A transchromosomic rat model with human chromosome 21 shows robust Down syndrome features.

Progress in earlier detection and clinical management has increased life expectancy and quality of life in people with Down syndrome (DS). However, no drug has been approved to help individuals with DS live independently and fully. Although rat models could support more robust physiological, behavioral, and toxicology analysis than mouse models during preclinical validation, no DS rat model is available as a result of technical challenges. We developed a transchromosomic rat model of DS, TcHSA21rat, which contains a freely segregating, EGFP-inserted, human chromosome 21 (HSA21) with >93% of its protein-coding genes. RNA-seq of neonatal forebrains demonstrates that TcHSA21rat expresses HSA21 genes and has an imbalance in global gene expression. Using EGFP as a marker for trisomic cells, flow cytometry analyses of peripheral blood cells from 361 adult TcHSA21rat animals show that 81% of animals retain HSA21 in >80% of cells, the criterion for a "Down syndrome karyotype" in people. TcHSA21rat exhibits learning and memory deficits and shows increased anxiety and hyperactivity. TcHSA21rat recapitulates well-characterized DS brain morphology, including smaller brain volume and reduced cerebellar size. In addition, the rat model shows reduced cerebellar foliation, which is not observed in DS mouse models. Moreover, TcHSA21rat exhibits anomalies in craniofacial morphology, heart development, husbandry, and stature. TcHSA21rat is a robust DS animal model that can facilitate DS basic research and provide a unique tool for preclinical validation to accelerate DS drug development.

Rapid Assessment of Fish Freshness for Multiple Supply-Chain Nodes Using Multi-Mode Spectroscopy and Fusion-Based Artificial Intelligence.

This study is directed towards developing a fast, non-destructive, and easy-to-use handheld multimode spectroscopic system for fish quality assessment. We apply data fusion of visible near infra-red (VIS-NIR) and short wave infra-red (SWIR) reflectance and fluorescence (FL) spectroscopy data features to classify fish from fresh to spoiled condition. Farmed Atlantic and wild coho and chinook salmon and sablefish fillets were measured. Three hundred measurement points on each of four fillets were taken every two days over 14 days for a total of 8400 measurements for each spectral mode. Multiple machine learning techniques including principal component analysis, self-organized maps, linear and quadratic discriminant analyses, k-nearest neighbors, random forest, support vector machine, and linear regression, as well as ensemble and majority voting methods, were used to explore spectroscopy data measured on fillets and to train classification models to predict freshness. Our results show that multi-mode spectroscopy achieves 95% accuracy, improving the accuracies of the FL, VIS-NIR and SWIR single-mode spectroscopies by 26, 10 and 9%, respectively. We conclude that multi-mode spectroscopy and data fusion analysis has the potential to accurately assess freshness and predict shelf life for fish fillets and recommend this study be expanded to a larger number of species in the future.

Glabridin, a bioactive component of licorice, ameliorates diabetic nephropathy by regulating ferroptosis and the VEGF/Akt/ERK pathways.

BACKGROUND: Glabridin (Glab) is a bioactive component of licorice that can ameliorate diabetes, but its role in diabetic nephropathy (DN) has seldom been reported. Herein, we explored the effect and underlying mechanism of Glab on DN. METHODS: The bioactive component-target network of licorice against DN was by a network pharmacology approach. The protective effect of Glab on the kidney was investigated by a high-fat diet with streptozotocin induced-diabetic rat model. High glucose-induced NRK-52E cells were used for in vitro studies. The effects of Glab on ferroptosis and VEGF/Akt/ERK pathways in DN were investigated in vivo and in vitro using qRT-PCR, WB, and IHC experiments. RESULTS: Bioinformatics analysis constructed a network comprising of 10 bioactive components of licorice and 40 targets for DN. 13 matching targets of Glab were mainly involved in the VEGF signaling pathway. Glab treatment ameliorated general states and reduced FBG, HOMA-ß, and HOMA-insulin index of diabetic rats. The renal pathological changes and the impaired renal function (the increased levels of Scr, BUN, UREA, KIM-1, NGAL, and TIMP-1) were also improved by Glab. Moreover, Glab repressed ferroptosis by increasing SOD and GSH activity, and GPX4, SLC7A11, and SLC3A2 expression, and decreasing MDA and iron concentrations, and TFR1 expression, in vivo and in vitro. Mechanically, Glab significantly suppressed VEGF, p-AKT, p-ERK1/2 expression in both diabetic rats and HG-induced NRK-52E cells. CONCLUSIONS: This study revealed protective effects of Glab on the kidney of diabetic rats, which might exert by suppressing ferroptosis and the VEGF/Akt/ERK pathway.

LiDAR integrated IR OWC system with the abilities of user localization and high-speed data transmission.

By using narrow infrared (IR) optical beams, optical wireless communication (OWC) system can realize ultra-high capacity and high-privacy data transmission. However, due to the point-to-point connection approach, a high accuracy localization system and beam-steering antenna (BSA) are required to steer the signal beam to user terminals. In this paper, we proposed an indoor beam-steering IR OWC system with high accuracy and calibration-free localization ability by employing a coaxial frequency modulated continuous wave (FMCW) light detection and ranging (LiDAR) system. In the meantime, benefitting from the mm-level ranging accuracy of the LiDAR system, a useful approach to assess the feasibility of the link alignment between beam-steering antenna and users is first demonstrated. With the assistance of the LiDAR system, we experimentally achieved the localization of user terminals with a 0.038-degree localization accuracy and on-off keying (OOK) downlink error-free transmission of 17 Gb/s in free space at a 3-m distance is demonstrated. The highest transmission data rate under the forward error correction (FEC) criterion (Bit error rate (BER) <3.8×103) can reach 24 Gb/s.

Deep learning for spirometry quality assurance with spirometric indices and curves.

BACKGROUND: Spirometry quality assurance is a challenging task across levels of healthcare tiers, especially in primary care. Deep learning may serve as a support tool for enhancing spirometry quality. We aimed to develop a high accuracy and sensitive deep learning-based model aiming at assisting high-quality spirometry assurance. METHODS: Spirometry PDF files retrieved from one hospital between October 2017 and October 2020 were labeled according to ATS/ERS 2019 criteria and divided into training and internal test sets. Additional files from three hospitals were used for external testing. A deep learning-based model was constructed and assessed to determine acceptability, usability, and quality rating for FEV1 and FVC. System warning messages and patient instructions were also generated for general practitioners (GPs). RESULTS: A total of 16,502 files were labeled. Of these, 4592 curves were assigned to the internal test set, the remaining constituted the training set. In the internal test set, the model generated 95.1%, 92.4%, and 94.3% accuracy for FEV1 acceptability, usability, and rating. The accuracy for FVC acceptability, usability, and rating were 93.6%, 94.3%, and 92.2%. With the assistance of the model, the performance of GPs in terms of monthly percentages of good quality (A, B, or C grades) tests for FEV1 and FVC was higher by ~ 21% and ~ 36%, respectively. CONCLUSION: The proposed model assisted GPs in spirometry quality assurance, resulting in enhancing the performance of GPs in quality control of spirometry.

Deep Learning for Automatic Upper Airway Obstruction Detection by Analysis of Flow-Volume Curve.

BACKGROUND: Due to the similar symptoms of upper airway obstruction to asthma, misdiagnosis is common. Spirometry is a cost-effective screening test for upper airway obstruction and its characteristic patterns involving fixed, variable intrathoracic and extrathoracic lesions. We aimed to develop a deep learning model to detect upper airway obstruction patterns and compared its performance with that of lung function clinicians. METHODS: Spirometry records were reviewed to detect the possible condition of airway stenosis. Then they were confirmed by the gold standard (e.g., computed tomography, endoscopy, or clinic diagnosis of upper airway obstruction). Images and indices derived from flow-volume curves were used for training and testing the model. Clinicians determined cases using spirometry records from the test set. The deep learning model evaluated the same data. RESULTS: Of 45,831 patients' spirometry records, 564 subjects with curves suggesting upper airway obstruction, after verified by the gold standard, 351 patients were confirmed. These cases and another 200 cases without airway stenosis were used as the training and testing sets. 432 clinicians evaluated 20 cases of each of the three patterns and 20 no airway stenosis cases (n = 80). They assigned an accuracy of 41.2% (±15.4) (interquartile range: 27.5-52.5%), with poor agreements (κ = 0.12). For the same cases, the model generated a correct detection of 81.3% (p < 0.0001). CONCLUSIONS: Deep learning could detect upper airway obstruction patterns from other classic patterns of ventilatory defects with high accuracy, whereas clinicians presented marked errors and variabilities. The model may serve as a support tool to enhance clinicians' correct diagnosis of upper airway obstruction using spirometry.

Clinical analysis of the "small plateau" sign on the flow-volume curve followed by deep learning automated recognition.

BACKGROUND: Small plateau (SP) on the flow-volume curve was found in parts of patients with suspected asthma or upper airway abnormalities, but it lacks clear scientific proof. Therefore, we aimed to characterize its clinical features. METHODS: We involved patients by reviewing the bronchoprovocation test (BPT) and bronchodilator test (BDT) completed between October 2017 and October 2020 to assess the characteristics of the sign. Patients who underwent laryngoscopy were assigned to perform spirometry to analyze the relationship of the sign and upper airway abnormalities. SP-Network was developed to recognition of the sign using flow-volume curves. RESULTS: Of 13,661 BPTs and 8,168 BDTs completed, we labeled 2,123 (15.5%) and 219 (2.7%) patients with the sign, respectively. Among them, there were 1,782 (83.9%) with the negative-BPT and 194 (88.6%) with the negative-BDT. Patients with SP sign had higher median FVC and FEV1% predicted (both P < .0001). Of 48 patients (16 with and 32 without the sign) who performed laryngoscopy and spirometry, the rate of laryngoscopy-diagnosis upper airway abnormalities in patients with the sign (63%) was higher than those without the sign (31%) (P = 0.038). SP-Network achieved an accuracy of 95.2% in the task of automatic recognition of the sign. CONCLUSIONS: SP sign is featured on the flow-volume curve and recognized by the SP-Network model. Patients with the sign are less likely to have airway hyperresponsiveness, automatic visualizing of this sign is helpful for primary care centers where BPT cannot available.

The nucleotide usages significantly impact synonymous codon usage in Mycoplasma hyorhinis.

Five annotated genomes of Mycoplasma hyorhinis were analyzed for clarifying evolutionary dynamics driving the overall codon usage pattern. Information entropy used for estimating nucleotide usage pattern at the gene level indicates that multiple evolutionary dynamics participate in forcing nucleotide usage bias at every codon position. Moreover, nucleotide usage bias directly contributes to synonymous codon usage biases with two different extremes. The overrepresented codons tended to have A/T in the third codon position, and the underrepresented codons strongly used G/C in the third position. Furthermore, correspondence analysis and neutrality plot reflect an obvious interplay between mutation pressure and natural selection mediating codon usage in M. hyorhinis genome. Due to significant bias in usages between A/T and G/C at the gene level, different selective forces have been proposed to contribute to codon usage preference in M. hyorhinis genome, including nucleotide composition constraint derived from mutation pressure, translational selection involved in natural selection, and strand-specific mutational bias represented by different nucleotide skew index. The systemic analyses of codon usage for M. hyorhinis can enable us to better understand the mechanisms of evolution in this species.

In Vitro Antioxidant Activities of Phenols and Oleanolic Acid from Mango Peel and Their Cytotoxic Effect on A549 Cell Line.

Mango peel, the main by-product of juice processing, possesses appreciable quantities of bioactive phenolic compounds and is worthy of further utilization. The present work reports for the first time the HPLC analysis and in vitro antioxidant evaluation of mango peel phenols (MPPs) and their cytotoxic effect on the A549 lung cancer cell line. These results indicated that mango peel has the total phenolic content of 723.2 ± 0.93 mg·kg−1 dry mango peel (DMP), which consisted mainly of vanillic aldehyde, caffeic acid, chlorogenic acid, gallic acid, procyanidin B2 and oleanolic acid. Antioxidant assays showed that MPPs had strong antioxidant activities, with 92 ± 4.2% of DPPH radical scavenging rate, 79 ± 2.5% of ABTS radical inhibition rate and 4.7 ± 0.5 µM Trolox equivalents per kg−1 DMP of ferric reducing power. Gallic acid possess a stronger antioxidant capacity than other phenols. In vitro cytotoxic tests suggested that mango peel extract (MPE) had an IC50 value of 15 mg·mL−1 and MPPs had a stronger inhibitory effect on the A549 cell line. Oleanolic acid exhibited the strongest cytotoxicity, with an IC50 value of 4.7 µM, which was similar with that of the positive control 5-fluorouracil.

Discrepancy in Insulin Regulation between Gestational Diabetes Mellitus (GDM) Platelets and Placenta.

Earlier findings have identified the requirement of insulin signaling on maturation and the translocation of serotonin (5-HT) transporter, SERT to the plasma membrane of the trophoblast in placenta. Because of the defect on insulin receptor (IR) in the trophoblast of the gestational diabetes mellitus (GDM)-associated placenta, SERT is found entrapped in the cytoplasm of the GDM-trophoblast. SERT is encoded by the same gene expressed in trophoblast and platelets. Additionally, alteration in plasma 5-HT levels and the 5-HT uptake rates are associated with the aggregation rates of platelets. Therefore, here, we investigated a novel hypothesis that GDM-associated defects in platelet IR should change their 5-HT uptake rates, and this should be a leading factor for thrombosis in GDM maternal blood. The maternal blood and the placentas were obtained at the time of cesarean section from the GDM and non-diabetic subjects (n = 6 for each group), and the platelets and trophoblasts were isolated to determine the IR activity, surface level of SERT, and their 5-HT uptake rates.Interestingly, no significant differences were evident in IR tyrosine phosphorylation or the downstream elements, AKT and S6K in platelets and their aggregation rates in both groups. Furthermore, insulin stimulation up-regulated 5-HT uptake rates of GDM-platelets as it does in the control group. However, the phosphorylation of IR and the downstream elements were significantly lower in GDM-trophoblast and showed no response to the insulin stimulation while they showed 4-fold increase to insulin stimulation in control group. Similarly, the 5-HT uptake rates of GDM-trophoblast and the SERT expression on their surface were severalfold lower compared with control subjects. IR is expressed in all tissues, but it is not known if diabetes affects IR in all tissues equally. Here, for the first time, our findings with clinical samples show that in GDM-associated defect on IR is tissue type-dependent. While IR is impaired in GDM-placenta, it is unaffected in GDM-platelet.

GDM-associated insulin deficiency hinders the dissociation of SERT from ERp44 and down-regulates placental 5-HT uptake.

Serotonin (5-HT) transporter (SERT) regulates the level of 5-HT in placenta. Initially, we found that in gestational diabetes mellitus (GDM), whereas free plasma 5-HT levels were elevated, the 5-HT uptake rates of trophoblast were significantly down-regulated, due to impairment in the translocation of SERT molecules to the cell surface. We sought to determine the factors mediating the down-regulation of SERT in GDM trophoblast. We previously reported that an endoplasmic reticulum chaperone, ERp44, binds to Cys200 and Cys209 residues of SERT to build a disulfide bond. Following this posttranslational modification, before trafficking to the plasma membrane, SERT must be dissociated from ERp44; and this process is facilitated by insulin signaling and reversed by the insulin receptor blocker AGL2263. However, the GDM-associated defect in insulin signaling hampers the dissociation of ERp44 from SERT. Furthermore, whereas ERp44 constitutively occupies Cys200/Cys209 residues, one of the SERT glycosylation sites, Asp208 located between the two Cys residues, cannot undergo proper glycosylation, which plays an important role in the uptake efficiency of SERT. Herein, we show that the decrease in 5-HT uptake rates of GDM trophoblast is the consequence of defective insulin signaling, which entraps SERT with ERp44 and impairs its glycosylation. In this regard, restoring the normal expression of SERT on the trophoblast surface may represent a novel approach to alleviating some GDM-associated complications.

Ferroptosis: emerging roles in lung cancer and potential implications in biological compounds.

Lung cancer has high metastasis and drug resistance. The prognosis of lung cancer patients is poor and the patients' survival chances are easily neglected. Ferroptosis is a programmed cell death proposed in 2012, which differs from apoptosis, necrosis and autophagy. Ferroptosis is a novel type of regulated cell death which is driven by iron-dependent lipid peroxidation and subsequent plasma membrane ruptures. It has broad prospects in the field of tumor disease treatment. At present, multiple studies have shown that biological compounds can induce ferroptosis in lung cancer cells, which exhibits significant anti-cancer effects, and they have the advantages in high safety, minimal side effects, and less possibility to drug resistance. In this review, we summarize the biological compounds used for the treatment of lung cancer by focusing on ferroptosis and its mechanism. In addition, we systematically review the current research status of combining nanotechnology with biological compounds for tumor treatment, shed new light for targeting ferroptosis pathways and applying biological compounds-based therapies.

Fingerprinting black tea: When spectroscopy meets machine learning a novel workflow for geographical origin identification.

Food fraud, along with many challenges to the integrity and sustainability, threatens the prosperity of businesses and society as a whole. Tea is the second most commonly consumed non-alcoholic beverage globally. Challenges to tea authenticity require the development of highly efficient and rapid solutions to improve supply chain transparency. This study has produced an innovative workflow for black tea geographical indications (GI) discrimination based on non-targeted spectroscopic fingerprinting techniques. A total of 360 samples originating from nine GI regions worldwide were analysed by Fourier Transform Infrared (FTIR) and Near Infrared spectroscopy. Machine learning algorithms (k-nearest neighbours and support vector machine models) applied to the test data greatly improved the GI identification achieving 100% accuracy using FTIR. This workflow will provide a low-cost and user-friendly solution for on-site and real-time determination of black tea geographical origin along supply chains.

Inflammation-Targeted Nanomedicines Alleviate Oxidative Stress and Reprogram Macrophages Polarization for Myocardial Infarction Treatment.

Myocardial infarction (MI) is a critical global health challenge, with current treatments limited by the complex MI microenvironment, particularly the excessive oxidative stress and intense inflammatory responses that exacerbate cardiac dysfunction and MI progression. Herein, a mannan-based nanomedicine, Que@MOF/Man, is developed to target the inflammatory infarcted heart and deliver the antioxidative and anti-inflammatory agent quercetin (Que), thereby facilitating a beneficial myocardial microenvironment for cardiac repair. The presence of mannan on the nanoparticle surface enables selective internalization by macrophages rather than cardiomyocytes. Que@MOF/Man effectively neutralizes reactive oxygen species in macrophages to reduce oxidative stress and promote their differentiation into a reparative phenotype, reconciling the inflammatory response and enhancing cardiomyocyte survival through intercellular communication. Owing to the recruitment of macrophages into inflamed myocardium post-MI, in vivo, administration of Que@MOF/Man in MI rats revealed the specific distribution into the injured myocardium compared to free Que. Furthermore, Que@MOF/Man exhibited favorable results in resolving inflammation and protecting cardiomyocytes, thereby preventing further myocardial remodeling and improving cardiac function in MI rats. These findings collectively validate the rational design of an inflammation-targeted delivery strategy to mitigate oxidative stress and modulate the inflammation response in the injured heart, presenting a therapeutic avenue for MI treatment.

An Injectable Thermosensitive Hydrogel Containing Resveratrol and Dexamethasone-Loaded Carbonated Hydroxyapatite Microspheres for the Regeneration of Osteoporotic Bone Defects.

Bone defects in osteoporosis usually present excessive reactive oxygen species (ROS), abnormal inflammation levels, irregular shapes and impaired bone regeneration ability; therefore, osteoporotic bone defects are difficult to repair. In this study, an injectable thermosensitive hydrogel poly (D, L-lactide)-poly (ethylene glycol)- poly (D, L-lactide) (PLEL) system containing resveratrol (Res) and dexamethasone (DEX) is designed to create a microenvironment conducive to osteogenesis in osteoporotic bone defects. This PLEL hydrogel is injected and filled irregular defect areas and achieving a rapid sol-gel transition in situ. Res has a strong anti-inflammatory effects that can effectively remove excess free radicals at the damaged site, guide macrophage polarization to the M2 phenotype, and regulate immune responses. Additionally, DEX can promote osteogenic differentiation. In vitro experiments showed that the hydrogel effectively promoted osteogenic differentiation of mesenchymal stem cells, removed excess intracellular ROS, and regulated macrophage polarization to reduce inflammatory responses. In vivo experiments showed that the hydrogel promoted osteoporotic bone defect regeneration and modulated immune responses. Overall, this study confirmed that the hydrogel can treat osteoporotic bone defects by synergistically modulating bone damage microenvironment, alleviating inflammatory responses, and promoting osteogenesis; thus, it represents a promising drug delivery strategy to repair osteoporotic bone defects.

WASPSYN: A Challenge for Domain Adaptive Synapse Detection in Microwasp Brain Connectomes.

The size of image volumes in connectomics studies now reaches terabyte and often petabyte scales with a great diversity of appearance due to different sample preparation procedures. However, manual annotation of neuronal structures (e.g., synapses) in these huge image volumes is time-consuming, leading to limited labeled training data often smaller than 0.001% of the large-scale image volumes in application. Methods that can utilize in-domain labeled data and generalize to out-of-domain unlabeled data are in urgent need. Although many domain adaptation approaches are proposed to address such issues in the natural image domain, few of them have been evaluated on connectomics data due to a lack of domain adaptation benchmarks. Therefore, to enable developments of domain adaptive synapse detection methods for large-scale connectomics applications, we annotated 14 image volumes from a biologically diverse set of Megaphragma viggianii brain regions originating from three different whole-brain datasets and organized the WASPSYN challenge at ISBI 2023. The annotations include coordinates of pre-synapses and post-synapses in the 3D space, together with their one-to-many connectivity information. This paper describes the dataset, the tasks, the proposed baseline, the evaluation method, and the results of the challenge. Limitations of the challenge and the impact on neuroscience research are also discussed. The challenge is and will continue to be available at https://codalab.lisn.upsaclay.fr/competitions/9169. Successful algorithms that emerge from our challenge may potentially revolutionize real-world connectomics research and further the cause that aims to unravel the complexity of brain structure and function.

SmartEM: machine-learning guided electron microscopy.

Connectomics provides essential nanometer-resolution, synapse-level maps of neural circuits to understand brain activity and behavior. However, few researchers have access to the high-throughput electron microscopes necessary to generate enough data for whole circuit or brain reconstruction. To date, machine-learning methods have been used after the collection of images by electron microscopy (EM) to accelerate and improve neuronal segmentation, synapse reconstruction and other data analysis. With the computational improvements in processing EM images, acquiring EM images has now become the rate-limiting step. Here, in order to speed up EM imaging, we integrate machine-learning into real-time image acquisition in a singlebeam scanning electron microscope. This SmartEM approach allows an electron microscope to perform intelligent, data-aware imaging of specimens. SmartEM allocates the proper imaging time for each region of interest - scanning all pixels equally rapidly, then re-scanning small subareas more slowly where a higher quality signal is required to achieve accurate segmentability, in significantly less time. We demonstrate that this pipeline achieves a 7-fold acceleration of image acquisition time for connectomics using a commercial single-beam SEM. We apply SmartEM to reconstruct a portion of mouse cortex with the same accuracy as traditional microscopy but in less time.