Search Results (731)

Pediatric brain tumor survivors remain at high risk of recurrence, yet current surveillance strategies relying on neuroimaging and cerebrospinal fluid (CSF) cytology have limited sensitivity for early or minimal disease. Tumor-specific peptides (TSPs) derived from individual tumors represent a promising class of highly specific biomarkers for longitudinal disease monitoring through CSF-based proteomic analysis. In this study, tumor tissue and serial CSF samples from six pediatric brain tumor patients (five medulloblastomas and one atypical teratoid/rhabdoid tumor (ATRT)) were analyzed using an integrated proteogenomic workflow combining discovery and targeted mass spectrometry. TSPs were identified from resected tumor tissue and matched against shotgun CSF proteomic datasets to nominate candidate biomarkers. High-confidence peptides were synthesized as isotopically labeled standards and quantified longitudinally using targeted multiple reaction monitoring. Two TSP biomarkers derived from individualized pediatric brain tumors (one medulloblastoma and one ATRT) demonstrated robust detection in serial CSF samples and exhibited temporal concordance with radiographic disease course, declining with treatment response and increasing during disease progression. These findings establish the feasibility of detecting and longitudinally quantifying TSPs in CSF and support further investigation of individualized proteomic biomarkers for treatment response monitoring and disease surveillance in pediatric brain tumors. Full article

Using disposable screen-printed electrodes faces major challenges when attempting to monitor a continuous process, especially in systems where there is pronounced adsorption, fouling, degradation, or in cases of irreversible electrochemical reactions. Methylene Blue (MB) exhibits some therapeutic properties and is commonly used as a redox reporter in DNA sensors, but is also considered a toxic pollutant in aquatic systems. MB demonstrates strong adsorption to carbon materials, which prevents its electroanalytical determination in multiple measurements with a single electrode. Our work details direct electrochemical determination of MB with only the native carbon screen-printed working electrode as sensing material and optimization of the analytical method. In batch mode, we significantly improved sensitivity and interelectrode reproducibility by introducing a prepolarization step, but successive measurements in lower concentrations were not feasible due to strong adsorption. A fully customizable, modular flow cell was 3D printed to allow in operando replacement of the planar screen-printed three-electrode system after measurement during continuous flow. As confirmed by mechanical properties testing, the rigid polyacrylate upper section of the flow cell provides structural stability, combined with a flexible TPU lower section which enables effortless sensor hot swapping and effective sealing during flow. With an optimized hot swapping flow detection method, MB was detected via square wave voltammetry with a sensitivity of 65.59 µA/µM and a calculated LOD of 7.75 nM, which outperforms similar systems from the literature. We envisage this approach can be integrated into low-cost continuous environmental monitoring systems or in-line quality control, especially in flow chemistry synthesis. Full article

The difficulties of continuously monitoring cardiac patients in general hospital wards are still present because of the manual charting system and the slow clinical reaction to worsening physiological state. This paper outlines an edge- and fog-based Internet of Things (IoT) healthcare system to acquire, process, and prioritize the vital signs of patients in real time to minimize the alert latency and increase the time of clinical interventions. Wearable 12-lead ECG sensors transmit physiological measurements, such as heart rate, blood pressure, and oxygen saturation, to an intelligent edge service, where preprocessing, triage by threshold, and machine learning ECG classification are performed, and selective synchronization of physiological data with a cloud backend and data delivery to the clinician are made possible by a mobile application. The proposed architecture combines a ribbon-like streaming scheme, Flask-based gateway services, and Firebase Firestore to coordinate scalable mob/cloud with the help of multi-client data dissemination. To encompass borderline clinical deterioration, which is often unnoticed by conventional threshold systems, physiological parameters are classified into normal, alarming, emergency, and a new state, average. The Pan–Tompkins++ peak detector algorithm and multiple edge-resident classifiers, such as random forest, XGBoost, decision tree, naive Bayes, K-nearest neighbor, and support vector machine, are used to analyze the ECG waveforms. Experimental analysis of PhysioNet datasets and tests in real wards prove that the ensemble models can reach the highest possible ECG classification precision of 91.96 percent and snapshot-driven mobile alerts can decrease routine patient evaluation time by several minutes, to an average of 15.23 ± 2.71 s. These results suggest that edge-centric IoT systems can be appropriate in latency-critical hospital settings and that fog-based coordination is useful in next-generation smart healthcare systems. Full article

Background: Long-haul travel and jet lag can disrupt athletes’ circadian, physiological, and performance systems, potentially impairing competition outcomes. This review aimed to study the effects of long-haul travel on athletes’ health and performance, differentiate travel fatigue from jet lag, and review mitigation strategies. Methods: A systematic narrative review was conducted following PRISMA 2020 guidelines. PubMed, Scopus, and Web of Science were searched for studies on jet lag, travel fatigue, and long-haul travel in athletes. Eligibility included studies reporting physiological, hemodynamic, or performance outcomes in athletes of any level and sex. Data were extracted on travel characteristics, interventions, physiological and performance markers, and risk of bias. Results: Overall, 284 records were identified, with 89 studies included. Travel directions were equally distributed between eastward and westward journeys, crossing 1–12 time zones. Interventions to mitigate travel effects were reported in 17 studies, primarily melatonin, caffeine, and light exposure. Common physiological changes included sleep disturbances (n = 36), body temperature alterations (n = 18), blood pressure changes, hormonal shifts (n = 9), heart rate variability (n = 4), and immune alterations (n = 4). Travel effects comprised fatigue (n = 25), sleep changes (n = 21), decreased physical performance (n = 18), mood changes (n = 15), and cognitive impairments (n = 9). Physical performance outcomes included anaerobic power (n = 18), strength (n = 14), velocity (n = 12), aerobic capacity (n = 10), coordination (n = 8), and reaction time (n = 7). Risk of bias was low in 49%, moderate in 17%, and high in 34% of studies. Conclusions: Long-haul travel negatively affects multiple physiological and performance domains in athletes, including sleep, hormonal balance, autonomic function, and physical performance. The magnitude of these effects seems to be influenced by travel direction, number of time zones crossed, and individual susceptibility. Eastward travel is generally associated with stronger circadian disruption and impaired aerobic capacity, coordination, and technical performance, whereas westward travel often induces greater fatigue and adversely affects team-sport outcomes. Monitoring key markers such as heart rate variability, sleep, and cortisol, combined with personalized strategies including circadian management, sleep hygiene, nutrition, recovery interventions, and training load adjustments, is essential to mitigate travel-related impairments and optimize performance. Full article

Photoionization detector (PID) sensors are widely used for ambient Volatile organic compound (VOC) monitoring because they are inexpensive, flexible, and fast. However, PID outputs are strongly influenced by environmental conditions (especially temperature and relative humidity) and exhibit substantial inter-sensor variability, limiting their quantitative reliability. Here we present a rapid machine-learning calibration workflow that maps PID signals and meteorological covariates to a photochemically relevant reference metric, PTR-derived VOC OH reactivity (R_OH,PTR, s⁻¹), calculated from online PTR-ToF-MS VOC measurements weighted by OH reaction rate constants. Four MiniPID sensors were co-located with a PTR-ToF-MS and a thermohygrometer, and data were harmonized to 10-s resolution. Multiple regression models were evaluated, with ensemble methods (RF and XGBoost) providing the best overall performance. To ensure realistic generalization under temporal autocorrelation, validation used a time-aware split: models were trained on a contiguous 24-h co-location period and evaluated on subsequent days (out-of-time). In this out-of-time evaluation, XGBoost achieved strong agreement with R_OH,PTR across sensors (Pearson’s r = 0.85, R² = 0.64, RMSE = 1.74 s⁻¹), while substantially improving inter-sensor consistency. This short-duration calibration approach supports practical co-location-based harmonization of PID networks for high-temporal-resolution VOC reactivity monitoring in urban and industrial environments. Full article

Oxidative alcohol metabolism in the liver relies on sequential enzymatic reactions involving alcohol dehydrogenase (ADH), cytochrome P450 2E1 (CYP2E1), and aldehyde dehydrogenase (ALDH) isozymes. However, the circadian regulation of these enzymes, their susceptibility to genetic, environmental, and metabolic disruption, and their functional implications toward alcohol-mediated tissue injury remain incompletely defined. To address this gap, we performed a comprehensive integrative analysis of the publicly available circadian transcriptome datasets spanning genetic clock disruption, acute sleep deprivation, chronic high-fat diet feeding, and occupational shift work to systematically characterize the temporal regulation and disruption vulnerability of the major alcohol-metabolizing enzymes. Mouse tissue-cycling analyses revealed pronounced gene- and tissue-specific diurnal regulation, with Adh1 oscillating primarily in adipose tissues; Cyp2e1 and mitochondrial Aldh2 cycling broadly across kidney, aorta, lung, adrenal gland, and liver; and cytosolic Aldh1b1 being uniformly arrhythmic. In the liver, Cyp2e1 and Aldh2 exhibited robust ~24 h oscillations that peaked during the light/resting phase, while Adh1 showed inconsistent rhythmicity and Aldh1b1 remained arrhythmic. Notably, Cyp2e1 and Aldh2 rhythms persisted in Bmal1 knockout and Clock mutant livers under light–dark conditions, despite complete loss of core clock gene oscillations, yet were abolished in constant darkness, revealing that systemic zeitgeber cues can mask the loss of intrinsic clock function to maintain apparent rhythmicity in these metabolic genes. Systematic cross-paradigm comparison established a novel gene-specific vulnerability hierarchy. Aldh2 was found to be most disrupted by environmental and metabolic perturbations, with acute sleep deprivation eliminating its rhythmicity and temporal expression pattern and a Western-style high-fat diet inducing pronounced phase delays and rhythm loss relative to low-fat diet controls. Both disruptions paralleled alterations in hepatocyte nuclear factor 4α (Hnf4a), newly implicating HNF4α as a potential mediator of ALDH2 circadian instability. In humans, ALDH2 and CYP2E1 exhibited conserved but phase-inverted circadian rhythms across multiple tissues relative to mice, and, importantly, night-shift workers showed markedly dampened and phase-shifted ALDH2 rhythms in peripheral blood mononuclear cells, providing the molecular link between occupational circadian misalignment and impaired acetaldehyde detoxification. Collectively, our detailed and innovative analytical approach reveals gene- and tissue-specific circadian regulation of alcohol-metabolizing enzymes, identifies ALDH2 as uniquely vulnerable to circadian misalignment, underscores the importance of circadian timing for optimal hepatic detoxification and resistance to tissue injury, and suggests that monitoring circadian rhythms could help tailor individualized advice on alcohol consumption for shift workers and populations with irregular sleep schedules, informing precision medicine approaches for alcohol-related disorders. Full article

Background: This study explored the interrelation between motor coordination abilities and cognitive functions in older adults, aiming to establish a preliminary diagnostic tool that may facilitate early detection of motor–cognitive decline. Methods: Utilizing a mixed-methods approach, we investigated the efficacy of the Stroop word test in conjunction with various motor coordination measurements to identify markers of cognitive aging in older adults. Results: The analysis revealed significant correlations between asymmetric spatial coordination (AC) and Stroop error effects (SEEs), indicating that better coordination correlates with reduced cognitive errors. Multiple-regression analysis showed that AC, simple reaction time (SRT), and anticipation time (AT) significantly predicted SEE (R² = 0.635), with AC emerging as the strongest predictor (β = −0.475). These results underscore the significance of asymmetric spatial motor coordination as a predictive factor for executive cognitive abilities affected by aging. We propose a potential tool for individuals to monitor their motor–cognitive health. Conclusions: The findings of this study contribute to the growing body of evidence linking physical coordination to cognitive function, emphasizing the importance of integrated diagnostic approaches in the management of aging-related cognitive impairments. Full article

Background: Host cell proteins (HCPs) are process-related impurities that must be monitored in biopharmaceutical products due to their potential impact on product quality and patient safety. Targeted LC–MS/MS approaches such as multiple reaction monitoring (MRM) enable protein-specific HCP quantification but are difficult to apply in highly multiplexed assays because of retention time (RT) variability across complex bioprocess matrices. Methods: Here, we show that conventional RT-scheduled MRM workflows lack transferability when applied to heterogeneous drug substances and process intermediates. Using a targeted assay comprising 240 peptides corresponding to 97 CHO-derived HCPs, RT shifts of several minutes resulted in truncated chromatographic peaks and peptide signal loss, even when wide scheduling windows were used. To overcome this limitation, a scout-triggered MRM (st-MRM) acquisition strategy based on event-driven monitoring was implemented. Results: This approach enabled robust peptide detection across diverse matrices within a single injection, without method re-optimization. Absolute quantification using stable isotope-labeled peptides spanned six orders of magnitude, with HCPs quantified down to 2.9 ppm in purified drug substances. Conclusion: Overall, st-MRM improves the robustness and transferability of highly multiplexed targeted proteomics workflows for HCP analysis. Full article

Lithium-ion batteries (LIBs) power devices from portable electronics to electric vehicles and grid storage, yet their reliable operation requires real-time monitoring of battery state, particularly at the anode where complex reactions and structural changes occur. Sensor technologies capable of capturing dynamic physical and chemical signals have therefore gained increasing attention for probing internal battery processes. This review summarizes recent operando and in situ monitoring strategies for carbon-based and silicon-based anodes, highlighting advances in electrical, optical, and acoustic sensing. These methods reveal degradation mechanisms and morphological evolution in real time. Multimodal sensing strategies that integrate multiple signals for improved battery state estimation are also discussed. Finally, future directions are outlined, focusing on real-time anode monitoring and the integration of sensing technologies with next-generation battery designs. This review aims to guide the development of smart battery sensing for artificial-intelligence-assisted and multimodal sensing, providing solutions for battery management system that enable accurate synchronous detection of mechanical, thermal, and electrical signals. Full article

Background: Multiple myeloma (MM) is a hematologic malignancy characterized by clonal plasma cell expansion and diverse genomic rearrangements, including immunoglobulin heavy chain (IGH) translocations. Although RNA sequencing enables the comprehensive detection of IGH-associated fusions, routine molecular monitoring remains limited, particularly in non-secretory MM (NSMM), which lacks measurable serologic markers. Methods: Here, we contracted an integrated system combining RNA sequencing (RNA-seq) and reverse transcription quantitative polymerase chain reaction (RT-qPCR) to identify and validate fusion gene-based molecular markers for minimal residual disease (MRD) monitoring. Results: The global fusion landscape was delineated by the sequencing analysis of bone marrow samples from 22 newly diagnosed patients with MM. A total of 362 fusion events were identified, of which 190 non-immunoglobulin fusions were selected for detailed characterization. Recurrent breakpoints were concentrated on chromosomes 1 and 19, and five recurrent fusions, DDX5::EEF1A1, OAZ1::KLF2, OAZ1::KLF16, PFKFB3::LINC02649, and PLXNB2::SCO2, were detected across nine patients. Functional enrichment analyses indicated the significant involvement of these genes in RNA splicing regulation, transcriptional misregulation in cancer-related pathways, and focal adhesion processes. Twenty-three fusion transcripts were validated using RT-PCR and Sanger sequencing, demonstrating high specificity for MM. Longitudinal monitoring revealed that the quantitative assessment of fusion transcript levels enabled earlier relapse detection than flow cytometry, including in NSMM, where conventional MRD tools are ineffective. Conclusions: These findings suggest that individualized fusion transcripts serve as robust molecular markers for MRD surveillance. The proposed RNA-seq–RT-qPCR pipeline offers a clinically practical strategy to enhance precision diagnosis and personalized treatment in MM. Full article

Imperfect first-trimester screening for hypertensive disorders of pregnancy (HDP) means many high-risk women miss the window for preventive aspirin, and the biological heterogeneity of HDPs is overlooked. This study aimed to leverage first-trimester serum proteomics to create a more precise tool for predicting preeclampsia (PE) and differentiating it from other HDPs. A prospective nested case–control study (n = 172) was conducted using targeted liquid chromatography-multiple reaction monitoring-mass spectrometry (LC-MRM-MS) proteomic profiling of 115 proteins. Machine learning (ML) methods were used to develop classifiers from the proteomic data. The signature predictive of PE was characterized by dysregulation of the complement and coagulation cascades (F10, C8A, C1QA, SERPING1, VTN). The profile differentiating gestational hypertension (GAH) from chronic hypertension (CAH) was linked to lipid metabolism (HRG, APOA4, APOC2). An 18-protein support vector machine (SVM) model for predicting PE demonstrated exceptional performance, with 94% sensitivity and 100% specificity, significantly outperforming the standard Fetal Medicine Foundation (FMF) screening algorithm. Pathway analysis confirmed that PE is associated with early activation of innate immunity and coagulation pathways, while GAH is linked to a pregnancy-induced metabolic response. A targeted serum proteomic combined with ML approach represents a new perspective diagnostic tool with strong potential to personalize monitoring for women at the highest risk for specific hypertensive pregnancy complications. Full article

Thrips are cosmopolitan agricultural pests and important vectors of plant viruses, and the increasing coexistence of multiple morphologically similar species has intensified the demand for species-specific molecular identification. However, traditional morphological identification and PCR assays using universal primers are often inadequate for mixed-species samples and field-adaptable application. In this study, we developed a species-specific molecular identification framework targeting a polymorphism-rich region of the mitochondrial cytochrome c oxidase subunit I (COI) gene, which is more time-efficient than sequencing-based COI DNA barcoding, for four economically important thrips species in southern China, including the globally invasive Frankliniella occidentalis. By aligning COI sequences, polymorphism-rich regions were identified and used to design four species-specific primer pairs, each containing a diagnostic 3′-terminal nucleotide. These primers were combined with a PBS-based DNA extraction workflow optimized for single-insect samples that minimizes dependence on column-based purification. The assay achieved a practical detection limit of 1 ng per reaction, demonstrated species-specific amplification, and maintained reproducible amplification at DNA inputs of ≥1 ng per reaction. Notably, PCR inhibition caused by crude extracts was effectively alleviated by fivefold dilution. Although the chemical identities of the inhibitors remain unknown, interspecific variation in inhibition strength was observed, with T. hawaiiensis exhibiting the strongest suppression, possibly due to differences in lysate composition. This integrated framework balances target specificity, operational simplicity, and dilution-mitigated inhibition, providing a field-adaptable tool for thrips species identification and invasive species monitoring. Moreover, it provides a species-specific molecular foundation for downstream integration with visual nucleic acid detection platforms, such as the CRISPR/Cas12a system, thereby facilitating the future development of portable molecular identification workflows for small agricultural pests. Full article

We present a methodology that enhances the analytical performance of organic electrochemical transistors (OECTs) by continuously cycling the devices through gate potential sweeps during sensing experiments. This continuous cycling methodology (CCM) enables real-time acquisition of full transfer curves, allowing simultaneous monitoring of multiple characteristic parameters. We show that the simultaneous temporal evolution of several OECT response parameters (threshold voltage (V_TH), maximum transconductance (g_max), and maximum transconductance potential (V_G,gmax)) provides highly sensitive descriptors for detecting pH changes and macromolecule adsorption on OECTs based on polyaniline (PANI) and poly(3,4-ethylenedioxythiophene) (PEDOT) channels. Moreover, the method allows reconstruction of I_DS–t (drain–source current vs. time) profiles at any selected gate potential, enabling the identification of optimal gate voltage (V_G) values for maximizing sensitivity. This represents a substantial improvement over traditional measurements at fixed V_G, which may suffer from reduced sensitivity and parasitic reactions associated with gate polarization. Moreover, the expanded set of parameters obtained with the CCM provides deeper insight into the physicochemical processes occurring at both gate and channel electrodes. We demonstrate its applicability in monitoring polyelectrolyte and enzyme adsorption, and detecting urea and glucose through enzyme-mediated reactions. Owing to its versatility and the richness of the information it provides, the CCM constitutes a significant advance for the development and optimization of OECT-based sensing platforms. Full article

Early recognition of a risk of Alzheimer’s disease (AD) remains a global challenge, and blood proteomic markers are of particular interest for wide-scale diagnostic use. Quantitative multiple reaction monitoring (MRM) approach demonstrates good reproducibility in the characteristic changes in the levels of reported candidate biomarkers (CBs) in different cohorts in AD. Following up on our previous study, we performed a joint analysis of 331 blood plasma samples from two different clinical cohorts of participants, comprising a total of 95 samples from patients with AD, 136 samples from patients with mild cognitive impairment (MCI), and 100 samples from controls. The obtained results confirm the significance of 37 CBs. A logistic regression-based algorithm was used to build protein classifiers, and a total of 21 important proteins were selected, 13 of which (ORM1, APOA4, LBP, HP, FN1, BCHE, APOE, PZP, A1BG, TF, SERPINA7, TTR, and F12) formed a universal panel that demonstrated strong classification performance in distinguishing AD patients from controls (ROC-AUC = 0.90) and in separating stable and progressing patients with MCI (ROC-AUC = 0.81). Overall, the analysis confirms the high potential of the MRM method for validating CBs in independent cohorts. Full article

In sensor-based monitoring systems, the rapid and accurate recognition of alarm semantic levels is essential for maintaining operational reliability. Traditional static visualizations often fail to communicate these distinctions effectively under time pressure, whereas dynamic stacked bar charts (DSBCs) integrate multiple semantic layers into a compact, dynamic display. This study systematically investigated how color cues applied to auxiliary visual elements (background, foreground, labels, and scale lines) and chart orientation (horizontal vs. vertical) affect users’ alarm recognition performance. Thirty-two participants completed a semantic alarm recognition task involving DSBCs with various combinations of color-coded elements and orientations. Reaction time (RT) and accuracy (ACC) were analyzed using mixed-effects regression models. The results revealed that color cues in foreground and labels significantly enhanced both RT and ACC, whereas background and scale line color cues produced negligible effects. Orientation exerted a significant main effect on RT but not on ACC. Participants responded faster to horizontally oriented charts, indicating improved scanning efficiency. Moreover, increasing the number of color cues yielded higher ACC and shorter RTs, supporting a redundancy gain effect. However, no interaction was found between color cues and orientation, suggesting that these factors influence performance through distinct cognitive pathways. The findings align with theories of attentional guidance, redundancy gain, and spatial compatibility, and offer practical recommendations for alarm visualization design. Consequently, designers are advised to prioritize color coding of perceptually dominant elements, employ horizontal layouts in time-critical contexts, and implement redundant but non-overwhelming cues to enhance alarm recognition in complex sensor-based monitoring environments. Full article