Search

Astragalus membranaceus is a medicinal plant mainly used in East Asia and contains abundant secondary metabolites. Despite the importance of this plant, the available genomic and genetic information is still limited. De novo transcriptome construction is recognized as an essential method for transcriptome research when reference genome information is incomplete. In this study, we constructed three individual transcriptome sets (unigene sets) for detailed analysis of the phenylpropanoid biosynthesis pathway, a major metabolite of A. membranaceus. Set-1 was a circular consensus sequence (CCS) generated using PacBio sequencing (PacBio-seq). Set-2 consisted of hybridized assembled unigenes with Illumina sequencing (Illumina-seq) reads and PacBio CCS using rnaSPAdes. Set-3 unigenes were assembled from Illumina-seq reads using the Trinity software. Construction of multiple unigene sets provides several advantages for transcriptome analysis. First, it provides an appropriate expression filtering threshold for assembly-based unigenes: a threshold transcripts per million (TPM) ≥ 5 removed more than 88% of assembly-based unigenes, which were mostly short and low-expressing unigenes. Second, assembly-based unigenes compensated for the incomplete length of PacBio CCSs: the ends of the 5`/3` untranslated regions of phenylpropanoid-related unigenes derived from set-1 were incomplete, which suggests that PacBio CCSs are unlikely to be full-length transcripts. Third, more isoform unigenes could be obtained from multiple unigene sets; isoform unigenes missing in Set-1 were detected in set-2 and set-3. Finally, gene ontology and Kyoto Encyclopedia of Genes and Genomes analyses showed that phenylpropanoid biosynthesis and carbohydrate metabolism were highly activated in A. membranaceus roots. Various sequencing technologies and assemblers have been developed for de novo transcriptome analysis. However, no technique is perfect for de novo transcriptome analysis, suggesting the need to construct multiple unigene sets. This method enables efficient transcript filtering and detection of longer and more diverse transcripts.

We experienced a case in which a central venous catheter (CVC) was misplaced into the wrong vein, which was mistaken for the internal jugular vein (IJV), identified by chest x-ray and ultrasound. The vertebral vein passes through the transverse foramina from the atlas to the 6th cervical vertebra. After exiting the transverse foramen of the 6th vertebra, the vein subsequently runs anterolateral to the vertebral artery and posterior to the IJV and drains the innominate vein. In this case, chest x-ray and ultrasound revealed that the inserted CVC had a course very similar to the vertebral vein. The misplacement of a CVC into the vertebral vein might occur from excessive rotation of the patient's head, which leads to alterations in the cervical vascular anatomy, and from deep insertion of the puncture needle. Therefore, it is advised, for safe CVC insertion, to minimize a patient's head rotation and to make use of ultrasound when the anatomical structures cannot be clearly identified.

ABSTRACTThis study presents a comparison of prediction performances by an artificial neural network (ANN), well-known deep convolutional neural network (D-CNN) models, and four proposed shallow convolutional neural network (S-CNN) models to forecast three key physicochemical properties (PCPs): salinity, °Brix, and moisture content of kimchi sauce (KS). The S-CNN models effectively minimized underfitting issues found in D-CNN models, predicting PCPs with a low error rate even with small image datasets. Furthermore, the ANN model using color values also allowed for competitive predictions. We used two nondestructive prediction strategies: (i) using color values with ANNs for immediate application in small-scale enterprises and (ii) using photographs as input for S-CNN models, allowing for faster and more accurate quality prediction. These results highlight the potential for image-based quality prediction in food science, possibly enhancing the efficiency and accuracy of real-time quality control. Future enhancements could incorporate additional data sources for improved predictive performance.

Abstract Navigating the challenges of data-driven speech processing, one of the primary hurdles is accessing reliable pathological speech data. While public datasets appear to offer solutions, they come with inherent risks of potential unintended exposure of patient health information via re-identification attacks. Using a comprehensive real-world pathological speech corpus, with over n $$=$$ = 3800 test subjects spanning various age groups and speech disorders, we employed a deep-learning-driven automatic speaker verification (ASV) approach. This resulted in a notable mean equal error rate (EER) of $$0.89 \pm 0.06 \%$$ 0.89 ± 0.06 % , outstripping traditional benchmarks. Our comprehensive assessments demonstrate that pathological speech overall faces heightened privacy breach risks compared to healthy speech. Specifically, adults with dysphonia are at heightened re-identification risks, whereas conditions like dysarthria yield results comparable to those of healthy speakers. Crucially, speech intelligibility does not influence the ASV system’s performance metrics. In pediatric cases, particularly those with cleft lip and palate, the recording environment plays a decisive role in re-identification. Merging data across pathological types led to a marked EER decrease, suggesting the potential benefits of pathological diversity in ASV, accompanied by a logarithmic boost in ASV effectiveness. In essence, this research sheds light on the dynamics between pathological speech and speaker verification, emphasizing its crucial role in safeguarding patient confidentiality in our increasingly digitized healthcare era.

Abstract Diabetic nephropathy (DN) is associated with kidney fibrosis. A previous study revealed that periostin (POSTN) contributes to kidney fibrosis. This study examined the role of POSTN in DN. The urinary concentrations of POSTN and TNC increased according to the severity of DN in human samples. Streptozotocin (STZ) was administered after unilateral nephrectomy (UNXSTZ) to induce DN in wild-type and Postn-null mice. Four experimental groups were generated: wild-typeham (WT Sham), wild-type UNXSTZ (WT STZ), Postn-null Sham (KO Sham), and Postn-null UNXSTZ (KO STZ). After 20 weeks, the KO STZ group had lower levels of urine albumin excretion, glomerular sclerosis, and interstitial fibrosis than those of the WT STZ group. Additionally, the KO STZ group had lower expression of fibrosis markers, including TNC. The KO STZ group showed better glucose regulation than the WT STZ model. Furthermore, the KO STZ group exhibited significantly preserved pancreatic islet integrity and insulin expression. HK-2 cells were used to observe the aggravation of fibrosis caused by POSTN under TGF-β conditions. We stimulated INS-1 cells with streptozotocin and evaluated the viability of these cells. The anti-POSTN antibody treatment of INS-1 cells with streptozotocin resulted in higher cell viability than that with treatment with streptozotocin alone. The absence of POSTN in DN contributes to renal fibrosis alleviation by improving pancreatic β-cell function. Additionally, there is an association between POSTN and TNC.

Background As a leading cause of chronic kidney disease, clinical demand for noninvasive biomarkers of diabetic kidney disease (DKD) beyond proteinuria is increasing. Metabolomics is a popular method to identify mechanisms and biomarkers. We investigated urinary targeted metabolomics in DKD patients. Methods We conducted a targeted metabolomics study of 26 urinary metabolites in consecutive patients with DKD stage 1 to 5 (n = 208) and healthy controls (n = 26). The relationships between estimated glomerular filtration rate (eGFR) or urine protein-creatinine ratio (UPCR) and metabolites were evaluated. Multivariate Cox analysis was used to estimate relationships between urinary metabolites and the target outcome, end-stage renal disease (ESRD). C statistics and time-dependent receiver operating characteristics (ROC) were used to assess diagnostic validity. Results During a median 4.5 years of follow-up, 103 patients (44.0%) progressed to ESRD and 65 (27.8%) died. The median fold changes of nine metabolites belonged to monosaccharide and tricarboxylic acid (TCA) cycle metabolites tended to increase with DKD stage. Myo-inositol, choline, and citrates were correlated with eGFR and choline, while mannose and myo-inositol were correlated with UPCR. Elevated urinary monosaccharide and TCA cycle metabolites showed associations with increased morality and ESRD progression. The predictive power of ESRD progression was high, in the order of choline, myo-inositol, and citrate. Although urinary metabolites alone were less predictive than serum creatinine or UPCR, myo-inositol had additive effect with serum creatinine and UPCR. In time-dependent ROC, myo-inositol was more predictive than UPCR of 1-year ESRD progression prediction. Conclusion Myo-inositol can be used as an additive biomarker of ESRD progression in DKD.

AbstractKimchi, a traditional fermented food in Korea, has gained global attention owing to its health benefits. Herein, we determined the fermentation and quality characteristics of radish kimchi containing different sweeteners using physicochemical and sensory evaluations. The characteristics of radish kimchi fermented with sucrose, saccharin, stevioside, or sorbitol were evaluated. Kimchi without the addition of any sweetener was used as a control. The kimchi samples were stored for 7 weeks at 4°C; thereafter, their physicochemical properties and sensory characteristics were measured. Kimchi containing sweeteners was found to have higher acidity and lower pH than the control during fermentation, except for kimchi with saccharin, which had a lower acetic acid content and higher hardness than kimchi with other sweeteners. Based on the results of sensory analysis, the preferences for kimchi with saccharin, sucrose, and sorbitol were similar. These results suggest that different types of sweeteners significantly influence kimchi fermentation and sensory characteristics.

Abstract Background Reducing salt intake helps prevent complications of cerebrovascular disease. To help patients accept a low-sodium diet, the salty taste test is used to evaluate how much salt an individual actually consumes. The aim of this study was to help patients with hypertension reduce their salt intake by helping them recognize the difference between their subjective perception of saltiness and the objective test results. Methods We enrolled workers who visited a local occupational health institution in the period from April to August 2019. Demographic and physical characteristics were recorded. Blood pressure measurement and use of medication were also recorded. A questionnaire was used to investigate whether people liked or disliked salty food, i.e., preference for saltiness, and whether they usually ate salty, normal, or fresh food, i.e., the subjective perception of saltiness. Subsequently, the taste determination kit provided by the Ministry of Food and Drug Safety was used to objectively test saltiness at various salty taste concentrations. The Ministry of Food and Drug Safety program (No. 10-093760) was used as the salty taste judgment tool. Results A total of 86 workers were surveyed. Eleven of 18 workers (61.1%) who reported usually eating fresh food actually ate normal or salty food. Thirteen of 37 workers (35.1%) who reported eating normal food actually ate salty food. Thirteen of 31 workers (41.9%) who reported eating salty food actually ate fresh or normal food. Of 46 workers who reported disliking salty food, 14 (30.4%) actually ate salty food, while 20 (43.5%) ate normal food. The subjective perception and preference for saltiness were not significantly correlated with the objective test results (P = ​​0.085 and P = 0.110, respectively). As for the subjective perception and preference for saltiness, Cohen’s weighted kappa for the taste judgment result were 0.23 and 0.22, respectively, indicating a low degree of agreement. Conclusions In dietary counseling to prevent cerebrovascular and cardiovascular diseases, rather than relying on the subjective perception of saltiness, a salty taste test should be performed such that people can recognize their salty food eating habits through objective evaluation.

In this paper, we propose a joint training framework that efficiently combines time-domain speech enhancement (SE) with an end-to-end (E2E) automatic speech recognition (ASR) system utilizing attention-based latent features. Using the latent feature to train E2E ASR implies that various time-domain SE models can be applied for noise-robust ASR and our modified framework is the first approach. We implement a fully E2E scheme pipelined from SE to ASR without domain knowledge and short-time Fourier transform (STFT) consistency constraints by applying a time-domain SE model. Therefore, using the latent feature of time-domain SE as appropriate features for ASR inputs is the main approach in our framework. Furthermore, we apply an attention algorithm to the time-domain SE model to selectively concentrate on certain latent features to achieve the better relevant feature for the task. Detailed experiments are conducted on the hybrid CTC/attention architecture for E2E ASR, and we demonstrate the superiority of our approach compared to baseline ASR systems trained with Mel filter bank coefficients features as input. Compared to the baseline ASR model trained only on clean data, the proposed joint training method achieves 63.6% and 86.8% relative error reductions on the TIMIT and WSJ “matched” test set, respectively.

Safety and efficacy are essential in the process of disease treatment. However, off-label medication use is inevitable because various medications do not contain regulatory labels for pediatric use. We aimed to examine off-label medication use and analyze the risk factors correlated with adverse drug reactions (ADRs). This study was performed retrospectively using electronic medical data from a pediatric intensive care unit (PICU) of a tertiary hospital in Korea from July 2019 to June 2020. A total 6,183 prescribed medications from 502 PICU patients were examined in the present study. A total of 80% were infants or children, and 96.0% of them were treated with off-label medications. It was discovered that 4,778 off-label cases (77.2%) of the top 100 drugs had prescriptions with dosage (67.8%). Drugs prescribed to patients admitted to the cardiothoracic department (odds ratio [OR], 3.248; p = 0.019), total number of medications (OR, 1.116; p = 0.001), and length of PICU stay of ≥ 7 days (OR, 4.981; p = 0.008) were significantly associated with ADRs. ADRs were noted to be more severe in off-label use (p = 0.0426). For appropriate medication use, evidence regarding the safety of off-label medications is required and ultimately reflected in the official regulation.

Abstract A highly contagious virus, severe acute respiratory syndrome coronavirus 2, caused the coronavirus disease 19 (COVID-19) pandemic (SARS-CoV-2). SARS-CoV-2 genetic variants have been reported to circulate throughout the COVID-19 pandemic. COVID-19 symptoms include respiratory symptoms, fever, muscle pain, and breathing difficulty. In addition, up to 30% of COVID-19 patients experience neurological complications such as headaches, nausea, stroke, and anosmia. However, the neurotropism of SARS-CoV-2 infection remains largely unknown. This study investigated the neurotropic patterns between the B1.617.2 (Delta) and Hu-1 variants (Wuhan, early strain) in K18-hACE2 mice. Despite both the variants inducing similar pathogenic patterns in various organs, B1.617.2-infected K18-hACE2 mice demonstrated a higher range of disease phenotypes such as weight loss, lethality, and conjunctivitis when compared to those in Hu-1-infected mice. In addition, histopathological analysis revealed that B1.617.2 infects the brain of K18-hACE2 mice more rapidly and effectively than Hu-1. Finally, we discovered that, in B1.617.2-infected mice, the early activation of various signature genes involved innate cytokines and that the necrosis-related response was most pronounced than that in Hu-1-infected mice. The present findings indicate the neuroinvasive properties of SARS-CoV-2 variants in K18-hACE2 mice and link them to fatal neuro-dissemination during the disease onset.

Abstract We aimed to determine the metabolomic profile of kidney cells under high glucose conditions and following sodium-glucose cotransporter 2 (SGLT2) inhibitor treatment. Targeted metabolomics using the Absolute IDQ-p180 kit was applied to quantify metabolites in kidney cells stimulated with high glucose (25 and 50 mM) and treated with SGLT2 inhibitor, dapagliflozin (2 µM). Primary cultured human tubular epithelial cells and podocytes were used to identify the metabolomic profile in high glucose conditions following dapagliflozin treatment. The levels of asparagine, PC ae C34:1, and PC ae C36:2 were elevated in tubular epithelial cells stimulated with 50 mM glucose and were significantly decreased after 2 µM dapagliflozin treatment. The level of PC aa C32:0 was significantly decreased after 50 mM glucose treatment compared with the control, and its level was significantly increased after dapagliflozin treatment in podocytes. The metabolism of glutathione, asparagine and proline was significantly changed in tubular epithelial cells under high-glucose stimulation. And the pathway analysis showed that aminoacyl-tRNA biosynthesis, arginine and proline metabolism, glutathione metabolism, valine, leucine and isoleucine biosynthesis, phenylalanine, tyrosine, and tryptophan biosynthesis, beta-alanine metabolism, phenylalanine metabolism, arginine biosynthesis, alanine, aspartate and glutamate metabolism, glycine, serine and threonine metabolism were altered in tubular epithelial cells after dapagliflozin treatment following 50 mM glucose compared to those treated with 50 mM glucose.

This study aimed to investigate the effects of salted shrimp on kimchi fermentation. Four types of kimchi (KWD, KWR, KWC, and KWY), based on the variety of salted shrimp (Detteugijeot, Red shrimp jeot, Chujeot, and Yukjeot, respectively), were prepared and stored at 4°C for 6 weeks. Amino nitrogen content in KWD was significantly higher after 4 weeks (P

Abstract Background Aggregated amyloid-β (Aβ) is considered a pathogenic initiator of Alzheimer’s disease (AD), in strong association with tau hyperphosphorylation, neuroinflammation, synaptic dysfunction, and cognitive decline. As the removal of amyloid burden from AD patient brains by antibodies has shown therapeutic potential, the development of small molecule drugs inducing chemical dissociation and clearance of Aβ is compelling as a therapeutic strategy. In this study, we synthesized and screened aryloxypropanolamine derivatives and identified 1-(3-(2,4-di-tert-pentylphenoxy)-2-hydroxypropyl)pyrrolidin-1-ium chloride, YIAD002, as a strong dissociator of Aβ aggregates. Methods The dissociative activity of aryloxypropanolamine derivatives against Aβ aggregates were evaluated through in vitro assays. Immunohistochemical staining, immunoblot assays, and the Morris water maze were used to assess the anti-Alzheimer potential in YIAD002-treated 5XFAD and transgenic APP/PS1 mice. Target-ligand interaction mechanism was characterized via a combination of peptide mapping, fluorescence dissociation assays, and constrained docking simulations. Results Among 11 aryloxypropanolamine derivatives, YIAD002 exerted strongest dissociative activity against β-sheet-rich Aβ aggregates. Upon oral administration, YIAD002 substantially reduced amyloid burden and accordingly, improved cognitive performance in the Morris water maze and attenuated major pathological hallmarks of AD including tauopathy, neuroinflammation, and synaptic protein loss. Mechanism studies suggest that YIAD002 interferes with intermolecular β-sheet fibrillation by directly interacting with KLVFFA and IGLMVG domains of Aβ. In addition, YIAD002 was found to possess dissociative activity against aggregates of pyroglutamate-modified Aβ and tau. Conclusions Collectively, our results evince the potential of chemical-driven dissociation of Aβ aggregates by aryloxypropanolamines as a therapeutic modality of the amyloid clearance approach.

BackgroundTechnology can assist in providing effective infectious disease management, but it can also become a source of social injustice and inequality. To control the rapidly increasing SARS-CoV-2 infections and promote effective vaccine administration, both South Korea and Japan have been using several technology-based systems and mobile apps. However, their different approaches to technology use have yielded contrasting social implications. ObjectiveThrough comparative studies of the use of digital technologies for pandemic management and its social implications in Japan and South Korea, this study aimed to discuss whether the active and optimal use of technology for pandemic management can occur without subverting or compromising important social values, such as privacy and equality. MethodsThis study compared the social implications of Japan’s and South Korea’s contrasting approaches to technology implementation for COVID-19 pandemic management in early 2022. ResultsDigital technologies have been actively and comprehensively used in South Korea, enabling effective COVID-19 management, but have raised serious concerns about privacy and social equality. In Japan, technologies have been more carefully implemented, thereby not causing similar social concerns, but their effectiveness in supporting COVID-19 regulations has been criticized. ConclusionsPotential social implications such as equality concerns, the balance between public interest and individual rights, and legal implications must be carefully assessed in conjunction with effective and optimal infectious disease control to achieve sustainable use of digital health technologies for infectious disease management in the future.

The COVID-19 pandemic is caused by the zoonotic SARS-CoV-2 virus. A wide range of animals that interact with humans have been investigated to identify potential infections. As the extent of infection became more apparent, extensive animal monitoring became necessary to assess their susceptibility. This study analyzed nasal swabs and blood samples collected from randomly selected Korean native cattle and Korean native black goats. The tests conducted included real-time qPCR to detect SARS-CoV-2 antigens, an ELISA to detect antibodies, and a plaque reduction neutralization test (PRNT) to determine the presence of neutralizing antibodies. Among the 1798 animals tested (consisting of 1174 Korean native cattle and 624 Korean native black goats), SARS-CoV-2 viral RNA was detected in one Korean native cattle and one Korean native black goat. ELISA testing revealed positive results for antibodies in 54 Korean native cattle (4.60%) and 16 Korean native black goats (2.56%), while PRNTs yielded positive results in 51 Korean native cattle (4.34%) and 14 Korean native black goats (2.24%). The presence of SARS-CoV-2 antigens and/or antibodies was identified in animals on farms where farmworkers were already infected. It is challenging to completely rule out the possibility of reverse zoonotic transmission from humans to livestock in Korea, although the transmission is not to the same extent as it is in highly susceptible animal species like minks, cats, and dogs. This is due to the limited geographical area and the dense, intensive farming practices implemented in these regions. In conclusion, continuous viral circulation between humans and animals is inevitable, necessitating ongoing animal monitoring to ensure public health and safety.

Ensuring operational reliability in machinery requires accurate fault detection. While time-domain vibration pulsation signals are intuitive for pattern recognition and feature extraction, downsampling can reduce analytical complexity, but may result in low-precision data, affecting fault detection performance. To address this, we propose time–frequency feature fusion, combining information from both the time and frequency domains for fault detection. Our approach transforms vibrational pulse data into instantaneous revolutions per minute (RPM) and employs statistical analysis for the time-domain features. For the frequency-domain features, we use the combined method of empirical mode decomposition and independent component analysis (EMD-ICA), along with the Wigner bispectrum method to capture the nonlinear characteristics and phase conjugation. Using a deep neural network (DNN), we classify the anomaly states, demonstrating the effectiveness and versatility of our approach in detecting anomalies and improving diagnostic precision. Compared to using time or frequency features alone, our time–frequency feature fusion model achieves higher accuracy, with 100% accuracy at lower downsampling rates and 96.3% accuracy at a downsampling rate of 100×.

The roles of neutrophils in renal inflammation are currently unclear. On examining these cells in the unilateral ureteral obstruction murine model of chronic kidney disease, we found that the injured kidney bore a large and rapidly expanding population of neutrophils that expressed the eosinophil marker Siglec-F. We first verified that these cells were neutrophils. Siglec-F+ neutrophils were recently detected in several studies in other disease contexts. We then showed that a) these cells were derived from conventional neutrophils in the renal vasculature by TGF-β1 and GM-CSF; b) they differed from their parent cells by more frequent hypersegmentation, higher expression of profibrotic inflammatory cytokines, and notably, expression of collagen 1; and c) their depletion reduced collagen deposition and disease progression, but adoptive transfer increased renal fibrosis. These findings have thus unveiled a subtype of neutrophils that participate in renal fibrosis and a potentially new therapeutic target in chronic kidney disease.

The core design elements of the motor for the co-robot joint are miniaturization and high torque. In this paper, a dual rotor axial-flux permanent magnet (DRAFPM) motor is proposed to improve the performance of robot joints. DRAFPM motors have the advantage of reducing iron loss and minimizing volume because they can remove stator yoke. When designing a motor with the same volume, it can be designed to increase the height of the fixed ruler by the thickness of the yoke of the fixed ruler and increase the number of turns. In the case of existing axial-flux permanent magnet (AFPM) motors, the shape of the three-dimensional structure is limited by radial laminating of stator. Therefore, considering the production of 3D printing, the shape of stator shoe is designed. The optimal design problem of DRAFPM motor consists of real and integer design variables. In addition, due to the structural characteristics of DRAFPM motors, 3D finite element analysis (FEA) is required, so it takes a long time to interpret. Therefore, this paper proposes an efficient optimal design process to optimize the remaining real design variables after prioritizing the integer design variables. The proposed optimization process is applied to the DRAFPM motor for robot joints, and the optimal design plan satisfying the design function is derived from various design variables to prove the validity of the optimization process.

Brassinin (BSN), a potent phytoalexin found in cruciferous vegetables, has been found to exhibit diverse anti-neoplastic effects on different cancers. However, the impact of BSN on chronic myelogenous leukemia (CML) cells and the possible mode of its actions have not been described earlier. We investigated the anti-cytotoxic effects of BSN on the KBM5, KCL22, K562, and LAMA84 CML cells and its underlying mechanisms of action in inducing programmed cell death. We noted that BSN could induce apoptosis, autophagy, and paraptosis in CML cells. BSN induced PARP cleavage, subG1 peak increase, and early apoptosis. The potential action of BSN on autophagy activation was confirmed by an LC3 expression and acridine orange assay. In addition, BSN induced paraptosis through increasing the reactive oxygen species (ROS) production, mitochondria damage, and endoplasmic reticulum (ER) stress. Moreover, BSN promoted the activation of the MAPK signaling pathway, and pharmacological inhibitors of this signaling pathway could alleviate all three forms of cell death induced by BSN. Our data indicated that BSN could initiate the activation of apoptosis, autophagy, and paraptosis through modulating the MAPK signaling pathway.

In breast cancer (BC), STAT3 is hyperactivated. This study explored the design of imidazopyridine-tethered pyrazolines as a de novo drug strategy for inhibiting STAT3 phosphorylation in human BC cells. This involved the synthesis and characterization of two series of compounds namely, 1-(3-(2,6-dimethylimidazo [1,2-a]pyridin-3-yl)-5-(3-nitrophenyl)-4,5-dihydro-1H-pyrazol-1-yl)-2-(4-(substituted)piperazin-1-yl)ethanone and N-substituted-3-(2,6-dimethylimidazo[1,2-a]pyridin-3-yl)-5-(3-nitrophenyl)-4,5-dihydro-1H-pyrazoline-1-carbothioamides. Compound 3f with 2,3-dichlorophenyl substitution was recognized among the tested series as a lead structure that inhibited the viability of MCF-7 cells with an IC50 value of 9.2 μM. A dose- and time-dependent inhibition of STAT3 phosphorylation at Tyr705 and Ser727 was observed in MCF-7 and T47D cells when compound 3f was added in vitro. Calculations using density functional theory showed that the title compounds HOMOs and LUMOs are situated on imidazopyridine-pyrazoline and nitrophenyl rings, respectively. Hence, compound 3f effectively inhibited STAT3 phosphorylation in MCF-7 and T47D cells, indicating that these structures may be an alternative synthon to target STAT3 signaling in BC.

In this study, a noncontact fabric loop sensor based on magnetic-field-induced conductivity, which can simultaneously detect cardiac activity and respiration signals, was developed and the effects of the sensor’s shape and measurement position on the sensing performance were analyzed. Fifteen male subjects in their twenties wore sleeveless shirts equipped with various types of fabric loop sensors (spiky, extrusion, and spiral), and the cardiac activity and respiratory signals were measured twice at positions P2, P4, and P6. The measurements were verified by comparing them against the reference electrocardiogram (ECG) and respiratory signals measured using BIOPAC® (MP150, ECG100B, RSP100C). The waveforms of the raw signal measured by the fabric loop sensor were filtered with a bandpass filter (1–20 Hz) and qualitatively compared with the ECG signal obtained from the Ag/AgCI electrode. Notwithstanding a slight difference in performance, the three fabric sensors could simultaneously detect cardiac activity and respiration signals at all measurement positions. In addition, it was verified through statistical analysis that the highest-quality signal was obtained at the measurement position of P4 or P6 using the spiral loop sensor.

Driver monitoring systems play an important role in lower to mid-level autonomous vehicles. Our work focuses on the detection of cognitive load as a component of driver-state estimation to improve traffic safety. By inducing single and dual-task workloads of increasing intensity on 51 subjects, while continuously measuring signals from multiple modalities, based on physiological measurements such as ECG, EDA, EMG, PPG, respiration rate, skin temperature and eye tracker data, as well as behavioral measurements such as action units extracted from facial videos, performance metrics like reaction time and subjective feedback using questionnaires, we create ADABase (Autonomous Driving Cognitive Load Assessment Database) As a reference method to induce cognitive load onto subjects, we use the well-established n-back test, in addition to our novel simulator-based k-drive test, motivated by real-world semi-autonomously vehicles. We extract expert features of all measurements and find significant changes in multiple modalities. Ultimately we train and evaluate machine learning algorithms using single and multimodal inputs to distinguish cognitive load levels. We carefully evaluate model behavior and study feature importance. In summary, we introduce a novel cognitive load test, create a cognitive load database, validate changes using statistical tests, introduce novel classification and regression tasks for machine learning and train and evaluate machine learning models.

Background: This study investigated the optimal coefficient of variance (%CV) for preventing hypoglycemia based on real-time continuous glucose monitoring (rt-CGM) data in people with type 1 diabetes mellitus (T1DM) already achieving their mean glucose (MG) target. Methods: Data from 172 subjects who underwent rt-CGM for at least 90 days and for whom 439 90-day glycemic profiles were available were analyzed. Receiver operator characteristic analysis was conducted to determine the cut-off value of %CV to achieve time below range (%TBR)<54 mg/dL <1 and =0. Results: Overall mean glycosylated hemoglobin was 6.8% and median %TBR<54 mg/dL was 0.2%. MG was significantly higher and %CV significantly lower in profiles achieving %TBR<54 mg/dL <1 compared to %TBR<54 mg/dL ≤1 (all P<0.001). The cut-off value of %CV for achieving %TBR<54 mg/dL <1 was 37.5%, 37.3%, and 31.0%, in the whole population, MG >135 mg/dL, and ≥135 mg/dL, respectively. The cut-off value for %TBR<54 mg/dL=0% was 29.2% in MG ≥135 mg/dL. In profiles with MG ≥135 mg/dL, 94.2% of profiles with a %CV <31 achieved the target of %TBR<54 mg/dL <1, and 97.3% with a %CV <29.2 achieved the target of %TBR<54 mg/dL=0%. When MG was >135 mg/dL, 99.4% of profiles with a %CV <37.3 achieved %TBR<54 mg/dL <1. Conclusion: In well-controlled T1DM with MG ≥135 mg/dL, we suggest a %CV <31% to achieve the %TBR<54 mg/dL <1 target. Furthermore, we suggest a %CV <29.2% to achieve the target of %TBR<54 mg/dL =0 for people at high risk of hypoglycemia. [ABSTRACT FROM AUTHOR]

Transglutaminase 2 (TG2) is a calcium-dependent transamidating acyltransferase enzyme of the protein-glutamine γ-glutamyltransferase family implicated in kidney injury. In this study, we identified associations between TG2 and chronic kidney disease (CKD) identified by visualizing TG2 in kidney biopsy samples derived from CKD patients using immunohistochemistry and measuring the plasma TG2 concentrations. Our study revealed a connection between TG2 and the pathological markers of kidney disease. We showed high plasma TG2 levels in samples from patients with advanced CKD. In addition, we observed an increase in TG2 expression in tissues concomitant with advanced CKD in human samples. Moreover, we investigated the effect of TG2 inhibition on kidney injury using cystamine, a well-known competitive inhibitor of TG2. TG2 inhibition reduced apoptosis and accumulation of extracellular molecules (ECM) such as fibronectin and pro-inflammatory cytokine IL-8. Collectively, the increased expression of TG2 that was observed in advanced CKD, hence inhibiting TG2 activity, could protect kidney cells from ECM molecule accumulation, apoptosis, and inflammatory responses, thereby preventing kidney fibrosis.