Recent studies have revealed the large potential of vertical-axis wind turbines (VAWTs) for high-energy-density wind farms due to their favorable wake recovery characteristics. The present study provides an experimental demonstration and proof-of-concept for the wake recovery mechanism of the novel X-Rotor VAWT. The phase-locked flowfield is measured at several streamwise locations along the X-Rotor’s wake using stereoscopic particle image velocimetry (PIV) with fixed-pitch offsets applied to the blades. The streamwise vortex system of the upper half of the X-Rotor is first hypothesized and then experimentally verified. The induced wake deformations of the vortex systems are discussed in comparison with previous studies concerning traditional H-type VAWTs. The results suggest that positive blade pitch is more favorable for accelerated wake recovery due to the dominant tip-vortex generated on the upwind windward quadrant of the cycle. Utilizing theoretical blade load variations along the span explains distinct unsteady flow features in the near wake generated at select quadrants of the rotor rotation, shedding light on the potential of the two pitch schemes.

Boehmeria nivea var. strigosa Zeng Y. Wu & Y. Zhao, a new variety of B. nivea (Urticaceae) from southwest China, is here described and illustrated based on evidence from morphology and molecular phylogeny. This new variety is mainly characterized by its green abaxial leaf blade, partly connate stipules, and densely patent strigose hairs. The phylogenetic relationship of rbcL showed that B. nivea var. strigosa formed a monophyletic group with a high support value. The conservation status of B. nivea var. strigosa is assessed as “Near Threatened” (NT) based on IUCN Criteria.

Echinococcus multiloculasis is a zoonotic parasitic disease caused by Echinococcus multilocularis, which can cause liver injury, but the mechanism of liver injury is still unclear. Here, Echinococcus multilocularis was injected via the hepatic portal vein to establish a mouse model of infection, and high-throughput RNA sequencing was performed for detecting the expression of miRNAs in the liver of mice infected with 2000 Echinococcus multilocularis after 3 months infection, in order to understand the potential molecular mechanism of liver injury caused by Echinococcus multilocularis infection. Overall, 71 differentially expressed miRNAs were found in liver in comparison with control and a total of 36 mouse miRNAs with |FC|>0.585 were screened out, respectively. In addition, Targetscan (V5.0) and miRanda (v3.3a) software were used to predict differential miRNAs target genes and functional enrichment of target genes. Functional annotation showed that “cytokine-cytokine interaction”, “ positive regulation of cytokine production”, “ inflammatory respose”, “ leukocute activation” were enriched in the liver of Echinococcus multilocularis-infected mice. Moreover, the pathways “human cytomegalovirus infection”, “cysteine and methonine metabolism”, “Notch signaling pathway” and “ferroptosis” were involved in liver disease. Furthermore, 4 miRNAs (mmu-miR-30e-3p, mmu-miR-203-3p, mmu-miR-125b-5p and mmu-miR-30c-2-3p) related to liver injury were screened and verified. This study revealed that the expression profiling of miRNAs in the livers was changed after Echinococcus multilocularis infection, and improved our understanding of the transcriptomic landscape of hepatic echinococcosis in mice.

Although over-expression of hypoxia-inducible factor-2 alpha (HIF-2α) can result in cartilage destruction and osteoarthritis (OA) development, the underlying mechanisms remain poorly understood. Here, we investigated the molecular mechanisms in chondrocytes over-expressing HIF-2α. The GeneCloud of Biotechnology Information platform was used to identify differentially expressed genes (DEGs). Using the GEO GSE104794 dataset of control (empty adenovirus, n = 4) and experimental (recombinant adenovirus expressing HIF-2α, n = 4) groups, we performed DEG, Gene Ontology, pathway, pathway network, and gene signal network analyses. Similarly, DEG analysis was performed for the GEO GSE51588 dataset of control (non-OA, n = 4) and experimental (OA, n = 20) groups. Thereafter, intersection of GSE104794 gene signal network analysis and GSE51588 DEG analysis was performed for the key genes, validated by quantitative reverse transcription-polymerase chain reaction. A total of 542 DEGs were identified, among which, the 10 most significant genes in the gene signal network were Nfkb1, Tlr2, Nt5e, Enpp1, Entpd3, Vegfa, Ptgs2, Socs3, Fos, and Epas1. The key genes in OA were LUM, ENTPD3, SMPD3, FGFR3, GPX3, IRAK3, EREG, HTR2A, TLR2, and CDA. Taken together, we screened key genes that are potentially involved in osteoarthritis, thereby providing a basis for identifying valuable markers for this disease.

A new species Cyanotis shrirangii is described from Konkan region of Maharashtra, India. The new species is allied with Cyanotis pilosa Schult. & Schult.f. but differs in having annual habit, stems densely hispid hairy, leaves distichous and hispid on both sides; flowers purple, 2–3-flowered cincinni, anthers black; seeds ovoid to pyramidal and rugose deeply pitted.

A document by Dr. Sheikh Sajad Ahmed. Click on the document to view its contents.

Cow’s milk protein allergy (CMPA) in Latin America is little studied. We conducted a prospective study in 64 patients with a median age of 3 months, 39/64 (60.94%) were male, 23/64 (35.93%) had malnutrition, 56/64 (87.5%) had familiar atopy, 34/64 (51.56%) were receiving exclusive breastfeeding (EBF). The patients passed a medical evaluation by the specialist and underwent: Prick Test, ImmunoCAP, Fecal Calprotectin (FC) and fecal eosinophil derived neurotoxin (EDN), double-blind placebo control oral food challenges (DBPCFC) and were observed for one year regarding their clinical evolution and acquisition of tolerance. 58/64 (90.63%) had gastrointestinal manifestations, 7/64 (10.94%) had a dermatological manifestation, 2/64 (3.1%) had respiratory manifestations and 2/64 (3.1%) had anaphylaxis. 6/64 (9.38%) had an IgE - mediated CMPA. In patients with Non - IgE Mediated CMPA, fecal calprotectin was found to have a median of 284 mg/dL and an interquartile range (IQR) of 138.5 - 415.5. EDN had a median of 508.5 mg/dL with an IQR of 160.25 - 868. One year after diagnosis, the median FC decreased significantly with p < 0.0001. Malnutrition after one year was reduced to 17.18%. Likewise, 52/64 (81%) acquired tolerance after practicing DBPCFC on them. 33/64 (52%) used nutritional replacement formulas from the time of diagnosis, those who used Nutramigen LGG 15/16 (94%) acquired tolerance after practicing DBPCFC while 12/17 (71%) of those who used other formulas acquired tolerance (p. 0.08). Our findings are a good starting point for future studies that explore the diagnosis, acquisition of CMPA tolerance, and the use of hypoallergenic formulas with the particularities of our region.

Aim: Both general medical and mental health services were disrupted during the pandemic. It is unclear how these disruptions played out for people with various mental health diagnoses. We compared change in mental health status and use of mental health services between four psychiatric groups: schizophrenia spectrum disorders, bipolar disorder, unipolar depression, and anxiety/obsessive-compulsive disorder (OCD). Method: Using a semi-structured interview, 492 psychiatric outpatients who had used psychiatric services of a university hospital before the pandemic were assessed on the phone during the pandemic. Results: About half of the sample reported a perceived need for contact with mental health services during the pandemic, half of whom actually used services. Need for contact was much lower in the schizophrenia group than other diagnostic groups, whereas actual use of services was lower in the unipolar depression and anxiety/OCD groups. Conclusions: Patients with severe mental disorders, such as schizophrenia or bipolar disorders, may not have been more disadvantaged than those with anxiety/depression spectrum disorders during the pandemic. The pandemic response structure of mental health services should be modified to fit the needs of anxiety/depression spectrum patients.

(1)Background: This study employs bibliometric methods to comprehensively understand the fundamental structure of research about Autism Spectrum Disorder (ASD) Signaling Pathways by examining key indicators such as nations, institutions, journals, authors, and keywords.(2) Methodology: We utilized the WoScc database to retrieve literature relevant to ASD Signaling Pathways. Through visual analysis and tools like CiteSpace and VosViewer, we explored nations, institutions, journals, authors, and keywords, thereby constructing relevant networks.(3) Results: The study encompasses 1396 articles, revealing a consistent increase in publications. The United States, China, and Germany are leading nations in this literature. Regarding research institutions, the University of California system and Eric Klann have garnered significant attention due to their substantial contributions to the field of ASD Signaling Pathways. Most relevant research is published in the journal “Molecular Autism.” Research interests are concentrated across various themes, including “elevating neuronal β-catenin levels,” “Tunisian children,” “FMR1 gene knockout mice,” “de novo mutations,” “autistic children,” “local translation,” “propionic acid-induced mouse models,” “neurosystems,” “glucose metabolism,” and “neuronal migration.” Future research may emphasize exploring aspects such as gut microbiota, genes, stress, maternal immune activation, memory, and neurodevelopmental disorders of ASD.(4) Conclusion: This study, through bibliometric analysis of key indicators such as nations, institutions, journals, authors, and keywords, provides a comprehensive overview of the current state of research on ASD Signaling Pathways.

Hepatocellular carcinoma (HCC) is the most prevalent form of primary liver cancer and ranks as the second leading cause of cancer-related mortality globally. Despite advances in current HCC treatment, it remains a malignancy with poor prognosis. Therefore, developing novel treatment options for patients with HCC is urgently needed. Chimeric antigen receptor (CAR)-modified natural killer (NK) cells have shown potent anti-tumor effects, making them as a promising immunotherapy strategy for cancer treatment. Glypican-3 (GPC3), a cell surface oncofetal glycoprotein, is highly expressed in most HCC tissues, but not in normal tissues, and functions as a key driver of carcinogenesis. Given its high expression level on the cell surface, GPC3 is considered as an attractive immunotherapy target for HCC. In this study, two GPC3-specific CAR-NK cells, NK92MI/NH3 and NK92MI/HS20, were established using NK92MI cells, a modified IL-2-independent NK cell line. These cell lines were engineered with third generation GPC3-specific CAR, and their activities were subsequently evaluated in the treatment of HCC. We found that NK92MI/NH3 cells, rather than NK92MI/HS20 cells, exhibited a significant cytotoxicity effect against GPC3 + HepG2 cells in vitro and efficiently suppressed tumor growth in a xenograft model using NSG mice. In addition, irradiated NK92MI/NH3 cells displayed similar anti-tumor efficacy to unirradiated NK92MI/NH3 cells. Furthermore, we observed that NK92MI/NH3 cells showed higher killing activity against the GPC3 isoform 2 overexpression cell line (SK-Hep1-v2) than those with GPC3 isoform 1 overexpression cell line (SK-Hep1-v1) both in vitro and in vivo. This suggest that the presence of different GPC3 isoforms in HCC may impact the cytotoxicity activity of NK92MI/NH3 cells and potentially influence therapeutic outcomes. These findings highlight the effective anti-HCC effects of NK92MI/HN3 cells, as well as the underlying therapy resistance, suggesting their potential as a promising therapy for HCC.

Water and soil are very important for crops and water and soil are interrelated components. Water and soil have an effect on each other. If the availability of water is sufficient in the soil, then the beneficial condition for the crop remains. The rotation of the soil is maintained through water, due to which the useless factors of the soil are removed. Due to the rotation of the soil through the medium of water, the new layer of the soil brought by the flow of water is deposited on the old layer of the soil and the utility of the soil remains. Soil rotation has been studied in this article and an attempt has been made to know how soil rotation is beneficial for the crop.

Introduction: ‘Let’s Talk About Children’ is a brief family focused intervention developed to improve mental health outcomes of children of parents with mental illness (COPMI). This study aims to assess the efficacy of LTC in improving mental health of children of parents with schizophrenia or bipolar disorder in China. Methods: The planned study is a multicentre parallel group randomized wait-list controlled trial. A total of 400 eligible families with children aged 8 to 18 years will be recruited, 200 each for families with parental schizophrenia or bipolar disorder. The intervention group will receive Let’s Talk About Children delivered by a trained therapist, while the control group will receive treatment as usual. The primary outcomes are child mental health measured by the strengths and difficulties questionnaire and parent-child communication measured using the parent-adolescent communication scale. Parental mental health and family functioning are secondary outcomes. This study also plans to explore mediating factors for the effect of Let’s Talk About Children on child mental health, as well as conduct a cost-effectiveness analysis on using Let’s Talk About Children in China. Conclusion: The present study will provide evidence for the efficacy of Let’s Talk About Children in families with parental schizophrenia and bipolar disorder in China. In addition, it will evaluate potential mechanisms of action and cost-effectiveness of Let’s Talk About Children, providing a basis for future implementation.

Nickel-catalyzed asymmetric intramolecular addition of aryl halides to a-ketoamides has been achieved to afford chiral 3-substituted-3-hydroxy-2-oxindoles in excellent yields and high enantioselectivities (up to 99% yield and 98% ee), which provides efficient access to valuable molecules containing 3-hydroxy-2-oxindole core. The gram-scale reaction proved the potential utility of the methodology.

Anti-VEGF therapy is commonly used to treat proliferative diabetic retinopathy (PDR), but the exact mechanism of VEGF signaling is not fully understood. Using data-independent acquisition mass spectrometry (DIA-MS), we analyzed proteomic changes in aqueous humor (AH) samples collected before and one week after intravitreal ranibizumab (IVR) treatment from 10 PDR patients to discover potential biomarkers. Resultantly, 875 proteins were quantified and 26 proteins were significantly altered in response to IVR treatment in PDR. Further investigation through gene ontology (GO) and pathway analysis revealed that these differentially expressed proteins were primarily involved in extracellular matrix (ECM) and platelet degranulation signaling. Protein-protein interaction analysis highlighted five hub proteins (COL3A1, DPT, VEGFA, SPP1, SERPING1) that were found to be ECM components. Enzyme-linked immunosorbent assay (ELISA) confirmed the decreased levels of VEGFA and increased levels of DPT proteins after IVR treatment in another 8 samples of AH in 4 PDR patients. Our study provided novel insights into aqueous proteins of PDR following IVR treatment. Targeting the ECM pathway, particularly the elevation of DPT protein, may provide a deeper understanding of the anti-VEGF resistance and VEGF signaling in PDR.

Purpose: Merozoites are the only extracellular form of blood stage parasites, making it a worthwhile target for vaccine and drug development. Multiple invasins that are stored in the merozoite apical organelles, are secreted just prior to invasion, and mediates its interaction with host cell. A comprehensive identification of all these secreted invasins is lacking and this study addresses that gap. Experimental design: Pf3D7 merozoites were enriched and were triggered to discharge apical organelle contents by exposure to ionic conditions mimicking that of blood plasma. The secreted proteins were separated from cellular contents and both the fractions were subjected to proteomic analysis. Also, the identified secreted proteins were subjected to gene ontology and PPI network analysis. Results: A total of 63 proteins were identified in the secretory fraction with membrane and apical organellar localization. This includes various MSPs, micronemal EBAs, various rhoptry bulb proteins, which plays a crucial role in initial and late attachment of the merozoite with the RBC, and harboured multiple linear B cell epitopes. Conclusion and clinical relevance: We, for the first time, report the secretory repertoire of merozoite. This information can be utilized to develop better invasion blocking multisubunit vaccines, comprising of immunological epitopes from several secreted invasins.

PROTEOMICS – State of the art in DATA INTEGRATION AND NETWORK BIOLOGY (Special Issue)Data integration and network biology are intertwined computational approaches for examining disparate experimental data sets as means of achieving a more holistic understanding of complex biological systems. Modern biomedical research produces an increasing amount and variety of high-throughput data types, ranging from hundreds of high-resolution protein structural models to spatiotemporally resolved expression profiles of tens of thousands of gene products. For example, proteomics, the large-scale study of protein structure and function, can provide pivotal insights into biological systems at the level of protein associations, folding, modifications, subcellular localization, and abundance within a cell or tissue over multiple time points. Unlike the genome, which is relatively static since an organism’s genetic material tends to change little over the course of an individual’s lifetime, the proteome is highly dynamic in response to changing physiological needs, as is metabolism, which reflects how different metabolite compounds are converted and utilized by diverse and overlapping enzymatic processes within a cell.Since different ‘omic layers provide orthogonal molecular information, integrating diverse datasets is essential to achieving a comprehensive understanding of how biological systems adapt to extrinsic stimuli and intrinsic cues, ranging from signal transduction cascades to adaptive metabolic remodeling. To assist with data integration, a plethora of bioinformatic pipelines have been devised to facilitate and automate the analysis of disparate data types, ranging from quantitative expression profiles through to post-translational modification landscapes. Network biology, the study of biological systems as dynamic networks of interacting components, has emerged as a powerful unifying framework for integrative data analysis. Computational network analysis techniques adapted from graph theory that use nodes and edges to represent binary associations can extract insights or hypotheses regarding causal dependencies for further experimental assessment. For instance, protein-protein interaction networks can reveal how large sets of proteins or enzymes carry out their cellular roles together within larger adaptive biochemical systems, such as metabolic pathways.Graph-based network visualization software tools, such as Cytoscape, can be used to display multiple datatypes, while more sophisticated statistical methods, including machine learning algorithms, are increasingly being used to merge and interpret different experimental data types with the aim of identifying recurrent patterns that bridge disparate ‘omic layers. Such an integrative framework also allows for a deeper mechanistic investigation of pathobiology, which is essential for rational drug discovery and the development of personalized medicines. For example, combining the genomic information obtained from a cancer patient together with the proteomic profile of their tumor can reveal biochemical maladaptations driving neoplastic growth, leading to the identification of potentially actionable targets that can be neutralized therapeutically. Public annotation repositories that systematically curate the literature, such as NCBI, EMBL, UniProt, or STRING, can then be cross-referenced to validate the reliability of computational inferences against independent lines of evidence.Especially notable techniques for generating and leveraging a vast body of biomedical information to empower the emerging domain of integrative data science are covered in this Special Issue in the form of primary Research Articles, Review Articles, a Technical Brief, and an expert Viewpoint.Diamandis and colleagues provide an informed perspective into the importance of integrating multidimensional models to decipher the vast intra-tumoral heterogeneity present in glioblastoma and likely many other tumors of the central nervous system, while the review from Demir and associates offers a generalizable framework for leveraging prior biological information from curated sources, using case studies to illustrate the advantages and pitfalls for network-based integrative omics data analyses. In a clinically relevant flourish, Gramolini and coauthors describe disparate strategies for multi-omic analyses of diseased tissue samples that illustrate the utility of a network biology foundation to explore in an iterative step-wise manner the pathobiological basis of multifactorial cardiovascular disorders.Li et al report primary research establishing the impact of integrating both intracellular and extracellular proteomic profiles to achieve a more in-depth mechanistic understanding of communication among multi-cellular systems, using a robust model of prostate cancer to illustrate key points, while the work of Sardiu and team illustrates the power of an integrative machine learning framework for identifying dynamic cytometric patterns that encapsulate key biological transitions.Crovella and colleagues review state-of-the-art computational methods, including innovative new machine-learning techniques such as deep learning, to predict protein-protein interactions on a large-scale with high accuracy and coverage, while Foster and friends capable demonstrate the utility of powerful global mass spectrometry screening techniques, such as data-independent data acquisition, as workhorse experimental platforms for large-scale interactome mapping efforts. In a complementary technical brief, Figeys and associates describes an economical isotope labeling approach for increasing the throughput of multi-sample proteomic surveys while ensuring data reliability. Highlighting the flexible application of computational and experimental methods, Youssef and partners deploy quantitative multiplex co-fractionation mass spectrometry followed by comparative scoring to examine how Escherichia coli remodels its protein interaction networks in response to changing environmental conditions, which is accompanied by a commentary by Low exploring biochemical rewiring of metabolic networks.In terms of maladaptative metabolic switches, Seifert and coworkers describe techniques for measuring the marked shift towards succinate-producing microbes after the microbiome is challenged with a widely used antibiotic, while Li and colleagues review recent advances in the experimental and integrative computational characterization of protein citrullination, which modulates a diverse array of cellular functions, outlining its increasingly recognized relevance to biomedical research.We trust the accompanying dozen manuscripts presented in this Special Issue will serve as valuable resources to support, inform and guide our sage readers efforts to exploit ‘omics data integration approaches and network biology tools to advance their own biomedical research programs.Sincerely,Andrew Emili, PhDProfessor in Network BiologyKnight Cancer Institute, Portland OR

The fruit anatomy of all species of the genus Prangos mentioned in the most recent literature from Iran was studied for the first time. In this study, comparative fruit anatomical properties based on mericarp shape in a transverse section of Prangos species. The species were divided into four groups based on anatomical characteristics such as the mericarp structure, vallecular vittae, vascular bundle and rib secretory ducts. The number of vittae distributed in the mesocarp demonstrates large differences between closely related species. Anatomical studies also confirmed the morphological and anatomical differences between sections Alococarpum, Latilobae, Ulopterae, and other members of the genus. As a result of this study, we were able to identify and classify the species of this genus by using anatomical features (similarities and differences between the species) of the fruit.

The present investigation was carried out as field experiment for evaluation of fifty genotypes for various traits and for multivariant analysis. The research aims to study correlation for grain yield and its yield contributing characters. The investigation was carried out at Bajara Research Scheme, College of Agriculture, Dhule in 2021. Correlation studies reveled that grain yield per plant showed significant positive association with the traits such as plant height, productive tillers, panicle girth, panicle length, test seed weight. While iron and zinc showed non- significant positive genotypic correlation; negative correlation with days 50 percent flowering, days to maturity, protein content.

Vertical axis wind turbine has been conceived as one of the best candidates for converting untapped kinetic energy of turbulent and unsteady airstream into electricity. Intelligible data on detailed analysis of performance of such a useful turbine is still scarcely found in the vast world of scientific literature. The report entitled “Design and performance analysis of Giromill Vertical Axis Wind Turbine” attempts to analyze performance of the VAWT at different tip speed ratios (TSR), for different number of blades, at different velocities and blade pitch angles. The Double Multiple Steam Tube model was used to find the design parameter of the Giromill Vertical Axis Wind Turbine after analyzing the performance curves at different solidity ratios and tip speed ratios. The turbine with radius 1.12m, chord length 0.11m obtained from the DMST model was used for various 2D computational fluid dynamics simulations. The result showed that the DMST model over-estimates the value of coefficient of performance than the 2D CFD analysis. Simulation results shows the dominance of three-bladed turbine, in performance, over four-bladed turbine at different design parameters, by around 4.3% for same inlet wind velocity of 4m/s. Also, the performance of three bladed wind turbine is found to be better for different wind velocity and it increases at suitable negative blade pitch angle.

Phishing attacks are thoroughly engineered attacks where the attackers use emails, messages, and websites of reputed sources as a medium to trick their targets into sharing sensitive content. This sensitive content primarily consists of their financial information, in the case of small attacks whereas some planned advanced attacks also target to obtain their login information. In the past few years, there has been a noticeable shift in attackers’ priorities, moving away from targeting individuals and instead concentrating on the organization’s employees. It is also observed that most cyber-attacks are the result of employee negligence. Due to the widespread availability of phishing kits and the expansion of ransomware as a service (RaaS), aspiring hackers now possess a straightforward method to defraud individuals. What is particularly worrisome about this growing trend is that individuals lacking technical expertise are engaging in such activities using simple tools and online instructional materials. Machine learning can help in recognizing different phishing attacks and patterns. We describe several classical algorithms to detect Phishing attacks. We aim to utilize machine learning techniques like Multilayer perceptron, Random Forest, XG Boost, and different classifiers for detection. This paper will compare various studies for detecting phishing attacks using each Artificial Intelligence technique: Deep Learning and Machine learning. In order to enhance our study, we have also listed various other conventional methods of detection that do not utilize the benefits of training through machine learning models.

We examined the association between maternal mental health symptomatology, sensitivity, and toddlers’ attachment security in a low-SES Turkish sample. Mothers (N= 148) and their 12- to 38-month-old toddlers (Mage = 23.18 months) from economically impoverished backgrounds were recruited. Home visits involved naturalistic home observations of mother–toddler interactions and filling out questionnaires including Brief Symptom Inventory. Trained coders independently assessed video-recorded mother-toddler interactions for maternal sensitivity and toddlers’ attachment security using the Maternal Behavior Q-Sort and with Attachment Q-Sort, respectively. Maternal symptoms were negatively associated while maternal sensitivity was positively associated with attachment security. Further, this relationship remained significant even after controlling for SES. However, maternal sensitivity did not significantly mediate the link between maternal symptoms and toddlers’ attachment security. We showed that the importance SES and maternal factors on attachment security in a disadvantaged group, which would facilitate developing preventive interventions specifically addressing maternal mental health and high-risk groups.

Objective  To investigate the expression of neuropeptide family members in the peripheral blood of children infected with COVID-19 and its correlation with inflammatory indicators. Methods  Blood samples were collected from 40 newly diagnosed children with COVID-19 infection and 17 hospitalized children with non-COVID-19 bronchial pneumonia in our hospital during the same period. Baseline clinical data were collected and analyzed. Expression and correlation analysis of neuropeptide-related molecules in peripheral blood were detected and analyzed by ELISA. Results  In this study, 43% of COVID-19 patients are male. 71% of non-COVID-19 patients are male. ACE and ACE2 in the COVID-19 group were not significantly higher than that in the non-COVID-19 group, and ACE2 in non-COVID-19 and moderate COVID-19 groups was higher than that in severe groups (p=0.04*; p=0.03*). ASCL1 in the non-COVID-19 group was higher than that in the COVID-19 group (p=0.04*). ASCL1 in the non-COVID group was higher than that in the severe COVID group (P=0.02*). There were no significant differences in SP, VIP, and GRP between COVID-19 and non-COVID-19 groups. ASCL1 respectively with N % (r = -0.534, p < 0.001 * * *), CRP (r =- 0.522, p < 0.001 * * *) negatively correlated, L % (r = 0.572, p< 0.001 * * *), AST (r = 0.496, p = 0.001 * *) were positively correlated. There was no significant correlation with WBC count, PLT count, ALT, LDH. Conclusions We found that, unlike adults, ACE and ACE2 were not high in children with COVID-19. ASCL1 in children with COVID-19 is lower than that in non-COVID-19 children, which may indicate that ASCL1 may be decreased in COVID-19 patients. At the same time, ASCL1 is negatively correlated with N% and CRP, suggesting that ASCL1 may play a certain role in COVID-19 inflammation.

Nowadays, Internet of Things applications today use hardware security modules to ensure secure communication. The elliptic curve is a fundamental aspect of contemporary cryptography. Due to its smaller key requirement than other public key algorithms, the elliptic curve is frequently used in wireless installations, mobile networks, and credit cards. Elliptic Curve Cryptography has the advantage of increasing system performance, utilising fewer resources, and providing security for a smaller key than other techniques. The technique of Elliptic Curve is carefully studied in this article by developing a categorization based on previous analyses and evaluations in this subject.

Interleukin-22 (IL-22) is an important cytokine in the intestinal environment. IL-22 is mainly produced by immune cells and targeted at non-immune cells such as epithelial and stromal cells in a broad array of tissues such as -but not restricted to- the liver and adipose tissue. IL-22 therefore connects immune functions with metabolic functions of the host, and since it is induced by the microbiota, connects host functioning to the outside environment. IL-22 induces epithelial cell proliferation aiding in rapid epithelium regeneration and wound healing. Additionally, IL-22 activates anti-apoptotic genes and DNA damage response pathways, enhancing epithelial cell survival. Recently, it has also been shown that IL-22 induces Paneth cell differentiation in humans. However, IL-22 can also contribute to intestinal epithelium damage and reduces microbial diversity in the intestine directly or indirectly by inducing excessive antimicrobial peptide production by epithelial cells. Moreover, IL-22 enhances angiogenesis and may therefore support tumorigenesis in the intestine. In conclusion, it appears that whether IL-22 has a beneficial or harmful effect in the mammalian intestine largely depends on its regulation. This review aims to provide a comprehensive overview of the current literature and emphasizes that IL-22 signalling outcome depends on the timing and duration of IL-22 production, the presence of it regulators such as IL-22BP, and the specific location of the cytokine production in the gastrointestinal tract.