Study on the mechanisms by which pumpkin polysaccharides regulate abnormal glucose and lipid metabolism in diabetic mice under oxidative stress.

Pumpkin polysaccharide (PPe-H) can perform physiological functions through its antioxidative and hypoglycemic effects; however, the mechanisms through which PPe-H regulates abnormal glucose and lipid metabolism caused by oxidative stress injury remain unclear. In the present study, streptozotocin was used to generate an acute diabetic mouse model, and the effects of PPe-H on glucose and lipid metabolism impaired by oxidative stress in diabetic mice were studied. PPe-H significantly reduced blood glucose levels and enhanced the oral glucose tolerance of diabetic mice under stress injury (p < 0.05). The analysis of liver antioxidant enzymes showed that PPe-H significantly enhanced the activities of SOD and CAT (p < 0.05), increased the GSH level, and decreased the level of MDA (p < 0.05). Transcriptomic and metabolomic analyses of the liver tissues of mice revealed characteristic differences in the genetic and metabolic levels of the samples, which showed that PPe-H treatment may play a positive role in regulating the metabolism of methionine, cysteine, glycerol phospholipid, and linoleic acid. These results indicated that PPe-H alleviated the symptoms of hyperglycemia by regulating metabolites related to oxidative stress and glycolipid metabolism in diabetic mice.

Effects of Auricularia auricula-judae (Bull.) Quél. polysaccharide acid hydrolysate on glucose metabolism in diabetic mice under oxidative stress.

BACKGROUND: Oxidative stress can lead to uncontrolled glucose metabolism and, thus, diabetes. Auricularia auricula-judae (Bull.) Quél. polysaccharides possess biological activities, such as antioxidant and hypoglycemic effects, but their mechanism of their acid hydrolysates on oxidative stress-injured glucose metabolism disorders is unclear. PURPOSE: Using diabetic mice, we investigated the effect of the acid hydrolysate of polysaccharides from Auricularia auricula-judae (Bull.) Quél. on improving diabetes. STUDY DESIGN AND METHODS: The structural information of sample polysaccharides was measured by high performance gel permeation chromatography, nuclear magnetic resolution, and high performance liquid chromatography. The diabetic model was established by intraperitoneal injection of streptozotocin. For eight consecutive weeks, the mice were orally administered sample polysaccharides (100, 200, and 300 mg/kg b.w. per day) for intervention. The improvement effect of the samples on diabetes was explored by detecting the changes in biochemical indicators in mice, and the underlying mechanism was studied by transcriptomic and metabolomic analysis. RESULTS: The results showed that acid hydrolysate of Auricularia auricula-judae (Bull.) Quél. polysaccharides consisted mainly of mannose, xylose, glucuronic acid, and glucose; its weight-averaged molecular weight was 6.3842 × 104 Dalton, its number average molecular weight was 2.9594 × 104 Dalton; and the molecule contained α-Glc(1→4)-, ß-Glc(1→3)-, and ß-Man(1→4)-linked glycosidic bonds. A total of 100 mg/kg b.w. per day sample was the best intervention concentration. After eight weeks of intervention, the sample polysaccharides significantly reduced dynamic blood glucose and serum lipids, enhanced antioxidant enzyme activities, promoted glucagon like peptide-1 and insulin secretion, improved insulin sensitivity and alleviated insulin resistance in diabetic mice. Transcriptomic and metabolomic analyses showed that sample polysaccharides was able to ameliorate disorders of glucose metabolism by modulating gene expression such as glucokinase; and modulate the state of oxidative stress in mice in vivo by regulating the glutathione metabolism pathway. CONCLUSION: Acid hydrolysate of Auricularia auricula-judae (Bull.) Quél. polysaccharides improved glucose metabolism disorders by slowing down the oxidative stress injury in mice, thereby alleviating diabetes. This study provided a basis for determining the underlying mechanism of the antidiabetic effect of Auricularia auricula-judae (Bull.) Quél. polysaccharides, which would significantly improve the deep development and application of these materials in diabetes control.

Learning Stiffness Tensors in Self-Activated Solids via a Local Rule.

Mechanical metamaterials are often designed with particular properties for specific load-bearing functions. Alternatively, this study aims to create a class of active lattice metamaterials, dubbed self-activated solids, that can learn desired stiffness tensors from the elastic deformations they experienced, a crucial feature to improve the performance, efficiency, and functionality of materials. Artificial adaptive matters that combine sensory, control, and actuation elements can offer appealing solutions. However, challenges still remain: The designs will rely on accurate off-line and global computations, as well as intricate coordination among individual elements. Here, a simple online and local learning strategy is initiated based on contrastive Hebbian learning to gradually guide self-activated solids to possess sought-after stiffness tensors autonomously and reversibly. During learning, the bond stiffness of the active lattice varies depending only on its local strain. The numerical tests show that the self-activated solid can not only achieve the desired bulk, shear, and coupling moduli but also manifest uni-mode and bi-mode extremal materials by itself after experiencing the corresponding elastic deformations. Further, the self-activated solid can also achieve the desired time-varying moduli when exposed to temporally different loads. The design is applicable to any lattice geometries and is resistant to damage and instabilities. The material design approach and the physical learning strategy suggested can benefit the design of autonomous materials, physical learning machines, and adaptive robots.

Effects of synergistic Fenton-microwave treatment on the antioxidant stress of soluble polysaccharides and the physicochemical properties of insoluble polysaccharides from Gelidium amansii.

In this study, we individually obtained crude Gelidium amansii water-soluble polysaccharides and water-insoluble polysaccharides (GAIPs) using an improved Fenton-microwave synergistic treatment. The former were purified by alcohol precipitation and deproteinization to obtain Gelidium amansii water-soluble polysaccharides (GASPs), and their effects on the oxidative stress resistance of Caenorhabditis elegans were investigated. GAIPs were studied for their physicochemical properties, including hydration characteristics, adsorption, and cation-exchange capacity. The results showed that compared with the negative control, 1.0 mg/mL GASPs significantly upregulated (>1.70-fold) the expression of antioxidant-related genes, such as daf-16, sir-2.1, and skn-1 (p < 0.05), which prolonged the mean survival time and increased the mean number of head bobbing (p < 0.05). The hydration characteristics and oil-holding capacity of GAIPs were lower than those of G. amansii powder (GAP) and G. amansii filtrate residue (GADP). However, the adsorption capacity of GAIPs for cholesterol (pH 7.0) and sodium cholate and the cation-exchange capacity were significantly better than those of GAP (5.17, 13.16 & 1.63 times, p < 0.05) and GADP (8.42, 6.39, & 2.05 times, p < 0.05). To conclude, the synergistic Fenton-microwave treatment contributed to the increase in the oxidative stress resistance of GASPs and improved the adsorption capacity and cation-exchange capacity of GAIPs.

Improving DNA 6mA Site Prediction via Integrating Bidirectional Long Short-Term Memory, Convolutional Neural Network, and Self-Attention Mechanism.

Identifying DNA N6-methyladenine (6mA) sites is significantly important to understanding the function of DNA. Many deep learning-based methods have been developed to improve the performance of 6mA site prediction. In this study, to further improve the performance of 6mA site prediction, we propose a new meta method, called Co6mA, to integrate bidirectional long short-term memory (BiLSTM), convolutional neural networks (CNNs), and self-attention mechanisms (SAM) via assembling two different deep learning-based models. The first model developed in this study is called CBi6mA, which is composed of CNN, BiLSTM, and fully connected modules. The second model is borrowed from LA6mA, which is an existing 6mA prediction method based on BiLSTM and SAM modules. Experimental results on two independent testing sets of different model organisms, i.e., Arabidopsis thaliana and Drosophila melanogaster, demonstrate that Co6mA can achieve an average accuracy of 91.8%, covering 89% of all 6mA samples while achieving an average Matthews correlation coefficient value (0.839), which is higher than the second-best method DeepM6A.

An unusual cause of recurrent abdominal pain in a middle-aged man.

A 54-year-old man was hospitalized with intermittent periumbilical pain for 1-month duration. Abdominal contrast-enhanced computed tomography (CT) revealed target-sign and a fat density mass measuring 2.0 × 2.5-cm in the distal ileum. Part mesenteric tissues and blood vessels were embedded and the wall of the affected intestinal tube was thickened and edematous. His symptom was alleviated after conservative treatment and he refused further management. The patient was hospitalized again with the same symptoms and abdominal CT findings 4 years later. Exploratory laparotomy was performed. A palpable mass in the ileum was found measuring 3.0 × 3.0-cm and partial enterectomy was performed. Postoperative histopathology revealed the resected mass was composed of proliferating mature adipocytes surrounded by few fibrous connective tissue. Hyperplastic fibroblast and inflammatory exudative necrotic tissue were found on the surface of the mass. The patient was diagnosed as ileum fibrolipoma with intussusception. He was discharged home uneventfully and no symptoms was observed in 12 months follow-up.

Free radical-mediated extraction of polysaccharides from Gelidium amansii and their modulation on abnormal glycometabolism in Caenorhabditis elegans.

An improved Fenton-microwave synergistic method was employed to extract polysaccharides from Gelidium amansii (GAPs), which were subsequently purified through alcohol precipitation, deproteinization, and gel chromatography. The effects of GAPs on oxidative stress resistance and abnormal glycometabolism were investigated using Caenorhabditis elegans. The polysaccharide yield reached 54.17 % ± 0.27 % under the following conditions: solid-liquid ratio of 1:102 g/mL, temperature of 80 °C, H2O2 concentration of 1.0 %, microwave power of 700 W, and 33 min. The purified GAPs were heteropolysaccharides primarily composed of mannose, ribose, glucuronic acid, glucose, galactose, xylose, and arabinose, with a molar ratio of 0.287:0.524:0.634:2.646:89.649:5.416:0.463. The weight-average and numerical-average molecular weights of the GAPs were determined to be 142.800 kDa and 75.255 kDa, respectively. Treatment of C. elegans with GAPs at 2.0 mg/mL resulted in a significant extension of the mean lifespan by 53.85 % compared to the negative control (p < 0.05). Furthermore, GAPs exhibited notable enhancements in the antioxidant system, including SOD by 56.90 % and CAT by 96.83 % (p < 0.05). Additionally, GAPs led to reductions in glucose-related metabolites, including glucose levels by 34.54 % and pyruvic acid levels by 149.54 % (p < 0.05). These findings demonstrate the excellent performance of GAPs in enhancing the antioxidant system and regulating abnormal glycometabolism.

Early and Sensitive Detection of Pathogens for Public Health and Biosafety: An Example of Surveillance and Genotyping of SARS-CoV-2 in Sewage Water by Cas12a-Facilitated Portable Plasmonic Biosensor.

Infectious diseases severely threaten public health and global biosafety. In addition to transmission through the air, pathogenic microorganisms have also been detected in environmental liquid samples, such as sewage water. Conventional biochemical detection methodologies are time-consuming and cost-ineffective, and their detection limits hinder early diagnosis. In the present study, ultrafine plasmonic fiber probes with a diameter of 125 µm are fabricated for clustered regularly interspaced short palindromic repeats/CRISPR-associated protein (CRISPR/Cas)-12a-mediated sensing of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Single-stranded DNA exposed on the fiber surface is trans-cleaved by the Cas12a enzyme to release gold nanoparticles that are immobilized onto the fiber surface, causing a sharp reduction in the surface plasmon resonance (SPR) wavelength. The proposed fiber probe is virus-specific with the limit of detection of ~2,300 copies/ml, and genomic copy numbers can be reflected as shifts in wavelengths. A total of 21 sewage water samples have been examined, and the data obtained are consistent with those of quantitative polymerase chain reaction (qPCR). In addition, the Omicron variant and its mutation sites have been fast detected using S gene-specific Cas12a. This study provides an accurate and convenient approach for the real-time surveillance of microbial contamination in sewage water.

Image-based real-time feedback control of magnetic digital microfluidics by artificial intelligence-empowered rapid object detector for automated in vitro diagnostics.

In vitro diagnostics (IVD) plays a critical role in healthcare and public health management. Magnetic digital microfluidics (MDM) perform IVD assays by manipulating droplets on an open substrate with magnetic particles. Automated IVD based on MDM could reduce the risk of accidental exposure to contagious pathogens among healthcare workers. However, it remains challenging to create a fully automated IVD platform based on the MDM technology because of a lack of effective feedback control system to ensure the successful execution of various droplet operations required for IVD. In this work, an artificial intelligence (AI)-empowered MDM platform with image-based real-time feedback control is presented. The AI is trained to recognize droplets and magnetic particles, measure their size, and determine their location and relationship in real time; it shows the ability to rectify failed droplet operations based on the feedback information, a function that is unattainable by conventional MDM platforms, thereby ensuring that the entire IVD process is not interrupted due to the failure of liquid handling. We demonstrate fundamental droplet operations, which include droplet transport, particle extraction, droplet merging and droplet mixing, on the MDM platform and show how the AI rectify failed droplet operations by acting upon the feedback information. Protein quantification and antibiotic resistance detection are performed on this AI-empowered MDM platform, and the results obtained agree well with the benchmarks. We envision that this AI-based feedback approach will be widely adopted not only by MDM but also by other types of digital microfluidic platforms to offer precise and error-free droplet operations for a wide range of automated IVD applications.

Effects of the Extraction Solvents on Dissolution Rate and Antioxidant Capacity of Auricularia auricula (Agaricomycetes) Polysaccharides In Vitro and In Vivo.

This paper reports the effects of solvents on the dissolution rate and antioxidant capacity of Auricularia auricula polysaccharides (AAPs). The ultra-low temperature combined with microwave extraction (UME) was used to compare the dissolution rates and molecular weights of AAPs using deionized water and deep eutectic solvents (DES) as solvents, respectively. Scanning electron microscope (SEM) was used to observe the effects of water extract (AAPs-FW) and DES extract (AAPs-FD) on the cell wall of A. auricula. The antioxidant capacity of polysaccharide extracts in vitro was assessed by using various methods (DPPH, ABTS, and hydroxyl radicals). In addition, in vivo oxidative stress was assessed using Caenorhabditis elegans models. The extract yield of AAPs varied among the extracts and was 19.58% ± 0.56% in AAPs-FW. Whereas DES-UME increased the yield of polysaccharides (AAPs-FD) by 9.81% in the extraction medium containing triethylene glycol-choline chloride, under the optimum conditions of 60 min freezing time, 350 W, and 90 s microwave time. The microstructure of the cell wall shown by SEM was consistent with the results of polysaccharide yields. The molecular weights of AAPs-FW and AAPs-FD were found to be 398.107 kDa and 89.099 kDa, respectively. The results demonstrated that AAPs-FD exhibited potent radical scavenging activity against DPPH and a weaker scavenging ability for ABTS and OH radicals compared to AAPs-FW. In addition, both polysaccharide extracts increased the survival rate of C. elegans under methyl viologen induced oxidative stress at specific concentrations (p < 0.05), and the antioxidant capacity of AAPs-FD was higher than that of AAPs-FW at low concentrations (0.125 mg/mL). This indicated that both polysaccharides had a protective effect against damage induced by intracellular free radical generators (methyl viologen).

Wastewater surveillance and an automated robot: effectively tracking SARS-CoV-2 transmission in the post-epidemic era.

Wastewater-based epidemiology (WBE) has exhibited great utility in the early and rapid identification of SARS-CoV-2. However, the efficacy of wastewater surveillance under China's previous strict epidemic prevention policy remains to be described. We collected the WBE data of wastewater treatment plants (WWTPs) in the Third People's Hospital of Shenzhen and several communities to determine the significant effectiveness of routine wastewater surveillance in monitoring the local spread of SARS-CoV-2 under tight containment of the epidemic. The results of 1 month of continuous wastewater surveillance showed that positive signals for SARS-CoV-2 RNA were detected in the wastewater samples, and a significant positive correlation was observed between the virus concentration and the number of daily cases. In addition, the community's domestic wastewater surveillance results were confirmed even 3 days before, or simultaneously with, the infected patient being confirmed as having the virus. Meanwhile, an automated sewage virus detection robot, ShenNong No.1 robot, was developed, showing a high degree of agreement with experimental data, offering the possibility of large-scale multi-point surveillance. Overall, our results illustrated the clear indicative role of wastewater surveillance in combating COVID-19 and provided a practical basis for rapidly expanding the feasibility and value of routine wastewater surveillance for future emerging infectious diseases.

CRISPR-Cas13a-powered electrochemical biosensor for the detection of the L452R mutation in clinical samples of SARS-CoV-2 variants.

Since the end of 2019, a highly contagious disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has deprived numerous lives worldwide, called COVID-19. Up to date, omicron is the latest variant of concern, and BA.5 is replacing the BA.2 variant to become the main subtype rampaging worldwide. These subtypes harbor an L452R mutation, which increases their transmissibility among vaccinated people. Current methods for identifying SARS-CoV-2 variants are mainly based on polymerase chain reaction (PCR) followed by gene sequencing, making time-consuming processes and expensive instrumentation indispensable. In this study, we developed a rapid and ultrasensitive electrochemical biosensor to achieve the goals of high sensitivity, the ability of distinguishing the variants, and the direct detection of RNAs from viruses simultaneously. We used electrodes made of MXene-AuNP (gold nanoparticle) composites for improved sensitivity and the CRISPR/Cas13a system for high specificity in detecting the single-base L452R mutation in RNAs and clinical samples. Our biosensor will be an excellent supplement to the RT-qPCR method enabling the early diagnosis and quick distinguishment of SARS-CoV-2 Omicron BA.5 and BA.2 variants and more potential variants that might arise in the future.

Endoscopic submucosal dissection of a rare antral mass: gastric fibrolipoma.

A 60-year-old man was hospitalized with upper abdominal pain for 2-month duration. He had no previous history of symptoms related to his upper gastrointestinal tract. Physical examination was unremarkable. A semicircular round mass with smooth surface was found at the gastric antrum measuring 2.0 × 2.0-cm by gastroscopy. Endoscopic ultrasound (EUS) demonstrated a heterogeneous hyperechoic mass originated from the submucosal layer. Abdominal computed tomography (CT) revealed a fat-density mass with internal mixed density at the gastric antrum. His complete blood test, liver, renal, coagulation function and tumor markers were within reference values. Endoscopic submucosal dissection (ESD) was performed. Postoperative histopathology examination revealed the tumor was composed of closely arranged proliferating mature adipocytes with consistent size and shape. Collagen fiber hyperplasia between adipocytes was found in focal area. The patient was diagnosed as gastric fibrolipoma. He was discharged home uneventful and no symptoms was found in 12 months follow-up.

Improving protein-protein interaction site prediction using deep residual neural network.

Accurate identification of protein-protein interaction (PPI) sites is significantly important for understanding the mechanism of life and developing new drugs. However, it is expensive and time-consuming to identify PPI sites using wet-lab experiments. Developing computational methods is a new road to identify PPI sites, which can accelerate the procedure of PPI-related research. In this study, we propose a novel deep learning-based method (called D-PPIsite) to improve the accuracy of sequence-based PPI site prediction. In D-PPIsite, four discriminative sequence-driven features, i.e., position specific scoring matrix, relative solvent accessibility, position information and physical properties, are employed to feed into a well-designed deep learning module, consisting of convolutional, squeeze and excitation, and fully connected layers, to learn a prediction model. To reduce the risk of a single prediction model getting stuck in local optima, multiple prediction models with different initialization parameters are selected and integrated into one final model using the mean ensemble strategy. Experimental results on five independent testing data sets demonstrate that the proposed D-PPIsite can achieve an average accuracy of 80.2% and precision of 36.9%, covering 53.5% of all PPI sites while achieving the average Matthews correlation coefficient value (0.330) that is significantly higher than most of existing state-of-the-art prediction methods. We implement a new standalone-version predictor for predicting PPI sites, which is freely available at https://github.com/MingDongup/D-PPIsite for academic use.

Overcoming the adhesion paradox and switchability conflict on rough surfaces with shape-memory polymers.

Smart adhesives that can be applied and removed on demand play an important role in modern life and manufacturing. However, current smart adhesives made of elastomers suffer from the long-standing challenges of the adhesion paradox (rapid decrease in adhesion strength on rough surfaces despite adhesive molecular interactions) and the switchability conflict (trade-off between adhesion strength and easy detachment). Here, we report the use of shape-memory polymers (SMPs) to overcome the adhesion paradox and switchability conflict on rough surfaces. Utilizing the rubbery-glassy phase transition in SMPs, we demonstrate, through mechanical testing and mechanics modeling, that the conformal contact in the rubbery state followed by the shape-locking effect in the glassy state results in the so-called rubber-to-glass (R2G) adhesion (defined as making contact in the rubbery state to a certain indentation depth followed by detachment in the glassy state), with extraordinary adhesion strength (>1 MPa) proportional to the true surface area of a rough surface, overcoming the classic adhesion paradox. Furthermore, upon transitioning back to the rubbery state, the SMP adhesives can detach easily due to the shape-memory effect, leading to a simultaneous improvement in adhesion switchability (up to 103, defined as the ratio of the SMP R2G adhesion to its rubbery-state adhesion) as the surface roughness increases. The working principle and the mechanics model of R2G adhesion provide guidelines for developing stronger and more switchable adhesives adaptable to rough surfaces, thereby enhancing the capabilities of smart adhesives, and impacting various fields such as adhesive grippers and climbing robots.

Once We Find Grade III Meconium Stained Amniotic Fluid, Must We Act as Early as Possible?

Background: Grade III meconium stained amniotic fluid (MSAF) is a common obstetric disease, and has the greatest impact on poor maternal and neonatal outcomes. Question or Hypothesis or Aim: There is no consensus on treatment, especially on the timing of delivery. Methods: We collected the medical records of 345 women who gave birth with grade III MSAF and analyzed the difference in baseline characteristics and maternal and neonatal outcomes relative to different labor stage, observation times in the first stage of labor, and the presence or absence of abnormal fetal heart rate (FHR) or thick amniotic fluid. Findings: Higher rate of cesarean section was observed when grade III MSAF was found in active labor. Intervention occurred at an observation time of 90-120 min, but there were no significant differences in maternal or neonatal outcomes shown when the observation time was greater than 3 or 4 hours. However, a higher rate of admission to the neonatal intensive care unit was demonstrated in cases with grade III MSAF with abnormal FHR either in the first or second stage of labor or in cases with thick MSAF in the second stage of labor. Discussion: Higher rate of composite adverse neonatal outcomes was found when secondary MSAF (a transition from clear AF to MSAF) was diagnosed >3 h before delivery. Conclusion: In the first stage of labor, an observation time of greater than 4 hours might be possible after grade III MSAF is found if the labor has progressed and is without abnormal FHR.

Chloroplast-boosted photodynamic therapy for effective drug-resistant bacteria killing and biofilm ablation.

Due to the misuse of various antibiotics, the problem of bacterial resistance has become more serious worldwide, and the associated diseases have significantly increased the medical burden of society. Antimicrobial photodynamic therapy (PDT) has received widespread attention because of its safety, efficiency, and facile implementation. Here, we report an oxygen-supply antibacterial agent (Ce6@CS/CP), which could enhance the efficacy of antibacterial PDT via photosynthesis of O2. Ce6@CS/CP displayed a robust interaction with bacteria, hence facilitating the delivery efficiency of Ce6. In vitro experiments demonstrated that the photodynamic bactericidal potency of Ce6@CS/CP was remarkably greater than that of free Ce6. Furthermore, Ce6@CS/CP also exhibited superior significant antibiofilm activity to free Ce6. As a live oxygen-supply antibacterial agent, Ce6@CS/CP possesses excellent bacteria delivery ability of Ce6 and could enhance the potency of antibacterial PDT by photosynthesis, offering a new strategy for fighting against drug-resistant bacteria.