Comparative evaluation of the STANDARD M10 and Xpert C. difficile assays for detection of toxigenic Clostridioides difficile in stool specimens.

This study compared the performance of two commercial molecular assays, the STANDARD M10 Clostridioides difficile assay (M10) and the Xpert C. difficile assay (Xpert), for detecting toxigenic C. difficile in stool specimens. A total of 487 consecutive stool specimens submitted for routine C. difficile testing between June and November 2023 were included. Following routine testing using C. DIFF QUIK CHEK COMPLETE (QCC), M10 and Xpert were tested in parallel, alongside toxigenic culture (reference standard). Additionally, two-step algorithms, using QCC on the first step and either M10 or Xpert on the second step, were assessed. Both M10 and Xpert demonstrated a sensitivity and negative predictive value (NPV) of 100%. M10 exhibited significantly higher specificity and positive predictive value (PPV; 91.9% and 64.2%, respectively) than Xpert (90.3% and 59.8%, respectively). Both two-step algorithms showed a sensitivity and NPV of 98.4% and 99.8%, respectively. The specificity and PPV of the two-step algorithm using M10 (95.2% and 75.0%, respectively) were slightly higher than those of the one using Xpert (94.8% and 73.2%, respectively), without statistical significance. Receiver operating characteristic curve analysis, assessing the predictive ability of cycle threshold (Ct) values for the detection of free toxin, exhibited an area under the curve of 0.825 for M10 and 0.843 for Xpert. This indicates the utility of Ct values as predictors for the detection of free toxin in both assays. In conclusion, M10 proves to be an effective diagnostic tool with performance comparable to Xpert, whether utilized independently or as part of a two-step algorithm.

Real-time analysis and prediction method of ion concentration using the effect of O-H stretching bands in aqueous solutions based on ATR-FTIR spectroscopy.

Analyzing the concentration of ions in aqueous solutions in real-time plays an important role in the fields of chemistry and biology. Traditional methods for measuring ion concentrations, such as concentration analysis by measuring electrical conductivity, inductively coupled plasma mass spectrometry, and ion chromatography, have been used in many research fields. However, these methods are limited in determining ion concentrations instantaneously. Fourier-transform infrared-attenuated total reflectance (ATR-FTIR) spectroscopy provides a new approach for determining ion concentrations in aqueous solutions. This allows for fast analysis without pretreatment and is scalable for real-time measurements. In this study, we present a method for measuring ion concentrations by examining ion-water interactions in the O-H stretching band of aqueous solutions using ATR-FTIR spectroscopy. Five aqueous solutions, namely LiCl + HCl, LiOH + HCl, LiOH, Li3PO4, and NaCl were used in the experiments and prepared at concentrations between 0.5-2 M. The ion concentrations in the prepared aqueous solutions were measured using ATR-FTIR spectroscopy. We observed that the difference in absorbance increased and decreased linearly with changes in concentration. The concentration of ions in the aqueous solution could be measured by validating the designed linear regression analysis function model. In this study, we proposed five linear regression analysis function models, all of which showed high coefficients of determination above 0.9, with the highest coefficient of determination reaching 0.9969. These results show that ATR-FTIR spectroscopy has the potential to be applied as a rapid and simple concentration analysis system.

Uncovering COVID-19 transmission tree: identifying traced and untraced infections in an infection network.

Introduction: This paper presents a comprehensive analysis of COVID-19 transmission dynamics using an infection network derived from epidemiological data in South Korea, covering the period from January 3, 2020, to July 11, 2021. The network illustrates infector-infectee relationships and provides invaluable insights for managing and mitigating the spread of the disease. However, significant missing data hinder conventional analysis of such networks from epidemiological surveillance. Methods: To address this challenge, this article suggests a novel approach for categorizing individuals into four distinct groups, based on the classification of their infector or infectee status as either traced or untraced cases among all confirmed cases. The study analyzes the changes in the infection networks among untraced and traced cases across five distinct periods. Results: The four types of cases emphasize the impact of various factors, such as the implementation of public health strategies and the emergence of novel COVID-19 variants, which contribute to the propagation of COVID-19 transmission. One of the key findings is the identification of notable transmission patterns in specific age groups, particularly in those aged 20-29, 40-69, and 0-9, based on the four type classifications. Furthermore, we develop a novel real-time indicator to assess the potential for infectious disease transmission more effectively. By analyzing the lengths of connected components, this indicator facilitates improved predictions and enables policymakers to proactively respond, thereby helping to mitigate the effects of the pandemic on global communities. Conclusion: This study offers a novel approach to categorizing COVID-19 cases, provides insights into transmission patterns, and introduces a real-time indicator for better assessment and management of the disease transmission, thereby supporting more effective public health interventions.

Medial entorhinal cortex mediates learning of context-dependent interval timing behavior.

Episodic memory requires encoding the temporal structure of experience and relies on brain circuits in the medial temporal lobe, including the medial entorhinal cortex (MEC). Recent studies have identified MEC 'time cells', which fire at specific moments during interval timing tasks, collectively tiling the entire timing period. It has been hypothesized that MEC time cells could provide temporal information necessary for episodic memories, yet it remains unknown whether they display learning dynamics required for encoding different temporal contexts. To explore this, we developed a new behavioral paradigm requiring mice to distinguish temporal contexts. Combined with methods for cellular resolution calcium imaging, we found that MEC time cells display context-dependent neural activity that emerges with task learning. Through chemogenetic inactivation we found that MEC activity is necessary for learning of context-dependent interval timing behavior. Finally, we found evidence of a common circuit mechanism that could drive sequential activity of both time cells and spatially selective neurons in MEC. Our work suggests that the clock-like firing of MEC time cells can be modulated by learning, allowing the tracking of various temporal structures that emerge through experience.

Observation of Long-Range Current-Induced Torque in Ni/Pt Bilayers.

The generation of current-induced torques through the spin Hall effect in Pt has been key to the development of spintronics. In prototypical ferromagnetic-metal/Pt devices, the characteristic length of the torque generation is known to be about 1 nm due to the short spin diffusion length of Pt. Here, we report the observation of a long-range current-induced torque in Ni/Pt bilayers. We demonstrate that when Ni is used as the ferromagnetic layer, the torque efficiency increases with the Pt thickness, even when it exceeds 10 nm. The torque efficiency is also enhanced by increasing the Ni thickness, providing evidence that the observed torque cannot be attributed to the spin Hall effect in the Pt layer. These findings, coupled with our semirealistic tight-binding calculations of the current-induced torque, suggest the possibility that the observed long-range torque is dominated by the orbital Hall effect in the Pt layer.

Improved weight initialization for deep and narrow feedforward neural network.

Appropriate weight initialization settings, along with the ReLU activation function, have become cornerstones of modern deep learning, enabling the training and deployment of highly effective and efficient neural network models across diverse areas of artificial intelligence. The problem of "dying ReLU," where ReLU neurons become inactive and yield zero output, presents a significant challenge in the training of deep neural networks with ReLU activation function. Theoretical research and various methods have been introduced to address the problem. However, even with these methods and research, training remains challenging for extremely deep and narrow feedforward networks with ReLU activation function. In this paper, we propose a novel weight initialization method to address this issue. We establish several properties of our initial weight matrix and demonstrate how these properties enable the effective propagation of signal vectors. Through a series of experiments and comparisons with existing methods, we demonstrate the effectiveness of the novel initialization method.

Evaporation-Driven Energy Generation Using an Electrospun Polyacrylonitrile Nanofiber Mat with Different Support Substrates.

Water evaporation-driven energy harvesting is an emerging mechanism for contributing to green energy production with low cost. Herein, we developed polyacrylonitrile (PAN) nanofiber-based evaporation-driven electricity generators (PEEGs) to confirm the feasibility of utilizing electrospun PAN nanofiber mats in an evaporation-driven energy harvesting system. However, PAN nanofiber mats require a support substrate to enhance its durability and stability when it is applied to an evaporation-driven energy generator, which could have additional effects on generation performance. Accordingly, various support substrates, including fiberglass, copper, stainless mesh, and fabric screen, were applied to PEEGs and examined to understand their potential impacts on electrical generation outputs. As a result, the PAN nanofiber mats were successfully converted to a hydrophilic material for an evaporation-driven generator by dip-coating them in nanocarbon black (NCB) solution. Furthermore, specific electrokinetic performance trends were investigated and the peak electricity outputs of Voc were recorded to be 150.8, 6.5, 2.4, and 215.9 mV, and Isc outputs were recorded to be 143.8, 60.5, 103.8, and 121.4 µA, from PEEGs with fiberglass, copper, stainless mesh, and fabric screen substrates, respectively. Therefore, the implications of this study would provide further perspectives on the developing evaporation-induced electricity devices based on nanofiber materials.

Magnon Orbital Nernst Effect in Honeycomb Antiferromagnets without Spin-Orbit Coupling.

Recently, topological responses of magnons have emerged as a central theme in magnetism and spintronics. However, resulting Hall responses are typically weak and infrequent, since, according to present understanding, they arise from effective spin-orbit couplings, which are weaker compared to the exchange energy. Here, by investigating transport properties of magnon orbital moments, we predict that the magnon orbital Nernst effect is an intrinsic characteristic of the honeycomb antiferromagnet and therefore, it manifests even in the absence of spin-orbit coupling. For the electric detection, we propose an experimental scheme based on the magnetoelectric effect. Our results break the conventional wisdom that the Hall transport of magnons requires spin-orbit coupling by predicting the magnon orbital Nernst effect in a system without it, which leads us to envision that our work initiates the intensive search for various magnon Hall effects in generic magnetic systems with no reliance on spin-orbit coupling.

Evaluating the HYDRASHIFT 2/4 Daratumumab assay: a powerful approach to assess treatment response in multiple myeloma.

OBJECTIVES: This study evaluates the HYDRASHIFT assay's effectiveness in mitigating daratumumab interference on serum protein tests during multiple myeloma (MM) treatment, aiming to ensure an accurate assessment of treatment response. METHODS: We analyzed 113 serum samples from 68 MM patients undergoing daratumumab treatment, employing both standard IF and the HYDRASHIFT assay. The assay's precision was determined through intra-day and inter-day variability assessments, while its specificity was verified using serum samples devoid of daratumumab. Comparative analysis of IF results, before and after the application of the HYDRASHIFT assay, facilitated the categorization of treatment responses in alignment with the International Myeloma Working Group's response criteria. RESULTS: The precision underscored the assay's consistent repeatability and reproducibility, successfully eliminating interference of daratumumab-induced Gκ bands. Specificity assessments demonstrated the assay's capability to distinguish daratumumab from both isatuximab and naturally occurring M-proteins. Of the analyzed cases, 91 exhibited successful migration of daratumumab-induced Gκ bands, thereby enhancing the accuracy of treatment response classification. The remaining 22 cases did not show a visible migration complex, likely due to the low concentration of daratumumab in the serum. These findings underscore the assay's critical role in distinguishing daratumumab from endogenous M-protein, particularly in samples with a single Gκ band on standard IF, where daratumumab and endogenous M-protein had co-migrated. CONCLUSIONS: The HYDRASHIFT assay demonstrates high precision, specificity, and utility in the accurate monitoring of treatment responses in MM patients receiving daratumumab. This assay represents a significant advancement in overcoming the diagnostic challenges posed by daratumumab interference.

Identification of the novel HLA-B*13:191 allele by next-generation sequencing.

The novel HLA-B*13:191 allele was detected during the HLA typing for kidney transplantation.

Highly Efficient Room-Temperature Spin-Orbit-Torque Switching in a Van der Waals Heterostructure of Topological Insulator and Ferromagnet.

All-Van der Waals (vdW)-material-based heterostructures with atomically sharp interfaces offer a versatile platform for high-performing spintronic functionalities at room temperature. One of the key components is vdW topological insulators (TIs), which can produce a strong spin-orbit-torque (SOT) through the spin-momentum locking of their topological surface state (TSS). However, the relatively low conductance of the TSS introduces a current leakage problem through the bulk states of the TI or the adjacent ferromagnetic metal layers, reducing the interfacial charge-to-spin conversion efficiency (qICS). Here, a vdW heterostructure is used consisting of atomically-thin layers of a bulk-insulating TI Sn-doped Bi1.1Sb0.9Te2S1 and a room-temperature ferromagnet Fe3GaTe2, to enhance the relative current ratio on the TSS up to ≈20%. The resulting qICS reaches ≈1.65 nm-1 and the critical current density Jc ≈0.9 × 106 Acm-2 at 300 K, surpassing the performance of TI-based and heavy-metal-based SOT devices. These findings demonstrate that an all-vdW heterostructure with thickness optimization offers a promising platform for efficient current-controlled magnetization switching at room temperature.

Core-insulator embedded nanosheet field-effect transistor for suppressing device-to-device variations.

Nanosheet field-effect transistors (NSFETs) have attracted considerable attention for their potential to achieve improved performance and energy efficiency compared to traditional FinFETs. Here, we present a comprehensive investigation of core-insulator-embedded nanosheet field-effect transistors (C-NSFETs), focusing on their improved performance and device-to-device (D2D) variability compared to conventional NSFETs through three-dimensional device simulations. The C-NSFETs exhibit enhanced direct-current (DC) performance, characterized by a steeper subthreshold slope and reduced off-current, indicating better gate electrostatic controllability. Furthermore, the structural design of C-NSFETs enables to demonstrate a notable resilience against D2D variations in nanosheet thickness and doping concentration. In addition, we investigate the effects of interface traps in C-NSFETs, emphasizing the importance of thermal oxidation processes in the formation of core-insulating layers to maintain optimal device performance.

The discovery of gut microbial metabolites as modulators of host susceptibility to acetaminophen-induced hepatotoxicity.

The mammalian gut microbiota plays diverse and essential roles in modulating host physiology. Key mediators determining the outcome of the microbiota-host interactions are the small molecule metabolites produced by the gut microbiota. The liver is the organ massively exposed to gut microbial metabolites, and it serves as the nexus, maintaining healthy interactions between the gut microbiota and host. At the same time, the liver is the primary target of harmful gut microbial metabolites. This review provides an up-to-date list of gut microbial metabolites identified to increase or decrease host susceptibility to APAP-induced liver injury. Signaling pathways and molecular factors involved in the progression of APAP-induced hepatotoxicity are well-established, and we propose that the mouse model of APAP-induced hepatotoxicity serves as an excellent system for uncovering gut microbial metabolites of previously unknown function. Moreover, we envision that gut microbial metabolites identified to alter APAP-induced hepatotoxicity likely have broader implications in other liver diseases. Significance Statement This review provides an overview of recent discoveries from investigating whether and how the gut microbiota modulates the host susceptibility to APAP-induced liver injury. It focuses on the roles of gut bacterial small molecule metabolites as mediators of the interaction between the gut microbiota and the liver. It also illustrates the utility of APAP-induced liver injury as a model to identify gut microbial metabolites with biological function.

Water-Based Generators with Cellulose Acetate: Uncovering the Mechanisms of Power Generation.

Power generation technologies based on water movement and evaporation use water, which covers more than 70% of the Earth's surface and can also generate power from moisture in the air. Studies are conducted to diversify materials to increase power generation performance and validate energy generation mechanisms. In this study, a water-based generator was fabricated by coating cellulose acetate with carbon black. To optimize the generator, Fourier-transform infrared spectroscopy, specific surface area, zeta potential, particle size, and electrical performance analyses were conducted. The developed generator is a cylindrical generator with a diameter of 7.5 mm and length of 20 mm, which can generate a voltage of 0.15 V and current of 82 µA. Additionally, we analyzed the power generation performance using three factors (physical properties, cation effect, and evaporation environment) and proposed an energy generation mechanism. Furthermore, we developed an eco-friendly and low-cost generator using natural fibers with a simple manufacturing process. The proposed generator can contribute to the identification of energy generation mechanisms and is expected to be used as an alternative energy source in the future.

Residual pure intralymphatic carcinoma component only (lymphovascular tumor emboli without invasive carcinoma) after neoadjuvant chemotherapy is associated with poor outcome: Not pathologic complete response.

Residual pure intralymphatic carcinoma (PIC) component only after neoadjuvant chemotherapy (NAC) is lymphovascular tumor emboli without invasive carcinoma and extremely rare form of residual tumor after NAC. Although several studies have been published, the prognostic influence of residual PIC component only had not been fully evaluated. This study aims to evaluate the clinicopathologic features and the prognostic value of residual PIC component only. We reviewed the 251 patients with no residual invasive carcinoma in breast after NAC and found 12 patients with residual PIC component only after NAC. Five cases were triple negative, 6 were HER2 positive, and 1 was estrogen receptor positive and HER2 negative. The extent of PIC component ranged from 0.18 to 50.00 mm. The detailed microscopic PIC component findings did not significantly correlate with regional lymph node metastasis, local recurrence, or distant metastasis (p > 0.05). In multivariate survival analysis, the presence of lymph node metastasis and pretreatment ki-67 labeling index more than 50 % was statistically associated with greater risk of relapse [Cox proportional hazards ratio (HR) = 3.236, 95 % confidence interval (CI), 1.461-7.280, p = 0.004; HR = 3.046, 95 % CI, 1.421-6.529, p = 0.004, respectively) and residual PIC component only tended to be associated with greater risk of relapse (HR = 2.378, 95 % CI, 0.853-6.631; p = 0.098), but not reached to statistically significance. In patients without lymph node metastasis, the presence of residual PIC component only was associated with worse disease-free survival (p = 0.004). Although the number of published studies still limited, residual residual PIC component only after NAC is associated with poor outcome, and it should not be considered as pathological complete response.

Medial entorhinal cortex plays a specialized role in learning of flexible, context-dependent interval timing behavior.

Episodic memory requires encoding the temporal structure of experience and relies on brain circuits in the medial temporal lobe, including the medial entorhinal cortex (MEC). Recent studies have identified MEC 'time cells', which fire at specific moments during interval timing tasks, collectively tiling the entire timing period. It has been hypothesized that MEC time cells could provide temporal information necessary for episodic memories, yet it remains unknown whether MEC time cells display learning dynamics required for encoding different temporal contexts. To explore this, we developed a novel behavioral paradigm that requires distinguishing temporal contexts. Combined with methods for cellular resolution calcium imaging, we find that MEC time cells display context-dependent neural activity that emerges with task learning. Through chemogenetic inactivation we find that MEC activity is necessary for learning of context-dependent interval timing behavior. Finally, we find evidence of a common circuit mechanism that could drive sequential activity of both time cells and spatially selective neurons in MEC. Our work suggests that the clock-like firing of MEC time cells can be modulated by learning, allowing the tracking of various temporal structures that emerge through experience.

Next-Generation Sequencing in Breast Cancer Patients: Real-World Data for Precision Medicine.

PURPOSE: Breast cancer is one of the most common causes of cancer-related death in females. Numerous drug-targetable biomarkers and predictive biomarkers have been developed. Some researchers have expressed doubts about the need for next-generation sequencing (NGS) studies in daily practice. This study analyzed the results of NGS studies on breast cancer at a single institute and evaluated the real-world applications of NGS data to precision medicine for breast cancer. MATERIALS AND METHODS: We retrospectively collected the results of NGS studies and analyzed the histopathologic features and genetic profiles of patients treated for breast cancer from 2010 to 2021. Seventy cases had data from CancerSCAN, a customized panel of 375 cancer-associated genes, and 110 cases had data from TruSight Oncology 500. RESULTS: The most frequently detected single nucleotide variant was the TP53 mutation (123/180, 68.3%), followed by PIK3CA mutations (51/180, 28.3%). Estrogen receptor 1 (ESR1) mutation was detected in 11 patients (6.1%), of whom 10 had hormone receptor-positive, human epidermal growth factor receptor 2-negative breast cancer, and two had no history of prior endocrine therapy. Based on their NGS study results, 13 patients (7.2%) received target therapy. Among them, four patients had a BRCA1 or BRCA2 germline mutation, and nine patients had a PIK3CA mutation. CONCLUSION: NGS can provide information about predictive biomarkers and drug-targetable biomarkers that can enable treatment and participation in clinical trials based on precision medicine. Further studies should be conducted to excavate novel drug-targetable biomarkers and develop additional target therapies.

Brain volume increase after discontinuing natalizumab therapy: Evidence for reversible pseudoatrophy.

BACKGROUND: The phenomenon of pseudoatropy after initiation of anti-inflammatory therapy is believed to be reversible, but a rebound in brain volume following cessation of highly-effective therapy has not been reported. OBJECTIVES: To evaluate brain volume change in a treatment interruption study (RESTORE) in which relapsing-remitting multiple sclerosis (RRMS) patients were randomized to switch from natalizumab to placebo, from natalizumab to once-monthly intravenous methylprednisolone (IVMP), or to remain on natalizumab. METHODS: T2 lesion volume (T2LV), baseline normalized brain volumes, and follow-up percent brain volume changes (PBVC) were calculated. Approximate T2 relaxation-time (pT2) was calculated within the brain mask and the T2 lesions to estimate changes in water content. Linear mixed effects models were used to detect differences in T2LV, pT2 in whole brain, pT2 in T2-weighted lesions, and PBVC among the placebo, natalizumab, and IVMP groups. We also estimated contributions of T2LV and pT2 (in whole brain and T2 lesions) to PBVC. RESULTS: T2LV increased in the placebo group (by 0.66 ml/year, p<0.0001) and IVMP (+1.98 ml/year, p = 0.05) groups relative to the natalizumab group. The rates of PBVC were significantly different: -0.239%/year with continued natalizumab and +0.126 %/year after switch to placebo (p = 0.03), while the IVMP group showed brain volume loss (-0.74 %/ year, p = 0.08). pT2 was not statistically different between the groups (p ≥ 0.29) and did not have significant effects on PBVC (p ≥ 0.25). CONCLUSION: The increase in the brain volume in patients witching from natalizumab to placebo is consistent with reversal of so-called pseudoatrophy after starting natalizumab.

Metagenomes and metagenome-assembled genomes from a sequentially fed anaerobic digester treating solid organic municipal waste.

We present a data set of four metagenomes and 281 metagenome-assembled genomes describing the microbial community of a laboratory-scale high solids anaerobic digester. Our objective was to obtain information on the coding potential of the microbial community and draft genomes of the most abundant organisms in the digester.

Low-Cost Angle Sensor for Robotics Applications Using Plastic Optical Fiber Based on Optical Loss Mechanism.

Robotic systems and the human body consist of numerous joint structures, all of which require precise angle adjustments. At present, encoder, strain gauge, and electrical resistance-based sensors are commonly used for angle measurement. However, these sensors have limitations when used in underwater or in environments with strong electromagnetic waves. Therefore, we have developed an angle sensor based on step-index profile plastic optical fiber (SI-POF), which is cost-effective and highly durable, in this study in order to overcome the limitations of existing angle measurement sensors. To this end, the amount of light loss according to the gab and angle changes that occur when the POF angle sensor is applied to the robot arm was experimentally measured, and based on the results, a simulation of the amount of light loss when the two losses occurred at the same time was conducted. In addition, the performance of the POF angle sensor was evaluated by measuring sensitivity and resolution, and comparative verification with a commonly used encoder was conducted to verify the reliability of sensors in extreme environments, such as those with electromagnetic fields and those that are underwater. Through this, the reliability and practicality of the POF angle sensor were confirmed. The results obtained in this study suggest that POF-based angle sensors can contribute to the development of the biomimetic robot industry as well as ordinary robots, especially in environments where existing sensors are difficult to apply, such as areas with underwater or electromagnetic interference (EMI).