Non-invasive Disease Diagnosis Research Articles

Glial fibrillary acidic protein in Alzheimer’s disease: a narrative review

Abstract Astrocytes are fundamental in neural functioning and homeostasis in the central nervous system. These cells respond to injuries and pathological conditions through astrogliosis, a reactive process associated with neurodegenerative diseases such as Alzheimer’s disease. This process is thought to begin in the early stages of these conditions. Glial fibrillary acidic protein (GFAP), a type III intermediate filament protein predominantly expressed in astrocytes, has emerged as a key biomarker for monitoring this response. During astrogliosis, GFAP is released into biofluids, making it a candidate for non-invasive diagnosis and tracking of neurodegenerative diseases. Growing evidence positions GFAP as a biomarker for Alzheimer’s disease with specificity and disease-correlation characteristics comparable to established clinical markers, such as Aβ peptides and phosphorylated tau protein. To improve diagnostic accuracy, particularly in the presence of confounders and comorbidities, incorporating a panel of biomarkers may be advantageous. This review will explore the potential of GFAP within such a panel, examining its role in early diagnosis, disease progression monitoring and its integration into clinical practice for Alzheimer’s disease management.

From Lab to Life: Self-Powered Sweat Sensors and Their Future in Personal Health Monitoring.

The rapid development of wearable sweat sensors has demonstrated their potential for continuous, non-invasive disease diagnosis and health monitoring. Emerging energy harvesters capable of converting various environmental energy sources-biomechanical, thermal, biochemical, and solar-into electrical energy are revolutionizing power solutions for wearable devices. Based on self-powered technology, the integration of the energy harvesters with wearable sweat sensors can drive the device for biosensing, signal processing, and data transmission. As a result, self-powered sweat sensors are able to operate continuously without external power or charging, greatly facilitating the development of wearable electronics and personalized healthcare. This review focuses on the recent advances in self-powered sweat sensors for personalized healthcare, covering sweat sensors, energy harvesters, energy management, and applications. The review begins with the foundations of wearable sweat sensors, providing an overview of their detection methods, materials, and wearable devices. Then, the working mechanism, structure, and a characteristic of different types of energy harvesters are discussed. The features and challenges of different energy harvesters in energy supply and energy management of sweat sensors are emphasized. The review concludes with a look at the future prospects of self-powered sweat sensors, outlining the trajectory of the field and its potential to flourish.

FRET-Based Dual-Color Carbon Dot Ratiometric Fluorescent Sensor Enables the Smartphone-Integrated Device for Noninvasive and Portable Diagnosis of Chronic Kidney Disease.

Cystatin C (Cys C), a crucial renal disease marker for chronic kidney disease (CKD), plays a vital role in early diagnosis and treatment guidance. However, most current methods for detecting Cys C rely on a single signal and find it difficult to perform noninvasive and portable diagnosis. Here, we developed a ratiometric fluorescent carbon dot (CD) detection system for point-of-care testing (POCT) of Cys C through fluorescence resonance energy transfer (FRET). The detection is based on the hydrolysis effect of papain on a bovine serum albumin (BSA) scaffold and the specific inhibitory effect of Cys C on papain, endowing high-resolution color variance. Moreover, a low-cost, portable, yet reliable smartphone-assisted miniaturized device for real-time quantitative POCT of Cys C has been developed with a limit of detection (LOD) as low as 0.4 μg/mL. This sensing platform can effectively differentiate patients from healthy volunteers, which facilitates self-screening for healthy individuals and home monitoring for CKD patients.

Ultra-Thin Highly Sensitive Electronic Skin for Temperature Monitoring.

Electronic skin capable of reliable monitoring of human skin temperature is crucial for the advancement of non-invasive clinical biomonitoring, disease diagnosis, and health surveillance. Ultra-thin temperature sensors, with excellent mechanical flexibility and robustness, can conformably adhere to uneven skin surfaces, making them ideal candidates. However, achieving high sensitivity often demands sacrificing flexibility, rendering the development of temperature sensors combining both qualities a challenging task. In this study, we utilized a low-cost drop-casting technique to print ultra-thin and lightweight (thickness: approximately 3 µm, weight: 0.61 mg) temperature sensors based on a combination of vanadium dioxide and PEDOT:PSS at room temperature and atmospheric conditions. These sensors exhibit high sensitivity (temperature coefficient of resistance: -5.11%/°C), rapid response and recovery times (0.36 s), and high-temperature accuracy (0.031 °C). Furthermore, they showcased remarkable durability in extreme bending conditions (bending radius = 400 µm), along with stable electrical performance over approximately 2400 bending cycles. This work offers a low-cost, simple, and scalable method for manufacturing ultra-thin and lightweight electronic skins for temperature monitoring, which seamlessly integrate exceptional temperature-measuring capabilities with optimal flexibility.

Acetone Sensors Based on Al-Coated and Ni-Doped Copper Oxide Nanocrystalline Thin Films.

Acetone detection is of significant importance in various industries, from cosmetics to pharmaceuticals, bioengineering, and paints. Sensor manufacturing involves the use of different semiconductor materials as well as different metals for doping and functionalization, allowing them to achieve advanced or unique properties in different sensor applications. In the healthcare field, these sensors play a crucial role in the non-invasive diagnosis of various diseases, offering a potential way to monitor metabolic conditions by analyzing respiration. This article presents the synthesis method, using chemical solutions and rapid thermal annealing technology, to obtain Al-functionalized and Ni-doped copper oxide (Al/CuO:Ni) nanostructured thin films for biosensors. The nanocrystalline thin films are subjected to a thorough characterization, with examination of the morphological properties by scanning electron microscopy (SEM), energy-dispersive X-ray spectroscopy (EDX), and X-ray diffraction (XRD) analysis. The results reveal notable changes in the surface morphology and structure following different treatments, providing insight into the mechanism of function and selectivity of these nanostructures for gases and volatile compounds. The study highlights the high selectivity of developed Al/CuO:Ni nanostructures towards acetone vapors at different concentrations from 1 ppm to 1000 ppm. Gas sensitivity is evaluated over a range of operating temperatures, indicating optimum performance at 300 °C and 350 °C with the maximum sensor signal (S) response obtained being 45% and 50%, respectively, to 50 ppm gas concentration. This work shows the high potential of developed technology for obtaining Al/CuO:Ni nanostructured thin films as next-generation materials for improving the sensitivity and selectivity of acetone sensors for practical applications as breath detectors in biomedical diagnostics, in particular for diabetes monitoring. It also emphasizes the importance of these sensors in ensuring industrial safety by preventing adverse health and environmental effects of exposure to acetone.

Breath Analysis: Identification of Potential Volatile Biomarkers for Non-Invasive Diagnosis of Chronic Kidney Disease (CKD).

Recently, volatile organic compound (VOC) determination in exhaled breath has seen growing interest due to its promising potential in early diagnosis of several pathological conditions, including chronic kidney disease (CKD). Therefore, this study aimed to identify the breath VOC pattern providing an accurate, reproducible and fast CKD diagnosis at early stages of disease. A cross-sectional observational study was carried out, enrolling a total of 30 subjects matched for age and gender. More specifically, the breath samples were collected from (a) 10 patients with end-stage kidney disease (ESKD) before undergoing hemodialysis treatment (DIAL); (b) 10 patients with mild-moderate CKD (G) including 3 patients in stage G2 with mild albuminuria, and 7 patients in stage G3 and (c) 10 healthy controls (CTRL). For each volunteer, an end-tidal exhaled breath sample and an ambient air sample (AA) were collected at the same time on two sorbent tubes by an automated sampling system and analyzed by Thermal Desorption-Gas Chromatography-Mass Spectrometry. A total of 110 VOCs were detected in breath samples but only 42 showed significatively different levels with respect to AA. Nonparametric tests, such as Wilcoxon/Kruskal-Wallis tests, allowed us to identify the most weighting variables able to discriminate between AA, DIAL, G and CTRL breath samples. A promising multivariate data mining approach incorporating only selected variables (showing p-values lower than 0.05), such as nonanal, pentane, acetophenone, pentanone, undecane, butanedione, ethyl hexanol and benzene, was developed and cross-validated, providing a prediction accuracy equal to 87% and 100% in identifying patients with both mild-moderate CKD (G) and ESKD (DIAL), respectively.

Editorial Comment: Quantitative Ultrasound 101-A Guide to Emerging Techniques in the Noninvasive Diagnosis and Staging of Liver Disease.

Editorial Comment: Quantitative Ultrasound 101-A Guide to Emerging Techniques in the Noninvasive Diagnosis and Staging of Liver Disease.

Optical chemical gas sensor based on spectral autocorrelation: A method for online detection of nitric oxide and ammonia in exhaled breath

The detection of nitric oxide (NO) and ammonia (NH3) in exhaled breath (EB) plays a crucial role in non-invasive diagnosis of airway inflammatory and kidney diseases. Here we report an optical chemical gas sensor for NO and NH3 based on spectral autocorrelation. In our sensor system, the target gas is removed through a chemical reaction with a sensing material and the spectrum of the target gas is extracted from the spectra obtained before and after the reaction by autocorrelation. Our method focuses on the ability of the material to eliminate the target gas without the need for detecting signals of the reaction, greatly relaxing the requirements on material properties. Fundamentally different from optical sensors that obtain spectra by spectral decoupling, our sensor relies on autocorrelation operation to acquire the spectrum of the target gas, thereby overcoming spectra overlap. Lab-based results show that the sensor enables detection of NO (2.28–451.82 ppb) and NH3 (11.52–12,725.42 ppb) with mean absolute errors (MAEs) of 0.79 ppb and 6.13 ppb and measurement accuracies of 0.8 % and 1.4 %, respectively. The EB experiment proves that the sensor can detect exhaled NO and NH3 and distinguish EB between healthy subjects and simulated patients.

Neutrophil elastase-activated carbon dots for non-invasive early diagnosis of inflammatory bowel disease by near-infrared-II fluorescence imaging

Most near-infrared-II (NIR-II) fluorescent probes produce an “always-on” output with poor signal specificity for biomarkers. Neutrophil elastase (NE) is a critical factor in chronic inflammation or in the acute response to infection and injury, and NE activatable NIR-II fluorescent probes can aid in the early clinical diagnosis of inflammatory bowel disease (IBD). In this paper, we synthesized OPDG-PFA@PEI carbon dots (CDs) through a two-step reaction of solvothermal/surface covalent bond modification, which can “turn on” specific NIR-II fluorescence signals related to the level of NE produced by neutrophils and target sites of inflammation effectively, thus realizing the monitoring of in-situ NE in cells and in vivo. OPDG-PFA@PEI CDs fluorescent nanoprobe show high-performance real-time non-invasive NIR-II fluorescence imaging for early diagnostic evaluation of IBD in mouse models of DSS-induced IBD, providing a promising approach for the development of clinical non-invasive and more convenient early diagnosis of IBD.

Exploring Total Immunoglobulin A's Impact on Non-Biopsy Diagnosis of Celiac Disease: Implications for Diagnostic Accuracy.

In the current debate surrounding the biopsy-free diagnosis of CeD, it is crucial to identify factors influencing the accuracy of results. This study investigated the impact of total IgA on the non-invasive diagnosis of celiac disease (CeD). We retrospectively assessed total IgA titers' influence on the diagnostic accuracy of different tTG-IgA thresholds compared to the upper reference value (UNL). Of 165 included patients, tTG-IgA values at 10× UNL and 6× UNL showed specificity of 82.6% and 73.9% and sensitivity of 49.3% and 69.0%, respectively, in predicting intestinal villous atrophy (Marsh 3). In 130 patients, total IgA levels were known at baseline. These patients were divided into three tertiles according to total IgA, i.e., patients with lower, intermediate, or higher total IgA within the population. For patients with total IgA ≥ 245 mg/dL, using a tTG-IgA cutoff of 6× UNL instead of 10× UNL resulted in decreased specificity from 71.4% to 42.8% and increased sensitivity from 67.6% to 81.1%. For patients with total IgA < 174 mg/dL and between 174 mg/dL and 245 mg/dL, using a tTG-IgA cutoff of 6× UNL instead of 10× UNL maintained specificity (75.0% and 85.7%, respectively) with increased sensitivity (from 46.2% to 64.1% and from 36.1% to 52.8%, respectively). In conclusion, total IgA influences the diagnostic accuracy of a predetermined tTG-IgA cutoff. Greater consideration should be given to total IgA, beyond its deficiency, in evaluating the applicability and accuracy of non-invasive CeD diagnosis.

A planar mounted SQUID full-tensor module for magnetoenterogram denoising detection

Magntoenterogram (MENG) is a functional imaging method with great clinical potential for non-invasive diagnosis of intestinal diseases. In measurements of MENG, the cardiac artifacts are a strong biological source of interference. Based on wire-wound SQUID gradiometers, we constructed a planar mounted full-tensor compensation module for MENG system, which can obtain cardiac artifacts while suppressing ambient noise, so as to denoise MENG signals without increasing external ECG recording channels. The cardiac artifacts are regressed out from the full-tensor compensation module outputs, which can denoise the MENG measurements. Using this module, the SNR is about 70 dB better than the raw data. At last, 36-point array MENG signals were detected successfully and the contour map of jejunal slow-wave frequency (SWF) was consistent with the jejunal area (one part of the small intestine). This method will save costs and avoid the new electromagnetic interferences introduced by ECG equipment. It will lay a good foundation for the rapid clinical application of the MENG system.

A microfluidic-based chemiluminescence biosensor for sensitive multiplex detection of exosomal microRNAs based on hybridization chain reaction

The analysis of microRNAs (miRNAs) in exosomes is of great importance for noninvasive early disease diagnosis. However, current techniques to detect exosomal miRNAs is hampered either by laborious exosome isolation or low abundance of miRNAs in exosomes. Here, we developed a microfluidic chemiluminescence (CL) analysis method for the multiplexed detection of exosomal miR-21 and miR-155. The microfluidic device contained three parts: a snake-shaped channel for fully mixing chemiluminescent reagents, a ship-shaped channel modified with CD63 protein aptamer for capturing exosomes, and another two parallel ship-shaped channels for hybridization chain reaction (HCR) amplification and CL detection. The multiple signal amplification was realized by Y-shaped arrays, HCR amplification, and poly-HRP catalyzed CL reaction. Using this multiple signal amplification method, our microfluidic CL biosensor achieves a limit of detection of miRNAs of 0.49 fM, with a linear range of 1 fM–10 pM, which is better or comparable to previously reported biosensors. What's more, the proposed microfluidic biosensor exhibits great specificity and selectivity to the target miRNA. Moreover, the microfluidic CL strategy exhibited excellent accuracy and could significantly distinguish different cancer subtypes as well as cancer patients and healthy people. These results suggest that this simple, high sensitive, and more accurate analytical strategy by analyzing different types of exosomal miRNAs has the potential applications in cancer diagnosis and stage monitoring.

Thermal energy and electro-osmotic for biomimetic artificial olfactory cilia in tri-hybrid nanofluids: entropy-defying approaches

Biomimetic artificial olfactory cilia have demonstrated potential in identifying specific volatile organic compounds linked to various diseases, including certain cancers, metabolic disorders, and respiratory conditions. These sensors may facilitate non-invasive disease diagnosis and monitoring. Cilia Motility is the coordinated movement of cilia, which are hair-like projections present on the surface of particular cells in different species. Cilia serve an important part in several biological functions, including motility, fluid movement, and sensory reception. Cilia motility is a complicated process that requires the coordinated interaction of structural components and molecular pathways. Cilia are made up of a highly structured structure known as the axoneme, which is made up of microtubules grouped in a unique pattern. The axoneme is made up of nine outer doublet microtubules and a core pair of singlet microtubules. This arrangement offers structural support and serves as a scaffold for the proteins involved in ciliary movement. Our latest endeavors investigate these Multiphysics phenomena in ciliary beating flows that are inspired by biology, utilizing copper, gold, and titania nanoparticles. We examine their functions in biological systems such as peristaltic transport computationally. Our models give precise two- and three-dimensional velocity, temperature, and concentration solutions by integrating transverse magnetohydrodynamics with laser heating. Furthermore, at the channel wall expressions, the skin friction coefficient, Sherwood number, Nusselt number and optimization of entropy generation are acquired and analyzed. Important properties of the velocity and scalar profiles are revealed by a thorough analysis of dimensionless parameters. The simplified examination provides more insight into the trapping patterns that result from the complex interaction between nanofluid rheology and optics. These findings greatly contribute to our knowledge and improvement of nanofluidic transport technologies in a variety of fields supporting industry, sustainability, and medicine. Our combined computational and experimental methodology clarifies the complex dynamics in these systems and provides design guidance for the engineering of improved fluidic devices that make use of multifunctional nanomaterial interfaces and peristaltic motion.

A Prototype of Graphene E-Nose for Exhaled Breath Detection and Label-Free Diagnosis of Helicobacter Pylori Infection.

Helicobacter pylori (HP), a common microanaerobic bacteria that lives in the human mouth and stomach, is reported to infect ≈50% of the global population. The current diagnostic methods for HP are either invasive, time-consuming, or harmful. Therefore, a noninvasive and label-free HP diagnostic method needs to be developed urgently. Herein, reduced graphene oxide (rGO) is composited with different metal-based materials to construct a graphene-based electronic nose (e-nose), which exhibits excellent sensitivity and cross-reactive response to several gases in exhaled breath (EB). Principal component analysis (PCA) shows that four typical types of gases in EB can be well discriminated. Additionally, the potential of the e-nose in label-free detection of HP infection is demonstrated through the measurement and analysis of EB samples. Furthermore, a prototype of an e-nose device is designed and constructed for automatic EB detection and HP diagnosis. The accuracy of the prototype machine integrated with the graphene-based e-nose can reach 92% and 91% in the training and validation sets, respectively. These results demonstrate that the highly sensitive graphene-based e-nose has great potential for the label-free diagnosis of HP and may become a novel tool for non-invasive disease screening and diagnosis.

Light-Activated Virtual Sensor Array with Machine Learning for Non-Invasive Diagnosis of Coronary Heart Disease

HighlightsPhotoresponsive black phosphorus (BP)/Ti3C2Tx composites were synthesized by a self-assembly strategy.Enhanced gas sensitive property was achieved by visible light modulation.Light activated virtual sensor array was fabricated based on BP/Ti3C2Tx composite.Diagnosis of coronary heart disease was achieved with the help of machine learning.

Sound as a bell: a deep learning approach for health status classification through speech acoustic biomarkers

BackgroundHuman health is a complex, dynamic concept encompassing a spectrum of states influenced by genetic, environmental, physiological, and psychological factors. Traditional Chinese Medicine categorizes health into nine body constitutional types, each reflecting unique balances or imbalances in vital energies, influencing physical, mental, and emotional states. Advances in machine learning models offer promising avenues for diagnosing conditions like Alzheimer's, dementia, and respiratory diseases by analyzing speech patterns, enabling complementary non-invasive disease diagnosis. The study aims to use speech audio to identify subhealth populations characterized by unbalanced constitution types.MethodsParticipants, aged 18–45, were selected from the Acoustic Study of Health. Audio recordings were collected using ATR2500X-USB microphones and Praat software. Exclusion criteria included recent illness, dental issues, and specific medical histories. The audio data were preprocessed to Mel-frequency cepstral coefficients (MFCCs) for model training. Three deep learning models—1-Dimensional Convolution Network (Conv1D), 2-Dimensional Convolution Network (Conv2D), and Long Short-Term Memory (LSTM)—were implemented using Python to classify health status. Saliency maps were generated to provide model explainability.ResultsThe study used 1,378 recordings from balanced (healthy) and 1,413 from unbalanced (subhealth) types. The Conv1D model achieved a training accuracy of 91.91% and validation accuracy of 84.19%. The Conv2D model had 96.19% training accuracy and 84.93% validation accuracy. The LSTM model showed 92.79% training accuracy and 87.13% validation accuracy, with early signs of overfitting. AUC scores were 0.92 and 0.94 (Conv1D), 0.99 (Conv2D), and 0.97 (LSTM). All models demonstrated robust performance, with Conv2D excelling in discrimination accuracy.ConclusionsThe deep learning classification of human speech audio for health status using body constitution types showed promising results with Conv1D, Conv2D, and LSTM models. Analysis of ROC curves, training accuracy, and validation accuracy showed all models robustly distinguished between balanced and unbalanced constitution types. Conv2D excelled with good accuracy, while Conv1D and LSTM also performed well, affirming their reliability. The study integrates constitution theory and deep learning technologies to classify subhealth populations using noninvasive approach, thereby promoting personalized medicine and early intervention strategies.

Fetuin-A: a relevant novel serum biomarker for non-invasive diagnosis of metabolic dysfunction-associated steatotic liver disease (MASLD): a retrospective case-control study

ObjectivesTo determine how fetuin-A contributes to diagnosing and assessing MASLD severity.MethodsFifty MASLD patients and fifty healthy control participants were involved in this retrospective case-control research. Abdominal ultrasonography, fibroscan with controlled attenuated parameter scan (CAP scan), laboratory investigation (including fetuin-A assessment), clinical examination, and history-taking were performed on every case.ResultsFetuin-A level was considerably higher in the Cases group (1154.85 ± 629.89) than in the Control group (505.29 ± 150.4) (p < 0.001). Fetuin-A had significant validity in the prediction of MASLD at a cut-off > 702.5 with 82% sensitivity, 90% specificity, and 86% overall accuracy.ConclusionOne possible marker for MASLD diagnosis could be fetuin-A. Furthermore, a substantial association between such marker and the severity of the disease as it revealed a significant correlation with ultrasound grading and fibroscan with controlled attenuated parameters.Trial registration1- Pan African Clinical Trial Registry.Unique Identifying number/registration ID: PACTR202309644280965.URL: https://pactr.samrc.ac.za/TrialDisplay.aspx?TrialID=26860.Registration Approval date: 21/09/2023.2- ClinicalTrials.gov.Unique Identifying number /registration ID: NCT06097039.URL: https://clinicaltrials.gov/study/NCT06097039?cond=NCT06097039&rank=1.Registration Approval date: 25/10/2023.

Viscosity-activated carbon dots for noninvasive disease diagnosis, therapeutic efficacy assessment and anticancer drug screening

Abnormal levels of intracellular microviscosity have a close relationship with some diseases and pathologies. Therefore, quantitative imaging of viscosity at the cellular and organ levels is beneficial for disease diagnosis, curative effect evaluation, and anticancer drug screening. Herein, we synthesized viscosity-activated orange-red emitting carbon dots (named as LP-CDs) from lignin and p-phenylenediamine using a one-step method, whose fluorescence intensity, lifetime, and quantum yield significantly increase with rising viscosity. Meanwhile, the fluorescence intensity of LP-CDs also has a good linear relationship with the solution viscosity. Taking the advantages of high sensitivity, noninvasiveness, and rapid result output of fluorescence imaging, LP-CDs can visualize the cellular viscosity changes induced by inflammation and hyperglycemia, and image tumor evolution. More importantly, LP-CDs can be used as powerful sensors to screen anticancer drugs in vivo by evaluating therapeutic effects. This work provides a new scheme for constructing robust nanoprobes to achieve the diagnosis and imaging-guided surgery of viscosity related diseases.

Multimodal Cardiac Imaging Revisited by Artificial Intelligence: An Innovative Way of Assessment or Just an Aid?

Cardiovascular disease remains a leading global health challenge, necessitating advanced diagnostic approaches. This review explores the integration of artificial intelligence (AI) in multimodal cardiac imaging, tracing its evolution from early X-rays to contemporary techniques such as CT, MRI, and nuclear imaging. AI, particularly machine learningand deep learning, significantly enhances cardiac diagnostics by estimating biological heart age, predicting disease risk, and optimizing heart failure management through adaptive algorithms without explicit programming or feature engineering. Key contributions include AI's transformative role in non-invasive coronary artery disease diagnosis, arrhythmia detection via wearable devices, and personalized treatment strategies. Despite substantial progress, challenges including data standardization, algorithm validation, regulatory approval, and ethical considerations must be addressed to fully harness AI's potential. Collaborative efforts among clinicians, scientists, industry stakeholders, and regulatory bodies are essential for the safe and effective deployment of AI in cardiac imaging, promising enhanced diagnostics and personalized patient care.

Skin Surface Sebum Analysis by ESI-MS.

The skin surface is an important sample source that the metabolomics community has only just begun to explore. Alterations in sebum, the lipid-rich mixture coating the skin surface, correlate with age, sex, ethnicity, diet, exercise, and disease state, making the skin surface an ideal sample source for future noninvasive biomarker exploration, disease diagnosis, and forensic investigation. The potential of sebum sampling has been realized primarily via electrospray ionization mass spectrometry (ESI-MS), an ideal approach to assess the skin surface lipidome. However, a better understanding of sebum collection and subsequent ESI-MS analysis is required before skin surface sampling can be implemented in routine analyses. Challenges include ambiguity in definitive lipid identification, inherent biological variability in sebum production, and methodological, technical variability in analyses. To overcome these obstacles, avoid common pitfalls, and achieve reproducible, robust outcomes, every portion of the workflow-from sample collection to data analysis-should be carefully considered with the specific application in mind. This review details current practices in sebum sampling, sample preparation, ESI-MS data acquisition, and data analysis, and it provides important considerations in acquiring meaningful lipidomic datasets from the skin surface. Forensic researchers investigating sebum as a means for suspect elimination in lieu of adequate fingerprint ridge detail or database matches, as well as clinical researchers interested in noninvasive biomarker exploration, disease diagnosis, and treatment monitoring, can use this review as a guide for developing methods of best-practice.