations. This study aims to assess the impact of individualised AKI-specific recommendations delivered by trained clinicians and pharmacists immediately after AKI detection in hospitalised patients. METHODS AND ANALYSIS: KAT-AKI is a multicentre randomised investigator-blinded trial being conducted across eight hospitals at two major US hospital systems planning to enrol 4000 patients over 3 years (between 1 November 2021 and 1 November 2024). A real-time electronic AKI alert system informs a dedicated team composed of a physician and pharmacist who independently review the chart in real time, screen for eligibility and provide combined recommendations across the following domains: diagnostics, volume, potassium, acid-base and medications. Recommendations are delivered to the primary team in the alert arm or logged for future analysis in the usual care arm. The planned primary outcome is a composite of AKI progression, dialysis and mortality within 14 days from randomisation. A key secondary outcome is the percentage of recommendations implemented by the primary team within 24 hours from randomisation. The study has enrolled 500 individuals over 8.5 months. Two-thirds were on a medical floor at the time of the alert and 17.8% were in an intensive care unit. Virtually all participants were recommended for at least one diagnostic intervention. More than half (51.6%) had recommendations to discontinue or dose-adjust a medication. The median time from AKI alert to randomisation was 28 (IQR 15.8-51.5) min. ETHICS AND DISSEMINATION: The study was approved by the ethics committee of each study site (Yale University and Johns Hopkins institutional review board (IRB) and a central IRB (BRANY, Biomedical Research Alliance of New York). We are committed to open dissemination of the data through clinicaltrials.gov and sharing of data on an open repository as well as publication in a peer-reviewed journal on completion. TRIAL REGISTRATION NUMBER: NCT04040296.

Optimization Design of MK-GGBS Based Geopolymer Repairing Mortar Based on Response Surface Methodology.

There are several influencing factors in the preparation of MK (metakaolin)-GGBS (ground granulated blast furnace slag)-based geopolymer repair mortars, including the MK-GGBS ratio, the alkalinity of the alkali activator solution, the modulus of the alkali activator solution, and the water-to-solid ratio. There are interactions between these factors, such as the different alkaline and modulus requirements of MK and GGBS, the interaction between the alkaline and modulus of the alkali activator solution, and the influence of water throughout the process. The effect of these interactions on the geopolymer repair mortar is not fully understood, making optimization of the MK-GGBS repair mortar ratio difficult. Therefore, in this paper, the response surface methodology (RSM) was used to optimize the preparation of the repair mortar, with GGBS content, SiO2/Na2O molar ratio, Na2O/binder ratio, and water/binder ratio as influencing factors and 1 d compressive strength, 1 d flexural strength, and 1 d bond strength as evaluation indices. Additionally, the repair mortar's overall performance was assessed in terms of setting time, long-term compressive and bond strength, shrinkage, water absorption, and efflorescence. The results show that RSM was successful in establishing a relationship between the repair mortar's properties and the factors. The recommended values of the GGBS content, Na2O/binder ratio, SiO2/Na2O molar ratio, and water/binder ratio are 60%, 10.1%, 1.19, and 0.41, respectively. The optimized mortar meets the standard's requirements for set time, water absorption, shrinkage values, and mechanical strength, with minimal visual efflorescence. The back-scattered electron (BSE) images and energy dispersive spectroscopy (EDS) analysis show that the geopolymer and cement have good interfacial adhesion, and a denser interfacial transition zone exists in the optimized proportion.

Visible-light-induced protein labeling in live cells with aryl azides.

Chemical labeling of proteins in live cells helps to probe their native functions in biological systems. Aryl azides are chemically inert under physiological conditions, but they are activated by certain external stimuli. Recently, photocatalytic live-cell applications of aryl azides by visible light irradiation have become a burgeoning new field in chemical biology. In this Feature Article, we focus on the recent progress of protein labeling in live cells with aryl azides induced by visible-light irradiation. Light irradiation activates aryl azides to generate highly reactive intermediates, which enables protein labeling for protein functionalization, crosslinking, and profiling. The activation mechanism of aryl azides by light irradiation is categorized as photolysis, energy-transfer, and electron-transfer. The extracellular and intracellular protein labeling applications in live cells with aryl azides induced by visible light are discussed, including recent advances in red-light-induced extracellular protein labeling.

Systemic inflammation markers and the prevalence of hypertension: A NHANES cross-sectional study.

Systemic inflammation markers have been highlighted recently as related to cardiac and non-cardiac disorders. However, few studies have estimated pre-diagnostic associations between these markers and hypertension. In the National Health and Nutritional Examination Survey from 1999 to 2010, 22,290 adult participants were included for analysis. We assessed associations between four systemic inflammation markers based on blood cell counts: systemic immune-inflammation index (SII), neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), lymphocyte-to-monocyte ratio (LMR), and hypertension prevalence in multivariate logistic regression analysis with odds ratio (OR) and 95% confidence interval (CI). To further explore their associations, subgroup and sensitivity analyses were performed. In continuous analyses, the ORs for hypertension prevalence per ln-transformed increment in SII and NLR were estimated at 1.115 and 1.087 (95% CI: 1.045-1.188; 1.008-1.173; respectively). Compared to those in the lowest tertiles, the hypertension risks for subjects in the highest SII and NLR tertiles were 1.20 and 1.11 times, respectively. Conversely, we found that PLR and LMR were negatively associated with hypertension prevalence in continuous analyses (1.060, 0.972-1.157; 0.926, 0.845-1.014; respectively), and the highest PLR and LMR tertiles (1.041, 0.959-1.129; 0.943, 0.866-1.028; respectively). Also, subgroup and sensitivity analyses indicated that SII had a greater correlation to hypertension. In conclusion, we find positive associations between SII and NLR and the prevalence of hypertension in this cross-sectional study. Our findings highlight that SII may be a superior systemic inflammation warning marker for hypertension.

Soft strain-insensitive bioelectronics featuring brittle materials.

Advancing electronics to interact with tissue necessitates meeting material constraints in electrochemical, electrical, and mechanical domains simultaneously. Clinical bioelectrodes with established electrochemical functionalities are rigid and mechanically mismatched with tissue. Whereas conductive materials with tissue-like softness and stretchability are demonstrated, when applied to electrochemically probe tissue, their performance is distorted by strain and corrosion. We devise a layered architectural composite design that couples strain-induced cracked films with a strain-isolated out-of-plane conductive pathway and in-plane nanowire networks to eliminate strain effects on device electrochemical performance. Accordingly, we developed a library of stretchable, highly conductive, and strain-insensitive bioelectrodes featuring clinically established brittle interfacial materials (iridium-oxide, gold, platinum, and carbon). We paired these bioelectrodes with different electrochemical probing methods (amperometry, voltammetry, and potentiometry) and demonstrated strain-insensitive sensing of multiple biomarkers and in vivo neuromodulation.

Femtosecond laser hybrid processing strategy of transparent hard and brittle materials.

With high hardness, high thermal stability, chemical inertness and excellent optoelectronic properties, transparent hard and brittle materials have drawn significant attentions in frontier domains such as aerospace, photoelectric detection, and high-intensity lasers. Femtosecond laser processing technology demonstrates great potential for transparent hard and brittle materials processing due to its outstanding advantages such as non-contact, true 3D processing and programmable design. However, high-energy laser ablation usually causes severe damage to the surface of the materials, resulting in low processing accuracy, low processing efficiency and poor surface quality. Femtosecond laser hybrid processing strategies have been proven to be an effective solution to solve the above problems. This mini-review summarizes the fundamentals and research progress of femtosecond laser hybrid processing strategies of transparent hard and brittle materials in recent years. Moreover, the challenges and application prospects of these techniques are discussed.

Wearable microneedle-based electrochemical aptamer biosensing for precision dosing of drugs with narrow therapeutic windows.

Therapeutic drug monitoring is essential for dosing pharmaceuticals with narrow therapeutic windows. Nevertheless, standard methods are imprecise and involve invasive/resource-intensive procedures with long turnaround times. Overcoming these limitations, we present a microneedle-based electrochemical aptamer biosensing patch (µNEAB-patch) that minimally invasively probes the interstitial fluid (ISF) and renders correlated, continuous, and real-time measurements of the circulating drugs' pharmacokinetics. The µNEAB-patch is created following an introduced low-cost fabrication scheme, which transforms a shortened clinical-grade needle into a high-quality gold nanoparticle-based substrate for robust aptamer immobilization and efficient electrochemical signal retrieval. This enables the reliable in vivo detection of a wide library of ISF analytes-especially those with nonexistent natural recognition elements. Accordingly, we developed µNEABs targeting various drugs, including antibiotics with narrow therapeutic windows (tobramycin and vancomycin). Through in vivo animal studies, we demonstrated the strong correlation between the ISF/circulating drug levels and the device's potential clinical use for timely prediction of total drug exposure.

U-Shaped Relationship of Non-HDL Cholesterol With All-Cause and Cardiovascular Mortality in Men Without Statin Therapy.

Background: Non-HDL-C is well established causal risk factor for the progression of atherosclerotic cardiovascular disease. However, there remains a controversial pattern of how non-HDL-C relates to all-cause and cardiovascular mortality, and the concentration of non-HDL-C where the risk of mortality is lowest is not defined. Methods: A population-based cohort study using data from the National Health and Nutrition Examination Survey (NHANES) from 1999 to 2014. Male participants without statin therapy were divided into the six groups according to non-HDL-C levels (<100, 100-129, 130-159, 160-189, 190-219, ≥220 mg/dl). Multivariable Cox proportional hazards models were conducted with a hazard ratio (HR) and corresponding 95% confidence interval (CI). To further explore the relationship between non-HDL-C and mortality, Kaplan-Meier survival curves, restricted cubic spline curves, and subgroup analysis were performed. Results: Among 12,574 individuals (average age 44.29 ± 16.37 years), 1,174(9.34%) deaths during a median follow-up 98.38 months. Both low and high non-HDL-C levels were significantly associated with increased risk of all-cause and cardiovascular mortality, indicating a U-shaped association. Threshold values were detected at 144 mg/dl for all-cause mortality and 142 mg/dl for cardiovascular mortality. Below the threshold, per 30 mg/dl increase in non-HDL-C reduced a 28 and 40% increased risk of all-cause (p < 0.0001) and cardiovascular mortality (p = 0.0037), respectively. Inversely, above the threshold, per 30 mg/dl increase in non-HDL-C accelerated risk of both all-cause mortality (HR 1.11, 95% CI 1.03-1.20, p = 0.0057) and cardiovascular mortality (HR 1.30, 95% CI 1.09-1.54, p = 0.0028). Conclusions: Non-HDL-C was U-shaped related to all-cause and cardiovascular mortality among men without statin therapy.

Use of municipal waste incineration fly ashes (MSWI FA) in metakaolin-based geopolymer.

The solidification/stabilization (S/S) through geopolymer is regarded as the ideal approach for the disposal of municipal waste incineration fly ashes (MSWI FA). This work aims to investigate the S/S behaviors of MSWI FA (up to 20 wt.% incorporations) in metakaolin-based geopolymer (MKG), with a focus on the effect of MSWI FA dosage on the performance of geopolymer. Results show that MSWI FA participates in the geopolymerization and alters the reaction products of geopolymer. MSWI FA imposes a dual effect on the performance of geopolymers. A dosage of MSWI FA lower than 5 wt.% can enhance the strength development of geopolymer, mainly due to the formation of C-A-S-H gels in the framework. However, an MSWI FA addition higher than 5 wt.% significantly decreases the strength of geopolymer. The efficiency of immobilization increases with the ionic radius of heavy metals, following the order of Pb > Zn > Cr > Cu. Heavy metals are immobilized in geopolymer framework through ions exchange and coordination to the nonbridging Si-O- and Al-O-. These results help to further understand the use of metakaolin-based geopolymer as an MSWI FA S/S binder.

Recent Advances in Silver Nanowires Electrodes for Flexible Organic/Perovskite Light-Emitting Diodes.

Flexible organic light-emitting diodes and perovskite light-emitting diodes (PeLEDs) have been investigated as an innovative category of revolutionary LED devices for next-generation flat display and lighting applications. A transparent conductive electrode is a key component in flexible OLEDs and PeLEDs, and has been the limitation of the development in this area. Silver nanowires (AgNWs) have been regarded as the most suitable alternative material in TCEs, due to the economical solution synthesis and compatibility with roll-to-roll technology. This mini-review addresses the advances in silver nanowires electrodes for flexible organic/perovskite light-emitting diodes, and the relationship between electrode optimization and device performance is demonstrated. Moreover, the potential strategies and perspectives for their further development of AgNWs-based flexible OLEDs and PeLEDs are presented.

A touch-based multimodal and cryptographic bio-human-machine interface.

The awareness of individuals' biological status is critical for creating interactive and adaptive environments that can actively assist the users to achieve optimal outcomes. Accordingly, specialized human­machine interfaces­equipped with bioperception and interpretation capabilities­are required. To this end, we devised a multimodal cryptographic bio-human­machine interface (CB-HMI), which seamlessly translates the user's touch-based entries into encrypted biochemical, biophysical, and biometric indices. As its central component, the CB-HMI features thin hydrogel-coated chemical sensors and inference algorithms to noninvasively and inconspicuously acquire biochemical indices such as circulating molecules that partition onto the skin (here, ethanol and acetaminophen). Additionally, the CB-HMI hosts physical sensors and associated algorithms to simultaneously acquire the user's heart rate, blood oxygen level, and fingerprint minutiae pattern. Supported by human subject studies, we demonstrated the CB-HMI's capability in terms of acquiring physiologically relevant readouts of target bioindices, as well as user-identifying and biometrically encrypting/decrypting these indices in situ (leveraging the fingerprint feature). By upgrading the common surrounding objects with the CB-HMI, we created interactive solutions for driving safety and medication use. Specifically, we demonstrated a vehicle-activation system and a medication-dispensing system, where the integrated CB-HMI uniquely enabled user bioauthentication (on the basis of the user's biological state and identity) prior to rendering the intended services. Harnessing the levels of bioperception achieved by the CB-HMI and other intelligent HMIs, we can equip our surroundings with a comprehensive and deep awareness of individuals' psychophysiological state and needs.

Physiochemical Responses of the Kernel Quality, Total Phenols and Antioxidant Enzymes of Walnut in Different Forms to the Low-Temperature Storage.

Fresh walnut is obtaining high attention due to its pleasant taste and health benefits. This study aimed to evaluate the influence of storage temperatures (0 °C and -20 °C) on the kernel quality, total phenols, and antioxidant enzyme activities of walnuts in three forms (fresh kernels, walnuts with green husk, and walnuts with shell). For a short storage within 3 months at 0 °C, the results revealed that walnuts with green husk provided a better walnut kernel quality resulting from its lower acid value and peroxide value, together with a higher total phenol content and total antioxidant activity, compared with other forms of walnuts. In comparison, frozen storage at -20 °C for a long duration (up to 10 months), found that walnuts with shell showed advantages in improving the kernel quality (fatty acid content, total phenols, and total antioxidant activity) and antioxidant enzyme (peroxidase, catalase, and superoxide dismutase) activities in the kernels, leading to an acceptable range of acid value and peroxide value, compared with other forms of walnuts. Thus, frozen storage at -20 °C showed a potential application in maintaining the walnut kernel quality, especially the walnuts with shell.

Machine learning-based prediction of survival prognosis in cervical cancer.

BACKGROUND: Accurately forecasting the prognosis could improve cervical cancer management, however, the currently used clinical features are difficult to provide enough information. The aim of this study is to improve forecasting capability by developing a miRNAs-based machine learning survival prediction model. RESULTS: The expression characteristics of miRNAs were chosen as features for model development. The cervical cancer miRNA expression data was obtained from The Cancer Genome Atlas database. Preprocessing, including unquantified data removal, missing value imputation, samples normalization, log transformation, and feature scaling, was performed. In total, 42 survival-related miRNAs were identified by Cox Proportional-Hazards analysis. The patients were optimally clustered into four groups with three different 5-years survival outcome (≥ 90%, ≈ 65%, ≤ 40%) by K-means clustering algorithm base on top 10 survival-related miRNAs. According to the K-means clustering result, a prediction model with high performance was established. The pathways analysis indicated that the miRNAs used play roles involved in the regulation of cancer stem cells. CONCLUSION: A miRNAs-based machine learning cervical cancer survival prediction model was developed that robustly stratifies cervical cancer patients into high survival rate (5-years survival rate ≥ 90%), moderate survival rate (5-years survival rate ≈ 65%), and low survival rate (5-years survival rate ≤ 40%).

A ferrobotic system for automated microfluidic logistics.

Automated technologies that can perform massively parallelized and sequential fluidic operations at small length scales can resolve major bottlenecks encountered in various fields, including medical diagnostics, -omics, drug development, and chemical/material synthesis. Inspired by the transformational impact of automated guided vehicle systems on manufacturing, warehousing, and distribution industries, we devised a ferrobotic system that uses a network of individually addressable robots, each performing designated micro-/nanofluid manipulation-based tasks in cooperation with other robots toward a shared objective. The underlying robotic mechanism facilitating fluidic operations was realized by addressable electromagnetic actuation of miniature mobile magnets that exert localized magnetic body forces on aqueous droplets filled with biocompatible magnetic nanoparticles. The contactless and high-strength nature of the actuation mechanism inherently renders it rapid (~10 centimeters/second), repeatable (>10,000 cycles), and robust (>24 hours). The robustness and individual addressability of ferrobots provide a foundation for the deployment of a network of ferrobots to carry out cross-collaborative logistics efficiently. These traits, together with the reconfigurability of the system, were exploited to devise and integrate passive/active advanced functional components (e.g., droplet dispensing, generation, filtering, and merging), enabling versatile system-level functionalities. By applying this ferrobotic system within the framework of a microfluidic architecture, the ferrobots were tasked to work cross-collaboratively toward the quantification of active matrix metallopeptidases (a biomarker for cancer malignancy and inflammation) in human plasma, where various functionalities converged to achieve a fully automated assay.

An autonomous wearable system for diurnal sweat biomarker data acquisition.

To track dynamically varying and physiologically relevant biomarker profiles in sweat, autonomous wearable platforms are required to periodically sample and analyze sweat with minimal or no user intervention. Previously reported sweat sensors are functionally limited to capturing biomarker information at one time-point/period, thereby necessitating repeated user intervention to increase the temporal granularity of biomarker data. Accordingly, we present a compact multi-compartment wearable system, where each compartment can be activated to autonomously induce/modulate sweat secretion (via iontophoretic actuation) and analyze sweat at set time points. This system was developed following a hybrid-flex design and a vertical integration scheme-integrating the required functional modules: miniaturized iontophoresis interfaces, adhesive thin film microfluidic-sensing module, and control/readout electronics. The system was deployed in a human subject study to track the diurnal variation of sweat glucose levels in relation to the daily food intake. The demonstrated autonomous operation for diurnal sweat biomarker data acquisition illustrates the system's suitability for large-scale and longitudinal personal health monitoring applications.

A programmable epidermal microfluidic valving system for wearable biofluid management and contextual biomarker analysis.

Active biofluid management is central to the realization of wearable bioanalytical platforms that are poised to autonomously provide frequent, real-time, and accurate measures of biomarkers in epidermally-retrievable biofluids (e.g., sweat). Accordingly, here, a programmable epidermal microfluidic valving system is devised, which is capable of biofluid sampling, routing, and compartmentalization for biomarker analysis. At its core, the system is a network of individually-addressable microheater-controlled thermo-responsive hydrogel valves, augmented with a pressure regulation mechanism to accommodate pressure built-up, when interfacing sweat glands. The active biofluid control achieved by this system is harnessed to create unprecedented wearable bioanalytical capabilities at both the sensor level (decoupling the confounding influence of flow rate variability on sensor response) and the system level (facilitating context-based sensor selection/protection). Through integration with a wireless flexible printed circuit board and seamless bilateral communication with consumer electronics (e.g., smartwatch), contextually-relevant (scheduled/on-demand) on-body biomarker data acquisition/display was achieved.

A wearable freestanding electrochemical sensing system.

To render high-fidelity wearable biomarker data, understanding and engineering the information delivery pathway from epidermally retrieved biofluid to a readout unit are critical. By examining the biomarker information delivery pathway and recognizing near-zero strained regions within a microfluidic device, a strain-isolated pathway to preserve biomarker data fidelity is engineered. Accordingly, a generalizable and disposable freestanding electrochemical sensing system (FESS) is devised, which simultaneously facilitates sensing and out-of-plane signal interconnection with the aid of double-sided adhesion. The FESS serves as a foundation to realize a system-level design strategy, addressing the challenges of wearable biosensing, in the presence of motion, and integration with consumer electronics. To this end, a FESS-enabled smartwatch was developed, featuring sweat sampling, electrochemical sensing, and data display/transmission, all within a self-contained wearable platform. The FESS-enabled smartwatch was used to monitor the sweat metabolite profiles of individuals in sedentary and high-intensity exercise settings.

Design Framework and Sensing System for Noninvasive Wearable Electroactive Drug Monitoring.

Wearable drug monitoring targeting epidermally retrievable biofluids (e.g., sweat) can enable a variety of applications, including drug compliance/abuse monitoring and personalized therapeutic drug dosing. In that regard, voltammetry-based approaches are suitable because they uniquely leverage the electroactive nature of target drug molecules for quantification, eliminating the reliance on the availability of recognition elements. However, to adapt such approaches for the envisioned application, three main challenges must be addressed: (1) constructing a sensitive voltammetric sensing interface with high signal-to-background ratio, (2) decoupling the confounding effect of endogenous electroactive species (naturally present in complex biofluid matrices) and baseline variation, and (3) realizing wireless voltammetric excitation and signal acquisition/transmission. To this end, first, a framework for the quantification of electroactive drugs is presented, which centers on the evaluation and determination of suitable sensing electrodes and characterization of the interference from a panel of physiologically relevant electroactive species. This framework was utilized to establish the design space and operational settings for the development of a coupled sensing system and analytical framework to render sample-to-answer drug readouts in complex biofluid matrices. The presented design framework and sensing system can serve as a basis for future wearable sensor development efforts aiming to monitor electroactive species such as pharmaceutical molecules.