Development and validation of a prognostic model for assessing long COVID risk following Omicron wave-a large population-based cohort study.

BACKGROUND: Long coronavirus disease (COVID) after COVID-19 infection is continuously threatening the health of people all over the world. Early prediction of the risk of Long COVID in hospitalized patients will help clinical management of COVID-19, but there is still no reliable and effective prediction model. METHODS: A total of 1905 hospitalized patients with COVID-19 infection were included in this study, and their Long COVID status was followed up 4-8 weeks after discharge. Univariable and multivariable logistic regression analysis were used to determine the risk factors for Long COVID. Patients were randomly divided into a training cohort (70%) and a validation cohort (30%), and factors for constructing the model were screened using Lasso regression in the training cohort. Visualize the Long COVID risk prediction model using nomogram. Evaluate the performance of the model in the training and validation cohort using the area under the curve (AUC), calibration curve, and decision curve analysis (DCA). RESULTS: A total of 657 patients (34.5%) reported that they had symptoms of long COVID. The most common symptoms were fatigue or muscle weakness (16.8%), followed by sleep difficulties (11.1%) and cough (9.5%). The risk prediction nomogram of age, diabetes, chronic kidney disease, vaccination status, procalcitonin, leukocytes, lymphocytes, interleukin-6 and D-dimer were included for early identification of high-risk patients with Long COVID. AUCs of the model in the training cohort and validation cohort are 0.762 and 0.713, respectively, demonstrating relatively high discrimination of the model. The calibration curve further substantiated the proximity of the nomogram's predicted outcomes to the ideal curve, the consistency between the predicted outcomes and the actual outcomes, and the potential benefits for all patients as indicated by DCA. This observation was further validated in the validation cohort. CONCLUSIONS: We established a nomogram model to predict the long COVID risk of hospitalized patients with COVID-19, and proved its relatively good predictive performance. This model is helpful for the clinical management of long COVID.

Multiscale brain age prediction reveals region-specific accelerated brain aging in Parkinson's disease.

Brain biological age, which measures the aging process in the brain using neuroimaging data, has been used to assess advanced brain aging in neurodegenerative diseases, including Parkinson disease (PD). However, assuming that whole brain degeneration is uniform may not be sufficient for assessing the complex neurodegenerative processes in PD. In this study we constructed a multiscale brain age prediction models based on structural MRI of 1240 healthy participants. To assess the brain aging patterns using the brain age prediction model, 93 PD patients and 91 healthy controls matching for sex and age were included. We found increased global and regional brain age in PD patients. The advanced aging regions were predominantly noted in the frontal and temporal cortices, limbic system, basal ganglia, thalamus, and cerebellum. Furthermore, region-level rather than global brain age in PD patients was associated with disease severity. Our multiscale brain age prediction model could aid in the development of objective image-based biomarkers to detect advanced brain aging in neurodegenerative diseases.

WTAP-mediated m6A modification of lncRNA Snhg1 improves myocardial ischemia-reperfusion injury via miR-361-5p/OPA1-dependent mitochondrial fusion.

BACKGROUND: Myocardial ischemia-reperfusion injury (MIRI) is caused by reperfusion after ischemic heart disease. LncRNA Snhg1 regulates the progression of various diseases. N6-methyladenosine (m6A) is the frequent RNA modification and plays a critical role in MIRI. However, it is unclear whether lncRNA Snhg1 regulates MIRI progression and whether the lncRNA Snhg1 was modified by m6A methylation. METHODS: Mouse cardiomyocytes HL-1 cells were utilized to construct the hypoxia/reoxygenation (H/R) injury model. HL-1 cell viability was evaluated utilizing CCK-8 method. Cell apoptosis, mitochondrial reactive oxygen species (ROS), and mitochondrial membrane potential (MMP) were quantitated utilizing flow cytometry. RNA immunoprecipitation and dual-luciferase reporter assays were applied to measure the m6A methylation and the interactions between lncRNA Snhg1 and targeted miRNA or target miRNAs and its target gene. The I/R mouse model was constructed with adenovirus expressing lncRNA Snhg1. HE and TUNEL staining were used to evaluate myocardial tissue damage and apoptosis. RESULTS: LncRNA Snhg1 was down-regulated after H/R injury, and overexpressed lncRNA Snhg1 suppressed H/R-stimulated cell apoptosis, mitochondrial ROS level and polarization. Besides, lncRNA Snhg1 could target miR-361-5p, and miR-361-5p targeted OPA1. Overexpressed lncRNA Snhg1 suppressed H/R-stimulated cell apoptosis, mitochondrial ROS level and polarization though the miR-361-5p/OPA1 axis. Furthermore, WTAP induced lncRNA Snhg1 m6A modification in H/R-stimulated HL-1 cells. Moreover, enforced lncRNA Snhg1 repressed I/R-stimulated myocardial tissue damage and apoptosis and regulated the miR-361-5p and OPA1 levels. CONCLUSION: WTAP-mediated m6A modification of lncRNA Snhg1 regulated MIRI progression through modulating myocardial apoptosis, mitochondrial ROS production, and mitochondrial polarization via miR-361-5p/OPA1 axis, providing the evidence for lncRNA as the prospective target for alleviating MIRI progression.

Baroreflex Sensitivity as a Surrogate Biomarker for Concurrently Assessing the Severity of Arterial Stiffness and Cardiovascular Autonomic Neuropathy in Individuals with Type 2 Diabetes.

This study aimed to investigate whether baroreflex sensitivity (BRS) could serve as a reliable metric for assessing cardiovascular autonomic neuropathy (CAN) and concurrently act as a surrogate biomarker for evaluating the severity of arterial stiffness and CAN in individuals diagnosed with type 2 diabetes mellitus (T2DM). Participants underwent brachial-ankle pulse wave velocity (baPWV) as well as autonomic function evaluations encompassing the Sudoscan-based modified composite autonomic scoring scale (CASS), baroreflex sensitivity, and heart rate variability in time domains and frequency domains. Linear regression analysis was performed to evaluate the influence of independent variables on baPWV and modified CASS. Participants with higher baPWV values were older, with longer diabetes duration, lower body weight, body mass index, waist circumference, elevated systolic and diastolic blood pressure, and mean arterial blood pressure. They also exhibited a higher prevalence of retinopathy as the underlying disease and reduced estimated glomerular filtration rate. Multiple linear regression analysis revealed that age and BRS were significantly associated with baPWV while diabetes duration, UACR, and BRS were significantly associated with modified CASS. Our study confirms the significant association of BRS with baPWV and modified CASS in T2DM, highlighting its pivotal role in linking microvascular and macrovascular complications. This supports BRS as a surrogate marker for assessing both the severity of arterial stiffness and cardiovascular autonomic neuropathy in T2DM, enabling the early identification of complications.

An Edge Computing System with AMD Xilinx FPGA AI Customer Platform for Advanced Driver Assistance System.

The convergence of edge computing systems with Field-Programmable Gate Array (FPGA) technology has shown considerable promise in enhancing real-time applications across various domains. This paper presents an innovative edge computing system design specifically tailored for pavement defect detection within the Advanced Driver-Assistance Systems (ADASs) domain. The system seamlessly integrates the AMD Xilinx AI platform into a customized circuit configuration, capitalizing on its capabilities. Utilizing cameras as input sensors to capture road scenes, the system employs a Deep Learning Processing Unit (DPU) to execute the YOLOv3 model, enabling the identification of three distinct types of pavement defects with high accuracy and efficiency. Following defect detection, the system efficiently transmits detailed information about the type and location of detected defects via the Controller Area Network (CAN) interface. This integration of FPGA-based edge computing not only enhances the speed and accuracy of defect detection, but also facilitates real-time communication between the vehicle's onboard controller and external systems. Moreover, the successful integration of the proposed system transforms ADAS into a sophisticated edge computing device, empowering the vehicle's onboard controller to make informed decisions in real time. These decisions are aimed at enhancing the overall driving experience by improving safety and performance metrics. The synergy between edge computing and FPGA technology not only advances ADAS capabilities, but also paves the way for future innovations in automotive safety and assistance systems.

Application of microbially induced calcium carbonate precipitation (MICP) process in concrete self-healing and environmental restoration to facilitate carbon neutrality: a critical review.

Soil contamination, land desertification and concrete cracking can have significant adverse impacts on sustainable human economic and societal development. Cost-effective and environmentally friendly approaches are recommended to resolve these issues. Microbially induced carbonate precipitation (MICP) is an innovative, attractive and cost-effective in situ biotechnology with high potential for remediation of polluted or desertified soils/lands and cracked concrete and has attracted widespread attention in recent years. Accordingly, the principles of MICP technology and its applications in the remediation of heavy metal-contaminated and desertified soils and self-healing of concrete were reviewed in this study. The production of carbonate mineral precipitates during the MICP process can effectively reduce the mobility of heavy metals in soils, improve the cohesion of dispersed sands and realize self-healing of cracks in concrete. Moreover, CO2 can be fixed during MICP, which can facilitate carbon neutrality and contribute to global warming mitigation. Overall, MICP technology exhibits great promise in environmental restoration and construction engineering applications, despite some challenges remaining in its large-scale implementation, such as the substantial impacts of fluctuating environmental factors on microbial activity and MICP efficacy. Several methods, such as the use of natural materials or wastes as nutrient and calcium sources and isolation of bacterial strains with strong resistance to harsh environmental conditions, are employed to improve the remediation performance of MICP. However, more studies on the efficiency enhancement, mechanism exploration and field-scale applications of MICP are needed.

Endoscopic Dyeing and Plasma Coblation-Assisted Open Neck Surgery for Pyriform Fossa Branchial Apparatus Anomalies in Adults.

Objective: Pyriform fossa (PF) branchial apparatus anomalies (PFBAA) are rare congenital third or fourth branchial apparatus anomalies (TBAA or FBAA). This article summarizes our paradigm in managing this condition by combining endoscopic procedures and open neck surgery. Methods: A retrospective review was undertaken concerning PFBAA cases treated at our tertiary medical institution between July 2020 and November 2023. Data were collected from case records. Three sequential steps were implemented: (1) direct laryngoscopy to identify internal orifice (IO), with injection of methylene blue into it; (2) open neck surgery to resect all inflammatory tissues, focusing on the ligation of the sinus tract out of PF; and (3) plasma coblation of IO mucosa. Results: In total, 7 cases (4 men and 3 women) were included (28-67 years old, median age 53). Presenting symptoms were various, with 6 lesions on the left and 1 on the right side. Preoperative (PO) fiberoptic laryngoscopy identified IO in 6 patients, while PO barium esophageal study identified outflow from PF in 4 patients. A preliminary diagnosis of PFBAA could be established in all cases (2 TBAA and 5 FBAA cases). Direct laryngoscopy after general anesthesia identified IO in all cases (2 on the base of PF and 5 on the apex of PF). All the surgical procedures were successful, with uneventful recovery in all the patients. No postoperative complications were observed. All the patients resumed oral fluid intake after confirmation of no pharyngeal fistula by barium esophageal study on the seventh postoperative day. The duration of follow-up was between 6 and 40 months (with a median duration of 27 months). No recurrence was observed. Conclusion: Open neck surgery, assisted by endoscopic dyeing of sinus tracts and plasma coblation of IO mucosa, is a suitable treatment for PFBAA in adults. This paradigm is effective and safe for senior surgeons.

Astrocytic ALKBH5 in stress response contributes to depressive-like behaviors in mice.

Epigenetic mechanisms bridge genetic and environmental factors that contribute to the pathogenesis of major depression disorder (MDD). However, the cellular specificity and sensitivity of environmental stress on brain epitranscriptomics and its impact on depression remain unclear. Here, we found that ALKBH5, an RNA demethylase of N6-methyladenosine (m6A), was increased in MDD patients' blood and depression models. ALKBH5 in astrocytes was more sensitive to stress than that in neurons and endothelial cells. Selective deletion of ALKBH5 in astrocytes, but not in neurons and endothelial cells, produced antidepressant-like behaviors. Astrocytic ALKBH5 in the mPFC regulated depression-related behaviors bidirectionally. Meanwhile, ALKBH5 modulated glutamate transporter-1 (GLT-1) m6A modification and increased the expression of GLT-1 in astrocytes. ALKBH5 astrocyte-specific knockout preserved stress-induced disruption of glutamatergic synaptic transmission, neuronal atrophy and defective Ca2+ activity. Moreover, enhanced m6A modification with S-adenosylmethionine (SAMe) produced antidepressant-like effects. Our findings indicate that astrocytic epitranscriptomics contribute to depressive-like behaviors and that astrocytic ALKBH5 may be a therapeutic target for depression.

Longitudinal artificial intelligence-based deep learning models for diagnosis and prediction of the future occurrence of polyneuropathy in diabetes and prediabetes.

OBJECTIVE: The objective of this study was to develop artificial intelligence-based deep learning models and assess their potential utility and accuracy in diagnosing and predicting the future occurrence of diabetic distal sensorimotor polyneuropathy (DSPN) among individuals with type 2 diabetes mellitus (T2DM) and prediabetes. METHODS: In 394 patients (T2DM=300, Prediabetes=94), we developed a DSPN diagnostic and predictive model using Random Forest (RF)-based variable selection techniques, specifically incorporating the combined capabilities of the Clinical Toronto Neuropathy Score (TCNS) and nerve conduction study (NCS) to identify relevant variables. These important variables were then integrated into a deep learning framework comprising Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks. To evaluate temporal predictive efficacy, patients were assessed at enrollment and one-year follow-up. RESULTS: RF-based variable selection identified key factors for diagnosing DSPN. Numbness scores, sensory test results (vibration), reflexes (knee, ankle), sural nerve attributes (sensory nerve action potential [SNAP] amplitude, nerve conduction velocity [NCV], latency), and peroneal/tibial motor NCV were candidate variables at baseline and over one year. Tibial compound motor action potential amplitudes were used for initial diagnosis, and ulnar SNAP amplitude for subsequent diagnoses. CNNs and LSTMs achieved impressive AUC values of 0.98 for DSPN diagnosis prediction, and 0.93 and 0.89 respectively for predicting the future occurrence of DSPN. RF techniques combined with two deep learning algorithms exhibited outstanding performance in diagnosing and predicting the future occurrence of DSPN. These algorithms have the potential to serve as surrogate measures, aiding clinicians in accurate diagnosis and future prediction of DSPN.

Anode-Free Aqueous Aluminum Ion Batteries.

Aqueous aluminum ion batteries (AAIBs) possess the advantages of high safety, cost-effectiveness, eco-friendliness and high theoretical capacity. However, the Al2O3 film on the Al anode surface, a natural physical barrier to the plating of hydrated aluminum ions, is a key factor in the decomposition of the aqueous electrolyte and the severe hydrogen precipitation reaction. To circumvent the obnoxious Al anode, a proof-of-concept of an anode-free AAIB is first proposed, in which Al2TiO5, as a cathode pre-aluminum additive (Al source), can replenish Al loss by over cycling. The Al-Cu alloy layer, formed by plating Al on the Cu foil surface during the charge process, possesses a reversible electrochemical property and is paired with a polyaniline (cathode) to stimulate the battery to exhibit high initial discharge capacity (175 mAh g-1), high power density (≈410 Wh L-1) and ultra-long cycle life (4000 cycles) with the capacity retention of ≈60% after 1000 cycles. This work will act as a primer to ignite the enormous prospective researches on the anode-free aqueous Al ion batteries.

Spatial distance between tumor and lymphocyte can predict the survival of patients with resectable lung adenocarcinoma.

Background and objective: Spatial interaction between tumor-infiltrating lymphocytes (TILs) and tumor cells is valuable in predicting the effectiveness of immune response and prognosis amongst patients with lung adenocarcinoma (LUAD). Recent evidence suggests that the spatial distance between tumor cells and lymphocytes also influences the immune responses, but the distance analysis based on Hematoxylin and Eosin (H&E) -stained whole-slide images (WSIs) remains insufficient. To address this issue, we aim to explore the relationship between distance and prognosis prediction of patients with LUAD in this study. Methods: We recruited patients with resectable LUAD from three independent cohorts in this multi-center study. We proposed a simple but effective deep learning-driven workflow to automatically segment different cell types in the tumor region using the HoVer-Net model, and quantified the spatial distance (DIST) between tumor cells and lymphocytes based on H&E-stained WSIs. The association of DIST with disease-free survival (DFS) was explored in the discovery set (D1, n = 276) and the two validation sets (V1, n = 139; V2, n = 115). Results: In multivariable analysis, the low DIST group was associated with significantly better DFS in the discovery set (D1, HR, 0.61; 95 % CI, 0.40-0.94; p = 0.027) and the two validation sets (V1, HR, 0.54; 95 % CI, 0.32-0.91; p = 0.022; V2, HR, 0.44; 95 % CI, 0.24-0.81; p = 0.009). By integrating the DIST with clinicopathological factors, the integrated model (full model) had better discrimination for DFS in the discovery set (C-index, D1, 0.745 vs. 0.723) and the two validation sets (V1, 0.621 vs. 0.596; V2, 0.671 vs. 0.650). Furthermore, the computerized DIST was associated with immune phenotypes such as immune-desert and inflamed phenotypes. Conclusions: The integration of DIST with clinicopathological factors could improve the stratification performance of patients with resectable LUAD, was beneficial for the prognosis prediction of LUAD patients, and was also expected to assist physicians in individualized treatment.

Geometries and stabilities of chromium doped nitrogen clusters: mass spectrometry and density functional theory studies.

Metal-doped nitrogen clusters serve as effective models for elucidating the geometries and electronic properties of nitrogen-rich compounds at the molecular scale. Herein, we have conducted a systematic study of VIB-group metal chromium (Cr) doped nitrogen clusters through a combination of mass spectrometry techniques and density functional theory (DFT) calculations. The laser ablation is employed to generate CrNn+ clusters. The results reveal that CrN8+ cluster exhibits the highest signal intensity in mass spectrometry. The photodissociation experiments with 266 nm photons confirm that the chromium heteroazide clusters are composed of chromium ions and N2 molecules. Further structural searches and electronic structure calculations indicate that the cationic CrN8+ cluster possesses an X shaped geometry with D2 symmetry and exhibits robust stability. Molecular orbital and chemical bonding analyses demonstrate the existence of strong interactions between Cr+ cation and N2 ligands. The present findings enrich the geometries of metal doped nitrogen clusters and provide valuable guidance for the rational design and synthesis of novel transition metal nitrides.

Deep learning reveals lung shape differences on baseline chest CT between mild and severe COVID-19: A multi-site retrospective study.

Severe COVID-19 can lead to extensive lung disease causing lung architectural distortion. In this study we employed machine learning and statistical atlas-based approaches to explore possible changes in lung shape among COVID-19 patients and evaluated whether the extent of these changes was associated with COVID-19 severity. On a large multi-institutional dataset (N = 3443), three different populations were defined; a) healthy (no COVID-19), b) mild COVID-19 (no ventilator required), c) severe COVID-19 (ventilator required), and the presence of lung shape differences between them were explored using baseline chest CT. Significant lung shape differences were observed along mediastinal surfaces of the lungs across all severity of COVID-19 disease. Additionally, differences were seen on basal surfaces of the lung when compared between healthy and severe COVID-19 patients. Finally, an AI model (a 3D residual convolutional network) characterizing these shape differences coupled with lung infiltrates (ground-glass opacities and consolidation regions) was found to be associated with COVID-19 severity.

Establishment of a Secretory Protein-Inducible CRISPR/Cas9 System for Nosema bombycis in Insect Cells.

Gene editing techniques are widely and effectively used for the control of pathogens, but it is difficult to directly edit the genes of Microsporidia due to its unique spore wall structure. Innovative technologies and methods are urgently needed to break through this limitation of microsporidia therapies. Here, we establish a microsporidia-inducible gene editing system through core components of microsporidia secreted proteins, which could edit target genes after infection with microsporidia. We identified that Nosema bombycis NB29 is a secretory protein and found to interact with itself. The NB29-N3, which lacked the nuclear localization signal, was localized in the cytoplasm, and could be tracked into the nucleus after interacting with NB29-B. Furthermore, the gene editing system was constructed with the Cas9 protein expressed in fusion with the NB29-N3. The system could edit the exogenous gene EGFP and the endogenous gene BmRpn3 after overexpression of NB29 or infection with N. bombycis.

Assessment of the antimicrobial and immunomodulatory activity of QS-CATH, a promising therapeutic agent isolated from the Chinese spiny frogs (Quasipaa spinosa).

Cathelicidins are important antimicrobial peptides in various vertebrate species where they are crucial parts of the innate immune system. The current understanding of amphibian cathelicidins is limited, particularly with regard to their immunomodulatory effects. To address this knowledge gap, we produced the cDNA sequence of the cathelicidin gene from a skin transcriptome of the Chinese spiny frog Quasipaa spinosa. The amino acid sequence of the Quasipaa spinosa cathelicidin (QS-CATH) was predicted to consist of a signal peptide, a cathelin domain, and a mature peptide. Comparative analysis of the QS-CATH amino acid sequence with that of other amphibian cathelicidins revealed high variability in the functional mature peptide among amphibians, whereas the cathelin domain was conserved. The QS-CATH gene was expressed in several tissues, with the highest level of expression in the spleen. Upregulation of QS-CATH after Aeromonas hydrophila infection occurred in the kidney, gut, spleen, skin, and liver. Chemically synthesized QS-CATH exhibited pronounced antibacterial activity against Shigella flexneri, Staphylococcus warneri, Escherichia coli, Salmonella enterica, and Listeria monocytogenes. Furthermore, QS-CATH disrupted the cell membrane integrity of S. flexneri, as evidenced by a lactate dehydrogenase release assay, and it hydrolyzed the genomic DNA of S. flexneri. Additionally, QS-CATH elicited chemotaxis and modulated the expression of inflammatory cytokine genes in RAW264.7 mouse leukemic monocyte/macrophage cells. These findings confirm the antimicrobial effects of amphibian cathelicidin and its ability to influence immune cell function. This will expedite the potential utilization of amphibian antimicrobial peptides as therapeutic agents.

Scale for time and space experience in anxiety (STEA): Phenomenology and its clinical relevance.

Anxiety is a pervasive emotional state where, phenomenologically, subjects often report changes in their experience of time and space. However, a systematic and quantified examination of time and space experience in terms of a self-report scale is still missing which eventually could also be used for clinical differential diagnosis. Based on historical phenomenological literature and patients' subjective reports, we here introduce, in a first step, the Scale for Time and Space Experience of Anxiety (STEA) in a smaller sample of 19 subjects with anxiety disorders and, in a second step, validate its shorter clinical version (cSTEA) in a larger sample of 48 anxiety subjects. The main findings are (i) high convergent and divergent validity of STEA with both Beck Anxiety Inventory (BAI) (r = 0.7325; p < 0.001) and Beck Depression Inventory (BDI) (r = 0.7749; p < 0.0001), as well as with spontaneous mind wandering (MWS) (r = 0.7343; p < 0.001) and deliberate mind wandering (MWD) (r = 0.1152; p > 0.05), (ii) statistical feature selection shows 8 key items for future clinical usage (cSTEA) focusing on the experience of temporal and spatial constriction, (iii) the effects of time and space experience (i.e., for both STEA and cSTEA scores) on the level of anxiety (BAI) are mediated by the degree of spontaneous mind wandering (MWS), (iv) cSTEA allows for differentiating high levels of anxiety from the severity of comorbid depressive symptoms, and (v) significant reduction in the cSTEA scores after a therapeutic intervention (breathing therapy). Together, our study introduces a novel fully quantified and highly valid self-report instrument, the STEA, for measuring time-space experiences in anxiety. Further we develop a shorter clinical version (cSTEA) which allows assessing time space experience in a valid, quick, and simple way for diagnosis, differential diagnosis, and therapeutic monitoring of anxiety.

Conv2Former: A Simple Transformer-Style ConvNet for Visual Recognition.

Vision Transformers have been the most popular network architecture in visual recognition recently due to the strong ability of encode global information. However, its high computational cost when processing high-resolution images limits the applications in downstream tasks. In this paper, we take a deep look at the internal structure of self-attention and present a simple Transformer style convolutional neural network (ConvNet) for visual recognition. By comparing the design principles of the recent ConvNets and Vision Transformers, we propose to simplify the self-attention by leveraging a convolutional modulation operation. We show that such a simple approach can better take advantage of the large kernels ( ≥ 7×7) nested in convolutional layers and we observe a consistent performance improvement when gradually increasing the kernel size from 5×5 to 21×21. We build a family of hierarchical ConvNets using the proposed convolutional modulation, termed Conv2Former. Our network is simple and easy to follow. Experiments show that our Conv2Former outperforms existent popular ConvNets and vision Transformers, like Swin Transformer and ConvNeXt in all ImageNet classification, COCO object detection and ADE20k semantic segmentation. Our code is available at https://github.com/HVision-NKU/Conv2Former.

MicroRNA bmo-miR-31-5p inhibits apoptosis and promotes BmNPV proliferation by targeting the CYP9e2 gene of Bombyx mori.

BACKGROUND: Bombyx mori nuclear polyhedrosis virus (BmNPV), as a typical baculovirus, is the primary pathogen that infects the silkworm B. mori, a lepidopteran species. Owing to the high biological safety of BmNPV in infecting insects, it is commonly utilized as a biological insecticide for pest control. Apoptosis is important in the interaction between the host and pathogenic microorganisms. MicroRNAs (miRNAs) influence immune responses and promote stability of the immune system via apoptosis. Therefore, the study of apoptosis-related miRNA in silkworms during virus infection can not only provide support for standardizing the prevention and control of diseases and insect pests, but also reduce the economic losses to sericulture caused by the misuse of biological pesticides. RESULTS: Through transcriptome sequencing, we identified a miRNA, miR-31-5p, and demonstrated that it can inhibit apoptosis in silkworm cells and promote the proliferation of BmNPV in BmE-SWU1 cells. We identified a target gene of miR-31-5p, B. mori cytochrome P450 9e2 (BmCYP9e2), and demonstrated that it can promote apoptosis in silkworm cells and inhibit the proliferation of BmNPV. Moreover, we constructed transgenic silkworm strains with miR-31-5p knockout and confirmed that they can inhibit the proliferation of BmNPV. CONCLUSION: These data indicate that miR-31-5p may exert functions of inhibiting apoptosis and promoting virus proliferation by regulating BmCYP9e2. The findings demonstrate how miRNAs influence host cell apoptosis and how they are involved in the host immune system response to viruses, providing important insights into the applications of biological insecticides for pest control. © 2024 Society of Chemical Industry.

Gypenoside XVII Reduces Synaptic Glutamate Release and Protects against Excitotoxic Injury in Rats.

Excitotoxicity is a common pathological process in neurological diseases caused by excess glutamate. The purpose of this study was to evaluate the effect of gypenoside XVII (GP-17), a gypenoside monomer, on the glutamatergic system. In vitro, in rat cortical nerve terminals (synaptosomes), GP-17 dose-dependently decreased glutamate release with an IC50 value of 16 µM. The removal of extracellular Ca2+ or blockade of N-and P/Q-type Ca2+ channels and protein kinase A (PKA) abolished the inhibitory effect of GP-17 on glutamate release from cortical synaptosomes. GP-17 also significantly reduced the phosphorylation of PKA, SNAP-25, and synapsin I in cortical synaptosomes. In an in vivo rat model of glutamate excitotoxicity induced by kainic acid (KA), GP-17 pretreatment significantly prevented seizures and rescued neuronal cell injury and glutamate elevation in the cortex. GP-17 pretreatment decreased the expression levels of sodium-coupled neutral amino acid transporter 1, glutamate synthesis enzyme glutaminase and vesicular glutamate transporter 1 but increased the expression level of glutamate metabolism enzyme glutamate dehydrogenase in the cortex of KA-treated rats. In addition, the KA-induced alterations in the N-methyl-D-aspartate receptor subunits GluN2A and GluN2B in the cortex were prevented by GP-17 pretreatment. GP-17 also prevented the KA-induced decrease in cerebral blood flow and arginase II expression. These results suggest that (i) GP-17, through the suppression of N- and P/Q-type Ca2+ channels and consequent PKA-mediated SNAP-25 and synapsin I phosphorylation, reduces glutamate exocytosis from cortical synaptosomes; and (ii) GP-17 has a neuroprotective effect on KA-induced glutamate excitotoxicity in rats through regulating synaptic glutamate release and cerebral blood flow.

A new nomogram to predict in-hospital mortality in patients with acute decompensated chronic heart failure and diabetes after 48 Hours of Intensive Care Unit.

BACKGROUND: The study set out to develop an accurate and clinically valuable prognostic nomogram to assess the risk of in-hospital death in patients with acute decompensated chronic heart failure (ADCHF) and diabetes. METHODS: We extracted clinical data of patients diagnosed with ADCHF and diabetes from the Medical Information Mart for Intensive Care III database. Risk variables were selected utilizing least absolute shrinkage and selection operator regression analysis, and were included in multivariate logistic regression and presented in nomogram. bootstrap was used for internal validation. The discriminative power and predictive accuracy of the nomogram were estimated using the area under the receiver operating characteristic curve (AUC), calibration curve and decision curve analysis (DCA). RESULTS: Among 867 patients with ADCHF and diabetes, In-hospital death occurred in 81 (9.3%) patients. Age, heart rate, systolic blood pressure, red blood cell distribution width, shock, ß-blockers, angiotensin converting enzyme inhibitors or angiotensin receptor blockers, assisted ventilation, and blood urea nitrogen were brought into the nomogram model. The calibration curves suggested that the nomogram was well calibrated. The AUC of the nomogram was 0.873 (95% CI: 0.834-0.911), which was higher that of the Simplified Acute Physiology Score II [0.761 (95% CI: 0.711-0.810)] and sequential organ failure assessment score [0.699 (95% CI: 0.642-0.756)], and Guidelines-Heart Failure score [0.782 (95% CI: 0.731-0.835)], indicating that the nomogram had better ability to predict in-hospital mortality. In addition, the internally validated C-index was 0.857 (95% CI: 0.825-0.891), which again verified the validity of this model. CONCLUSIONS: This study constructed a simple and accurate nomogram for predicting in-hospital mortality in patients with ADCHF and diabetes, especially in those who admitted to the intensive care unit for more than 48 hours, which contributed clinicians to assess the risk and individualize the treatment of patients, thereby reducing in-hospital mortality.