Distinct Information Research Articles

A comparative analysis of axial and appendicular skeletal maturity staging systems through assessment of longitudinal growth and curve modulation after VBT surgery.

Appendicular skeleton markers are commonly used for maturity assessment for Adolescent Idiopathic Scoliosis (AIS) patients. Traditionally, Risser has been a standard skeletal maturity assessment method. More recently, Sanders classification (SSMS), as a more comprehensive system, became popular, especially in decision-making for Vertebral Body Tethering (VBT). Thumb-Ossification Composite Index (TOCI), using ossification of thumb epiphyses, has been claimed to more accurately stage patients around their peak height velocity. However, growth peaks may occur separately at lower limbs and trunk. Hence, Cervical Vertebral Maturity (CVM), using cervical spine morphology, possesses a potential to better estimate spinal growth as it uses axial skeleton markers instead of appendicular skeleton markers. The aim of the study was to compare various axial and appendicular skeletal maturity assessment methods for longitudinal growth and curve modulation after VBT. A retrospective analysis of prospectively collected data was conducted. Skeletal maturity was determined using Risser, SSMS, TOCI and CVM for each patient. Crosstabulations of axial vs. appendicular markers were formed to analyze their concordance and discordance. Logistic and logarithmic regression models were run to assess longitudinal growth (postoperative height gain and leg-length growth) and curve modulation (follow-up instrumented Cobb correction after index operation), respectively. Models were compared using Akaike information criterion (AIC). 34 patients (32F/2M, mean age: 12.8 ± 1.5 years, mean follow-up: 47.7 (24-80) months) were included. The median preoperative maturity stages were: Risser: 1 (-1-4), SSMS: 4 (1-7), TOCI: 6 (1-8) and CVM: 4 (1-6). At latest follow-up, all patients reached skeletal maturity. Concordance and discordance were observed between axial vs. appendicular systems that demonstrated a range of possible distributions of CVM, where trunk peak height velocity occurred before, simultaneously with or after the standing height peak height velocity. R-squared values for Risser, SSMS, TOCI and CVM were 0.701, 0.783, 0.810 and 0.811, respectively, for prediction of final height; 0.759, 0.821, 0.831 and 0.775 for final leg-length, and 0.507, 0.588, 0.668 and 0.673 for curve modulation. Delta AIC values demonstrated that different skeletal maturity assessment methods provided distinctive information regarding follow-up height gain, leg-length growth and curve behavior. Risser score provided considerably less information for all three outcome variables. TOCI and SSMS provided substantial information regarding remaining leg-length assessments, while in terms of assessment of total height gain and curve modulation after surgery, CVM and TOCI offered substantial information and SSMS offered strong information. Mutual use of axial and appendicular markers may provide valuable insight concerning timing of surgery and magnitude of surgical correction.

Chemical Landscape of Adipocytes Derived from 3T3-L1 Cells Investigated by Fourier Transform Infrared and Raman Spectroscopies.

Adipocytes derived from 3T3-L1 cells are a gold standard for analyses of adipogenesis processes and the metabolism of fat cells. A widely used histological and immunohistochemical staining and mass spectrometry lipidomics are mainly aimed for examining lipid droplets (LDs). Visualizing other cellular compartments contributing to the cellular machinery requires additional cell culturing for multiple labeling. Here, we present the localization of the intracellular structure of the 3T3-L1-derived adipocytes utilizing vibrational spectromicroscopy, which simultaneously illustrates the cellular compartments and provides chemical composition without extensive sample preparation and in the naïve state. Both vibrational spectra (FTIR-Fourier transform infrared and RS-Raman scattering spectroscopy) extended the gathered chemical information. We proved that both IR and RS spectra provide distinct chemical information about lipid content and their structure. Despite the expected presence of triacylglycerols and cholesteryl esters in lipid droplets, we also estimated the length and unsaturation degree of the fatty acid acyl chains that were congruent with known MS lipidomics of these cells. In addition, the clustering of spectral images revealed that the direct surroundings around LDs attributed to lipid-associated proteins and a high abundance of mitochondria. Finally, by using quantified markers of biomolecules, we showed that the fixative agents, paraformaldehyde and glutaraldehyde, affected the cellular compartment differently. We concluded that PFA preserves LDs better, while GA fixation is better for cytochromes and unsaturated lipid analysis. The proposed analysis of the spectral images constitutes a complementary tool for investigations into the structural and molecular features of fat cells.

Abstract A008: Adaptive immune receptor repertoire characterization highlights the importance of interplay between B and T cells in disease progression

Abstract Understanding the adaptive immune system is integral to disease progression studies, development of diagnostic biomarkers, identification of therapeutic targets, and discerning varied treatment responses. Despite growing interest in profiling B and T cells in cancer research, the interaction between these cells and also the crucial roles of γδ T cells often go overlooked. In this research project, we explored the role of crucial yet frequently ignored γδ T cells in cancer research, using immune repertoire data from bladder cancer patients. We also studied the synergy between B and T cells in the immune repertoires of COVID-19 patients.Roche has developed immunoPETE (immune repertoire Primer Extension Target Enrichment), a DNA- based target enrichment assay to capture adaptive immune repertoire data*. immunoPETE efficiently identifies the heavy chains of B cells, alongside αβ and γδ T cells in one reaction, facilitating normalized clone counts across the adaptive immune system. To explore the significance of γδ T cells, we profiled the immune repertoires of 24 Non-Muscle Invasive Bladder Cancer patients treated with Bacillus Calmette-Guerin (BCG). Additionally, we evaluated the interplay between B and T cells in 117 peripheral blood immune repertoire from healthy and COVID-19 afflicted individuals, ranging from mild to severe cases.Immune repertoire data analysis indicates that underrepresentation of T cell receptor delta locus (TRD) is associated with higher risk of bladder cancer recurrence or progress. Urine pellet DNA was available for a subset of the patients in this study. Comparing immune repertoires from PBMC and urine revealed that urine repertoires represent the tumor repertoire more accurately than PBMC. Analysis of the immune repertoires from the COVID-19 patients linked disease severity with immune repertoire measures across all three cell types, highlighting the importance of multiple lymphocyte classes in disease progression. Using immunoPETE technology, which is capable of capturing the heavy chain of multiple immune cell types simultaneously in a single reaction, we have demonstrated that both B cell and T cell compartments provide distinct and valuable information in cancer and infectious diseases, each responding uniquely to the disease. Additionally, we emphasized the significance of γδ T cells in predicting cancer prognosis. Note: immunoPETE is for Research Use Only. Not for use in diagnostic procedures. Citation Format: Hossein Asgharian, Hamid Mirebrahim, Dilduz Telman, Hahn Zhao, Ulrich Schlecht, Sylvie McNamara, Rajiv Dua, Jan Berka, Patrick Bogard, Dinesh Kumar. Adaptive immune receptor repertoire characterization highlights the importance of interplay between B and T cells in disease progression [abstract]. In: Proceedings of the AACR Special Conference: Liquid Biopsy: From Discovery to Clinical Implementation; 2024 Nov 13-16; San Diego, CA. Philadelphia (PA): AACR; Clin Cancer Res 2024;30(21_Suppl):Abstract nr A008.

Granzyme mRNA-miRNA interaction and its implication to functional impact.

Granzyme activity can affect the processing and stability of miRNAs within target cells. They also could induce changes in miRNA expression that impact apoptotic signaling. Granzyme-induced apoptosis might result in changes to the miRNA profile, which can further influence the apoptosis and inflammation processes. The aim of this study was to bioinformatically analyze which miRNAs and transcription factors bind to the CDS and UTR regions of the granzyme family to regulate gene expression in relation to granzyme evolution and their association with human cancer diseases. The expression patterns of granzyme genes were analyzed in various human tissues. MiRNAs binding to the CDS and UTR of the granzyme family were examined, and the transcription factors binding to these miRNAs binding sites were also analyzed. Cytoscape program was used to visualize and analyze the networks of interactions between granzyme mRNA and miRNAs. Additionally, the evolutionary patterns of the granzyme family in relation to miRNAs and transcription factors binding were investigated. Analysis of the expression patterns of the granzyme family in various human tissues shows that GZMA and GZMK are strongly expressed in lymph nodes. GZMB exhibits strong expression in the bone marrow, while GZMA is prominently expressed in the spleen. Twenty-two miRNAs bind to both GZMK and GZMB mRNA, while six miRNAs bind to both GZMK and GZMM mRNA. The only miRNA that binds to GZMK, GZMB, GZMM, and GZMA mRNA is hsa-miR-146a-5p. Transcription factors JUND, FOS, and JUN are distinctly interconnected with has-miR-5696 and GZMK. Association data between the granzyme family and cancers showed that various miRNAs were consistently implicated and exhibited either upregulation or downregulation. Although the granzyme family possesses distinct genetic information, it shows relatively high expression levels in the lymph node, spleen, and bone marrow. Many miRNAs specifically regulate granzyme gene expression, and various transcription factors are involved. Analyzing the granzyme genes-miRNAs-transcription factors-related network will provide crucial insights into the mechanisms of cancer development and suppression.

MonoFG: Monocular 3D Object Detection with Knowledge Distillation for Human-Centric Autonomous Driving Systems

Monocular 3D object detection is essential for identifying objects in road images, thus offering valuable environmental perception data that are crucial for human-centric autonomous driving systems. However, due to the inherent limitations of camera imaging, obtaining precise depth information from images alone is challenging, which hampers the accuracy of within-scene object localization. In this paper, we introduce a monocular 3D object detection method called MonoFG that uses knowledge distillation with separated foreground and background components to improve the accuracy of object localization. First, detached foreground and background distillation processes can strategically leverage the distinct positional information acquired from each location to optimize the produced global distillation effects. This step serves as the foundation for the subsequent feature and response distillation process, which focuses on the distilled foreground and background rather than isolated object distillation. Second, triple attention mechanism-based feature distillation intensifies the feature imitation and feature representation capabilities of the student network. Spatial and channel attention mechanisms encourage the student network to capture crucial pixels and channels from the teacher network, whereas a self-attention mechanism globally transfers the learned relationships between pixels. Third, localization error-based response distillation facilitates a clearer transfer of positional information from the teacher network to the student network. Only when the positioning ability of the teacher network exceeds that of the student network can knowledge be comprehensively distilled across both the foreground and background. Therefore, the distillation process is constrained to specific content, which is delineated by positioning errors that serve as the boundaries. Finally, experiments conducted on the KITTI benchmark dataset demonstrate that our method outperforms many well-known baseline methods in several representative evaluation tasks (e.g., 3D object detection and bird's-eye view (BEV) detection) involving human-centric autonomous driving systems.

A distributed brain response predicting the facial expression of acute nociceptive pain.

Pain is a private experience observable through various verbal and non-verbal behavioural manifestations, each of which may relate to different pain-related functions. Despite the importance of understanding the cerebral mechanisms underlying those manifestations, there is currently limited knowledge of the neural correlates of the facial expression of pain. In this functional magnetic resonance imaging (fMRI) study, noxious heat stimulation was applied in healthy volunteers and we tested if previously published brain signatures of pain were sensitive to pain expression. We then applied a multivariate pattern analysis to the fMRI data to predict the facial expression of pain. Results revealed the inability of previously developed pain neurosignatures to predict the facial expression of pain. We thus propose a facial expression of pain signature (FEPS) conveying distinctive information about the brain response to nociceptive stimulations with minimal or no overlap with other pain-relevant brain signatures associated with nociception, pain ratings, thermal pain aversiveness, or pain valuation. The FEPS may provide a distinctive functional characterization of the distributed cerebral response to nociceptive pain associated with the socio-communicative role of non-verbal pain expression. This underscores the complexity of pain phenomenology by reinforcing the view that neurosignatures conceived as biomarkers must be interpreted in relation to the specific pain manifestation(s) predicted and their underlying function(s). Future studies should explore other pain-relevant manifestations and assess the specificity of the FEPS against simulated pain expressions and other types of aversive or emotional states.

A distributed brain response predicting the facial expression of acute nociceptive pain

Pain is a private experience observable through various verbal and non-verbal behavioural manifestations, each of which may relate to different pain-related functions. Despite the importance of understanding the cerebral mechanisms underlying those manifestations, there is currently limited knowledge of the neural correlates of the facial expression of pain. In this functional magnetic resonance imaging (fMRI) study, noxious heat stimulation was applied in healthy volunteers and we tested if previously published brain signatures of pain were sensitive to pain expression. We then applied a multivariate pattern analysis to the fMRI data to predict the facial expression of pain. Results revealed the inability of previously developed pain neurosignatures to predict the facial expression of pain. We thus propose a facial expression of pain signature (FEPS) conveying distinctive information about the brain response to nociceptive stimulations with minimal or no overlap with other pain-relevant brain signatures associated with nociception, pain ratings, thermal pain aversiveness, or pain valuation. The FEPS may provide a distinctive functional characterization of the distributed cerebral response to nociceptive pain associated with the socio-communicative role of non-verbal pain expression. This underscores the complexity of pain phenomenology by reinforcing the view that neurosignatures conceived as biomarkers must be interpreted in relation to the specific pain manifestation(s) predicted and their underlying function(s). Future studies should explore other pain-relevant manifestations and assess the specificity of the FEPS against simulated pain expressions and other types of aversive or emotional states.

Aberrant dynamic functional network connectivity in patients with diffuse axonal injury

Diffuse axonal injury (DAI) results in aberrant functional connectivity and is significantly linked to cognitive impairment. Nevertheless, the network mechanisms influencing neurocognitive function following DAI remain unclear. This study aimed to examine the characteristics of static and dynamic functional network connectivity (FNC) in patients with DAI. Resting-state functional magnetic resonance imaging data were collected from 26 patients with DAI and 27 healthy controls. Resting-state networks were extracted using independent component analysis. We evaluated the connectivity strength through spatial maps and static FNC, and then further dynamic properties were identified using a sliding time-window approach and k-means clustering, and investigated their associations with clinical variables. Patients with DAI showed stronger intra-network spatial maps in the default mode network and subcortical network than healthy controls, but static inter-network functional connectivity remained stable. Furthermore, three recurring states for dynamic connectivity were identified in all participants, and state 1 occurred most frequently in patients with DAI and exhibited higher fractional time, and as well as longer mean dwell time, which was positively associated with MMSE scores. Meanwhile, patients with DAI exhibited mostly increased functional connectivity strength of dynamic FNC in all states, particularly within the default mode network and visual network. These findings suggest that patients with DAI are characterized by altered dynamic FNC and temporal properties, which provide distinct complementary information different from static functional connectivity, and new insights into the neural pathophysiology of DAI associated with cognitive impairment.

Unveiling Frequency-Specific Microstate Correlates of Anxiety and Depression Symptoms.

Electroencephalography (EEG) microstates are canonical voltage topographies that reflect the temporal dynamics of brain networks on a millisecond time scale. Abnormalities in broadband microstate parameters have been observed in subjects with psychiatric symptoms, indicating their potential as clinical biomarkers. Considering distinct information provided by specific frequency bands of EEG, we hypothesized that microstates in decomposed frequency bands could provide a more detailed depiction of the underlying neuropathological mechanism. In this study, with a large open access resting-state dataset (n = 203), we examined the properties of frequency-specific microstates and their relationship with anxiety and depression symptoms. We conducted clustering on EEG topographies in decomposed frequency bands (delta, theta, alpha and beta), and determined the number of clusters with a meta-criterion. Microstate parameters, including global explained variance (GEV), duration, coverage, occurrence and transition probability, were calculated for eyes-open and eyes-closed states, respectively. Their ability to predict the severity of depression and anxiety symptoms were systematically identified by correlation, regression and classification analyses. Distinct microstate patterns were observed across different frequency bands. Microstate parameters in the alpha band held the best predictive power for emotional symptoms. Microstates B (GEV, coverage) and parieto-central maximum microstate E (coverage, occurrence, transitions from B to E) in the alpha band exhibited significant correlations with depression and anxiety, respectively. Microstate parameters of the alpha band achieved predictive R-square of 0.100 for anxiety scores, which is much higher than those of broadband (R-square = -0.026, p < 0.01). Similar results were found in classification of participants with high and low anxiety symptom scores (68% accuracy in alpha vs. 52% in broadband). These results suggested the value of frequency-specific microstates in predicting emotional symptoms.

The External Globus Pallidus as the Hub of the Auditory Cortico-Basal Ganglia Loop.

The cortico-basal ganglia loop has traditionally been conceptualized as consisting of three distinct information networks: motor, limbic, and associative. However, this three-loop concept is insufficient to comprehensively explain the diverse functions of the cortico-basal ganglia system, as emerging evidence suggests its involvement in sensory processing, including the auditory systems. In the present study, we demonstrate the auditory cortico-basal ganglia loop by using transgenic mice and viral-assisted labelings. The caudal part of the external globus pallidus (GPe) emerged as a major output nucleus of the auditory cortico-basal ganglia loop with the cortico-striato-pallidal projections as its input pathway and pallido-cortical and pallido-thalamo-cortical projections as its output pathway. GABAergic neurons in the caudal GPe dominantly innervated the nonlemniscal auditory pathway. They also projected to various regions, including the substantia nigra pars lateralis, cuneiform nucleus, and periaqueductal gray. Considering the functions associated with these GPe-projecting regions, auditory cortico-basal ganglia circuits may play a pivotal role in eliciting defensive behaviors against acoustic stimuli.

FORECASTING THE REALIZED VOLATILITY OF ISLAMIC EQUITIES USING MULTIVARIATE HAR-TYPE MODELS

This study proposes nine multivariate intraday models using various realized variation measures with the aim to improve volatility forecasting in the Islamic stock market in Malaysia using a dataset from 1st April 2008 to 31st March 2018. The findings show that considering independently the jump-robust realized volatility, additional daily jump realized volatility, and continuous and discontinuous jump sample path variations improved the in-sample predictive regressions compared to using the standard realized volatility. For the out-of-sample volatility forecasts evaluation, it is observed that the volatility models that disentangled the realized volatility into its continuous and discontinuous jump components have outperformed the rest of the proposed models. This is because both the continuous and discontinuous variation of returns exhibit distinctive substantial information in yielding the final volatility dynamic and thus should be modeled disjointedly. However, the empirical results suggest that the simple autoregressive specification using the standard realized volatility is often performing better or as well as the new extension models. Lastly, this study may provide useful insight in portfolio management, risk assessment, and asset pricing, particularly in the Shariah-compliant equities.

Finite-Time Synchronization Criteria for Caputo Fractional-Order Uncertain Memristive Neural Networks with Fuzzy Operators and Transmission Delay Under Communication Feedback

Unlike existing memristive neural networks or fuzzy neural networks, this article investigates a class of Caputo fractional-order uncertain memristive neural networks (CFUMNNs) with fuzzy operators and transmission delay to realistically model complex environments. Especially, the fuzzy symbol AND and the fuzzy symbol OR as well as nonlinear activation behaviors are all concerned in the generalized master-slave networks. Based on the characteristics of the neural networks being studied, we have designed distinctive information feedback control protocols including three different functional sub-modules. Combining comparative theorems, inequality techniques, and stability theory, novel delay-independent conditions can be derived to ensure the finite-time synchronization (FTS) of fuzzy CFUMNNs. Besides, the upper bound of the settling time can be effectively evaluated based on feedback coefficients and control parameters, which makes the achievements of this study more practical for engineering applications such as signal encryption and secure communications. Ultimately, simulation experiments show the feasibility of the derived results.

Cassava disease detection using a lightweight modified soft attention network.

Cassava is a high-carbohydrate crop that is at risk of viral infections. The production rate and quality of cassava crops are affected by several diseases. However, the manual identification of diseases is challenging and requires considerable time because of the lack of field professionals and the limited availability of clear and distinct information. Consequently, the agricultural management system is seeking an efficient and lightweight method that can be deployable to edged devices for detecting diseases at an early stage. To address these issues and accurately categorize different diseases, a very effective and lightweight framework called CDDNet has been introduced. We used MobileNetV3Small framework as a backbone feature for extracting optimized, discriminating, and distinct features. These features are empirically validated at the early intermediate stage. Additionally, we modified the soft attention module to effectively prioritize the diseased regions and enhance significant cassava plant disease-related features for efficient cassava disease detection. Our proposed method achieved accuracies of 98.95%, 97.03%, and 98.25% on Cassava Image Dataset, Cassava Plant Disease Merged (2019-2020) Dataset, and the newly created Cassava Plant Composite Dataset, respectively. Furthermore, the proposed technique outperforms previous state-of-the-art methods in terms of accuracy, parameter count, and frames per second values, ultimately making the proposed CDDNet the best one for real-time processing. Our findings underscore the importance of a lightweight and efficient technique for cassava disease detection and classification in a real-time environment. Furthermore, we highlight the impact of modified soft attention on model performance. © 2024 The Author(s). Pest Management Science published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.

Online health [mis]information processing and evaluation among older adults: Comparing individuals with varied abilities in information discernment

Older adults are vulnerable to online health misinformation. This study fills a research gap by exposing older adults to a mixture of health-related misinformation and factual information and employing the think-aloud protocol in a semi-structured interview to understand information processing and evaluation in the moment of information engagement. The participants were categorized into two groups based on their abilities to discern misinformation. Via thematic analysis, we compared the two groups and found distinctive information processing patterns between them. We propose guidelines for evaluating online misinformation based on the best practices of those who have a high capacity to discern the veracity of the information and pitfalls to avoid based on the experience of those who failed to correctly differentiate true and misinformation.

Determinism in Current Physics. Is It Possible?

We discuss the possibilities of determinism in reality, taking under consideration both quantum and classical physics. We present this firstly by questioning the supposed nature of quantum physics as non-deterministic, following the proposal of Penrose: the collapse of the wavefunction interpreted as particular measurements which seem to indicate certain contingency does not actually give the full picture of the reality of the former. In addition to what Penrose suggests, we consider this collapse as part of a bigger deterministic picture. Secondly, we analyse the distinction between this microphysical scenario and our macrophysical experience, in the light of determinism as well. We suggest that this experience can be understood as particular “measurements” similar to what happens in quantum mechanics. For instance, the image of a person with certain identity features is a highlight or particularization of all the possibilities the identity of this person experienced, experiences and will experience through time. The “collapse” is thus linked to individuation, not less real, but incomplete of reality. By linking the domain of quantum physics in a deterministic fashion to the phenomenological or macrophysical realm, we aim to show that a non-contingent character of reality is possible when accepting measurements or particular instances of things as forms of comprehension given by the physical world (thus not just mere subjective interventions). We argue that the complete picture (closer to the wavefunction) cannot give distinctive information (understanding this as differentiation of elements, such as particles in the microphysical domain and a certain colour in the macrophysical one).

Connectomic reconstruction predicts visual features used for navigation

Many animals use visual information to navigate1–4, but how such information is encoded and integrated by the navigation system remains incompletely understood. In Drosophila melanogaster, EPG neurons in the central complex compute the heading direction5 by integrating visual input from ER neurons6–12, which are part of the anterior visual pathway (AVP)10,13–16. Here we densely reconstruct all neurons in the AVP using electron-microscopy data17. The AVP comprises four neuropils, sequentially linked by three major classes of neurons: MeTu neurons10,14,15, which connect the medulla in the optic lobe to the small unit of the anterior optic tubercle (AOTUsu) in the central brain; TuBu neurons9,16, which connect the AOTUsu to the bulb neuropil; and ER neurons6–12, which connect the bulb to the EPG neurons. On the basis of morphologies, connectivity between neural classes and the locations of synapses, we identify distinct information channels that originate from four types of MeTu neurons, and we further divide these into ten subtypes according to the presynaptic connections in the medulla and the postsynaptic connections in the AOTUsu. Using the connectivity of the entire AVP and the dendritic fields of the MeTu neurons in the optic lobes, we infer potential visual features and the visual area from which any ER neuron receives input. We confirm some of these predictions physiologically. These results provide a strong foundation for understanding how distinct sensory features can be extracted and transformed across multiple processing stages to construct higher-order cognitive representations.

A-165 Value Of Monocyte Distribution Width In Bacteremia Assessment In Emergency Department Patients

Abstract Background Monocyte distribution width (MDW) is an immune response biomarker and part of a CBC differential. MDW is derived from the distribution of monocyte volumes and elevated MDW values correlate with severe infections and sepsis in Emergency Department (ED) adult patients. Neutrophil to lymphocyte ratio (NLR) is predictive of infections and inflammatory stress. Evaluating these biomarkers could be valuable in bacteremia risk assessment. Bacteremia is the presence of viable bacteria in the bloodstream and is present in up to 20% of septic patients. Blood cultures establish pathogen identity but require culture time, potentially delaying appropriate treatment. Our goal was to characterize MDW and NLR clinical performance in ED patients with suspected bacteremia. Methods This was an observational cohort study of ED patients 18 years and older with CBC differential and blood cultures obtained. MDW values ≥20 is the published cut-off for increased infection risk. Clinical literature suggests that NLR ≥3 is abnormal. We assessed the diagnostic performance of MDW and NLR for positive blood cultures using SPSS Version 25. Results From 07/2021-09/2023, 9,400 blood cultures were ordered and 5174 patients had differential CBCs. The overall blood culture positivity rate was 14.4%. MDW and NLR performance for positive blood cultures is shown in Table 1. Conclusions Our analysis provides evidence that MDW and NLR may be useful for early recognition of bacteremia in ED patients. CBC differential results are reported sooner than culture results. MDW is directly reported, NLR can be readily calculated from the differential. Therefore both could contribute to earlier risk stratification and our findings suggest potentially distinct information from MDW and NLR. Combining MDW with other CBC parameters may provide additional diagnostic advantage. Further analysis will incorporate added clinical data for enhanced decision support in interpreting MDW values in the context of pending blood culture results.

Label-Free Exosome Analysis by Surface-Enhanced Raman Scattering Spectroscopy with Laser-Ablated Silver Nanoparticle Substrate.

Early diagnostics of breast cancer is crucial to reduce the risk of cancer metastasis and late relapse. Exosome, which contains distinct information of its origin, can be the target object as a liquid biopsy. However, its low sensitivity and inadequate diagnostic tools interfere with the point-of-care testing (POCT) of the exosome. Recently, Surface-enhanced Raman Scattering (SERS) spectroscopy, which amplifies the Raman scattering, has been proved as a promising tool for exosome detection. However, the fabrication process of SERS probe or substrate is still inefficient and far from large-scale production. This study proposes rapid and label-free detection of breast cancer-derived exosomes by statistical analysis of SERS spectra using silver-nanoparticle-based SERS substrate fabricated by selective laser ablation and melting (SLAM). Employing silver nanowires and optimizing laser process parameters enable rapid and low-energy fabrication of SERS substrate. The functionalities including sensitivity, reproducibility, stability, and renewability are evaluated using rhodamine 6G as a probe molecule. Then, the feasibility of POCT is examined by the statistical analysis of SERS spectra of exosomes from malignant breast cancer cells and non-tumorigenic breast epithelial cells. The presented framework is anticipated to be utilized in other biomedical applications, facilitating cost-effective and large-scale production performance.

Evaluation of Haptic Textures for Tangible Interfaces for the Tactile Internet

Every texture in the real world provides us with the essential information to identify the physical characteristics of real objects. In addition to sight, humans use the sense of touch to explore their environment. Through haptic interaction we obtain unique and distinct information about the texture and the shape of objects. In this paper, we enhance X3D 3D graphics files with haptic features to create 3D objects with haptic feedback. We propose haptic attributes such as static and dynamic friction, stiffness, and maximum altitude that provide the optimal user experience in a virtual haptic environment. After numerous optimization attempts on the haptic textures, we propose various haptic geometrical textures for creating a virtual 3D haptic environment for the tactile Internet. These tangible geometrical textures can be attached to any geometric shape, enhancing the haptic sense. We conducted a study of user interaction with a virtual environment consisting of 3D objects enhanced with haptic textures to evaluate performance and user experience. The goal is to evaluate the realism and recognition accuracy of each generated texture. The findings of the study aid visually impaired individuals to better understand their physical environment, using haptic devices in conjunction with the enhanced haptic textures.

Enhanced electro-optical properties of polymer dispersed liquid crystals doped with functional nanofibers for applications of efficient smart windows and advanced anti-counterfeiting

The development of polymer-dispersed liquid crystal (PDLC) capable of infrared smart tuning and displaying distinct information under ultraviolet, visible, and infrared light presents significant challenges. These challenges include the tuning of electro-optical properties and IR reflectivity, the preparation of complex patterns, and the display of different display states under various stimuli. In this work, nanofibers doped with alumina-zinc oxide nanoparticles (AZO NPs) and loaded with cesium tungstate nanoparticles (Cs0.33WO3 NPs) into PDLC not only enhanced their electro-optical properties but also added infrared shielding. Temperature regulation tests demonstrate that PDLC films can intelligently modulate infrared light reflection, offering superior shading and temperature regulation capabilities. PDLC smart windows integrated with nanofibers achieve a temperature coordination function, maintaining a temperature differential of 12.1 °C compared to standard PDLC. This significant enhancement underscores their potential for applications in thermally regulated smart windows. In addition, a full-wavelength anti-counterfeiting mode for PDLC was developed using a screen-printing strategy to display different information under various wavelengths of light. Noteworthily, the anti-counterfeiting system exhibits a bright fluorescent pattern under UV light, a scattering pattern under visible light, and an infrared-modulated pattern when viewed with an infrared detector. Furthermore, an advanced dynamic anti-counterfeiting mode with time response was created using nanofibers loaded with different concentrations of Cs0.33WO3 NPs. This advanced anti-counterfeiting material can be used as multimodal and dynamic anti-counterfeiting label, which greatly facilitates the development of novel anti-counterfeiting and optical sensing materials.