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In this paper, with the method of epidemic dynamics, we assess the spread and prevalence of COVID-19 after the policy adjustment of prevention and control measure in December 2022 in Taiyuan City in China, and estimate the excess population deaths caused by COVID-19. Based on the transmission mechanism of COVID-19 among individuals, a dynamic model with heterogeneous contacts is established to describe the change of control measures and the population's social behavior in Taiyuan city. The model is verified and simulated by basing on reported case data from November 8th to December 5th, 2022 in Taiyuan city and the statistical data of the questionnaire survey from December 1st to 23rd, 2022 in Neijiang city. Combining with reported numbers of permanent residents and deaths from 2017 to 2021 in Taiyuan city, we apply the dynamic model to estimate theoretical population of 2022 under the assumption that there is no effect of COVID-19. In addition, we carry out sensitivity analysis to determine the propagation character of the Omicron strain and the effect of the control measures. As a result of the study, it is concluded that after adjusting the epidemic policy on December 6th, 2022, three peaks of infection in Taiyuan are estimated to be from December 22nd to 31st, 2022, from May 10th to June 1st, 2023, and from September 5th to October 13th, 2023, and the corresponding daily peaks of new cases can reach 400 000, 44 000 and 22 000, respectively. By the end of 2022, excess deaths can range from 887 to 4887, and excess mortality rate can range from 3.06% to 14.82%. The threshold of the infectivity of the COVID-19 variant is estimated 0.0353, that is if the strain infectivity is above it, the epidemic cannot be control with the previous normalization measures.

Abstract The current study explores the potential function and the underlying mechanisms of endothelial cell-derived R-spondin 3 (RSPO3) neuroprotection against ischemia/reperfusion-induced neuronal cell injury. In both neuronal cells (Neuro-2a) and primary murine cortical neurons, pretreatment with RSPO3 ameliorated oxygen and glucose deprivation (OGD)/re-oxygenation (OGD/R)-induced neuronal cell death and oxidative injury. In neurons RSPO3 activated the Akt, Erk and β-Catenin signaling cascade, but only Erk inhibitors reversed RSPO3-induced neuroprotection against OGD/R. In mouse embryonic fibroblasts (MEFs) and neuronal cells, RSPO3-induced LGR4-Gab1-Gαi1/3 association was required for Erk activation, and either silencing or knockout of Gαi1 and Gαi3 abolished RSPO3-induced neuroprotection. In mice, middle cerebral artery occlusion (MCAO) increased RSPO3 expression and Erk activation in ischemic penumbra brain tissues. Endothelial knockdown or knockout of RSPO3 inhibited Erk activation in the ischemic penumbra brain tissues and increased MCAO-induced cerebral ischemic injury in mice. Conversely, endothelial overexpression of RSPO3 ameliorated MCAO-induced cerebral ischemic injury. We conclude that RSPO3 activates Gαi1/3-Erk signaling to protect neuronal cells from ischemia/reperfusion injury.

Abstract Neuropeptides are ubiquitous intercellular signaling molecules in the CNS and play diverse roles in modulating physiological functions by acting on specific G-protein coupled receptors (GPCRs). Among them, the elevenin signaling system is now believed to be present primarily in protostomes. Although elevenin was first identified from the L11 neuron of the abdominal ganglion in mollusc Aplysia californica, no receptors have been described in Aplysia, nor in any other molluscs. Here, using two elevenin receptors in annelid Platynereis dumerilii, we found three putative elevenin GPCRs in Aplysia. We cloned the three receptors and tentatively named them apElevR1, apElevR2, and apElevR3. Using an inositol monophosphate (IP1) accumulation assay, we demonstrated that Aplysia elevenin with the disulfide bond activated the three putative receptors with low EC50 values (ranging from 1.2 to 25 nM), supporting that they are true receptors for elevenin. In contrast, elevenin without the disulfide bond could not activate the receptors, indicating that the disulfide bond is required for receptor activity. Using alanine substitution of individual conserved residues other than the two cysteines, we showed that these residues appear to be critical to receptor activity, and the three different receptors had different sensitivities to the single residue substitution. Finally, we examined the roles of those residues outside the disulfide bond ring by removing these residues and found that they also appeared to be important to receptor activity. Thus, our study provides an important basis for further study of the functions of elevenin and its receptors in Aplysia and other molluscs.

Abstract Background To develop an artificial intelligence (AI) model with radiomics and deep learning (DL) features extracted from CT images to distinguish benign from malignant ovarian tumors. Methods We enrolled 149 patients with pathologically confirmed ovarian tumors. A total of 185 tumors were included and divided into training and testing sets in a 7:3 ratio. All tumors were manually segmented from preoperative contrast-enhanced CT images. CT image features were extracted using radiomics and DL. Five models with different combinations of feature sets were built. Benign and malignant tumors were classified using machine learning (ML) classifiers. The model performance was compared with five radiologists on the testing set. Results Among the five models, the best performing model is the ensemble model with a combination of radiomics, DL, and clinical feature sets. The model achieved an accuracy of 82%, specificity of 89% and sensitivity of 68%. Compared with junior radiologists averaged results, the model had a higher accuracy (82% vs 66%) and specificity (89% vs 65%) with comparable sensitivity (68% vs 67%). With the assistance of the model, the junior radiologists achieved a higher average accuracy (81% vs 66%), specificity (80% vs 65%), and sensitivity (82% vs 67%), approaching to the performance of senior radiologists. Conclusions We developed a CT-based AI model that can differentiate benign and malignant ovarian tumors with high accuracy and specificity. This model significantly improved the performance of less-experienced radiologists in ovarian tumor assessment, and may potentially guide gynecologists to provide better therapeutic strategies for these patients.

In the present study, phenolic compounds from Gomphrena globosa L. (GGLPCs) including total phenols, flavonoids, flavonols, flavanols, and phenolic acids were measured by spectrophotometric method. The antioxidant activities of GGLPCs were assessed by radical scavengings of 1,1-diphenyl-2-picrylhydrazyl (DPPH) and 2,2′-azinobis-(3-ethylbenzothiazoline -6-sulfonic acid)-diammonium salt (ABTS) cation, oxygen radical absorbance capacity (ORAC), phosphomolybdenum antioxidative power (PAP), ferric reducing antioxidant power (FRAP), and cupric ion reducing antioxidant capacity (CUPRAC) assays. Antimicrobial and enzyme inhibitory activities of GGLPCs in vitro were investigated using micro-plate dilution method and colorimetry, respectively. The results showed that GGLPCs containing abundant phenolic compounds, had obvious antioxidant and antimicrobial activities (especially against Escherichia coli and Listeria monocytogenes), and significant inhibitory effects on lipase, α-amylase, α-glucosidase, and trypsin in vitro. Thus, the results showed that GGLPC may be used as a natural antioxidant and antibacterial agent, as well as drugs for the treatment of hyperlipidemia, diabetes, and inflammation-related diseases.

The vasopressin/oxytocin signaling system is present in both protostomes and deuterostomes and plays various physiological roles. Although there were reports for both vasopressin-like peptides and receptors in mollusc Lymnaea and Octopus, no precursor or receptors have been described in mollusc Aplysia. Here, through bioinformatics, molecular and cellular biology, we identified both the precursor and two receptors for Aplysia vasopressin-like peptide, which we named Aplysia vasotocin (apVT). The precursor provides evidence for the exact sequence of apVT, which is identical to conopressin G from cone snail venom, and contains 9 amino acids, with two cysteines at position 1 and 6, similar to nearly all vasopressin-like peptides. Through inositol monophosphate (IP1) accumulation assay, we demonstrated that two of the three putative receptors we cloned from Aplysia cDNA are true receptors for apVT. We named the two receptors as apVTR1 and apVTR2. We then determined the roles of post-translational modifications (PTMs) of apVT, i.e., the disulfide bond between two cysteines and the C-terminal amidation on receptor activity. Both the disulfide bond and amidation were critical for the activation of the two receptors. Cross-activity with conopressin S, annetocin from an annelid, and vertebrate oxytocin showed that although all three ligands can activate both receptors, the potency of these peptides differed depending on their residue variations from apVT. We, therefore, tested the roles of each residue through alanine substitution and found that each substitution could reduce the potency of the peptide analog, and substitution of the residues within the disulfide bond tended to have a larger impact on receptor activity than the substitution of those outside the bond. Moreover, the two receptors had different sensitivities to the PTMs and single residue substitutions. Thus, we have characterized the Aplysia vasotocin signaling system and showed how the PTMs and individual residues in the ligand contributed to receptor activity.

Abstract Neuropeptides, as pervasive intercellular signaling molecules in the CNS, modulate a variety of behavioral systems in both protostomes and deuterostomes. Allatostatins are neuropeptides in arthropods that inhibit the biosynthesis of juvenile hormones. Based on amino acid sequences, they are divided into three different types in arthropods: allatostatin A, allatostatin B, allatostatin C. Allatostatin C (AstC) was first isolated from Manduca sexta, and it has an important conserved feature of a disulfide bridge formed by two cysteine residues. Moreover, AstC appears to be the ortholog of mammalian somatostatin, and it has functions in common with somatostatin, such as modulating feeding behaviors. The AstC signaling system has been widely studied in arthropods, but minimally studied in molluscs. In this study, we seek to identify the AstC signaling system in the marine mollusc Aplysia californica. We cloned the AstC precursor from the cDNA of Aplysia. We predicted a 15-amino acid peptide with a disulfide bridge, i.e., AstC, using NeuroPred. We then cloned two putative allatostatin C-like receptors and through NCBI Conserved Domain Search we found that they belonged to the G protein-coupled receptor (GPCR) family. In addition, using an inositol monophosphate 1 (IP1) accumulation assay, we showed that Aplysia AstC could activate one of the putative receptors, i.e., the AstC-R, at the lowest EC50, and AstC without the disulfide bridge (AstC') activated AstC-R with the highest EC50. Moreover, four molluscan AstCs with variations of sequences from Aplysia AstC but with the disulfide bridge activated AstC-R at intermediate EC50. In summary, our successful identification of the Aplysia AstC precursor and its receptor (AstC-R) represents the first example in molluscs, and provides an important basis for further studies of the AstC signaling system in Aplysia and other molluscs.

At present, new elements such as distributed new energy and electric vehicles have emerged in the distribution network, which changes the composition of loads, enriches the connotation of loads, and poses severe challenges to load forecasting. In fact, loads are aggregated in a bottom-up manner in multiple voltage levels of the distribution network, but such hierarchical characteristics are rarely considered in current load forecasting researches. Therefore, a multi-level load collaborative forecasting method based on the distributed optimization algorithm is proposed aimed at ensuring the bottom-up aggregation consistency of loads and jointly improving the performance of load forecasting at all levels. First, the distributed optimization concept based on the alternating direction method of multipliers is adopted to construct a multi-level load collaborative forecasting framework which adapts to the hierarchical characteristics of distribution network and has less data interaction. Then, a specific forecasting method based on the long short term-memory neural network and federated learning is proposed. By aggregating the bottom load forecasting results step by step, the bottom-up integrated load forecasting of distribution network can be realized. The results of calculation examples show that the proposed method has a high accuracy and a great application prospect.