Effect of Oxidizing Agent on the Synthesis of ZnO Nanoparticles for Inverted Phosphorescent Organic Light-Emitting Devices without Multiple Interlayers.

Inverted organic light-emitting devices (OLEDs) have been aggressively developed because of their superiorities such as their high stability, low driving voltage, and low drop of brightness in display applications. The injection of electrons is a critical issue in inverted OLEDs because the ITO cathode has an overly high work function in injecting electrons into the emission layer from the cathode. We synthesized hexagonal wurtzite ZnO nanoparticles using different oxidizing agents for an efficient injection of electrons in the inverted OLEDs. Potassium hydroxide (KOH) and tetramethylammonium hydroxide pentahydrate (TMAH) were used as oxidizing agents for synthesizing ZnO nanoparticles. The band gap, surface defects, surface morphology, surface roughness, and electrical resistivity of the nanoparticles were investigated. The inverted devices with phosphorescent molecules were prepared using the synthesized nanoparticles. The inverted devices with ZnO nanoparticles using TMAH exhibited a lower driving voltage, lower leakage current, and higher maximum external quantum efficiency. The devices with TMAH-based ZnO nanoparticles exhibited the maximum external quantum efficiency of 19.1%.

Perfluorooctanoic acid induces cardiac dysfunction in human induced pluripotent stem cell-derived cardiomyocytes.

Perfluorooctanoic acid (PFOA), commonly found in drinking water, leads to widespread exposure through skin contact, inhalation, and ingestion, resulting in detectable levels of PFOA in the bloodstream. In this study, we found that exposure to PFOA disrupts cardiac function in human induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs). We observed reductions in field and action potentials in hiPSC-CMs exposed to PFOA. Furthermore, PFOA demonstrated a dose-dependent inhibitory effect on various ion channels, including the calcium, sodium, and potassium channels. Additionally, we noted dose-dependent inhibition of the expression of these ion channels in hiPSC-CMs following exposure to PFOA. These findings suggest that PFOA exposure can impair cardiac ion channel function and decrease the transcription of genes associated with these channels, potentially contributing to cardiac dysfunction such as arrhythmias. Our study sheds light on the electrophysiological and epigenetic consequences of PFOA-induced cardiac dysfunction, underscoring the importance of further research on the cardiovascular effects of perfluorinated compounds (PFCs).

Differential regulations of neural activity and survival in primary cortical neurons by PFOA or PFHpA.

Perfluorinated compounds (PFCs), organofluoride compounds comprising carbon-fluorine and carbon-carbon bonds, are used as water and oil repellents in textiles and pharmaceutical tablets; however, they are associated with potential neurotoxic effects. Moreover, the impact of PFCs on neuronal survival, activity, and regulation within the brain remains unclear. Additionally, the mechanisms through which PFCs induce neuronal toxicity are not well-understood because of the paucity of data. This study elucidates that perfluorooctanoic acid (PFOA) and perfluoroheptanoic acid (PFHpA) exert differential effects on the survival and activity of primary cortical neurons. Although PFOA triggers apoptosis in cortical neurons, PFHpA does not exhibit this effect. Instead, PFHpA modifies dendritic spine morphogenesis and synapse formation in primary cortical neuronal cultures, additionally enhancing neural activity and synaptic transmission. This research uncovers a novel mechanism through which PFCs (PFHpA and PFOA) cause distinct alterations in dendritic spine morphogenesis and synaptic activity, shedding light on the molecular basis for the atypical behaviors noted following PFC exposure. Understanding the distinct effects of PFHpA and PFOA could guide regulatory policies on PFC usage and inform clinical approaches to mitigate their neurotoxic effects, especially in vulnerable population.

Virucidal activity of Cu-doped TiO2 nanoparticles under visible light illumination: Effect of Cu oxidation state.

Copper (Cu) is an effective antimicrobial material; however, its activity is inhibited by oxidation. Titanium dioxide (TiO2) photocatalysis prevents Cu oxidation and improves its antimicrobial activity and stability. In this study, the virucidal efficacy of Cu-doped TiO2 nanoparticles (Cu-TiO2) with three different oxidation states of the Cu dopant (i.e., zero-valent Cu (Cu0), cuprous (CuI), and cupric (CuII) oxides) was evaluated for the phiX174 bacteriophage under visible light illumination (Vis/Cu-TiO2). CuI-TiO2 exhibited superior virucidal activity (5 log inactivation in 30 min) and reusability (only 11 % loss of activity in the fifth cycle) compared to Cu0-TiO2 and CuII-TiO2. Photoluminescence spectroscopy and photocurrent measurements showed that CuI-TiO2 exhibited the highest charge separation efficiency and photocurrent density (approximately 0.24 µA/cm2) among the three materials, resulting in the most active redox reactions of Cu. Viral inactivation tests under different additives and viral particle integrity analyses (i.e., protein oxidation and DNA damage analyses) revealed that different virucidal species played key roles in the three Vis/Cu-TiO2 systems; Cu(III) was responsible for the viral inactivation by Vis/CuI-TiO2. The Vis/CuI-TiO2 system exhibited substantial virucidal performance for different viral species and in different water matrices, demonstrating its potential practical applications. The findings of this study offer valuable insights into the design of effective and sustainable antiviral photocatalysts for disinfection.

Effects of Natural Extract Mixtures on the Quality Characteristics of Sausages during Refrigerated Storage.

Owing to the residual toxicity and adverse health effects of chemical preservatives, there is an increasing demand for using natural preservatives in food. Although many natural extracts have been evaluated, research on their antibacterial effects remains insufficient. Therefore, this study aimed to explore the possibility of developing Psidium guajava, Ecklonia cava, and Paeonia japonica (Makino) Miyabe & Takeda extracts as natural food preservatives. Further, the effect of mixing these extracts on microbial growth and quality was evaluated during the refrigeration of sausages. Optimal mixing ratios were determined based on the minimum inhibitory and bactericidal concentrations of each mixed extract against the Listeria monocytogenes, Salmonella spp. and Escherichia coli. D-optimal mixing design optimization tool was further used to obtain an optimum mixing ratio of Formulation 1 (F1). The antibacterial activity of F1 increased with increasing concentration, with similar activities at 0.5% and 1%. The sausages with synthetic or natural preservatives showed significantly lower lipid oxidation than those of the control and grapefruit extract-treated sausages after 4 wk of refrigeration. Total plate counts were observed only in the control and treatment groups stored for 3 wk, and no significant effect of ascorbic acid was observed. Compared to the other samples, sausages with added natural extracts showed the highest overall acceptability scores initially and after 4 wk. Therefore, similar amounts of grapefruit seed and natural extracts had the same effect on microbiological analysis and lipid rancidity during sausage storage. Hence, this mixture can serve as a potential natural preservative in meat products.

Prenatal Di-methoxyethyl phthalate exposure impairs cortical neurogenesis and synaptic activity in the mice.

Di-methoxyethyl phthalate (DMEP) is a well-known environmentally prevalent endocrine disruptor and may be associated with neurodevelopmental disorders including attention deficit/hyperactivity disorder and intellectual disability. However, the regulatory mechanisms leading to these neurodevelopmental disorders are still poorly understood. Here, we demonstrate that prenatal DMEP exposure causes abnormal brain morphology and function in the mice. DMEP (50 mg/kg) was chronically administered to pregnant mice orally once a day starting on embryonic day 0 (E0) to breast-feeding cessation for the fetus. We found that prenatal DMEP exposure significantly reduced the number of neurons in the parietal cortex by impairing neurogenesis and gliogenesis during the developing cortex. Moreover, we found that prenatal DMEP exposure impaired dendritic spine architectures and synaptic activity in the parietal cortex. Finally, prenatal DMEP exposure in mice induces hyperactivity and reduces anxiety behaviors. Altogether, our study demonstrates that prenatal DMEP exposure leads to abnormal behaviors via impairment of neurogenesis and synaptic activity.

Fabrication of biodegradable cellulose acetate nanofibers containing Rose Bengal dye by electrospinning technique and their antiviral efficacy under visible light irradiation.

Biodegradable cellulose acetate (CA) nanofibers containing Rose Bengal (RB) dye were fabricated by electrospinning technique. RB dye, an anionic photosensitizer, has been used in photodynamic therapy due to its excellent biocompatibility and ability to absorb light to generate reactive oxygen species (ROS), but has a decisive disadvantage of water solubility on infection prevention. Firstly, water-insoluble RB dye was synthesized through complexation with cationic ionic liquid (IL) for antiviral agents. The synthesized water-insoluble RB dyes were embedded into biodegradable CA nanofibers by electrospinning. The electrospun nanofibers passed both antiviral test for φx174 virus under visible light irradiation and biodegradability-test using enzymes. The fabricated RB nanofibers absorbed light and generated ROS to inactivate the virus. As a result, the log reduction (-Log10(N/N0)) of φx174 titer under visible light reached a detection limit of 5.00 within 30 min. Also, the fabricated nanofibers were degraded up to 34 wt % in 9 weeks by lipase and cellulase enzymes compared with non-biodegradable nanofibers.

Density Multiplication of Highly Periodic Sub-5 nm Supramolecular Dendrimer Cylinders on Block Copolymer Lamellar-Assisted High-Resolution Patterns.

Density multiplication in nanopatterning is one of the most efficient techniques for increasing the resolution of the inherent patterns. Thus far, most of the density multiplication techniques integrate bottom-up (or top-down) patterning onto guide patterns prepared by the top-down approach. Although the bottom-up approach exhibits several advantages of cost-effectiveness and high resolution, very few studies have reported bottom-up patterning within a bottom-up template. In this study, the density multiplication of supramolecular cylinders into a block copolymer (BCP)-based guide lamellar pattern is demonstrated by the directed self-assembly (DSA) of a dendrimer and BCPs for the first time. Supramolecular cylinders of sub-5 nm scale are confined into trenches based on 50 and 100 nm scales of a lamellar polystyrene (PS)-poly(methyl methacrylate) (PMMA) BCP, which led to 10×-level to 20×-level density multiplication. Moreover, the orientation of the dendrimer is dependent on the dendrimer film thickness, and the corresponding mechanism is revealed. Notably, the strong guiding effect from the high-resolution guide patterns improved the ordering behavior in the highly curved pattern. Graphoepitaxy via the confinement of an ultrahigh-resolution dendrimer into the guide pattern based on BCP demonstrates promise as a density multiplication method for generating highly ordered nanostructures and complex structures.

The biological standard of living of Korean men under Confucianism, colonialism, capitalism, and communism.

This study focuses on analysing the heights of 10,953 Korean men aged 20 to 40 years who were measured during the Joseon dynasty, the Japanese colonialisation period, and the contemporary period, the latter including both North and South Korea. This study thus provides rare long-term statistical evidence on how biological living standards have developed over several centuries, encompassing Confucianism, colonialism, capitalism, and communism. Using error bar analysis of heights for each historical sample period, this study confirms that heights rose as economic performance improved. For instance, economically poorer North Koreans were expectedly shorter, by about 6 cm, than their peers living in the developed South. Similarly, premodern inhabitants of present-day South Korea, who produced a gross domestic product (GDP) per capita below the world average, were about 4 cm shorter than contemporary South Koreans, who have a mean income above the world average. Along similar lines, North Koreans, who have a GDP per capita akin to that of the premodern Joseon dynasty, have not improved much in height. On the contrary, mean heights of North Koreans were even slightly below (by about 2.4 cm) heights of Joseon dynasty Koreans. All in all, the heights follow a U-shaped pattern across time, wherein heights were lowest during the colonial era. Heights bounced back to Joseon dynasty levels during the interwar period, a time period where South Korea benefitted from international aid, only to rise again and surpass even premodern levels under South Korea's flourishing market economy.

Application of multiple-finding segmentation utilizing Mask R-CNN-based deep learning in a rat model of drug-induced liver injury.

Drug-induced liver injury (DILI) presents significant diagnostic challenges, and recently artificial intelligence-based deep learning technology has been used to predict various hepatic findings. In this study, we trained a set of Mask R-CNN-based deep algorithms to learn and quantify typical toxicant induced-histopathological lesions, portal area, and connective tissue in Sprague Dawley rats. We compared a set of single-finding models (SFMs) and a combined multiple-finding model (MFM) for their ability to simultaneously detect, classify, and quantify multiple hepatic findings on rat liver slide images. All of the SFMs yielded mean average precision (mAP) values above 85%, suggesting that the models had been successfully established. The MFM showed better performance than the SFMs, with a total mAP value of 92.46%. We compared the model predictions for slide images with ground-truth annotations generated by an accredited pathologist. For the MFM, the overall and individual finding predictions were highly correlated with the annotated areas, with R-squared values of 0.852, 0.952, 0.999, 0.990, and 0.958 being obtained for portal area, infiltration, necrosis, vacuolation, and connective tissue (including fibrosis), respectively. Our results indicate that the proposed MFM could be a useful tool for detecting and predicting multiple hepatic findings in basic non-clinical study settings.

Detection of thiocyanate in postmortem blood of a fatal potassium thiocyanate-poisoning patient using gas chromatography-mass spectrometry.

Thiocyanate is an inorganic compound used in industrial applications. Here, we report a case of suicidal death due to acute thiocyanate overdose. A 44-year-old man who consumed an unknown amount of thiocyanate solution was transferred to the emergency room and died 2 h after admission. An autopsy was performed 2 days after death. General toxicological analysis of blood using gas chromatography-mass spectrometry (GC-MS) and liquid chromatography-tandem mass spectrometry found no drug or alcohol. Quantification using GC-MS post-derivatization with pentafluorobenzyl bromide revealed 2,290 and 1,920 mg/L of thiocyanate in the heart and femoral blood samples, respectively. Thus, the cause of death was attributed to thiocyanate overdose. This study provides useful information for the interpretation of thiocyanate-related fatalities.

Subway station dust-induced pulmonary inflammation may be due to the dysfunction of alveolar macrophages: Possible contribution of bound elements.

With its increasing value as a means of public transportation, the health effects of the air in subway stations have attracted public concern. In the current study, we investigated the pulmonary toxicity of dust collected from an air purifier installed on the platform of the busiest subway station in Seoul. We found that the dust contained various elements which are attributable to the facilities and equipment used to operate the subway system. Particularly, iron (Fe), chromium (Cr), zirconium (Zr), barium (Ba), and molybdenum (Mo) levels were more notable in comparison with those in dust collected from the ventilation chamber of a subway station. To explore the health effects of inhaled dust, we first instilled via the trachea in ICR mice for 13 weeks. The total number of pulmonary macrophages increased significantly with the dose, accompanying hematological changes. Dust-laden alveolar macrophages and inflammatory cells accumulated in the perivascular regions in the lungs of the treated mice, and pulmonary levels of CXCL-1, TNF-α, and TGF-ß increased clearly compared with the control. The CCR5 and CD54 level expressed on BAL cell membranes was also enhanced following exposure to dust, whereas the CXCR2 level tended to decrease in the same samples. In addition, we treated the dust to alveolar macrophages (known as dust cells), lysosomal and mitochondrial function decreased, accompanied by cell death, and NO production was rapidly elevated with concentration. Moreover, the expression of autophagy- (p62) and anti-oxidant (SOD-2)-related proteins increased, and the expression of inflammation-related genes was dramatically up-regulated in the dust-treated cells. Therefore, we suggest that dysfunction of alveolar macrophages may importantly contribute to dust-induced inflammatory responses and that the exposure concentrations of Cr, Fe, Mo, Zr, and Ba should be considered carefully when assessing the health risks associated with subway dust. We also hypothesize that the bound elements may contribute to dust-induced macrophage dysfunction by inhibiting viability.

A comparative study on the implementation of deep learning algorithms for detection of hepatic necrosis in toxicity studies.

Deep learning has recently become one of the most popular methods of image analysis. In non-clinical studies, several tissue slides are generated to investigate the toxicity of a test compound. These are converted into digital image data using a slide scanner, which is then studied by researchers to investigate abnormalities, and the deep learning method has been started to adopt in this study. However, comparative studies evaluating different deep learning algorithms for analyzing abnormal lesions are scarce. In this study, we applied three algorithms, SSD, Mask R-CNN, and DeepLabV3+, to detect hepatic necrosis in slide images and determine the best deep learning algorithm for analyzing abnormal lesions. We trained each algorithm on 5750 images and 5835 annotations of hepatic necrosis including validation and test, augmented with 500 image tiles of 448 × 448 pixels. Precision, recall, and accuracy were calculated for each algorithm based on the prediction results of 60 test images of 2688 × 2688 pixels. The two segmentation algorithms, DeepLabV3+ and Mask R-CNN, showed over 90% of accuracy (0.94 and 0.92, respectively), whereas SSD, an object detection algorithm, showed lower accuracy. The trained DeepLabV3+ outperformed all others in recall while also successfully separating hepatic necrosis from other features in the test images. It is important to localize and separate the abnormal lesion of interest from other features to investigate it on a slide level. Therefore, we suggest that segmentation algorithms are more appropriate than object detection algorithms for use in the pathological analysis of images in non-clinical studies. Supplementary Information: The online version contains supplementary material available at 10.1007/s43188-023-00173-5.

Long-chain perfluoroalkyl carboxylates induce cytoskeletal abnormalities and activate epithelial-mesenchymal transition in both renal cell carcinoma 3D cultures and Caki-1 xenografted mouse model.

Exposure to perfluorooctanoate (PFOA; a type of perfluoroalkyl carboxylates [PFACs]) may be correlated with the incidence of kidney cancer in individuals exposed to high levels of PFOA. However, mechanistic studies on the influence of PFACs on renal cell carcinoma (RCC) development are lacking. We explored the effects of five types of PFACs on RCC using in vitro and in vivo models to fill this knowledge gap and provide information for environmental/usage regulations. Using 2D/3D cultures of Caki-1 cells, a human clear cell RCC line, we examined the effects of short-chain (SC) PFACs and long-chain (LC) PFACs on RCC physio/pathological markers, including the cytoskeleton, epithelial-mesenchymal transition (EMT)-related proteins, and Na+/K+-ATPase. We also administered three different PFACs orally to mice harboring Caki-1 xenografts to assess the impact of these compounds on engrafted RCC in vivo. Compared with the effects of SCPFACs, mice with Caki-1 xenografts treated with LCPFACs showed increased EMT-related protein expression and exhibited liver toxicity. Therefore, LCPFACs induced EMT, influencing cancer metastasis activity, and displayed higher toxicity in vivo compared with SCPFACs. These findings improve our understanding of the effects of PFACs on RCC development and their corresponding in vivo toxicity, which is crucial for regulating these substances to protect public health.

Segmentation algorithm can be used for detecting hepatic fibrosis in SD rat.

BACKGROUND: Liver fibrosis is an early stage of liver cirrhosis. As a reversible lesion before cirrhosis, liver failure, and liver cancer, it has been a target for drug discovery. Many antifibrotic candidates have shown promising results in experimental animal models; however, due to adverse clinical reactions, most antifibrotic agents are still preclinical. Therefore, rodent models have been used to examine the histopathological differences between the control and treatment groups to evaluate the efficacy of anti-fibrotic agents in non-clinical research. In addition, with improvements in digital image analysis incorporating artificial intelligence (AI), a few researchers have developed an automated quantification of fibrosis. However, the performance of multiple deep learning algorithms for the optimal quantification of hepatic fibrosis has not been evaluated. Here, we investigated three different localization algorithms, mask R-CNN, DeepLabV3+, and SSD, to detect hepatic fibrosis. RESULTS: 5750 images with 7503 annotations were trained using the three algorithms, and the model performance was evaluated in large-scale images and compared to the training images. The results showed that the precision values were comparable among the algorithms. However, there was a gap in the recall, leading to a difference in model accuracy. The mask R-CNN outperformed the recall value (0.93) and showed the closest prediction results to the annotation for detecting hepatic fibrosis among the algorithms. DeepLabV3+ also showed good performance; however, it had limitations in the misprediction of hepatic fibrosis as inflammatory cells and connective tissue. The trained SSD showed the lowest performance and was limited in predicting hepatic fibrosis compared to the other algorithms because of its low recall value (0.75). CONCLUSIONS: We suggest it would be a more useful tool to apply segmentation algorithms in implementing AI algorithms to predict hepatic fibrosis in non-clinical studies.

The seamless integration of dietary plant-derived natural flavonoids and gut microbiota may ameliorate non-alcoholic fatty liver disease: a network pharmacology analysis.

We comprised metabolites of gut microbiota (GM; endogenous species) and dietary plant-derived natural flavonoids (DPDNFs; exogenous species) were known as potent effectors against non-alcoholic fatty liver disease (NAFLD) via network pharmacology (NP). The crucial targets against NAFLD were identified via GM and DPDNFs. The protein interaction (PPI), bubble chart and networks of GM or natural products- metabolites-targets-key signalling (GNMTK) pathway were described via R Package. Furthermore, the molecular docking test (MDT) to verify the affinity was performed between metabolite(s) and target(s) on a key signalling pathway. On the networks of GNMTK, Enterococcus sp. 45, Escherichia sp.12, Escherichia sp.33 and Bacterium MRG-PMF-1 as key microbiota; flavonoid-rich products as key natural resources; luteolin and myricetin as key metabolites (or dietary flavonoids); AKT Serine/Threonine Kinase 1 (AKT1), CF Transmembrane conductance Regulator (CFTR) and PhosphoInositide-3-Kinase, Regulatory subunit 1 (PIK3R1) as key targets are promising components to treat NAFLD, by suppressing cyclic Adenosine MonoPhosphate (cAMP) signalling pathway. This study shows that components (microbiota, metabolites, targets and a key signalling pathway) and DPDNFs can exert combinatorial pharmacological effects against NAFLD. Overall, the integrated pharmacological approach sheds light on the relationships between GM and DPDNFs.

Inhibitory effect of natural extract mixtures on microbial growth and lipid oxidation of sausages during storage.

Large amounts of additives are used during meat product processing to maintain product quality and shelf life. With the growing interest in healthy foods, natural plant-based additives are being used as alternatives to synthetic additives. In this study, six types of natural extracts with excellent antibacterial activity were selected, and their antibacterial and antioxidant activities against four types of pathogens were evaluated in various combinations. In addition, the pH, color, amount of thiobarbituric acid reactive substances (TBARS), and growth of pathogenic microorganisms were analyzed during the storage of sausages treated with various combinations of these extracts. The natural extract mixtures exhibited different antibacterial activities, depending on the combination. Compared to grapefruit seed extract, a mixture of natural extracts extracted with ethanol (M4) reduced the Escherichia coli content by more than 99.9% after 8 days of storage and slowed the growth of Listeria monocytogenes and Salmonella spp. by more than 80% after 14 days. Compared to untreated (NC) and grapefruit extract (PC)-treated sausages, sausages treated with the natural extract mixtures showed a significant decrease in CIE L* and an increase in CIE a* and CIE b* (p < 0.05). The pH value was significantly lower in sausages containing natural extract mixtures than in the NC and PC sausages (p < 0.05). The natural plant extract mixtures significantly prevented lipid oxidation (p < 0.05). In summary, different types of natural extract mixtures have a synergistic effect when used together, suggesting that natural preservatives can generally inhibit the growth of microorganisms and oxidation of processed meat.

Progressive search in tandem mass spectrometry.

BACKGROUND: High-throughput Proteomics has been accelerated by (tandem) mass spectrometry. However, the slow speed of mass spectra analysis prevents the analysis results from being up-to-date. Tandem mass spectrometry database search requires O(|S||D|) time where S is the set of spectra and D is the set of peptides in a database. With usual values of |S| and |D|, database search is quite time consuming. Meanwhile, the database for search is usually updated every month, with 0.5-2% changes. Although the change in the database is usually very small, it may cause extensive changes in the overall analysis results because individual PSM scores such as deltaCn and E-value depend on the entire search results. Therefore, to keep the search results up-to-date, one needs to perform database search from scratch every time the database is updated, which is very inefficient. RESULTS: Thus, we present a very efficient method to keep the search results up-to-date where the results are the same as those achieved by the normal search from scratch. This method, called progressive search, runs in O(|S||ΔD|) time on average where ΔD is the difference between the old and the new databases. The experimental results show that the progressive search is up to 53.9 times faster for PSM update only and up to 16.5 times faster for both PSM and E-value update. CONCLUSIONS: Progressive search is a novel approach to efficiently obtain analysis results for updated database in tandem mass spectrometry. Compared to performing a normal search from scratch, progressive search achieves the same results much faster. Progressive search is freely available at: https://isa.hanyang.ac.kr/ProgSearch.html .

Preparing pathological data to develop an artificial intelligence model in the nonclinical study.

Artificial intelligence (AI)-based analysis has recently been adopted in the examination of histological slides via the digitization of glass slides using a digital scanner. In this study, we examined the effect of varying the staining color tone and magnification level of a dataset on the result of AI model prediction in hematoxylin and eosin stained whole slide images (WSIs). The WSIs of liver tissues with fibrosis were used as an example, and three different datasets (N20, B20, and B10) were prepared with different color tones and magnifications. Using these datasets, we built five models trained Mask R-CNN algorithm by a single or mixed dataset of N20, B20, and B10. We evaluated their model performance using the test dataset of three datasets. It was found that the models that were trained with mixed datasets (models B20/N20 and B10/B20), which consist of different color tones or magnifications, performed better than the single dataset trained models. Consequently, superior performance of the mixed models was obtained from the actual prediction results of the test images. We suggest that training the algorithm with various staining color tones and multi-scaled image datasets would be more optimized for consistent remarkable performance in predicting pathological lesions of interest.

New insight into gut microbiota-derived metabolites to enhance liver regeneration via network pharmacology study.

We intended to identify favourable metabolite(s) and pharmacological mechanism(s) of gut microbiota (GM) for liver regeneration (LR) through network pharmacology. We utilized the gutMGene database to obtain metabolites of GM, and targets associated with metabolites as well as LR-related targets were identified using public databases. Furthermore, we performed a molecular docking assay on the active metabolite(s) and target(s) to verify the network pharmacological concept. We mined a total of 208 metabolites in the gutMGene database and selected 668 targets from the SEA (1,256 targets) and STP (947 targets) databases. Finally, 13 targets were identified between 61 targets and the gutMGene database (243 targets). Protein-protein interaction network analysis showed that AKT1 is a hub target correlated with 12 additional targets. In this study, we describe the potential microbe from the microbiota (E. coli), chemokine signalling pathway, AKT1 and myricetin that accelerate LR, providing scientific evidence for further clinical trials.