Detection Threshold Research Articles

In LIGO’s Sight? Vigorous Coherent Gravitational Waves from Cooled Collapsar Disks

We present the first numerical study of gravitational waves (GWs) from collapsar disks, using state-of-the-art 3D general relativistic magnetohydrodynamic simulations of collapsing stars. These simulations incorporate a fixed Kerr metric for the central black hole (BH) and employ simplified prescriptions for disk cooling. We find that cooled disks with an expected scale height ratio of H/R ≳ 0.1 at ∼10 gravitational radii induce Rossby instability in compact, high-density rings. The trapped Rossby vortices generate vigorous coherent emission regardless of disk magnetization and BH spin. For BH mass of ∼10 M ⊙, the GW spectrum peaks at ∼100 Hz, with some breadth due to various nonaxisymmetric modes. The spectrum shifts toward lower frequencies as the disk viscously spreads and the circularization radius of the infalling gas increases. Weaker-cooled disks with H/R ≳ 0.3 form a low-density extended structure of spiral arms, resulting in a broader, lower-amplitude spectrum. Assuming an optimistic detection threshold with a matched-filter signal-to-noise ratio of 20 and a rate similar to Type Ib/c supernovae, LIGO–Virgo–KAGRA (LVK) could detect ≲1 event annually, suggesting that GW events may already be hidden in observed data. Third-generation GW detectors could detect dozens to hundreds of collapsar disks annually, depending on the cooling strength and the disk formation rate. The GW amplitudes from collapsar disks are ≳100 times higher with a substantially greater event rate than those from core-collapse supernovae, making them potentially the most promising burst-type GW class for LVK and Cosmic Explorer. This highlights the importance of further exploration and modeling of disk-powered GWs, promising insights into collapsing star physics.

Transformer and Adaptive Threshold Sliding Window for Improving Violence Detection in Videos.

This paper presents a comprehensive approach to detect violent events in videos by combining CrimeNet, a Vision Transformer (ViT) model with structured neural learning and adversarial regularization, with an adaptive threshold sliding window model based on the Transformer architecture. CrimeNet demonstrates exceptional performance on all datasets (XD-Violence, UCF-Crime, NTU-CCTV Fights, UBI-Fights, Real Life Violence Situations, MediEval, RWF-2000, Hockey Fights, Violent Flows, Surveillance Camera Fights, and Movies Fight), achieving high AUC ROC and AUC PR values (up to 99% and 100%, respectively). However, the generalization of CrimeNet to cross-dataset experiments posed some problems, resulting in a 20-30% decrease in performance, for instance, training in UCF-Crime and testing in XD-Violence resulted in 70.20% in AUC ROC. The sliding window model with adaptive thresholding effectively solves these problems by automatically adjusting the violence detection threshold, resulting in a substantial improvement in detection accuracy. By applying the sliding window model as post-processing to CrimeNet results, we were able to improve detection accuracy by 10% to 15% in cross-dataset experiments. Future lines of research include improving generalization, addressing data imbalance, exploring multimodal representations, testing in real-world applications, and extending the approach to complex human interactions.

Abnormal lower limb posture recognition based on spatial gait feature dynamic threshold detection

Lower limb rehabilitation training often involves the use of assistive standing devices. However, elderly individuals frequently experience reduced exercise effectiveness or suffer muscle injuries when utilizing these devices. The ability to recognize abnormal lower limb postures can significantly enhance training efficiency and minimize the risk of injury. To address this, we propose a model based on dynamic threshold detection of spatial gait features to identify such abnormal postures. A human-assisted standing rehabilitation device platform was developed to build a lower limb gait depth dataset. RGB data is employed for keypoint detection, enabling the establishment of a 3D lower limb posture recognition model that extracts gait, time, spatial features, and keypoints. The predicted joint angles, stride length, and step frequency demonstrate errors of 4 %, 8 %, and 1.3 %, respectively, with an average confidence of 0.95 for 3D key points. We employed the WOA-BP neural network to develop a dynamic threshold algorithm based on gait features and propose a model for recognizing abnormal postures. Compared to other models, our model achieves a 96 % accuracy rate in recognizing abnormal postures, with a recall rate of 83 % and an F1 score of 90 %. ROC curve analysis and AUC values reveal that the WOA-BP algorithm performs farthest from the pure chance line, with the highest AUC value of 0.89, indicating its superior performance over other models. Experimental results demonstrate that this model possesses a strong capability in recognizing abnormal lower limb postures, encouraging patients to correct these postures, thereby reducing muscle injuries and improving exercise effectiveness.

Ovarian Cancer Risk Prediction - a Clinical Epidemiology Perspective.

Ovarian cancer is a rare and highly heterogeneous disease usually detected at late stages when outcomes are poor. Population-based screening approaches have not been successful at reducing ovarian cancer mortality, but preventive bilateral salpingo-oophorectomy is highly effective at preventing ovarian cancer in high-risk populations. Ovarian cancer risk prediction models may allow identification of populations at increased risk of ovarian cancer for preventive interventions or targeted early detection. We propose a life-course approach to ovarian cancer risk prediction based on the time at which a risk model should be applied and the risk factors that are available. The discriminative ability of ovarian cancer risk prediction models published so far is limited, with areas under the curve ranging from 0.58-0.65 for different combinations of risk factors and genetic susceptibility markers. Currently proposed absolute risk thresholds for preventive surgery are around 4% lifetime risk. The absolute risk predicted by ovarian cancer risk models ranges from 0.6-2.5% lifetime risk in the general population, highlighting the need to improve ovarian cancer risk prediction models and evaluating new preventive approaches that can be offered to individuals at lower risk.

Light vector bosons and the weak mixing angle in the light of future germanium-based reactor CEνNS experiments

In this work, the sensitivity of future germanium-based reactor neutrino experiments to the weak mixing angle sin2θW, and to the presence of new light vector bosons is investigated. By taking into account key experimental features with their uncertainties and the application of a data-driven and state-of-the-art reactor antineutrino spectrum, the impact of detection threshold and experimental exposure is assessed in detail for an experiment relying on germanium semiconductor detectors. With the established analysis framework, the precision on the Weinberg angle, and capability of probing the parameter space of a universally coupled mediator model, as well as a U(1)B−L-symmetric model are quantified. Our investigation finds the next-generation of germanium-based reactor neutrino experiments in good shape to determine the Weinberg angle sin2θW with < 10% precision using the low-energetic neutrino channel of CEνNS. In addition, the current limits on new light vector bosons determined by reactor experiments can be lowered by about an order of magnitude via the combination of both CEνNS and EνeS. Consequently, our findings provide strong phenomenological support for future experimental endeavours close to a reactor site.

Ischemic core detection threshold of computed tomography perfusion (CTP) in acute stroke.

This study aimed to determine the accuracy of detecting ischemic core volume using computed tomography perfusion (CTP) in patients with suspected acute ischemic stroke compared to diffusion-weighted magnetic resonance imaging (DW-MRI) as the reference standard. This retrospective monocentric study included patients who underwent CTP and DW-MRI for suspected acute ischemic stroke. The ischemic core size was measured at DW-MRI. The detectability threshold volume was defined as the lowest volume detected by each method. Clinical data on revascularization therapy, along with the clinical decision that influenced the choice, were collected. Volumes of the ischemic cores were compared using the Mann-Whitney U test. Of 83 patients who underwent CTP, 52 patients (median age 73years, IQR 63-80, 36 men) also had DW-MRI and were included, with a total of 70 ischemic cores. Regarding ischemic cores, only 18/70 (26%) were detected by both CTP and DW-MRI, while 52/70 (74%) were detected only by DW-MRI. The median volume of the 52 ischemic cores undetected on CTP (0.6mL, IQR 0.2-1.3mL) was significantly lower (p < 0.001) than that of the 18 ischemic cores detected on CTP (14.2mL, IQR 7.0-18.4mL). The smallest ischemic core detected on CTP had a volume of 5.0mL. Among the 20 patients with undetected ischemic core on CTP, only 10% (2/20) received thrombolysis treatment. CTP maps failed in detecting ischemic cores smaller than 5mL. DW-MRI remains essential for suspected small ischemic brain lesions to guide a correct treatment decision-making.

MOF-derived Mo-doped Co3O4: A hierarchical yeast-like structure for superior carbon monoxide sensing

Carbon monoxide (CO) is one of the most dangerous gases owing to its dual threat, role in global warming, and severe impact on human health. Metal-organic framework (MOF) gas-sensing materials have drawn the interest of many different sectors due to their unique structural characteristics. This study used a solvothermal technique and subsequent annealing to create a hierarchical, yeast-shaped Mo-Co3O4 material from a Co-MOF precursor. The prepared materials have been employed in the development of a CO-detection gas sensor. Gas-sensing experiments revealed that Mo-Co3O4 exhibited significantly improved sensing capabilities compared to pure Co3O4. Notably, at 200 °C, 2 mol% Mo-Co3O4 showed high response levels of about 136 at 100 ppm CO concentrations, approximately 50.4 times higher than pure Co3O4. Furthermore, the Mo-doped material exhibited a low detection threshold, excellent reproducibility, long-term stability, good selectivity, and rapid response and recovery times (78.5/55.3 s). Introducing Mo dopants into the Co3O4 material and the hierarchical yeast-like nanostructure are responsible for these improvements in gas-sensing capability.

Multi-scenario failure diagnosis for lithium-ion battery based on coupling PSO-SA-DBSCAN algorithm

The battery failure diagnosis methods are the main algorithms for online battery management system, but the conventional methods are mainly developed from calibrated thresholds detection, which will suffer from the degradation for full-lifespan operation and have no consideration of utilization for battery system information. Here we show innovative diagnosis methods for detecting battery failure both from online battery management system and cloud monitoring platform based on a particle swarm optimization-simulated annealing- density based spatial clustering of applications with noise (PSO-SA-DBSCAN) algorithm. By using the battery electrochemical impedance spectroscopy and voltage, the proposed method can solve the problem of battery abnormal degradation diagnosis, thermal runaway diagnosis and sampling failure diagnosis. Compared against conventional methods, the proposed method has less reliance on parameterization with better precision, which is validated from commercial prismatic cells and cloud data from a 117 serial-cell pack, with high accuracy and lower misinformation compared against conventional detecting methods, confirming the actual effectiveness for online battery pack.

Wildfire Threshold Detection and Progression Monitoring Using an Improved Radar Vegetation Index in California

To address the recent increase in wildfire severity and incidence, as well as the subsequent financial and physical costs, forest managers and wildland firefighting agencies rely on remotely sensed products for better decision-making and mitigation efforts. To address the remote sensing needs of these agencies, which include high spatial resolution, immunity to atmospheric and solar illumination effects, and day/night capabilities, the use of synthetic aperture radar (SAR) is under investigation for application in current and upcoming systems for all phases of a wildfire. Focusing on the active phase, a method for monitoring wildfire activity is presented based on changes in the radar vegetation index (RVI). L-band backscatter measurements from NASA/JPL’s UAVSAR instrument are used to obtain RVI images on multiple dates during the 2020 Bobcat (located in Southern CA, USA) and Hennessey (located in Northern CA, USA) fires and the 2021 Caldor (located in the Sierra Nevada region of CA, USA) fire. Changes in the RVI between measurement dates of a single fire are then compared to indicators of fire activity such as ancillary GIS-based burn extent perimeters and the Landsat 8-based difference normalized burn ratio (dNBR). An RVI-based wildfire “burn” detector/index is then developed by thresholding the RVI change. A combination of the receiver operating characteristic (ROC) curves and F1 scores for this detector are used to derive change detection thresholds at varying spatial resolutions. Six repeat-track UAVSAR lines over the 2020 fires are used to determine appropriate threshold values, and the performance is subsequently investigated for the 2021 Caldor fire. The results show good performance for the Bobcat and Hennessey fires at 100 m resolution, with optimum probability of detections of 67.89% and 71.98%, F1 scores of 0.6865 and 0.7309, and Matthews correlation coefficients of 0.5863 and 0.6207, respectively, with an overall increase in performance for all metrics as spatial resolution becomes coarser. The results for pixels identified as “burned” compare well with other fire indicators such as soil burn severity, known progression maps, and post-fire agency publications. Good performance is also observed for the Caldor fire where the percentage of pixels identified as burned within the known fire perimeters ranges from 37.87% at ~5 m resolution to 88.02% at 500 m resolution, with a general increase in performance as spatial resolution increases. All detections for Caldor show dense collections of burned pixels within the known perimeters, while pixels identified as burned that lie outside of the know perimeters have a sparse spatial distribution similar to noise that decreases as spatial resolution is degraded. The Caldor results also align well with other fire indicators such as soil burn severity and vegetation disturbance.

Y-switch: a spring-loaded synthetic gene switch for robust DNA/RNA signal amplification and detection.

Nucleic acid tests (NATs) are essential for biomedical diagnostics. Traditional NATs, often complex and expensive, have prompted the exploration of toehold-mediated strand displacement (TMSD) circuits as an economical alternative. However, the wide application of TMSD-based reactions is limited by 'leakage'-the spurious activation of the reaction leading to high background signals and false positives. Here, we introduce the Y-Switch, a new TMSD cascade design that recognizes a custom nucleic acid input and generates an amplified output. The Y-Switch is based on a pair of thermodynamically spring-loaded DNA modules. The binding of a predefined nucleic acid target triggers an intermolecular reaction that activates a T7 promoter, leading to the perpetual transcription of a fluorescent aptamer that can be detected by a smartphone camera. The system is designed to permit the selective depletion of leakage byproducts to achieve high sensitivity and zero-background signal in the absence of the correct trigger. Using Zika virus (ZIKV)- and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)-derived nucleic acid sequences, we show that the assay generates a reliable target-specific readout. Y-Switches detect native RNA under isothermal conditions without reverse transcription or pre-amplification, with a detection threshold as low as ∼200 attomole. The modularity of the assay allows easy re-programming for the detection of other targets by exchanging a single sequence domain. This work provides a low-complexity and high-fidelity synthetic biology tool for point-of-care diagnostics and for the construction of more complex biomolecular computations.

Visualized detection of tobacco anthracnose by RPA-LFD.

Anthracnose caused by Colletotrichum spp. is a widespread fungal disease that is detrimental to tobacco growth and inflicts economic damage up to 100 million in tobacco-growing regions in China. An early diagnostic tool is vital for the accurate determination and management of anthracnose in the field. This study investigated the diversity of Colletotrichum spp. on tobacco leaves with anthracnose and developed a recombinase polymerase amplification-lateral flow dipstick (RPA-LFD) diagnostic method for the rapid and equipment-independent detection of the main Colletotrichum spp. causing tobacco anthracnose. This assay targeted the chitin synthase gene (chs1) and could be performed in a few minutes (6-10 min). All isolates of C. kastii, C. fructicola and C. gloeosporioides yielded positive results using the RPA-LFD assay, and no cross-reaction occurred with other fungal species from tobacco or other hosts. The detection threshold was 1 pg of genomic DNA under optimal reaction conditions. The entire RPA-LFD assay enabled the detection of pathogen visualization within 30 min without specialized equipment by combining a polyethylene glycol-KOH method for extracting DNA rapidly from tobacco leaves infected with C. kastii, C. fructicola and C. gloeosporioides. Based on these results, the RPA-LFD assay is easy to operate, rapid and equipment independent and is promising for development as a kit to diagnose tobacco anthracnose in resource-limited settings at point-of-care.

Regions of Homozygocity size patterns among diverse ethnic groups in Israel: Toward tailored diagnostic reporting thresholds.

Long contiguous stretches of homozygosity or regions of homozygosity (ROH) are frequently detected via microarray and sequencing technologies. However, consensus on the establishment of specific size cutoffs for reporting ROH remains elusive. This study aims to assess the Total ROH Percentages (TRPS) and size of ROH segments across different ethnic origins, exploring potential disparities and proposing tailored diagnostic thresholds. This retrospective study included 13,035 microarray analyses conducted between 2017 to 2023. ROH segments on autosomal chromosomes were retrieved, and samples lacking ROH segments were excluded. The cohort was categorized based on reported ethnic origins, and TRPS and ROH segment size were analyzed for each origin. Distinct TRPS values were noted among different ethnic groups, ranging from median 0.36% in Ethiopian Jewish cohort and up to 6.42% in the Bedouin population. Wide range of 99th percentiles of ROH segment size for various origins was noted, ranging from 10.6 to 51.5 Mb. A significant correlation between ROH segment sizes and TRPS was noted in each origin. Statistically significant differences in ROH segment sizes were noted between the Jewish and the Israeli Arab/Druze origins in TRPS from 1% to 9.99%, whereas extremities of low (0.11%-0.99%) and high (over 10%) TRPS yielded no significant differences. In conclusion, as fixed absolute size thresholds may overlook pathogenic segments in certain populations while generating excessive reports in others, tailored approaches to define ROH reporting thresholds can be considered to facilitate the accuracy and clinical relevance of genomic analyses.

Quantum-Dot-Encoded Beads-Enhanced CRISPR/Cas-Based Lateral-Flow Assay for the Amplification-Free, Sensitive, and Rapid Detection of Nucleic Acids in Breast Cancer.

Nucleic acid detection plays a pivotal role in the accurate diagnosis of diseases. The CRISPR/Cas detection system, noted for its significant utility in a variety of applications, often necessitates enhanced sensitivity or specific signal amplification strategies, particularly for detecting low-abundance biomarkers. In this study, we present a quantum-dot-encoded beads (QDB)-energized CRISPR/Cas12-based lateral-flow assay (QDB-CRISPR-LFA). This method enables amplification-free, sensitive, and rapid detection (<40 min) of BRCA-1. We validated our method using contrived reference samples and nucleic acids extracted from tumor cells. The QDB-CRISPR-LFA provides a visual, more rapid alternative to the traditional BRCA-1 real-time RT-PCR assay. Significantly, through the integration of CRISPR's specificity and the high signal output of QDB, the detection threshold for BRCA-1 has been reduced to the femtomolar level, representing an enhancement of 2-4 orders of magnitude over existing CRISPR/Cas detection methods. This advancement underscores the potential of our approach in advancing nucleic acid detection techniques, which is crucial for the early and precise diagnosis of diseases.

Stability of generalized models for HIV-1 dynamics with impaired CTL immunity and three pathways of infection

Highly active antiretroviral therapy (HAART) stands out as the most effective treatment for human immunodeficiency virus type-1 (HIV-1). While total eradication is still difficult, HAART can dramatically lower the virus's plasma viral level below the detection threshold. The activation of latently infected cells is considered to be the main obstacle to the total eradication of HIV-1 infection. This study investigates the dynamic characteristics of two generalized HIV-1 infection models taking into account the impairment of cytotoxic T lymphocytes (CTLs). These models include CD4+T cells that are latently infected and equipped with the capability to engage in cell-to-cell infection and elude immune responses. We introduce models featuring three infection pathways: virus-to-cell (VTC), latent cell-to-cell (L-CTC), and active cell-to-cell (A-CTC). The three pathways' infection rates are characterized by general functions, which cover the many types of infection rates documented in the literature. The second model integrates three distinct types of distributed-time delays. We demonstrate the validity of the suggested models, through their well-posedness. We determine the basic reproduction ratio (ℜ0) of the systems. Lyapunov functions and LaSalle's invariance principle are employed to verify that the global stability of both the virus-free steady state (O0) and the virus-persistence steady state (O1). More precisely, O0 achieves global asymptotic stability when ℜ0 ≤ 1, whereas O1 attains global asymptotic stability when ℜ0 &amp;gt; 1. To demonstrate the impact of the parameter values on ℜ0, we examine the sensitivity analysis. It is illustrated that ℜ0 comprises three components, namely ℜ0E, ℜ0P, and ℜ0K, corresponding to the transmissions of VTC, L-CTC, and A-CTC, respectively. Thus, if the L-CTC pathway is disregarded in the HIV-1 infection model, ℜ0 may be underestimated, which could lead to inadequate or erroneous medication therapy focused on eradicating HIV-1 within the body. To demonstrate the associated mathematical outcomes, we conduct numerical simulations through an illustrative example. Specifically, we delve into how the dynamics of HIV-1 are influenced by both immune impairment and time delay. Our findings suggest a significant role of reduced immunity in the progression of the infection. Furthermore, time delays possess the potential to markedly reduce ℜ0, thereby impeding the replication of HIV-1.

Discover the secrets of salty taste in Manila clam (Ruditapes philippinarum): MALDI-TOF MS and flavor fingerprint

In this work, the ultrasound-assisted enzymatic (UAE) conditions for salty compounds from Manila clam were optimized using a combination of fractional factorial design (FFD) and Box-Behnken design (BBD). The optimum protein extraction rate (40.13 ± 0.37), DH (25.92 ± 0.78), and salty score (4.01 ± 0.02) of salty extract were achieved with a solid-liquid ratio of 1:4 (w/w), hydrolysis temperature of 49 °C, and ultrasound time of 10 min. Sensory results showed that the UAE hydrolysates exhibited pronounced saltiness (6.25 ± 0.03) and umami (6.62 ± 0.03), with an impressively low detection threshold for salty taste at 69 mmol/L. The electronic tongue analysis revealed that the UAE hydrolysate had a distinct saltiness (3.73 ± 0.05), umami (8.96 ± 0.02) and richness (0.83 ± 0.00). The molecular weights of the peptides in the UAE solution were 84.497 Da, 211.710 Da, 380.530 Da, 568.644 Da and 830.696 Da, respectively. It was rich in alcohols, ketones and furans, and total of 79 volatile compounds (VOCs) were identified. After ultrasonic pretreatment, the content of organic acids was significantly increased, the content of succinic acid was 8.5 ± 0.24, and the content of malic acid was 4.68 ± 0.07. In conclusion, ultrasound-assisted enzymatic is an effective treatment for the extraction of salty compounds.

Novel electrochemical sensing platform based on zinc metal organic frameworks/carbon nanofiber nanocomposite for detection of creatinine in human urine

A biomarker known as creatinine is a very important determinant of kidney failure. Creatinine concentrations in blood and urine can identify renal insufficiencies and muscle diseases. Monitoring the creatinine level and quick sensing requires a rapid, robust and simple method for determining the creatinine in human body. To overcome the universal challenge postured by this waste product of muscle, this study presents a novel strategy to fabricate an electrochemical sensor based on zinc MOFs, synthesized using terephthalic di- hydrazide (TPDD) as the organic ligand using a one-pot solvothermal method in the presence of carbon nanofiber matrix for enzyme less sensing the creatinine. The Zn-TPDD/CNF nanocomposite was well characterized by XRD, TGA, BET, XPS, FESEM, CV, DPV, and Raman spectroscopic techniques. Notably, this MOF anchored electrode owns important fundamental features such as large electro active exterior area, plentiful active sites, an immense operational linear range (5 to 250 μM), a nominal detection limit (10.04 nM), and an outstanding sensitivity towards creatinine. This Zn-TPDD/CNF/GCE electrode not only showcased more extraordinary electrochemical attributes, but its anti-interference stability, flexibility, reproducibility, storage stability, selectivity, and low detection threshold emphasize its ability for real-world applications with reasonable recoveries. Therefore, this modified electrode could be a potential tool for operative creatinine detection in urine.

First results on the final readout chip for the High-Luminosity LHC upgrade of the CMS Inner Tracker

The CMS Readout Chip (CROC) is a 65nm CMOS hybrid pixel readout chip for the High-Luminosity LHC upgrade of the CMS Inner Tracker. The chip has been developed by the CERN RD53 Collaboration in order to withstand radiation doses up to 10MGy and hit rates up to 3GHzcm−2 on the innermost tracking layer. Moreover, it must handle an increased sensor granularity (pixel size of 2500µm2) with respect to current detectors and operate at detection thresholds as low as 1000e. Twenty wafers of the prototype chip (CROCv1) have been produced in 2021. The CROCv1 characterisation and verification campaign has, overall, demonstrated the radiation resistance of the chip and its performance. The production version of the chip (CROCv2) was submitted in October 2023 and the engineering run wafers have been received in January 2024. In this contribution, the first results from CROCv2 wafer-level testing of more than 1900 chips from the engineering run will be outlined.

Investigating Stochastic Resonance in Tactile Discrimination

Stochastic resonance (SR) is a phenomenon where addition of noise to a system enhances the system’s signal-to-noise ratio. SR can improve the ability to detect weak signals. Lugo et al. showed that tactile detection threshold decreased when participants received auditory white noise. We aimed to replicate the results of Lugo et al., except that we measured tactile discrimination. We examined whether discrimination between durations of vibrotactile stimuli would be better when auditory noise was presented. Participants performed a 2AFC task for three noise conditions: no noise, 45 dB, 55 dB. A total of 17 out of 24 participants demonstrated increased tactile sensitivity when white noise was present compared to when it was absent, which is not statistically significant. The optimal noise level varied among individuals. Eight participants performed best at 45 dB and 4 at 55 dB. Hence, determining the ideal noise level for each person is crucial for enhancing tactile sensitivity.

Quinoline yellow acts as a novel amyloid fibrillation probe by using surface-enhanced Raman spectroscopy

Protein amyloid fibrillation is linked to a wide range of neurodegenerative diseases. Protein oligomer is an intermediate substance in the process of fibrillation, which is neurotoxic and formed by the aggregation of protein molecules under physiological stress. Early detection of protein oligomers could make timely intervention of protein fibrillation related diseases. Therefore, it is crucial to develop efficient inhibitors and probes for monitoring amyloid fibril formation. In this study, we developed a novel amyloid inhibitor quinoline yellow (QY), which was proved to be effective in inhibiting insulin protein fibrillation as demonstrated by fluorescence, morphology characterization and circular dichroism. When QY binds to insulin, it exerts inhibitory effects on the nucleation process and effectively impedes the formation of fibrillar fibrils. In addition, we present the application of surface-enhanced Raman spectroscopy (SERS) as an extremely sensitive technique for identifying amyloid oligomers. The investigation employed the probe QY, which demonstrated a linear reaction for identifying oligomers in the concentration range of 1.0–58.0 μM. Impressively, it showcased an exceptionally sensitive detection threshold of 0.2 μM. And also illustrating the binding sites and interaction mechanisms between small molecules of QY and insulin by SERS. The aforementioned methodology was also employed for the identification of insulin oligomers in human serum samples. Thereby, the proposed approach presenting a promising avenue with extensive implications in the realms of pharmaceutical exploration and disease diagnosis.

Assessment of small nerve fiber function as an early marker of peripheral neuropathy in children and adolescents with type 1 diabetes mellitus (T1DM).

This study aimed to assess subclinical peripheral diabetic neuropathy (PDN) in adolescents with type 1 diabetes mellitus (T1DM). Subjects included 53 T1DM patients (age (mean ± SE): 15.8 ± 0.54 years, disease duration: 6.0 ± 0.51 years and HbA1c: 7.9 ± 0.19%), and 37 healthy gender matched controls (age: 15.6 ± 0.52 years). PDN was assessed by vibration perception threshold (VPT) and by quantitative sensory testing (QST). In controls, 95% confidence intervals were calculated. Among patients, VPT prevalence of abnormality ranged from 60-73.4% on different sites. Higher VPT was found in patients on all examined sites (p < 0.01). In controls, VPT correlated with height (r = 0.48, p = 0.05). Regarding QST prevalence of abnormality, cold detection threshold (CDT) ranged 7.3-39.0%, cold pain threshold (CPT) ranged 22.22-29.63%, hot detection threshold (HDT) ranged 34.14-63.41%, and hot pain threshold (HPT) ranged 15.79-36.84%. In patients, CPT correlated with BMI (r = 0.42, p = 0.05) and diabetes duration, (r = 0.40, p = 0.05), HPT correlated with age (r = 0.36, p = 0.05) and height (r = 0.35, p = 0.05), while in controls with BMI (r = 0.51, p = 0.05). No correlation of VPT or QST with HbA1c was observed. Adolescents with T1DM in this study, although asymptomatic, showed a high prevalence of impaired indices of PDN, highlighting potential clinical implications of early identification of PDN.