PRODH safeguards human naive pluripotency by limiting mitochondrial oxidative phosphorylation and reactive oxygen species production.

Naive human embryonic stem cells (hESCs) that resemble the pre-implantation epiblasts are fueled by a combination of aerobic glycolysis and oxidative phosphorylation, but their mitochondrial regulators are poorly understood. Here we report that, proline dehydrogenase (PRODH), a mitochondria-localized proline metabolism enzyme, is dramatically upregulated in naive hESCs compared to their primed counterparts. The upregulation of PRODH is induced by a reduction in c-Myc expression that is dependent on PD0325901, a MEK inhibitor routinely present in naive hESC culture media. PRODH knockdown in naive hESCs significantly promoted mitochondrial oxidative phosphorylation (mtOXPHOS) and reactive oxygen species (ROS) production that triggered autophagy, DNA damage, and apoptosis. Remarkably, MitoQ, a mitochondria-targeted antioxidant, effectively restored the pluripotency and proliferation of PRODH-knockdown naive hESCs, indicating that PRODH maintains naive pluripotency by preventing excessive ROS production. Concomitantly, PRODH knockdown significantly slowed down the proteolytic degradation of multiple key mitochondrial electron transport chain complex proteins. Thus, we revealed a crucial role of PRODH in limiting mtOXPHOS and ROS production, and thereby safeguarding naive pluripotency of hESCs.

Prevalence, distribution, and hepatic fibrosis burden of the different subtypes of steatotic liver disease in primary care settings.

BACKGROUND AND AIM: In relation to the new umbrella terminology for steatotic liver disease (SLD), we aimed to elucidate the prevalence, distribution, and clinical characteristics of the SLD subgroups in the primary care setting. APPROACH AND RESULTS: We retrospectively collected data from 2535 individuals who underwent magnetic resonance elastography and MRI proton density fat fraction during health checkups in 5 primary care health promotion clinics. We evaluated the presence of cardiometabolic risk factors according to predefined criteria and divided all the participants according to the new SLD classification. The prevalence of SLD was 39.13% in the total cohort, and 95.77% of the SLD cases had metabolic dysfunction (one or more cardiometabolic risk factors). The prevalence of metabolic dysfunction-associated steatotic liver disease (MASLD) was 29.51%, with those of metabolic dysfunction and alcohol associated steatotic liver disease (MetALD) and alcohol-associated liver disease (ALD) at 7.89% and 0.39%, respectively. According to the old criteria, the prevalence of NAFLD was 29.11%, and 95.80% of the NAFLD cases fulfilled the new criteria for MASLD. The distribution of SLD subtypes was highest for MASLD, at 75.40%, followed by MetALD at 20.06%, cryptogenic SLD at 3.33%, and ALD at 1.01%. The MetALD group had a significantly higher mean magnetic resonance elastography than the MASLD or ALD group. CONCLUSION: Almost all the patients with NAFLD met the new criteria for MASLD. The fibrosis burden of the MetALD group was higher than those of the MASLD and ALD groups.

Roles of PD-L1 in human adipose-derived mesenchymal stem cells under inflammatory microenvironment.

Mesenchymal stem cells (MSCs) display unique homing and immunosuppression features which make them promising candidates for cell therapy in inflammatory disorders. It is known that C-X-C chemokine receptor type 4 (CXCR4, also known as CD184) is a critical receptor implicated in MSCs migration, and the protein programmed death ligand-1 (PD-L1) is involved in MSC's immunosuppression. However, it remains unclear how the molecular mechanisms regulate PD-L1 expression for migration and immunosuppression of MSCs under the inflammatory microenvironment. In this article, we used the human adipose-derived mesenchymal stem cells (hADMSCs) treated with lipopolysaccharide (LPS) as an in vitro inflammatory model to explore the roles of PD-L1 on the migration and immunosuppression of MSC. Our results demonstrate that in hADMSCs, LPS significantly increased PD-L1 expression, which mediated the migration of the LPS-treated hADMSCs via CXCR4. In addition, we found that the increased PD-L1 expression in the LPS-treated hADMSCs inhibited B cell proliferation and immunoglobulin G secretion through nuclear factor-κB. Our study suggests that the PD-L1 plays critical roles in the homing and immunosuppression of MSCs which are a promising cell therapy to treat inflammatory diseases.

Large-scale comparison of machine learning algorithms for target prediction of natural products.

Natural products (NPs) and their derivatives are important resources for drug discovery. There are many in silico target prediction methods that have been reported, however, very few of them distinguish NPs from synthetic molecules. Considering the fact that NPs and synthetic molecules are very different in many characteristics, it is necessary to build specific target prediction models of NPs. Therefore, we collected the activity data of NPs and their derivatives from the public databases and constructed four datasets, including the NP dataset, the NPs and its first-class derivatives dataset, the NPs and all its derivatives and the ChEMBL26 compounds dataset. Conditions, including activity thresholds and input features, were explored to access the performance of eight machine learning methods of target prediction of NPs, including support vector machines (SVM), extreme gradient boosting, random forests, K-nearest neighbor, naive Bayes, feedforward neural networks (FNN), convolutional neural networks and recurrent neural networks. As a result, the NPs and all their derivatives datasets were selected to build the best NP-specific models. Furthermore, the consensus models, as well as the voting models, were additionally applied to improve the prediction performance. More evaluations were made on the external validation set and the results demonstrated that (1) the NP-specific model performed better on the target prediction of NPs than the traditional models training on the whole compounds of ChEMBL26. (2) The consensus model of FNN + SVM possessed the best overall performance, and the voting model can significantly improve recall and specificity.

A mouse xenograft long-term replication yields a SARS-CoV-2 Delta mutant with increased lethality.

We recently established a long-term SARS-CoV-2 infection model using lung-cancer xenograft mice and identified mutations that arose in the SARS-CoV-2 genome during long-term propagation. Here, we applied our model to the SARS-CoV-2 Delta variant, which has increased transmissibility and immune escape compared with ancestral SARS-CoV-2. We observed limited mutations in SARS-CoV-2 Delta during long-term propagation, including two predominant mutations: R682W in the spike protein and L330W in the nucleocapsid protein. We analyzed two representative isolates, Delta-10 and Delta-12, with both predominant mutations and some additional mutations. Delta-10 and Delta-12 showed lower replication capacity compared with SARS-CoV-2 Delta in cultured cells; however, Delta-12 was more lethal in K18-hACE2 mice compared with SARS-CoV-2 Delta and Delta-10. Mice infected with Delta-12 had higher viral titers, more severe histopathology in the lungs, higher chemokine expression, increased astrocyte and microglia activation, and extensive neutrophil infiltration in the brain. Brain tissue hemorrhage and mild vacuolation were also observed, suggesting that the high lethality of Delta-12 was associated with lung and brain pathology. Our long-term infection model can provide mutant viruses derived from SARS-CoV-2 Delta and knowledge about the possible contributions of emergent mutations to the properties of new variants.

Analysis of SARS-CoV-2 omicron mutations that emerged during long-term replication in a lung cancer xenograft mouse model.

SARS-CoV-2 Omicron has the largest number of mutations among all the known SARS-CoV-2 variants. The presence of these mutations might explain why Omicron is more infectious and vaccines have lower efficacy to Omicron than other variants, despite lower virulence of Omicron. We recently established a long-term in vivo replication model by infecting Calu-3 xenograft tumors in immunodeficient mice with parental SARS-CoV-2 and found that various mutations occurred majorly in the spike protein during extended replication. To investigate whether there are differences in the spectrum and frequency of mutations between parental SARS-CoV-2 and Omicron, we here applied this model to Omicron. At 30 days after infection, we found that the virus was present at high titers in the tumor tissues and had developed several rare sporadic mutations, mainly in ORF1ab with additional minor spike protein mutations. Many of the mutant isolates had higher replicative activity in Calu-3 cells compared with the original SARS-CoV-2 Omicron virus, suggesting that the novel mutations contributed to increased viral replication. Serial propagation of SARS-CoV-2 Omicron in cultured Calu-3 cells resulted in several rare sporadic mutations in various viral proteins with no mutations in the spike protein. Therefore, the genome of SARS-CoV-2 Omicron seems largely stable compared with that of the parental SARS-CoV-2 during extended replication in Calu-3 cells and xenograft model. The sporadic mutations and modified growth properties observed in Omicron might explain the emergence of Omicron sublineages. However, we cannot exclude the possibility of some differences in natural infection.

Removal of Cu(II) by sodium hexametaphosphate and nano zero-valent iron modified calcium bentonite: characteristic, adsorption performance and mechanism.

Cu (II) is a toxic heavy metal commonly identified in groundwater contaminants. Bentonite-based cutoff wall is the most used method in isolating and adsorbing contaminants, while the bentonite in it easily to fail due to Cu(II) exchange. This study synthesized a novel material through the modification of calcium bentonite (CaB) utilizing sodium hexametaphosphate (SHMP) and nano zero-valent iron (NZVI). The characteristics, adsorption performance, and mechanism of the NZVI/SHMP-CaB were investigated comprehensively. The results showed that SHMP can disperse CaB and reduce flocculation, while NZVI can be further stabilized without agglomeration. The best adsorption performance of NZVI/SHMP-CaB could be obtained at the dosage of 2% SHMP and 4% NZVI. The NZVI/SHMP-CaB exhibited an outstanding removal efficiency of over 60% and 90% at a high Cu(II) concentration (pH = 6, Cu(II) = 300 mg/L) and acidic conditions (pH = 3-6, Cu(II) = 50 mg/L), respectively. The adsorption of Cu(II) by NZVI/SHMP-CaB followed a pseudo-second-order kinetic model, and fitting results from the Freundlich isothermal model suggested that the adsorption process occurred spontaneously. Besides the rapid surface adsorption on the NZVI/SHMP-CaB and ion exchange with interlayer ions in bentonite, the removal mechanism of Cu(II) also involved the chemical reduction to insoluble forms such as Cu0 and Cu2O. The generated FePO4 covered the surface of the homogenized NZVI particles, enhancing the resistance of NZVI/SHMP-CaB to acidic and oxidative environments. This study indicates that NZVI/SHMP-CaB is a promising alternative material which can be used for heavy metal removal from contaminated soil and water.

Diagnostic Performance of Noninvasive Tests for Advanced Hepatic Fibrosis in Young Age Population.

BACKGROUND & AIMS: Most noninvasive tests (NITs) for hepatic fibrosis are designed for middle-aged patients with chronic liver disease. We compared the diagnostic performance of major NITs (aspartate aminotransferase-to-platelet ratio index [APRI], Fibrosis-4 index, and nonalcoholic fatty liver disease fibrosis score) for a community-based cohort. METHODS: This cross-sectional study analyzed 8775 participants who underwent magnetic resonance elastography at community health check-up centers. Advanced hepatic fibrosis (≥F3) was defined by magnetic resonance elastography thresholds of 3.6 kPa. The diagnostic performance of 3 NITs was evaluated according to the etiology of liver disease, sex, metabolic syndrome, obesity, and increased aminotransferase levels in 4 age groups. RESULTS: The APRI generally showed the best area under the receiver operating characteristic curve in patients aged 45 years or younger, and it was statistically significant in patients with chronic viral hepatitis and alcoholic fatty liver disease (P < .043). The best APRI cut-off value for detecting advanced hepatic fibrosis was 0.4, with a sensitivity and specificity of 75.8% and 73.5%, respectively, in the community-based cohort. The APRI showed balanced sensitivity and specificity across all age groups, whereas the other metrics showed low sensitivity in those aged <45 and low specificity in those >65 years. CONCLUSIONS: The APRI showed better sensitivity and negative predictive value than the Fibrosis-4 index and the nonalcoholic fatty liver disease fibrosis score in community-based populations with mixed etiology, and, thus, can be performed as the primary test in young adults (age, ≤45 y).

A computational study on the optimization of transcranial temporal interfering stimulation with high-definition electrodes using unsupervised neural networks.

Transcranial temporal interfering stimulation (tTIS) can focally stimulate deep parts of the brain related to specific functions using beats at two high frequencies that do not individually affect the human brain. However, the complexity and nonlinearity of the simulation limit it in terms of calculation time and optimization precision. We propose a method to quickly optimize the interfering current value of high-definition electrodes, which can finely stimulate the deep part of the brain, using an unsupervised neural network (USNN) for tTIS. We linked a network that generates the values of electrode currents to another network, which is constructed to compute the interference exposure, for optimization by comparing the generated stimulus with the target stimulus. Further, a computational study was conducted using 16 realistic head models. We also compared tTIS with transcranial alternating current stimulation (tACS), in terms of performance and characteristics. The proposed method generated the strongest stimulation at the target, even when targeting deep areas or performing multi-target stimulation. The high-definition tTISl was less affected than tACS by target depth, and mis-stimulation was reduced compared with the case of using two-pair inferential stimulation in deep region. The optimization of the electrode currents for the target stimulus could be performed in 3 min. Using the proposed USNN for tTIS, we demonstrated that the electrode currents of tTIS can be optimized quickly and accurately. Moreover, we confirmed the possibility of precisely stimulating the deep parts of the brain via transcranial electrical stimulation.

Identification of VPS34-PI(3)P-FEN1-mediated DNA repair pathway as a potential drug target to overcome chemoresistance.

Intrinsic or acquired chemoresistance represents a major obstacle in cancer treatment. Multiple mechanisms can contribute to cancer cells' resistance to chemotherapy. Among them, an aberrantly strengthened DNA repair mechanism is responsible for a large proportion of drug resistance to alkylating agents and radiation therapy. In cancer cells, damping overactivated DNA repair system can overcome survival advantages conferred by chromosomal translocations or mutations and lead to cytostatic effects or cytotoxic. Therefore, selectively targeting DNA repair system in cancer cells holds promise for overcoming chemoresistance. In this study, we revealed that the endonuclease Flap Endonuclease 1 (FEN1), essential for DNA replication and repair, directly interacts with phosphatidylinositol 3-phosphate [PI(3)P], and FEN1-R378 is the primary PI(3)P-binding site. PI(3)P-binding deficient FEN1 mutant (FEN1-R378A) cells exhibited abnormal chromosomal structures and were hypersensitized to DNA damage. The PI(3)P-mediated FEN1 functionality was essential for repairing DNA damages caused by multiple mechanisms. Furthermore, VPS34, the major PI(3)P synthesizing enzyme, was negatively associated with patients' survival in various cancer types, and VPS34 inhibitors significantly sensitized chemoresistant cancer cells to genotoxic agents. These findings open up an avenue for counteracting chemoresistance by targeting VPS34-PI(3)P-mediated DNA repair pathway, and call for assessing the efficacy of this strategy in patients suffering from chemoresistance-mediated cancer recurrence in clinical trials.

A Reappraisal of the Diagnostic Performance of B-Mode Ultrasonography for Mild Liver Steatosis.

INTRODUCTION: Previous studies have shown that ultrasonography has high specificity (80%-100%) but low sensitivity (50%-70%) in diagnosing fatty liver; sensitivity is especially low for mild steatosis. In this study, we aimed to reappraise the diagnostic performance of B-mode ultrasonography (B-USG) for fatty liver disease. METHODS: We performed a retrospective, multinational, multicenter, cross-sectional, observational study (6 referral centers from 3 nations). We included 5,056 participants who underwent both B-USG and magnetic resonance proton density fat fraction (MRI-PDFF) within a 6-month period. The diagnostic performance of B-USG was compared with that of MRI-PDFF as a reference standard for fatty liver diagnosis, using sensitivity, specificity, positive and negative predictive values, diagnostic accuracy, and area under the receiver operating characteristic curve (AUC). RESULTS: B-USG showed a sensitivity of 83.4%, specificity of 81.0%, and AUC of 0.822 in diagnosing mild liver steatosis (6.5% ≤MRI-PDFF ≤14%). The sensitivity, specificity, and AUC in diagnosing the presence of fatty liver disease (MRI-PDFF ≥6.5%) were 83.4%, 81.0%, and 0.822, respectively. The mean PDFF of B-USG-diagnosed nonfatty liver differed significantly from that of diagnosed mild liver steatosis (3.5% ± 2.8% vs 8.5% ± 5.0%, P < 0.001). The interinstitutional variability of B-USG in diagnosing fatty liver was similar in diagnostic accuracy among the 6 centers (range, 82.8%-88.6%, P = 0.416). DISCUSSION: B-USG was an effective, objective method to detect mild liver steatosis using MRI-PDFF as comparison, regardless of the etiologies and comorbidities.

In vitro and in vivo suppression of SARS-CoV-2 replication by a modified, short, cell-penetrating peptide targeting the C-terminal domain of the viral spike protein.

Peptides are promising therapeutic agents for COVID-19 because of their specificity, easy synthesis, and ability to be fine-tuned. We previously demonstrated that a cell-permeable peptide corresponding to the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) Spike C-terminal domain (CD) inhibits the interaction between viral spike and nucleocapsid proteins that results in SARS-CoV-2 replication in vitro. Here, we used docking studies to design R-t-Spike CD(D), a more potent short cell-penetrating peptide composed of all D-form amino acids and evaluated its inhibitory effect against the replication of SARS-CoV-2 S clade and other variants. R-t-Spike CD(D) was internalized into Vero cells and Calu-3 cells and suppressed the replication of SARS-CoV-2 S clade, delta variant, and omicron variant with higher potency than the original peptide. In hemizygous K18-hACE2 mice, intratracheal administration of R-t-Spike CD(D) effectively delivered the peptide to the trachea and lungs, whereas intranasal administration delivered the peptide mostly to the upper respiratory system and stomach, and a small amount to the lungs. Administration by either route reduced viral loads in mouse lungs and turbinates. Furthermore, intranasally administered R-t-Spike CD(D) mitigated pathological change in the lungs and increased the survival of mice after infection with the SARS-CoV-2 S clade or delta variant. Our data suggest that R-t-Spike CD(D) has potential as a therapeutic agent against SARS-CoV-2 infection.

Sarcopenia assessed with DXA and CT increases the risk of perioperative complications in patients with gastrectomy.

OBJECTIVES: We investigated sarcopenia prevalence using various diagnostic criteria based on dual-energy X-ray absorptiometry (DXA) and computed tomography (CT) in gastric cancer patients who underwent gastrectomy, and evaluated the association between sarcopenia and perioperative complications. METHODS: This retrospective study included consecutive patients with gastric cancer who underwent gastrectomy, and preoperative DXA and CT from January 2013 to November 2020. Body composition was measured using DXA and CT. Height-adjusted DXA-based Appendicular Skeletal Muscle Mass Index (ASMI) and CT-based skeletal muscle cross-sectional area at the L3 level (SMI) were measured. Sarcopenia and sarcopenic obesity were defined using reported cutoff values. The chi-square test and univariate analysis were performed to determine risk factors for significant and severe perioperative complications (Clavien-Dindo Grades ≥ 2 and ≥ 3, respectively). RESULTS: In total, 77 males and 43 females aged 61.4 ± 11.0 years were included. ASMI and SMI were correlated (r = 0.819), but sarcopenia prevalence varied (20.0-63.3%), depending on the criteria applied. Univariate analysis revealed sarcopenia defined using the Asian Working Group on Sarcopenia (AWGS) criteria and sarcopenic obesity as risk factors for significant (odds ratio [OR] 2.76, p = 0.030 vs. OR 4.31, p = 0.002) and severe perioperative complications (OR 3.77, p = 0.036 vs. OR 4.78, p = 0.010). In subgroup analyses, sarcopenia and sarcopenic obesity were significantly associated with perioperative complications only in males. CONCLUSION: Perioperative complication risk can be predicted from sarcopenia defined using the AWGS criteria and sarcopenic obesity measured using DXA and CT, particularly in males. KEY POINTS: • The prevalence of sarcopenia varies due to definition differences. • Sarcopenia and sarcopenic obesity are risk factors for significant and severe perioperative complications, particularly in males. • Our results suggest that physicians need to pay attention to perioperative complications after surgical treatment of male patients with sarcopenia and sarcopenic obesity.

High-resolution pancreatic computed tomography for assessing pancreatic ductal adenocarcinoma resectability: a multicenter prospective study.

OBJECTIVE: This prospective multicenter study aimed to evaluate the diagnostic performance of 80-kVp thin-section pancreatic CT in determining pancreatic ductal adenocarcinoma (PDAC) resectability according to the recent National Comprehensive Cancer Network (NCCN) guidelines. METHODS: We prospectively enrolled surgical resection candidates for PDAC from six tertiary referral hospitals (study identifier: NCT03895177). All participants underwent pancreatic CT using 80 kVp tube voltage with 1-mm reconstruction interval. The local resectability was prospectively evaluated using NCCN guidelines at each center and classified into three categories: resectable, borderline resectable, and unresectable. RESULTS: A total of 138 patients were enrolled; among them, 60 patients underwent neoadjuvant therapy. R0 resection was achieved in 103 patients (74.6%). The R0 resection rates were 88.7% (47/53), 52.4% (11/21), and 0.0% (0/4) for resectable, borderline resectable, and unresectable disease, respectively, in 78 patients who underwent upfront surgery. Meanwhile, the rates were 90.9% (20/22), 76.7% (23/30), and 25.0% (2/8) for resectable, borderline resectable, and unresectable PDAC, respectively, in patients who received neoadjuvant therapy. The area under curve of high-resolution CT in predicting R0 resection was 0.784, with sensitivity, specificity, and accuracy of 87.4% (90/103), 48.6% (17/35), and 77.5% (107/138), respectively. Tumor response was significantly associated with the R0 resection after neoadjuvant therapy (odds ratio [OR] = 38.99, p = 0.016). CONCLUSION: An 80-kVp thin-section pancreatic CT has excellent diagnostic performance in assessing PDAC resectability, enabling R0 resection rates of 88.7% and 90.9% for patients with resectable PDAC who underwent upfront surgery and patients with resectable PDAC after neoadjuvant therapy, respectively. KEY POINTS: • The margin-negative (R0) resection rates were 88.7% (47/53), 52.4% (11/21), and 0.0% (0/4) for resectable, borderline resectable, and unresectable pancreatic ductal adenocarcinoma (PDAC), respectively, on 80-kVp thin-section pancreatic CT in the 78 patients who underwent upfront surgery. • Among the 60 patients who underwent neoadjuvant therapy, the R0 rates were 90.9% (20/22), 76.7% (23/30), and 25.0% (2/8) for resectable, borderline resectable, and unresectable PDAC, respectively. • Tumor response, along with the resectability status on pancreatic CT, was significantly associated with the R0 resection rate after neoadjuvant therapy.

Role of SLC5A2 polymorphisms and effects of genetic polymorphism on sodium glucose cotransporter 2 inhibitorsinhibitor response.

Type 2 diabetes mellitus (T2DM) is a complex metabolic disease characterized by hyperglycaemia. T2DM is a highly heterogeneous polygenic disease. Due to genetic variation, variations in lifestyle and other environmental exposures, there are certain variations in the phenotype of T2DM patients. Sodium glucose cotransporter 2 (SGLT2) inhibitors are novel hypoglycaemic agents that increase urinary glucose excretion by inhibiting glucose reabsorption in the proximal tubules of the kidney. For glucagon-like peptide-1 receptor agonists and dipeptidyl peptidase-4 inhibitors, studies have confirmed a variety of gene variants that may modify their effects. For SGLT2 inhibitors, research has focused on the SLC5A2 gene encoding SGLT2 and UGT1A9 gene polymorphisms affecting SGLT2 inhibitor metabolism. The SLC5A2 polymorphism rs9934336 have been associated with decreased HbA1c during the oral glucose tolerance test. Common variants of the SLC5A2 gene are related to blood glucose and insulin concentrations, but not glucagon concentrations. SLC5A2 rs9934336 and rs3116150 are related to a lower risk of heart failure. SGLT2 inhibitor exposure of UGT1A9*3 carriers is commonly higher than that of noncarriers, while these effects commonly have no obvious clinical significance on SGLT2 inhibitor pharmacokinetics. In terms of efficacy, general SLC5A2 variants show no significant effect on the response to the SGLT2 inhibitor empagliflozin. At present, research on the relationship between genetic polymorphisms and the efficacy of SGLT2 inhibitors is limited. The main purpose of this review is to elucidate the general effects of SGLT2 polymorphisms and the association between polymorphisms and the treatment response to SGLT2 inhibitors.

Table-Balancing Cooperative Robot Based on Deep Reinforcement Learning.

Reinforcement learning is one of the artificial intelligence methods that enable robots to judge and operate situations on their own by learning to perform tasks. Previous reinforcement learning research has mainly focused on tasks performed by individual robots; however, everyday tasks, such as balancing tables, often require cooperation between two individuals to avoid injury when moving. In this research, we propose a deep reinforcement learning-based technique for robots to perform a table-balancing task in cooperation with a human. The cooperative robot proposed in this paper recognizes human behavior to balance the table. This recognition is achieved by utilizing the robot's camera to take an image of the state of the table, then the table-balance action is performed afterward. Deep Q-network (DQN) is a deep reinforcement learning technology applied to cooperative robots. As a result of learning table balancing, on average, the cooperative robot showed a 90% optimal policy convergence rate in 20 runs of training with optimal hyperparameters applied to DQN-based techniques. In the H/W experiment, the trained DQN-based robot achieved an operation precision of 90%, thus verifying its excellent performance.

Agricultural Robot-Centered Recognition of Early-Developmental Pest Stage Based on Deep Learning: A Case Study on Fall Armyworm (Spodoptera frugiperda).

Accurately detecting early developmental stages of insect pests (larvae) from off-the-shelf stereo camera sensor data using deep learning holds several benefits for farmers, from simple robot configuration to early neutralization of this less agile but more disastrous stage. Machine vision technology has advanced from bulk spraying to precise dosage to directly rubbing on the infected crops. However, these solutions primarily focus on adult pests and post-infestation stages. This study suggested using a front-pointing red-green-blue (RGB) stereo camera mounted on a robot to identify pest larvae using deep learning. The camera feeds data into our deep-learning algorithms experimented on eight ImageNet pre-trained models. The combination of the insect classifier and the detector replicates the peripheral and foveal line-of-sight vision on our custom pest larvae dataset, respectively. This enables a trade-off between the robot's smooth operation and localization precision in the pest captured, as it first appeared in the farsighted section. Consequently, the nearsighted part utilizes our faster region-based convolutional neural network-based pest detector to localize precisely. Simulating the employed robot dynamics using CoppeliaSim and MATLAB/SIMULINK with the deep-learning toolbox demonstrated the excellent feasibility of the proposed system. Our deep-learning classifier and detector exhibited 99% and 0.84 accuracy and a mean average precision, respectively.

Effect of Contrast Level and Image Format on a Deep Learning Algorithm for the Detection of Pneumothorax with Chest Radiography.

Under the black-box nature in the deep learning model, it is uncertain how the change in contrast level and format affects the performance. We aimed to investigate the effect of contrast level and image format on the effectiveness of deep learning for diagnosing pneumothorax on chest radiographs. We collected 3316 images (1016 pneumothorax and 2300 normal images), and all images were set to the standard contrast level (100%) and stored in the Digital Imaging and Communication in Medicine and Joint Photographic Experts Group (JPEG) formats. Data were randomly separated into 80% of training and 20% of test sets, and the contrast of images in the test set was changed to 5 levels (50%, 75%, 100%, 125%, and 150%). We trained the model to detect pneumothorax using ResNet-50 with 100% level images and tested with 5-level images in the two formats. While comparing the overall performance between each contrast level in the two formats, the area under the receiver-operating characteristic curve (AUC) was significantly different (all p < 0.001) except between 125 and 150% in JPEG format (p = 0.382). When comparing the two formats at same contrast levels, AUC was significantly different (all p < 0.001) except 50% and 100% (p = 0.079 and p = 0.082, respectively). The contrast level and format of medical images could influence the performance of the deep learning model. It is required to train with various contrast levels and formats of image, and further image processing for improvement and maintenance of the performance.

Polyamines in Ovarian Aging and Disease.

Ovarian aging and disease-related decline in fertility are challenging medical and economic issues with an increasing prevalence. Polyamines are a class of polycationic alkylamines widely distributed in mammals. They are small molecules essential for cell growth and development. Polyamines alleviate ovarian aging through various biological processes, including reproductive hormone synthesis, cell metabolism, programmed cell death, etc. However, an abnormal increase in polyamine levels can lead to ovarian damage and promote the development of ovarian disease. Therefore, polyamines have long been considered potential therapeutic targets for aging and disease, but their regulatory roles in the ovary deserve further investigation. This review discusses the mechanisms by which polyamines ameliorate human ovarian aging and disease through different biological processes, such as autophagy and oxidative stress, to develop safe and effective polyamine targeted therapy strategies for ovarian aging and the diseases.

Exploration of the Antioxidant Effect of Spermidine on the Ovary and Screening and Identification of Differentially Expressed Proteins.

Spermidine is a naturally occurring polyamine compound that has many biological functions, such as inducing autophagy and anti-inflammatory and anti-aging effects. Spermidine can affect follicular development and thus protect ovarian function. In this study, ICR mice were fed exogenous spermidine drinking water for three months to explore the regulation of ovarian function by spermidine. The results showed that the number of atretic follicles in the ovaries of spermidine-treated mice was significantly lower than that in the control group. Antioxidant enzyme activities (SOD, CAT, T-AOC) significantly increased, and MDA levels significantly decreased. The expression of autophagy protein (Beclin 1 and microtubule-associated protein 1 light chain 3 LC3 II/I) significantly increased, and the expression of the polyubiquitin-binding protein p62/SQSTM 1 significantly decreased. Moreover, we found 424 differentially expressed proteins (DEPs) were upregulated, and 257 were downregulated using proteomic sequencing. Gene Ontology and KEGG analyses showed that these DEPs were mainly involved in lipid metabolism, oxidative metabolism and hormone production pathways. In conclusion, spermidine protects ovarian function by reducing the number of atresia follicles and regulating the level of autophagy protein, antioxidant enzyme activity, and polyamine metabolism in mice.