Novel approach exploring the correlation between presepsin and routine laboratory parameters using explainable artificial intelligence.

Although presepsin, a crucial biomarker for the diagnosis and management of sepsis, has gained prominence in contemporary medical research, its relationship with routine laboratory parameters, including demographic data and hospital blood test data, remains underexplored. This study integrates machine learning with explainable artificial intelligence (XAI) to provide insights into the relationship between presepsin and these parameters. Advanced machine learning classifiers provide a multilateral view of data and play an important role in highlighting the interrelationships between presepsin and other parameters. XAI enhances analysis by ensuring transparency in the model's decisions, especially in selecting key parameters that significantly enhance classification accuracy. Utilizing XAI, this study successfully identified critical parameters that increased the predictive accuracy for sepsis patients, achieving a remarkable ROC AUC of 0.97 and an accuracy of 0.94. This breakthrough is possibly attributed to the comprehensive utilization of XAI in refining parameter selection, thus leading to these significant predictive metrics. The presence of missing data in datasets is another concern; this study addresses it by employing Extreme Gradient Boosting (XGBoost) to manage missing data, effectively mitigating potential biases while preserving both the accuracy and relevance of the results. The perspective of examining data from higher dimensions using machine learning transcends traditional observation and analysis. The findings of this study hold the potential to enhance patient diagnoses and treatment, underscoring the value of merging traditional research methods with advanced analytical tools.

Column Row Convolutional Neural Network: Reducing Parameters for Efficient Image Processing.

Recent advancements in deep learning have achieved significant progress by increasing the number of parameters in a given model. However, this comes at the cost of computing resources, prompting researchers to explore model compression techniques that reduce the number of parameters while maintaining or even improving performance. Convolutional neural networks (CNN) have been recognized as more efficient and effective than fully connected (FC) networks. We propose a column row convolutional neural network (CRCNN) in this letter that applies 1D convolution to image data, significantly reducing the number of learning parameters and operational steps. The CRCNN uses column and row local receptive fields to perform data abstraction, concatenating each direction's feature before connecting it to an FC layer. Experimental results demonstrate that the CRCNN maintains comparable accuracy while reducing the number of parameters and compared to prior work. Moreover, the CRCNN is employed for one-class anomaly detection, demonstrating its feasibility for various applications.

Purely self-rectifying memristor-based passive crossbar array for artificial neural network accelerators.

Memristor-integrated passive crossbar arrays (CAs) could potentially accelerate neural network (NN) computations, but studies on these devices are limited to software-based simulations owing to their poor reliability. Herein, we propose a self-rectifying memristor-based 1 kb CA as a hardware accelerator for NN computations. We conducted fully hardware-based single-layer NN classification tasks involving the Modified National Institute of Standards and Technology database using the developed passive CA, and achieved 100% classification accuracy for 1500 test sets. We also investigated the influences of the defect-tolerance capability of the CA, impact of the conductance range of the integrated memristors, and presence or absence of selection functionality in the integrated memristors on the image classification tasks. We offer valuable insights into the behavior and performance of CA devices under various conditions and provide evidence of the practicality of memristor-integrated passive CAs as hardware accelerators for NN applications.

Fibriform Organic Electrochemical Diodes with Rectifying, Complementary Logic and Transient Voltage Suppression Functions for Wearable E-Textile Embedded Circuits.

In this study, a fibriform electrochemical diode capable of performing rectifying, complementary logic and device protection functions for future e-textile circuit systems is fabricated. The diode was fabricated using a simple twisted assembly of metal/polymer semiconductor/ion gel coaxial microfibers and conducting microfiber electrodes. The fibriform diode exhibited a prominent asymmetrical current flow with a rectification ratio of over 102, and its performance was retained after repeated bending deformations and washings. Fundamental studies on the electrochemical interactions of polymer semiconductors with ions reveal that the Faradaic current generated in polymer semiconductors by electrochemical reactions results in an abrupt current increase under a forward bias, in which the threshold voltages of the device are determined by the oxidation or reduction potential of the polymer semiconductor. Textile-embedded full-wave rectifiers and logic gate circuits were implemented by simply integrating the fibriform diodes, exhibiting AC-to-DC signal conversion and logic operation functions, respectively. It was also confirmed that the proposed fibriform diode can suppress transient voltages and thus protect a low-voltage operational wearable e-textile circuit.

Bio-inspired electronic fingerprint PUF device with single-walled carbon nanotube network surface mediated by M13 bacteriophage template.

Human fingerprints are randomly created during fetal activity in the womb, resulting in unique and physically irreproducible fingerprint patterns that are applicable as a biological cryptographic primitive. Similarly, stochastically knitted single-walled carbon nanotube (SWNT) network surfaces exhibit inherently random and unique electrical characteristics that can be exploited as a physical unclonable function (PUF) in the authentication. In this study, filamentous M13 bacteriophages are used as a biological gluing template to create a random SWNT network surface with mechanical flexibility, with electrical properties determined by random variation during fabrication. The resistance profile between two adjacent electrodes was mapped for these M13-mediated SWNT network surfaces, with the results demonstrating a unique resistance profile for each M13-SWNT device, similar to that of human fingerprints. Randomness and uniqueness measures were evaluated as respectively 50.5% and 50% using generated challenge-response pairs. Min-entropy for unpredictability evaluation of the M13-SWNT based PUFs resulted in 0.98. Our results showed that M13-SWNT random network exhibits cryptographic characteristics when used in a bio-inspired PUF device.

Dendritic Network Implementable Organic Neurofiber Transistors with Enhanced Memory Cyclic Endurance for Spatiotemporal Iterative Learning.

Dendritic network implementable organic neurofiber transistors with enhanced memory cyclic endurance for spatiotemporal iterative learning are proposed. The architecture of the fibrous organic electrochemical transistors consisting of a double-stranded assembly of electrode microfibers and an iongel gate insulator enables the highly sensitive multiple implementation of synaptic junctions via simple physical contact of gate-electrode microfibers, similar to the dendritic connections of a biological neuron fiber. In particular, carboxylic-acid-functionalized polythiophene as a semiconductor channel material provides stable gate-field-dependent multilevel memory characteristics with long-term stability and cyclic endurance, unlike the conventional poly(alkylthiophene)-based neuromorphic electrochemical transistors, which exhibit short retention and unstable endurance. The dissociation of the carboxylic acid of the polythiophene enables reversible doping and dedoping of the polythiophene channel by effectively stabilizing the ions that penetrate the channel during potentiation and depression cycles, leading to the reliable cyclic endurance of the device. The synaptic weight of the neurofiber transistors with a dendritic network maintains the state levels stably and is independently updated with each synapse connected with the presynaptic neuron to a specific state level. Finally, the neurofiber transistor demonstrates successful speech recognition based on iterative spiking neural network learning in the time domain, showing a substantial recognition accuracy of 88.9%.

Distribution of petrogenic polycyclic aromatic hydrocarbons (PAHs) in seafood following Deepwater Horizon oil spill.

A community-based participatory research was utilized to address the coastal community's concern regarding Deepwater Horizon oil contamination of seafood. Therefore, we analyzed polycyclic aromatic hydrocarbons (PAHs), major toxic constituents of crude oil, in the seafood collected from gulf coast (Louisiana, Alabama and Mississippi) during December 2011-February 2014. PAHs were extracted from edible part of shrimp, oysters, and crabs by the QuEChERS/dsPE procedure and analyzed by gas chromatography-mass spectrometry. The total PAHs data were further analyzed using the General Linear Mixed Model procedure of the SAS (Version 9.3, SAS Institute, Inc., Cary, NC) statistical software. Brown shrimp showed statistically significant differences in PAHs levels with respect to time and locations while white shrimp showed differences at various time points. PAHs levels in oyster and crab samples were not statistically different at the Type I error of 0.05. Overall, the PAHs levels are far below FDA levels of concern for human consumption.

A New Architecture for Fibrous Organic Transistors Based on a Double-Stranded Assembly of Electrode Microfibers for Electronic Textile Applications.

Herein, a unique device architecture is proposed for fibrous organic transistors based on a double-stranded assembly of electrode microfibers for electronic textile applications. A key feature of this work is that the semiconductor channel of the fiber transistor comprises a twist assembly of the source and drain electrode microfibers that are coated by an organic semiconductor. This architecture not only allows the channel dimension of the device to be readily controlled by varying the thickness of the semiconductor layer and the twisted length of the two electrode microfibers, but also passivates the device without affecting interconnections with other electrical components. It is found that the control of crystalline nanostructure of the semiconductor layer is critical for improving both the production yield of the device and the charge-carrier transport in the device. The resulting fibrous organic transistors show a high output current of over -5 mA at a low operation voltage of -1.3 V and a good on/off current ratio of 105 . The device performance is maintained after repeated bending deformation and washing with a strong detergent solution. Application of the fibrous organic transistors to switch current-driven LED devices and detection of electrocardiography signals from a human body are demonstrated.

How do plants see the world? - UV imaging with a TiO2 nanowire array by artificial photosynthesis.

The concept of plant vision refers to the fact that plants are receptive to their visual environment, although the mechanism involved is quite distinct from the human visual system. The mechanism in plants is not well understood and has yet to be fully investigated. In this work, we have exploited the properties of TiO2 nanowires as a UV sensor to simulate the phenomenon of photosynthesis in order to come one step closer to understanding how plants see the world. To the best of our knowledge, this study is the first approach to emulate and depict plant vision. We have emulated the visual map perceived by plants with a single-pixel imaging system combined with a mechanical scanner. The image acquisition has been demonstrated for several electrolyte environments, in both transmissive and reflective configurations, in order to explore the different conditions in which plants perceive light.

Four-Bits-Per-Cell Operation in an HfO2 -Based Resistive Switching Device.

The quadruple-level cell technology is demonstrated in an Au/Al2 O3 /HfO2 /TiN resistance switching memory device using the industry-standard incremental step pulse programming (ISPP) and error checking/correction (ECC) methods. With the highly optimistic properties of the tested device, such as self-compliance and gradual set-switching behaviors, the device shows 6σ reliability up to 16 states with a state current gap value of 400 nA for the total allowable programmed current range from 2 to 11 µA. It is demonstrated that the conventional ISPP/ECC can be applied to such resistance switching memory, which may greatly contribute to the commercialization of the device, especially competitively with NAND flash. A relatively minor improvement in the material and circuitry may enable even a five-bits-per-cell technology, which can hardly be imagined in NAND flash, whose state-of-the-art multiple-cell technology is only at three-level (eight states) to this day.

Bile Cast Nephropathy in Cirrhotic Patients: Effects of Chronic Hyperbilirubinemia.

OBJECTIVES: The aim of this study was to determine the prevalence of bile cast nephropathy (BCN) in autopsied cirrhotic patients and to correlate BCN with clinical and laboratory data to direct attention to this underrecognized renal complication of liver failure. METHODS: We assessed 114 autopsy cases of cirrhosis for the presence of renal intratubular bile casts using Hall stain for bile. Presence of bile casts was correlated with etiology of cirrhosis, clinical and laboratory data, and histologic findings. RESULTS: Bile casts were identified in 55% of cases. The most common etiology of cirrhosis was hepatitis C virus (HCV) infection (52%), and serum creatinine ( P = .02) and serum urea nitrogen ( P = .01) were significantly higher in the Hall-positive group. Conjugated bilirubin was below 20 mg/dL in 90%, and levels below 10 mg/dL were noted in 80% of cases. CONCLUSIONS: To our knowledge, this is the largest study of BCN in human subjects and a first report describing the association of BCN with HCV-related cirrhosis. We demonstrated that in the face of protracted chronic hyperbilirubinemia, bile casts are formed at much lower bilirubin levels than previously thought. Furthermore, we proposed an algorithm to assist in better identification of bile casts.

Riboflavin as an independent and accurate biomarker for adherence in a randomized double-blind and placebo-controlled clinical trial.

BACKGROUND: Medication adherence is critical for success of clinical trials. OBJECTIVE: To assess oral riboflavin is an adherence marker. METHODS: Riboflavin was incorporated into active treatment and placebo pills for a clinical trial lasting for 2 years. RESULTS: The accuracy (area under the receiver operating curve) of urinary riboflavin was 0.91 as a binary classifier of adherence, and was similar or better than for two active study ingredients daidzein (0.92) and genistein (0.87) (all p < 0.0001). Decreased adherence over time was similar in the two study groups. CONCLUSION: Riboflavin is an accurate and useful biomarker for study pill ingestion.

Statistical Approaches to Candidate Biomarker Panel Selection.

The statistical analysis of robust biomarker candidates is a complex process, and is involved in several key steps in the overall biomarker development pipeline (see Fig. 22.1, Chap. 19 ). Initially, data visualization (Sect. 22.1, below) is important to determine outliers and to get a feel for the nature of the data and whether there appear to be any differences among the groups being examined. From there, the data must be pre-processed (Sect. 22.2) so that outliers are handled, missing values are dealt with, and normality is assessed. Once the processed data has been cleaned and is ready for downstream analysis, hypothesis tests (Sect. 22.3) are performed, and proteins that are differentially expressed are identified. Since the number of differentially expressed proteins is usually larger than warrants further investigation (50+ proteins versus just a handful that will be considered for a biomarker panel), some sort of feature reduction (Sect. 22.4) should be performed to narrow the list of candidate biomarkers down to a more reasonable number. Once the list of proteins has been reduced to those that are likely most useful for downstream classification purposes, unsupervised or supervised learning is performed (Sects. 22.5 and 22.6, respectively).

Peripheral Venous Pressure as an Indicator of Preload Responsiveness During Volume Resuscitation from Hemorrhage.

BACKGROUND: Fluid resuscitation of hypovolemia presumes that peripheral venous pressure (PVP) increases more than right atrial pressure (RAP), so the net pressure gradient for venous return (PVP-RAP) rises. However, the heart and peripheral venous system function under different compliances that could affect their respective pressures during fluid infusion. In a porcine model of hemorrhage resuscitation, we examined whether RAP increases more than PVP, thereby reducing the venous return pressure gradient and blood flow. METHODS: Anesthetized pigs (n = 8) were bled to a mean arterial blood pressure of 40 mm Hg and resuscitated with stored blood and albumin for pulmonary artery occlusion pressures (PAOPs) of 5, 10, 15, and 20 mm Hg. Venous pressures, inferior vena cava blood flow (ultrasonic flowprobe), and left ventricular diastolic compliance (Doppler echocardiography) were measured. Stroke volume variability was calculated. RESULTS: With volume resuscitation, the slope of RAP exceeded PVP (P ≤ 0.0001) when PAOP is 10 to 20 mm Hg, causing the pressure gradient for venous return to progressively decrease. Inferior vena cava blood flow did not further increase after PAOP > 10 mm Hg. The E/e' ratio increased (P = 0.001) during resuscitation indicating reduced diastolic compliance. A significant curvilinear relationship was found between PVP and stroke volume variability (R = 0.62; P < 0.001), where fluid responders had PVP < 15 mm Hg. CONCLUSIONS: Fluid resuscitation above a PAOP 10 mm Hg reduces myocardial compliance and reduces the venous return pressure gradient. The hemodynamic response to fluid resuscitation becomes limited by diastolic properties of the heart. PVP measurement during hemorrhage resuscitation may predict fluid responsiveness and nonresponsiveness.

Identifying effective and feasible interventions to accelerate functional recovery from hospitalization in older adults: A randomized controlled pilot trial.

Hospitalization induces functional decline in older adults. Many geriatric patients fail to fully recover physical function after hospitalization, which increases the risk of frailty, disability, dependence, re-hospitalization, and mortality. There is a lack of evidence-based therapies that can be implemented following hospitalization to accelerate functional improvements. The aims of this Phase I clinical trial are to determine 1) the effect size and variability of targeted interventions in accelerating functional recovery from hospitalization and 2) the feasibility of implementing such interventions in community-dwelling older adults. Older patients (≥65years, n=100) will be recruited from a single site during hospitalization for an acute medical condition. Subjects will be randomized to one of five interventions initiated immediately upon discharge: 1. protein supplementation, 2. in-home rehabilitation plus placebo supplementation, 3. in-home rehabilitation plus protein supplementation, 4. single testosterone injection, or 5. isocaloric placebo supplementation. Testing will occur during hospitalization (baseline) and at 1 and 4weeks post-discharge. Each testing session will include measures of muscle strength, physical function/performance, body composition, and psychological function. Physical activity levels will be continuously monitored throughout study participation. Feasibility will be determined through collection of the number of eligible, contacted, and enrolled patients; intervention adherence and compliance; and reasons for declining enrollment and study withdrawal. This research will determine the feasibility of post-hospitalization strategies to improve physical function in older adults. These results will also provide a foundation for performing larger, multi-site clinical trials to improve physical function and reduce readmissions in geriatric patents.

Improved Detection of Invasive Pulmonary Aspergillosis Arising during Leukemia Treatment Using a Panel of Host Response Proteins and Fungal Antigens.

Invasive pulmonary aspergillosis (IPA) is an opportunistic fungal infection in patients undergoing chemotherapy for hematological malignancy, hematopoietic stem cell transplant, or other forms of immunosuppression. In this group, Aspergillus infections account for the majority of deaths due to mold pathogens. Although early detection is associated with improved outcomes, current diagnostic regimens lack sensitivity and specificity. Patients undergoing chemotherapy, stem cell transplantation and lung transplantation were enrolled in a multi-site prospective observational trial. Proven and probable IPA cases and matched controls were subjected to discovery proteomics analyses using a biofluid analysis platform, fractionating plasma into reproducible protein and peptide pools. From 556 spots identified by 2D gel electrophoresis, 66 differentially expressed post-translationally modified plasma proteins were identified in the leukemic subgroup only. This protein group was rich in complement components, acute-phase reactants and coagulation factors. Low molecular weight peptides corresponding to abundant plasma proteins were identified. A candidate marker panel of host response (9 plasma proteins, 4 peptides), fungal polysaccharides (galactomannan), and cell wall components (ß-D glucan) were selected by statistical filtering for patients with leukemia as a primary underlying diagnosis. Quantitative measurements were developed to qualify the differential expression of the candidate host response proteins using selective reaction monitoring mass spectrometry assays, and then applied to a separate cohort of 57 patients with leukemia. In this verification cohort, a machine learning ensemble-based algorithm, generalized pathseeker (GPS) produced a greater case classification accuracy than galactomannan (GM) or host proteins alone. In conclusion, Integration of host response proteins with GM improves the diagnostic detection of probable IPA in patients undergoing treatment for hematologic malignancy. Upon further validation, early detection of probable IPA in leukemia treatment will provide opportunities for earlier interventions and interventional clinical trials.

Direct evidence on Ta-Metal Phases Igniting Resistive Switching in TaO(x) Thin Film.

A Ta/TaOx/Pt stacked capacitor-like device for resistive switching was fabricated and examined. The tested device demonstrated stable resistive switching characteristics including uniform distribution of resistive switching operational parameters, highly promising endurance, and retention properties. To reveal the resistive switching mechanism of the device, micro structure analysis using high-resolution transmission electron microscope (HR-TEM) was performed. From the observation results, two different phases of Ta-metal clusters of cubic α-Ta and tetragonal ß-Ta were founded in the amorphous TaOx mother-matrix after the device was switched from high resistance state (HRS) to low resistance state (LRS) by externally applied voltage bias. The observed Ta metal clusters unveiled the origin of the electric conduction paths in the TaOx thin film at the LRS.

Moving Block Bootstrap for Analyzing Longitudinal Data.

In a longitudinal study subjects are followed over time. I focus on a case where the number of replications over time is large relative to the number of subjects in the study. I investigate the use of moving block bootstrap methods for analyzing such data. Asymptotic properties of the bootstrap methods in this setting are derived. The effectiveness of these resampling methods is also demonstrated through a simulation study.

Mixed-effects model of epithelial-mesenchymal transition reveals rewiring of signaling networks.

The type II epithelial-mesenchymal transition (EMT) produces airway fibrosis and remodeling, contributing to the severity of asthma and chronic obstructive pulmonary disease. While numerous studies have been done on the mechanisms of the transition itself, few studies have investigated the system effects of EMT on signaling networks. Here, we use mixed effects modeling to develop a computational model of phospho-protein signaling data that compares human small airway epithelial cells (hSAECs) with their EMT-transformed counterparts across a series of perturbations with 8 ligands and 5 inhibitors, revealing previously uncharacterized changes in signaling in the EMT state. Strong couplings between menadione, TNFα and TGFß and their known phospho-substrates were revealed after mixed effects modeling. Interestingly, the overall phospho-protein response was attenuated in EMT, with loss of Mena and TNFα coupling to heat shock protein (HSP)-27. These differences persisted after correction for EMT-induced changes in phospho-protein substrate abundance. Construction of network topology maps showed significant changes between the two cellular states, including a linkage between glycogen synthase kinase (GSK)-3α and small body size/mothers against decapentaplegic (SMAD)2. The model also predicted a loss of p38 mitogen activated protein kinase (p38MAPK)-independent HSP27 signaling, which we experimentally validated. We further characterized the relationship between HSP27 and signal transducers and activators of transcription (STAT)3 signaling, and determined that loss of HSP27 following EMT is only partially responsible for the downregulation of STAT3. These rewired connections represent therapeutic targets that could potentially reverse EMT and restore a normal phenotype to the respiratory mucosa.

Molecular classification of outcomes from dengue virus -3 infections.

OBJECTIVES: Dengue virus (DENV) infection is a significant risk to over a third of the human population that causes a wide spectrum of illness, ranging from sub-clinical disease to intermediate syndrome of vascular complications called dengue fever complicated (DFC) and severe, dengue hemorrhagic fever (DHF). Methods for discriminating outcomes will impact clinical trials and understanding disease pathophysiology. STUDY DESIGN: We integrated a proteomics discovery pipeline with a heuristics approach to develop a molecular classifier to identify an intermediate phenotype of DENV-3 infectious outcome. RESULTS: 121 differentially expressed proteins were identified in plasma from DHF vs dengue fever (DF), and informative candidates were selected using nonparametric statistics. These were combined with markers that measure complement activation, acute phase response, cellular leak, granulocyte differentiation and viral load. From this, we applied quantitative proteomics to select a 15 member panel of proteins that accurately predicted DF, DHF, and DFC using a random forest classifier. The classifier primarily relied on acute phase (A2M), complement (CFD), platelet counts and cellular leak (TPM4) to produce an 86% accuracy of prediction with an area under the receiver operating curve of >0.9 for DHF and DFC vs DF. CONCLUSIONS: Integrating discovery and heuristic approaches to sample distinct pathophysiological processes is a powerful approach in infectious disease. Early detection of intermediate outcomes of DENV-3 will speed clinical trials evaluating vaccines or drug interventions.