Association of Higher Hemoglobin Level With Significant Carotid Artery Plaque in the General Population.

Objective: Serum hemoglobin (Hb) level affects the viscosity of blood. Several studies have reported that Hb level is associated with adverse cardiovascular outcome. However, there is a paucity of evidence on the association between serum Hb level and the risk of subclinical atherosclerosis. Thus, the objective of this study was to investigate the relationship between Hb level and risk of carotid plaque in a health checkup cohort. Methods: This retrospective study analyzed a total of 3,805 individuals without history of cardiovascular disease (CVD) who underwent carotid ultrasonography (USG) between January 2016 and June 2018. Participants were divided into 4 groups based on Hb quartiles in each of male and female. Carotid plaque score was calculated based on USG reports. Multivariable logistic regression analysis was performed for each index of quartile groups regarding the risk of carotid plaque. Results: Of 3,805 individuals (mean age, 52.62±10.25 years; 2,674 [70.28%] males), mean Hb level was 15.11±0.75 g/dL in male and 13.35±0.74 g/dL in female. When the Q1 group was compared to the Q4, increasing quartile of Hb was associated with the presence of significant carotid plaque (plaque score ≥3) in male (adjusted odds ratio [OR], 1.538; 95% confidence interval [CI], 1.182-2.001; p=0.001) and female (adjusted OR, 1.749; 95% CI, 1.058-2.676; p=0.01). Conclusion: A high Hb level is associated with an increased risk of carotid plaques in individuals without history of CVD. This finding may support the need for early screening of CVD in individuals with high Hb levels.

A multimodal machine learning model for predicting dementia conversion in Alzheimer's disease.

Alzheimer's disease (AD) accounts for 60-70% of the population with dementia. Mild cognitive impairment (MCI) is a diagnostic entity defined as an intermediate stage between subjective cognitive decline and dementia, and about 10-15% of people annually convert to AD. We aimed to investigate the most robust model and modality combination by combining multi-modality image features based on demographic characteristics in six machine learning models. A total of 196 subjects were enrolled from four hospitals and the Alzheimer's Disease Neuroimaging Initiative dataset. During the four-year follow-up period, 47 (24%) patients progressed from MCI to AD. Volumes of the regions of interest, white matter hyperintensity, and regional Standardized Uptake Value Ratio (SUVR) were analyzed using T1, T2-weighted-Fluid-Attenuated Inversion Recovery (T2-FLAIR) MRIs, and amyloid PET (αPET), along with automatically provided hippocampal occupancy scores (HOC) and Fazekas scales. As a result of testing the robustness of the model, the GBM model was the most stable, and in modality combination, model performance was further improved in the absence of T2-FLAIR image features. Our study predicts the probability of AD conversion in MCI patients, which is expected to be useful information for clinician's early diagnosis and treatment plan design.

Information technology-supported integrated health service for older adults in long-term care settings.

BACKGROUND: To examine the effectiveness and safety of a data sharing and comprehensive management platform for institutionalized older patients. METHODS: We applied information technology-supported integrated health service platform to patients who live at long-term care hospitals (LTCHs) and nursing homes (NHs) with cluster randomized controlled study. We enrolled 555 patients aged 65 or older (461 from 7 LTCHs, 94 from 5 NHs). For the intervention group, a tablet-based platform comprising comprehensive geriatric assessment, disease management, potentially inappropriate medication (PIM) management, rehabilitation program, and screening for adverse events and warning alarms were provided for physicians or nurses. The control group was managed with usual care. Co-primary outcomes were (1) control rate of hypertension and diabetes, (2) medication adjustment (PIM prescription rate, proportion of polypharmacy), and (3) combination of potential quality-of-care problems (composite quality indicator) from the interRAI assessment system which assessed after 3-month of intervention. RESULTS: We screened 1119 patients and included 555 patients (control; 289, intervention; 266) for analysis. Patients allocated to the intervention group had better cognitive function and took less medications and PIMs at baseline. The diabetes control rate (OR = 2.61, 95% CI 1.37-4.99, p = 0.0035), discontinuation of PIM (OR = 4.65, 95% CI 2.41-8.97, p < 0.0001), reduction of medication in patients with polypharmacy (OR = 1.98, 95% CI 1.24-3.16, p = 0.0042), and number of PIMs use (ꞵ = - 0.27, p < 0.0001) improved significantly in the intervention group. There was no significant difference in hypertension control rate (OR = 0.54, 95% CI 0.20-1.43, p = 0.2129), proportion of polypharmacy (OR = 1.40, 95% CI 0.75-2.60, p = 0.2863), and improvement of composite quality indicators (ꞵ = 0.03, p = 0.2094). For secondary outcomes, cognitive and motor function, quality of life, and unplanned hospitalization were not different significantly between groups. CONCLUSIONS: The information technology-supported integrated health service effectively reduced PIM use and controlled diabetes among older patients in LTCH or NH without functional decline or increase of healthcare utilization. TRIAL REGISTRATION: Clinical Research Information Service, KCT0004360. Registered on 21 October 2019.

Transport Dynamics of Water Molecules Confined between Lipid Membranes.

Water molecules confined between biological membranes exhibit a distinctive non-Gaussian displacement distribution, far different from that of bulk water. Here, we introduce a new transport equation for water molecules in the intermembrane space, quantitatively explaining molecular dynamics simulation results. We find that the unique transport dynamics of water molecules stems from the lateral diffusion coefficient fluctuation caused by their longitudinal motion in the direction perpendicular to the membranes. We also identify an interfacial region where water possesses distinct physical properties, which is unaffected by changes in the intermembrane separation.

Predicting the wicking rate of nitrocellulose membranes from recipe data: a case study using ANN at a membrane manufacturing in South Korea.

Lateral flow assays have been widely used for detecting coronavirus disease 2019 (COVID-19). A lateral flow assay consists of a Nitrocellulose (NC) membrane, which must have a specific lateral flow rate for the proteins to react. The wicking rate is conventionally used as a method to assess the lateral flow in membranes. We used multiple regression and artificial neural networks (ANN) to predict the wicking rate of NC membranes based on membrane recipe data. The developed ANN predicted the wicking rate with a mean square error of 0.059, whereas the multiple regression had a square error of 0.503. This research also highlighted the significant impact of the water content on the wicking rate through images obtained from scanning electron microscopy. The findings of this research can cut down the research and development costs of novel NC membranes with a specific wicking rate significantly, as the algorithm can predict the wicking rate based on the membrane recipe.

Taxonomy and Phylogeny of the Aphid Genus Nippolachnus Matsumura, 1917, with Synonymy of the Mysterious Neonippolachnus Shinji, 1924 (Hemiptera: Aphididae: Lachninae).

Nippolachnus Matsumura, 1917 is a small aphid genus from the tribe Tuberolachnini (Hemiptera: Lachninae) occurring in Southeast Asia. Species from this genus are quite characteristic and stand out among lachnids for their morphology and ecological associations. We have performed a revision and phylogenetic analyses to elucidate the relationships within Nippolachnus and other representatives of Tuberolachnini. Here, the taxonomy of the genus is revised based on morphological data to include seven species, three of them newly described: Nippolachnus chakrabartiisp. nov. from India, Nippolachnus sinensissp. nov. from China, and Nippolachnus malayaensissp. nov. from Indonesia. Nippolachnus appear to be non monophyletic genus and a new genus, Indolachnusgen. nov., is described to accommodate Nippolachnus himalayensis (van der Goot, 1917) as Indolachnus himalayensis (van der Goot, 1917) comb. nov. The new genus is a sister group to the remaining Nippolachnus species, which created a monophyletic clade. Neonippolachnus Shinji, 1924 syn. nov. is recognised as a synonym of Nippolachnus, and Neonippolachnus betulae Shinji, 1924 syn. nov. as a synonym of Nippolachnus micromeli Shinji, 1924. For the first time, a scanning electron microscopy study of the sexual generation of N. piri Matsumura, 1917 has been performed. Apterous and alate viviparous females of N. bengalensis Basu and Hille Ris Lambers, 1968, N. piri, and N. micromeli, and alate viviparous females of N. xitianmushanus Zhang and Zhong, 1982 are re-described and illustrated, as well as apterous and alate viviparous females of I. himalayensiscomb. nov. Hitherto unknown morphs of N. micromeli, N. piri, and N. xitianmushanus are described. A lectotype and paralectotypes of N. xitianmushanus are designated herein. Notes on distribution and host plants are given, and keys to apterous and alate viviparous females of the genera Nippolachnus and Indolachnus are also provided.

Percutaneous coronary intervention versus medical therapy in stable angina: a matched cohort study.

OBJECTIVE: It is uncertain whether percutaneous coronary intervention (PCI) in addition to optimal medical therapy (OMT) can reduce adverse clinical events in the long term as compared with OMT alone in patients with pure stable angina. METHODS: We enrolled patients from 2006 to 2010 using the Korean national insurance data. 58 742 patients with pure stable angina with no history of myocardial infarction (MI) nor PCI were candidate, and finally, 5673 patients in the PCI plus OMT group and 5673 in the OMT alone group were selected with 1:1 propensity matching. They were followed up for 9.3 years. RESULTS: Primary endpoint, a composite of MI, stroke and cardiac death rate was significantly higher in the PCI group than in the OMT group, 13.5/1000 vs 11.5/1000 person-year with HR of 1.18 (95% CI 1.06 to 1.32, p=0.003). Individual event rate of MI and cardiac death rate was higher in the PCI group than in the OMT group at 9.3 years, 2.9 vs 2.1 (HR 1.38, 95% CI 1.09 to 1.7, p=0.009) and 4.8 vs 3.4/1000 person-year (HR 1.40, 95% CI 1.16 to 1.69, p=0.001), respectively. Revascularisation and total death occurred more in the PCI group as compared with the OMT group, 30.3 vs 8.2 (HR 3.64, 95% CI 3.27 to 4.05, p<0.001) and 13.5 vs 10.6/1000 person-year (HR 1.23, 95% CI 1.12 to 1.40, p<0.001), respectively. In subgroup analysis, the same trend of more event in the PCI group was detected. CONCLUSIONS: PCI plus OMT was associated with higher rate of primary endpoint of MI, stroke, cardiac death as compared with OMT alone in patients with pure stable angina at 9.3-year follow-up in large population.

Comparative Transcriptomic Analysis of Flagellar-Associated Genes in Salmonella Typhimurium and Its rnc Mutant.

Salmonella enterica serovar Typhimurium (S. Typhimurium) is a globally recognized foodborne pathogen that affects both animals and humans. Endoribonucleases mediate RNA processing and degradation in the adaptation of bacteria to environmental changes and have been linked to the pathogenicity of S. Typhimurium. Not much is known about the specific regulatory mechanisms of these enzymes in S. Typhimurium, particularly in the context of environmental adaptation. Thus, this study carried out a comparative transcriptomic analysis of wild-type S. Typhimurium SL1344 and its mutant (∆rnc), which lacks the rnc gene encoding RNase III, thereby elucidating the detailed regulatory characteristics that can be attributed to the rnc gene. Global gene expression analysis revealed that the ∆rnc strain exhibited 410 upregulated and 301 downregulated genes (fold-change > 1.5 and p < 0.05), as compared to the wild-type strain. Subsequent bioinformatics analysis indicated that these differentially expressed genes are involved in various physiological functions, in both the wild-type and ∆rnc strains. This study provides evidence for the critical role of RNase III as a general positive regulator of flagellar-associated genes and its involvement in the pathogenicity of S. Typhimurium.

Construction of a High-Performance Composite Solid Electrolyte Through In-Situ Polymerization within a Self-Supported Porous Garnet Framework.

Composite solid electrolytes (CSEs) have emerged as promising candidates for safe and high-energy-density solid-state lithium metal batteries (SSLMBs). However, concurrently achieving exceptional ionic conductivity and interface compatibility between the electrolyte and electrode presents a significant challenge in the development of high-performance CSEs for SSLMBs. To overcome these challenges, we present a method involving the in-situ polymerization of a monomer within a self-supported porous Li6.4La3Zr1.4Ta0.6O12 (LLZT) to produce the CSE. The synergy of the continuous conductive LLZT network, well-organized polymer, and their interface can enhance the ionic conductivity of the CSE at room temperature. Furthermore, the in-situ polymerization process can also construct the integration and compatibility of the solid electrolyte-solid electrode interface. The synthesized CSE exhibited a high ionic conductivity of 1.117 mS cm-1, a significant lithium transference number of 0.627, and exhibited electrochemical stability up to 5.06 V vs. Li/Li+ at 30 °C. Moreover, the Li|CSE|LiNi0.8Co0.1Mn0.1O2 cell delivered a discharge capacity of 105.1 mAh g-1 after 400 cycles at 0.5 C and 30 °C, corresponding to a capacity retention of 61%. This methodology could be extended to a variety of ceramic, polymer electrolytes, or battery systems, thereby offering a viable strategy to improve the electrochemical properties of CSEs for high-energy-density SSLMBs.

Where is VALDO? VAscular Lesions Detection and segmentatiOn challenge at MICCAI 2021.

Imaging markers of cerebral small vessel disease provide valuable information on brain health, but their manual assessment is time-consuming and hampered by substantial intra- and interrater variability. Automated rating may benefit biomedical research, as well as clinical assessment, but diagnostic reliability of existing algorithms is unknown. Here, we present the results of the VAscular Lesions DetectiOn and Segmentation (Where is VALDO?) challenge that was run as a satellite event at the international conference on Medical Image Computing and Computer Aided Intervention (MICCAI) 2021. This challenge aimed to promote the development of methods for automated detection and segmentation of small and sparse imaging markers of cerebral small vessel disease, namely enlarged perivascular spaces (EPVS) (Task 1), cerebral microbleeds (Task 2) and lacunes of presumed vascular origin (Task 3) while leveraging weak and noisy labels. Overall, 12 teams participated in the challenge proposing solutions for one or more tasks (4 for Task 1-EPVS, 9 for Task 2-Microbleeds and 6 for Task 3-Lacunes). Multi-cohort data was used in both training and evaluation. Results showed a large variability in performance both across teams and across tasks, with promising results notably for Task 1-EPVS and Task 2-Microbleeds and not practically useful results yet for Task 3-Lacunes. It also highlighted the performance inconsistency across cases that may deter use at an individual level, while still proving useful at a population level.

Increased Likelihood of Dementia with Coexisting Atrophy of Multiple Regions of Interest.

BACKGROUND: Brain volume is associated with cognitive decline in later life, and cortical brain atrophy exceeding the normal range is related to inferior cognitive and behavioral outcomes in later life. OBJECTIVE: To investigate the likelihood of cognitive decline, mild cognitive impairment (MCI), or dementia, when regional atrophy is present in participants' magnetic resonance imaging (MRI). METHODS: Multi-center MRI data of 2,545 adults were utilized to measure regional volumes using NEUROPHET AQUA. Four lobes (frontal, parietal, temporal, and occipital), four Alzheimer's disease-related regions (entorhinal, fusiform, inferior temporal, and middle temporal area), and the hippocampus in the left and right hemispheres were measured and analyzed. The presence of regional atrophy from brain MRI was defined as ≤1.5 standard deviation (SD) compared to the age- and sex-matched cognitively normal population. The risk ratio for cognitive decline was investigated for participants with regional atrophy in contrast to those without regional atrophy. RESULTS: The risk ratio for cognitive decline was significantly higher when hippocampal atrophy was present (MCI, 1.84, p < 0.001; dementia, 4.17, p < 0.001). Additionally, participants with joint atrophy in multiple regions showed a higher risk ratio for dementia, e.g., 9.6 risk ratio (95% confidence interval, 8.0-11.5), with atrophy identified in the frontal, temporal, and hippocampal gray matter, than those without atrophy. CONCLUSIONS: Our study showed that individuals with multiple regional atrophy (either lobar or AD-specific regions) have a higher likelihood of developing dementia compared to the age- and sex-matched population without atrophy. Thus, further consideration is needed when assessing MRI findings.

Functional conservation of specialized ribosomes bearing genome-encoded variant rRNAs in Vibrio species.

Heterogeneity of ribosomal RNA (rRNA) sequences has recently emerged as a mechanism that can lead to subpopulations of specialized ribosomes. Our previous study showed that ribosomes containing highly divergent rRNAs expressed from the rrnI operon (I-ribosomes) can preferentially translate a subset of mRNAs such as hspA and tpiA in the Vibrio vulnificus CMCP6 strain. Here, we explored the functional conservation of I-ribosomes across Vibrio species. Exogenous expression of the rrnI operon in another V. vulnificus strain, MO6-24/O, and in another Vibrio species, V. fischeri (strain MJ11), decreased heat shock susceptibility by upregulating HspA expression. In addition, we provide direct evidence for the preferential synthesis of HspA by I-ribosomes in the V. vulnificus MO6-24/O strain. Furthermore, exogenous expression of rrnI in V. vulnificus MO6-24/O cells led to higher mortality of infected mice when compared to the wild-type (WT) strain and a strain expressing exogenous rrnG, a redundant rRNA gene in the V. vulnificus CMCP6 strain. Our findings suggest that specialized ribosomes bearing heterogeneous rRNAs play a conserved role in translational regulation among Vibrio species. This study shows the functional importance of rRNA heterogeneity in gene expression control by preferential translation of specific mRNAs, providing another layer of specialized ribosome system.

Ratiometric electrochemical detection of kojic acid based on glassy carbon modified MXene nanocomposite.

The significance of developing a selective and sensitive sensor for quality control purposes is underscored by the prevalent use of kojic acid (KA) in cosmetics, pharmaceuticals, and food items. KA's utility stems from its ability to inhibit tyrosinase activity. However, the instability of KA and its potential adverse effects have created a pressing need for accurate and sensitive sensors capable of analyzing real samples. This research introduces an electrochemical ratiometric sensor designed to accurately detect KA in actual cosmetic and food samples. The ratiometric sensor offers distinct advantages such as enhanced selectivity, reproducibility, and sensitivity. It achieves this by leveraging the ratio between two output signals, thereby producing reliable and undistorted results. The sensor is constructed by modifying a Glassy Carbon Electrode (GCE) with a nanocomposite consisting of Ti3C2 MXene, Prussian blue, and gold nanoparticles. The incorporation of MXene and gold nanoparticles heightens sensitivity and reduces impedance. Meanwhile, the Prussian blue signal diminishes proportionally with increasing KA concentration, forming the basis for the ratiometric sensing mechanism. The outcomes of the study reveal a broad linear range (1-600 µM), a low detection limit (1 µM), and strong selectivity for KA. These findings suggest the sensor's potential efficacy in quality control across cosmetics, pharmaceuticals, and food products.

Phenylalkyl Glycosides from the Flowers of Brugmansia arborea L. and Their Radical Scavenging Effect and Protective Effect on Pancreatic Islets Damaged by Alloxan in Zebrafish (Danio rerio) Larvae.

The study aimed to investigate the antioxidant and antidiabetic activity of Brugmansia arborea L. flower extracts, solvent fractions, and isolated compounds. B. arborea L flowers were extracted with aqueous methanol, and concentrated extract was successively partitioned into EtOAc, n-BuOH, and H2O fractions. Repeated silica gel and octadecyl silica gel column chromatographies for EtOAc and n-BuOH fractions led to the isolation of a new phenylalkyl glycoside (6), along with five known ones. Several spectroscopic data led to the structure determination of one new phenylalky glycoside as brugmansioside C (named) (6) and five known ones as benzyl-O-ß-D-glucopyranoside (1), benzyl-O-ß-D-glucosyl-(1→6)-ß-D-glucopyranoside (2), 2-phenylethyl-O-ß-D-glucopyranoside (3), 2-phenylethyl-O-ß-D-glucosyl-(1→6)-ß-D-glucopyranoside (4), and 3-phenylpropyl-O-ß-D-glucopyranoside (5). The five known ones (1-5) were isolated from B. arborea flowers for the first time in this study. The extract, solvent fractions, and all isolated compounds showed radical scavenging activities using ABTS radical, and EtOAc fraction showed the highest scavenging capacity, whereas compounds 2, 4, and 6 did not display the capacity to use the DPPH radical. The extract, solvent fractions, and all isolated compounds showed a protective effect on pancreatic islets damaged by alloxan treatment in zebrafish larvae. The pancreatic islet size treated with EtOAc, n-BuOH fractions, and all compounds significantly increased by 64.0%, 69.4%, 82.0%, 89.8%, 80.0%, 97.8%, 103.1%, and 99.6%, respectively, compared to the alloxan-induced group. These results indicate that B. arborea flowers and their isolated compounds are useful as potential antioxidant and antidiabetic agents.

A prospective approach to detect advanced persistent threats: Utilizing hybrid optimization technique.

Advanced Persistent Threat (APT) attacks pose significant challenges for AI models in detecting and mitigating sophisticated and highly effective cyber threats. This research introduces a novel concept called Hybrid HHOSSA which is the grouping of Harris Hawk Optimization (HHO) and Sparrow Search Algorithm (SSA) characteristics for optimizing the feature selection and data balancing in the context of APT detection. In addition, the light GBM as well as the weighted average Bi-LSTM are optimized by the proposed hybrid HHOSSA optimization. The HHOSSA-based attribute selection is used to choose the most important attributes from the provided dataset in the early step of the quasi-identifier detection. The HHOSSA-SMOTE algorithm effectively balances the unbalanced data, such as the lateral movements and the data exfiltration in the DAPT 2020 database, which further improves the classifier performance. The light GBM and the Bi-LSTM classifier hyperparameters are well attuned and classified by the HHOSSA optimization for the precise classification of the attacks. The outcome of both the optimized light GBM and the Bi-LSTM classifier generates the final prediction of the attacks existing in the network. According to the research findings, the HHOSSA-hybrid classifier achieves high accuracy in detecting attacks, with an accuracy rate of 94.468 %, a sensitivity of 94.650 %, and a specificity of 95.230 % with a K-fold value of 10. Also, the HHOSSA-hybrid classifier achieves the highest AUC percentage of 97.032, highlighting its exceptional performance in detecting APT attacks.

Automatic segmentation of white matter hyperintensities in T2-FLAIR with AQUA: A comparative validation study against conventional methods.

White matter hyperintensities (WMHs) are lesions in the white matter of the brain that are associated with cognitive decline and an increased risk of dementia. The manual segmentation of WMHs is highly time-consuming and prone to intra- and inter-variability. Therefore, automatic segmentation approaches are gaining attention as a more efficient and objective means to detect and monitor WMHs. In this study, we propose AQUA, a deep learning model designed for fully automatic segmentation of WMHs from T2-FLAIR scans, which improves upon our previous study for small lesion detection and incorporating a multicenter approach. AQUA implements a two-dimensional U-Net architecture and uses patch-based training. Additionally, the network was modified to include Bottleneck Attention Module on each convolutional block of both the encoder and decoder to enhance performance for small-sized WMH. We evaluated the performance and robustness of AQUA by comparing it with five well-known supervised and unsupervised methods for automatic segmentation of WMHs (LGA, LPA, SLS, UBO, and BIANCA). To accomplish this, we tested these six methods on the MICCAI 2017 WMH Segmentation Challenge dataset, which contains MRI images from 170 elderly participants with WMHs of presumed vascular origin, and assessed their robustness across multiple sites and scanner types. The results showed that AQUA achieved superior performance in terms of spatial (Dice = 0.72) and volumetric (logAVD = 0.10) agreement with the manual segmentation compared to the other methods. While the recall and F1-score were moderate at 0.49 and 0.59, respectively, they improved to 0.75 and 0.82 when excluding small lesions (≤ 6 voxels). Remarkably, despite being trained on a different dataset with different ethnic backgrounds, lesion loads, and scanners, AQUA's results were comparable to the top 10 ranked methods of the MICCAI challenge. The findings suggest that AQUA is effective and practical for automatic segmentation of WMHs from T2-FLAIR scans, which could help identify individuals at risk of cognitive decline and dementia and allow for early intervention and management.

A regional brain volume-based age prediction model for neonates and the derived brain maturation index.

OBJECTIVE: To develop a postmenstrual age (PMA) prediction model based on segmentation volume and to evaluate the brain maturation index using the proposed model. METHODS: Neonatal brain MRIs without clinical illness or structural abnormalities were collected from four datasets from the Developing Human Connectome Project, the Catholic University of Korea, Hammersmith Hospital (HS), and Dankook University Hospital (DU). T1- and T2-weighted images were used to train a brain segmentation model. Another model to predict the PMA of neonates based on segmentation data was developed. Accuracy was assessed using mean absolute error (MAE), root mean square error (RMSE), and mean error (ME). The brain maturation index was calculated as the difference between the PMA predicted by the model and the true PMA, and its correlation with postnatal age was analyzed. RESULTS: A total of 247 neonates (mean gestation age 37 ± 4 weeks; range 24-42 weeks) were included. Thirty-one features were extracted from each neonate and the three most contributing features for PMA prediction were the right lateral ventricle, left caudate, and corpus callosum. The predicted and true PMA were positively correlated (coefficient = 0.88, p < .001). MAE, RMSE, and ME of the external dataset of HS and DU were 1.57 and 1.33, 1.79 and 1.37, and 0.37 and 0.06 weeks, respectively. The brain maturation index negatively correlated with postnatal age (coefficient = - 0.24, p < .001). CONCLUSION: A model that calculates the regional brain volume can predict the PMA of neonates, which can then be utilized to show the brain maturation degree. CLINICAL RELEVANCE STATEMENT: A brain maturity index based on regional volume of neonate's brain can be used to measure brain maturation degree, which can help identify the status of early brain development. KEY POINTS: • Neonatal brain MRI segmentation model could be used to assess neonatal brain maturation status. • A postmenstrual age (PMA) prediction model was developed based on a neonatal brain MRI segmentation model. • The brain maturation index, derived from the PMA prediction model, enabled the estimation of the neonatal brain maturation status.

Incremental Learning for Online Data Using QR Factorization on Convolutional Neural Networks.

Catastrophic forgetting, which means a rapid forgetting of learned representations while learning new data/samples, is one of the main problems of deep neural networks. In this paper, we propose a novel incremental learning framework that can address the forgetting problem by learning new incoming data in an online manner. We develop a new incremental learning framework that can learn extra data or new classes with less catastrophic forgetting. We adopt the hippocampal memory process to the deep neural networks by defining the effective maximum of neural activation and its boundary to represent a feature distribution. In addition, we incorporate incremental QR factorization into the deep neural networks to learn new data with both existing labels and new labels with less forgetting. The QR factorization can provide the accurate subspace prior, and incremental QR factorization can reasonably express the collaboration between new data with both existing classes and new class with less forgetting. In our framework, a set of appropriate features (i.e., nodes) provides improved representation for each class. We apply our method to the convolutional neural network (CNN) for learning Cifar-100 and Cifar-10 datasets. The experimental results show that the proposed method efficiently alleviates the stability and plasticity dilemma in the deep neural networks by providing the performance stability of a trained network while effectively learning unseen data and additional new classes.

Development of Gelatin Methacryloyl/Sodium Alginate Interpenetrating Polymer Network Hydrogels for Bone Regeneration by Activating the Wnt/ß-Catenin Signaling Pathway via Lithium Release.

Hydrogels have gained significant attention as biomaterials due to their remarkable properties resembling those of the extracellular matrix (ECM). In the present investigation, we successfully synthesized interpenetrating polymer network (IPN) hydrogels using gelatin methacryloyl (GelMA) and sodium alginate (SA), incorporating various concentrations of lithium chloride (LiCl; 0, 5, and 10 mM), aiming to develop a hydrogel scaffold for bone regeneration. Notably, the compressive modulus of the IPN hydrogels remained largely unaffected upon the inclusion of LiCl. However, the hydrogel with the high concentration of LiCl exhibited reduced fragmentation after compression testing. Intriguingly, we observed a significant improvement in cellular biocompatibility, primarily attributed to activation of the Wnt/ß-catenin signaling pathway induced by LiCl. Subsequently, we evaluated the efficacy of the newly developed IPN-Li hydrogels in a rat cranial defect model and found that they substantially enhanced bone regeneration. Nevertheless, it is important to note that the introduction of high concentrations of LiCl did not significantly promote osteogenesis. This outcome can be attributed to the excessive release of Li+ ions into the extracellular matrix, hindering the desired effect. Overall, the IPN-Li hydrogel developed in this study holds great promise as a biodegradable material for bone regeneration applications.

Protamine dose to neutralize heparin at the completion of cardiopulmonary bypass can be reduced significantly without affecting post-operative bleeding.

BACKGROUND: Systemic anticoagulation with heparin during cardiopulmonary bypass (CPB) should be neutralized by protamine administration to restore normal hemostasis. Our previous study showed the protamine-to-heparin ratio (P-to-H) of 1:1 (1 mg protamine:100 IU circulating heparin; 1.0 Ratio) is likely an overestimation. Thus, we reduced the P-to-H in the HMS Plus Hemostasis Management System to 0.9:1 (0.9 Ratio) for 5 months and then to 0.8:1 (0.8 Ratio). We monitored post-operative (post-op) bleeding in the setting of reduced protamine dose (PD). METHODS: We performed a retrospective study of 632 patients (209 for the 1.0 Ratio, 211 for 0.9 Ratio, 212 for 0.8 Ratio group) who underwent cardiac surgery to measure the reduction of PD and how it affects 24-hour (24 h) post-op chest tube output. We also analyzed the entire data set to explore whether further reduction of P-to-H is warranted. RESULTS: While there was no difference in the indexed heparin dose among the three groups, we achieved a significant reduction in the indexed actual protamine dose (APDi) by 24% (0.9 Ratio) and 31% (0.8 Ratio) reductions compared to the 1.0 Ratio group. On average, APDi was 88 ± 22, 67 ± 18, and 61 ± 15 mg/m2 in the 1.0, 0.9, and 0.8 Ratio groups, respectively. We found no significant difference in 24 h post-op bleeding among the three groups. CONCLUSION: 1.0 Ratio at the completion of CPB is likely an excessive administration of protamine. With the stepwise reduction of PD, we observed no increase in post-op bleeding, which may indicate that no meaningful increase in heparin rebound occurred. In addition, further analysis of the entire data set demonstrates that a 0.75 Ratio is likely sufficient to neutralize the heparin completely.