Chemotherapy resistance is a huge barrier for head and neck cancer (HNC) patients and therefore requires close attention to understand its underlay mechanisms for effective strategies. In this review, we first summarize the molecular mechanisms of chemotherapy resistance that occur during the treatment with cisplatin, 5-fluorouracil, and docetaxel/paclitaxel, including DNA/RNA damage repair, drug efflux, apoptosis inhibition, and epidermal growth factor receptor/focal adhesion kinase/nuclear factor-κB activation. Next, we describe the potential approaches to combining conventional therapies with previous cancer treatments such as immunotherapy, which may improve the treatment outcomes and prolong the survival of HNC patients. Overall, by parsing the reported molecular mechanisms of chemotherapy resistance within HNC patient’s tumors, we can improve the prediction of chemotherapeutic responsiveness, and reveal new therapeutic targets for the future. The global incidence of head and neck cancers (HNCs) continued to rise from 6.55 to 10.91% in the 10 years from 2008 to 2018. In 2018, over 1.9 million individuals were diagnosed with HNC including 354,864 lip and oral cavity, 177,422 larynx, 129,079 nasopharynx (NPC), 92,887 oropharynx, and 80,608 hypopharynx tumors , and docetaxel/paclitaxel –9. A comHowever, the overall situation is still not optimal. The number of deaths from HNC continues to rise globally, from 586,400 deaths in 2008 to 980,787 deaths in 2018 . In this7 position on either nuclear DNA or mitochondrial DNA. Finally, cisplatin-induced DNA damage leads to mitochondrial outer membrane permeabilization (MOMP). Bcl-2-associated X/Bcl interacting domain (BAX/BID) forms a pore to release mitochondrial protein cytochrome c into the cytoplasm. Released cytochrome c can activate the apoptotic protease-activating factor (Apaf)-1 apoptosome, which eventually results in the activation of caspases and induction of cell apoptosis. On the other hand, aquated cisplatin also binds to cytoplasmic molecules, including reduced glutathione (GSH) and metallothionein (MT), which results in the generation of reactive oxygen species (ROS) that also trigger MOMP and DNA damages  differs from paclitaxel (C47H51NO14) in the 10-position on the baccatin ring and in the 3’-position of the lateral chain. Efficiently, docetaxel is approximately twice higher binding affinity to tubulin compared with paclitaxel. Clinically, the respective response rates of paclitaxel and docetaxel were 43% in 28 HNC patients and 44% in HNC 38 patients, suggesting both paclitaxel and docetaxel are active in HNC patients , mismatch repair (MMR), base excision repair (BER), and nucleotide excision repair (NER) –40. For Drug efflux enables cancer cells to resist cisplatin by reducing intracellular drug levels. The ATP-binding cassette (ABC) transporter superfamily is one of the platinum-drug efflux transporters, which are mediated by multidrug resistance (MDR) genes . Some ABp = 0.036) and poor clinical outcomes (p = 0.025) (p < 0.001) (Apoptosis inhibition is one of the acquired capabilities used by cancer cells to resist cisplatin. The inhibitor of apoptosis protein (IAP) can prevent activation of the apoptosis signaling pathway by blocking caspases , 65. As = 0.025)  , integrins, and G-protein-coupled receptors (GPCRs) . Activatpatients  , cleavage the phosphodiester bond at the resulting AP site by an endonuclease, clean-up the 3’ or 5’ terminal end, replace the excised nucleotides by a polymerase, and seal the final DNA nick by a ligase. In 5-FU resistant cancer cells, after FdUTP is incorporated into DNA, the UDG can lyse the uracil-deoxyribose glycosyl bond of the dUTP and 5-FdUTP residues in DNA . MER-medLivin gene-knockdown in those three HNSCC cell lines  enhanced 5-FU-induced apoptosis   inhibits cell apoptosis d 6-10B) . Survivid 6-10B) , 54, 83 d 6-10B) Table 1,d 6-10B) . For exad 6-10B) , 54. On d 6-10B) .Changes in microtubule assembly alter the sensitivity of cancer cells to paclitaxel due to mismatched binding of paclitaxel to β-tubulin isotypes. Common β-tubulin isotypes include βI, βII, βIII, βIVa, βIVb, βV, and βVI. In particular, it is widely accepted that increased levels of βIII-tubulin cause paclitaxel resistance by rendering microtubules less sensitive to its effects , 86. HowIt is worth noting that multidrug cross-resistance mechanisms possibly occur on HNC cells. As previously reported, triple drugs  −resistant HNSCC cell lines (Hep−2 and CAL−27) exhibited higher chemotherapy resistance, reduced apoptotic cell death, and an increased expression of MDR1, MRP2, ERCC1, CTR, Survivin, and TS , which sA rare subset of cells with stem cell features known as cancer stem cells (CSCs) have been demonstrated to highly tumorigenic, metastatic, and therapeutic resistance in both chemotherapy and radiotherapy . In HNC DNA repair inhibitors can be used in combination with cisplatin. It is possible to inhibit different DNA repair pathways by blocking several targets, such as blocking ERCC1-XPF on the NER process by E-X PPI2 or E-X AS7 , 96. ERCWnt/β-catenin signaling inhibitors were examined in clinical trials. WNT974 was used as a Wnt/β-catenin inhibitor in a phase II trial of HNSCC patients (NCT02649530). These HNSCC patients received 10 mg of WNT974 daily for a month, and those patients showed tumor regression and improved disease-free survival (DFS) and OS rates with a tolerable toxicity profile. Combined treatment with cisplatin and WNT974 can be expected   antibody that blocks the EGFR . The HNSVascular endothelial growth factor (VEGF) and its receptor (VEGFR) mediate tumor angiogenesis, which is associated with tumor progression and metastasis. Approximately 60~67% of NPC patients showed higher VEGF/VEGFR expressions and exhibited lower OS . The mulBevacizumab is an antibody against the VEGF approved by the US Food and Drug Administration (FDA) for combined treatment with chemotherapy of lung cancer in 2006, recurrent ovarian cancer in 2016, and non-squamous non-small cell lung cancer in 2018 . So far,Defactinib  is an inhibitor of FAK , a primavia the NF-κB pathway. PD-L1-expressing cancer cells can suppress cytotoxic activity and proliferation of CD8+ T cells by a PD-L1/programmed cell death (PD)-1 interaction (Treatment of HNC cells with cisplatin or 5-FU upregulates the expression of programmed cell death ligand 1 (PD-L1) eraction –121. A peraction Table 2.eraction . Two yeaeraction   or drug efflux  are still under investigation, and none of them had clinical trials for HNSCC patients in the past , 98. Curressions . Thus, aressions .Chemotherapy resistance largely influences the therapeutic efficacy and results in poor prognoses in HNC patients. This review summarizes and updates the mechanisms underlying chemotherapy resistance on HNCs. Four primary resistance mechanisms, including DNA/RNA damage repair, drug efflux, apoptosis inhibition, and EGFR/FAK/NF-κB activation after cisplatin, 5-FU, and paclitaxel/docetaxel treatments, have been described. The corresponding strategies to those four mechanisms are listed, which can be translated into developing innovative cancer therapeutics to overcome chemotherapy resistance in HNC patients.YK, C-YC, H-LL, J-FC, and Y-JC were involved in the design, wrote the article, and supervised the research. All authors contributed to the article and approved the submitted version.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Endometrial cancer (EC) is one of the most common gynecologic cancers worldwide. There were 417,367 newly diagnosed cases and 97,370 deaths due to this disease worldwide in 2020. The incidence rates have increased over time, especially in countries with rapid socioeconomic transitions, and EC has been the most prevalent gynecologic malignancy in Taiwan since 2012. The new EC molecular classifications of The Cancer Genome Atlas (TCGA) Research Network include clear-cell carcinoma, serous carcinoma, and carcinosarcoma, while undifferentiated/dedifferentiated EC (UDEC) is not mentioned, and most previous clinical trials for EC have not included UDEC. UDEC is rare, has an aggressive growth pattern, tends to be diagnosed at an advanced stage, and is resistant to conventional chemotherapy. In this review, case series or case reports on the clinical features and genomic/epigenetic and expression profiles on UDEC data are summarized in order to identify potential molecular targets for current and future research. Endometrial cancer (EC) is one of the most common gynecologic cancers worldwide. There were 61,880 newly diagnosed EC cases and 12,160 related deaths in the United States in 2019 , with prUDEC harbors specific genetic features different from endometrioid carcinoma. Generally, endometrioid carcinoma is a hormone-dependent tumor that expresses hormone receptors that may respond to hormone therapy. Unfortunately, UDEC seldom has detectable hormone receptors and its tumorigenesis pathway has distinct features, such as microsatellite instability (MSI-H)/mismatch repair (MMR) protein ,9 and thThus, our aim is to review the literature concerning clinical features and molecular targets for novel therapy.Histologically, UEC is characterized by a proliferation of dyscohesive tumor cells growing in patternless, sheet-like arrangements ,13. The POLE/ultramutated, microsatellite instability (MSI)/hypermutated, copy-number-low/TP53-wild-type, and copy-number-high/p53-abnormal [POLE mutation, and 18.6% with p53 abnormality [POLE group (12.4%) was also higher than EC (7.3%) in the TCGA cohort without UDEC. As the majority of UDECs belonged to the MSI and POLE groups, a high mutational load is a feature of UDECs different from other high-risk histological subtypes of EC [TCGA documented four molecular categories of EC, known as ormality , for whiormality ,24 underes of EC . Since aes of EC .SMARCA4) and INI1 (encoded by SMARCB1) mutations are known to be related to several types of human malignancies [SMARCA4 or SMARCB1 cause the loss of BRG1 or INI1 expression, which is a significant subset of DEC [As component proteins of the switch/sucrose non-fermenting (SWI/SNF) chromatin remodeling complex, BRG1 , postmeiotic segregation increased 2 (PMS2), mutS protein homolog 2 (MSH2), and mutS protein homolog 6 (MSH6). MMR defects were significantly associated with poor clinical outcomes in all types of EC. A higher tumor grade and the presence of lymphovascular space invasion (LVSI) were related to both epigenetic MMR defects and mutations . Howeverp = 0.026) [Programmed death receptor (PD-1) and its ligand PD-L1 are known as important biomarkers for immune checkpoint inhibitors. As a co-inhibitory transmembrane receptor expressed by T cells, PD-1 can inhibit the proliferation, survival, and cytokine production of T cells by engaging with PD-L1 , resulti= 0.026) . However= 0.026) , which s= 0.026) . A DEC c= 0.026) .POLE mutation for 11% (2/18); PTEN mutation accounted for 28% of endometrioid-like ECs (5/18); TP53 mutation accounted for 11% of serous-like ECs (2/18); and there was one unclassified case [POLE groups in this series (two cases) harbored a TP53 mutation. One-third of UEC in this series were low-copy-number endometrial carcinomas. The most frequent mutation was the PTEN mutation, associated with the PIK3CA pathway. Another report of sequencing analysis results also found UDEC with somatic mutations in PIK3CA (50%), CTNNB1 (30%), TP53 (30%), FBXW7 (20%), and PPP2R1A (20%) [The UDEC, as previously described, are genetically heterogeneous, with cases belonging to all four groups of TCGA classification. To further investigate genetic alteration, Rosa-Rosa, J.M. et al. carried out exome sequencing in 18 samples of UDEC. MSI hypermutation accounted for 44% (8/18) of UDEC cases, and ied case . The MSIied case . One of 1A (20%) .Due to the rarity of UDEC, there was little experience with novel treatment in case series. Real-world experience was limited to conventional chemotherapy and/or radiotherapy ,50, as lDespite such a heavy treatment setting, a fulminant recurrence pattern with short survival was reported by Han et al. All four patients in this series were LVSI positive. One stage II and one stage IIIB patient relapsed and died within a short interval. Although one patient (stage II) refused adjuvant therapy, the other patient with stage IIIB and vaginal wall extension received complete chemotherapy and radiotherapy, but still relapsed one month after she had completed treatment .Pfaendler treated two chemoresistant UEC patients with a rapid progression pattern. As no hormone receptor was present, nor a microsatellite stable feature, case 1 with stage IA, LVSI-negative UEC, and 20% myometrial invasion, received six cycles of paclitaxel and carboplatin, but still had recurrence after 6 months, and was deceased 6 weeks after recurrence. In case 2, with MLH1 and PMS2 loss, pembrolizumab was tried for one dose, but the patient moved overseas .Most cases of DEC coexisted with UEC of low-grade (FIGO grade 1-2) EC (DEC-LG). However, DEC can arise with a background of high-grade carcinoma (DEC-HG). A case series (n = 18) compared DEC-HG with DEC-LG, in which DEC-HG presented with advanced disease (stage III–IV) , whereas most cases of DEC-LG were stage 1 . A total of four DEC-HG patients recurred or died, while only one DEC-LG patient recurred, with a mean follow-up of 23.2 months . Goh et p = 0.042). For stages III and IV disease, the medial survival was 4 months for SWI/SNF-deficient tumors . SWI/SNF-deficient tumors possessed a highly progressive disease pattern and were resistant to conventional chemotherapy [The genomic inactivation of core SWI/SNF complex protein was recently shown to account for two-thirds of DEC/UECs. When comparing SWI/SNF-deficient tumors with SWI/SNF-intact tumors, a poorer prognosis in the SWI/SNF-deficient group was noted. The 2-year disease-specific survival (DSS) for stages I and II disease was 65 % in deficient tumors and 100% in intact tumors (otherapy . Furtherp < 0.05) [In a recent case series of 52 UDECs, in which MMR status was checked, 30 of 43 (69.8%) were MMRd. The 5-year disease-free survival was 80% for stage I/II, 29% for stage III, and 10% for stage IV. Multivariate analysis suggested that adjuvant chemotherapy, adjuvant radiotherapy, and a lower FIGO stage contributed to better disease-free survival ( < 0.05) .FGFR2) mutation was identified in approximately 10% of patients with primary endometrial cancer and more than 90% of patients with PIK3CA activation [FGFR2 mutation and CCNE2 amplification. The former was targeted with the FGFR inhibitor lenvatinib, while the latter was treated with the cell cycle inhibitor palbociclib in patient-derived xenograft (PDX) mice with UDEC. Compared with control mice, the tumor shrank significantly in the treatment arm [FGFR-mutated solid tumors, including EC . For a rare disease such as UDEC, the use of a precise genetic survey can find an effective target therapy for a specific molecular alteration of cancer.Several new drugs have been tested in UDEC in preclinical studies. Fibroblast Growth Factor Receptor  for longer than in the placebo plus fulvestrant arm [Aurora A was reported to be a potential therapeutic target in ARID1A-deficient colorectal cancer cells . Wild-tyrant arm .SMARCB1, SMARCA4, or EZH2 mutations [SMARCB1), which demonstrated a response rate of 15%, and of the patients who responded, 67% had a response lasting 6 months or longer [The enhancer of zeste homologue 2 (EZH2) inhibitor tazemetostat is under testing in adults with diffuse large B cell lymphoma or SMARCB1-negative or SMARCA4-negative solid tumors, and in pediatric patients with rhabdoid tumors, synovial sarcoma, epithelioid sarcoma, or other cancers with utations ,65,66,67r longer . Howeverp53 and restores its wild-type function, showed synergistic effects with chemotherapy in ovarian cancer cell lines and resensitized platinum-resistant ovarian cancer cells [TP53 mutation is one of the features of UEDC [Finally, as we know, MMR-d and PD-L1 account for about 60% of these UDECs. Immune checkpoint inhibitors should be considered, although there is a lack of trials or large databases grounded in real world experience specific for UEC/DEC. In recent EC trials, KENOTE-158 used pembrolizumab monotherapy for noncolorectal high-microsatellite-instability/MMR-d cancers, which included 49 cases (21.0%) of endometrial cancer and yielded the best response rate (57.1%) and longest progression-free survival (25.7 months) among gastric, cholangiocarcinoma, pancreatic, small intestine, ovarian, and brain cancer . Pembroler cells . Adavoseer cells . Since a of UEDC ,48, agenWe summarize the potential therapeutic agents targeting members of these pathways in UDECs are clinically aggressive diseases with chemoresistance. Recurrent disease almost always has a dismal outcome. The prevalence of UDEC might have been underestimated, and the prevalence of DEC-HG versus DEC-LG needs more research for clarification. UDEC patients should be encouraged to enroll in EC clinical trials, and the sponsors should be able to conduct subgroup analyses for UDEC. Since about half of these tumors have MMR-d features and the expression of PD-L1, immune therapy may be eligible for treatment options. The SWI/SNF complex plays a crucial role, especially in patients with a poor prognosis. Targeting the SWI/SNF pathway and subunit may open a new therapeutic window for UDECs. Comprehensive genetic sequencing is also helpful in finding possible potent drugs.https://pubmed.ncbi.nlm.nih.gov), UpToDate, and Google Scholar databases by using the following keywords: undifferentiated or dedifferentiated carcinoma of uterus, genetic analysis, molecular markers, and prognosis and treatment of UDEC. The most relevant reviews and case series were included, and the reference lists of these selected articles were read. By focusing on future directions, the latest case reports or preclinical data of special or target therapies were also presented.We searched articles published up to February 2022 in the PubMed (


INS, IL6, LEP, TNF, IL1B, EGFR, and FOS had the highest betweenness centrality, while GPR142, MBOAT4, SLC5A4, IGFBP6, PPY, G6PC1, SLC2A2, GYS2, GCGR, and AQP7 demonstrated the highest cross-talk specificity. Hypoglycemia-related genes were overrepresented in the gene networks of diabetic complications and comorbidity; moreover, 14 genes were mutual for all studied disorders. Eleven GO biological processes  were overrepresented in all reconstructed networks. The obtained results expand our understanding of the molecular mechanisms underlying the deteriorating effects of hypoglycemia in diabetes-associated vascular disease and cognitive dysfunction.Hypoglycemia has been recognized as a risk factor for diabetic vascular complications and cognitive decline, but the molecular mechanisms of the effect of hypoglycemia on target organs are not fully understood. In this work, gene networks of hypoglycemia and cardiovascular disease, diabetic retinopathy, diabetic nephropathy, diabetic neuropathy, cognitive decline, and Alzheimer’s disease were reconstructed using ANDSystem, a text-mining-based tool. The gene network of hypoglycemia included 141 genes and 2467 interactions. Enrichment analysis of Gene Ontology (GO) biological processes showed that the regulation of insulin secretion, glucose homeostasis, apoptosis, nitric oxide biosynthesis, and cell signaling are significantly enriched for hypoglycemia. Among the network hubs,  Hypoglycemia is a life-threatening complication and a barrier to achieving good glycemic control in patients with diabetes . The lonIn individuals with diabetes, severe hypoglycemia is an established risk factor for cognitive decline and dementia ,13,14. RIt is well known that a glucose-deprived condition triggers a cascade of adaptive and pathophysiological events in the cardiovascular and nervous systems. Cardiovascular effects of hypoglycemia include increase in cardiac work load and potential attenuation of myocardial perfusion, potentially arrhythmogenic electrophysiological changes, induction of a prothrombotic state, and release of inflammatory mediators . An episArtificial intelligence and bioinformatics open up new possibilities for systems analysis of molecular events in human diseases. Text-mining is a field in artificial intelligence that aims to extract information from collections of text documents based on machine learning and natural language processing techniques. Text-mining is considered a useful tool for integrative biomedical research involving genes, proteins and phenotypes . In thisThe main goal of the ANDSystem is to allow the generation of new hypotheses related to understanding of the molecular mechanisms of complex biological processes by reconstruction and analysis of associative molecular (gene) networks where biological objects are presented as nodes, and interactions between them are presented as edges. For that purpose, a high-throughput technology of automatic knowledge extraction from texts of scientific publications is utilized. For the first step, the text-mining approach performs automated recognition of the names of biological entities in texts. For the second step, it reveals interactions between biological objects using more than 3000 specific semantic templates. The information extracted by the text-mining is stored in the huge ANDCell knowledge base which is updated annually. Information from the ANDCell knowledge base could be queried by users through the ANDVisio client module. The ANDVisio supports network visualization and analysis. For example, ANDVisio functions allow to calculate the connectivity and the centrality coefficients of nodes ,23,24. POne of the well-established ways to find relations between gene sets obtained in the research and the studied conditions  is the gene set enrichment analysis. As a result of applying this method, it is possible to identify sets of genes for which the frequency of occurrence in the analyzed set, associated with the target condition, is significantly different from the background frequency . Such sets of genes are called overrepresented (if the frequency is higher than the background) or underrepresented (if the frequency is below the background). The hypergeometric distribution is commonly used as a statistical model to assess the significance of enrichment. The examples of web tools that perform the gene set enrichment analysis are DAVID  and TopADespite the obvious clinical importance of the issue, a gene network of hypoglycemia has not yet been analyzed. The comparative analysis of a network of hypoglycemia and diabetes-related diseases has not been performed also. Therefore, in this study, we reconstructed and matched the gene networks of hypoglycemia, cardiovascular disease, microvascular diabetes complications, cognitive dysfunction and AD with the use of ANDSystem to identify the principal molecules and processes that can mediate the effects of hypoglycemia on the target organs in diabetes.In our previous work , we haveThe network of hypoglycemia consisted of molecules with different structures and functions. It included insulin and other hormones, cytokines and growth factors, enzymes, transporters, transcription factors, neuropeptides, structural and binding proteins, and microRNAs .INS), glucagon (GCG), glucagon-like peptide 1 (GCG), glucose-dependent insulinotropic polypeptide (GIP), islet amyloid polypeptide (IAPP), growth hormone (GH1), and some hormonal receptors  turned out to be the central hubs of this network. Among identified hormones, insulin plays a key role as an inducer of hypoglycemia, glucagon and growth hormone are involved in the response to hypoglycemia, and other hormones act as modulators of insulin secretion or sensitivity. Alternatively, hypoglycemia itself may affect the secretion of a number of these regulators [HNF1A, HNF4A, and TCF7L2) are essential for glucose homeostasis. A group of neuropeptides included modulators of the neuroendocrine system, such as adenylate cyclase-activating polypeptide 1(ADCYAP1), neuromedin C (GRP), and chromogranin A (CHGA), and some regulators of appetite and food intake, namely, hypocretin neuropeptide precursor (HCRT), neuropeptide Y (NPY), pancreatic polypeptide Y (PPY), and urocortin (UCN) participated in the network.Expectedly, genes encoding hormones that regulate glucose metabolism including insulin  identified in the networks were both involved in glucose metabolism. Specifically, in mice, global overexpression of miR155 resulted in hypoglycemia, improved glucose tolerance and enhanced insulin sensitivity of peripheral tissues [Two microRNAs genes  showed over 100 connections with other elements of the network. All these junction genes were present among the top 15 genes with the highest betweenness centrality , cell proliferation, differentiation and survival , inflammatory pathways , angiogenesis , and other processes.The distribution of the number of gene connections in the network turned out to be exponential . Only sentrality , which sGPR142, MBOAT4, SLC5A4, IGFBP6, PPY, G6PC1, SLC2A2, GYS2, GCGR and AQP7) demonstrated the highest cross-talk specificity values (GPR142) was discussed as a potential target for the treatment of type 2 diabetes as it stimulates glucose-dependent insulin secretion [IGFBP6) was reported to be a marker of non-islet tumor-induced hypoglycemia [G6PC1), glycogen synthase 2 (GYS2) and aquaporin 7 (AQP7) may cause fasting hypoglycemia due to impaired liver metabolism [GCGR), pancreatic polypeptide Y (PPY), glucose transporter 2  and ghrelin o-acyltransferase (MBOAT4) could be involved in the counterregulatory response to hypoglycemia [Ten genes . The network of diabetic retinopathy contained 424 genes/proteins, fifty of them were also present in the network of hypoglycemia , 104 human genes are associated with microvascular endothelial cells (CD36), claudin 5 (CLDN5), epidermal growth factor receptor (EGFR), fibroblast growth factor 2 (FGF2), insulin receptor (INSR), leptin (LEP), leptin receptor (LEPR), notch 1 (NOTCH1), solute carrier family 16 member 1 (SLC16A1), solute carrier family 22 member 1 (SLC22A1), solute carrier family 2 member 1 (SLC2A1), solute carrier family 5 member 1 (SLC5A1), tumor necrosis factor (TNF), TNF receptor-associated factor 6 (TRAF6), and vascular endothelial growth factor A (VEGFA). The chance of finding so many genes for random reasons is extremely low .According to the AmiGO2 database  and insulin receptor (INSR), endothelin-1 (EDN1), erythropoietin (EPO), adiponectin (ADIPOQ), interleukin-1β (IL1B), interleukin-6 (IL6), tumor necrosis factor α (TNF), glucagon-like peptide-1 receptor (GLP1R), insulin-like growth factor-1 (IGF1), vascular endothelial growth factor A (VEGFA), C-reactive protein (CRP), nuclear factor, erythroid 2 like 2 (NFE2L2) and neuropeptide Y (NPY). A wide range of biological activities of these molecules is consistent with the concept of their key role in the pathophysiology of diabetic vascular complications [ications ,54,55,56NRP1), adenylate cyclase-activating polypeptide 1 (ADCYAP1) and fibroblast growth factor 2 (FGF2) were mutual for the networks of hypoglycemia and all microvascular complications. Neuropilin-1 is a membrane-bound receptor for vascular endothelial growth factor and semaphorin family members, it is important for angiogenesis, axon guidance, cell survival, migration, and invasion. The role of neuropilin-1 in diabetic complications is discussed [ADCYAP1 gene, is involved in neuroendocrine stress response; in pancreatic islets, it may produce a glucose-sensitive effect and decrease insulin levels required to control hyperglycemia [In addition, the genes of neuropilin 1 , islet amyloid polypeptide (IAPP), somatostatin (SST), leptin (LEP), adiponectin (ADIPOQ), erythropoietin (EPO), interleukin-6 (IL6), tumor necrosis factor α (TNF), AKT serine/threonine kinase 1 (AKT1) and C-C motif chemokine ligand 2 (CCL2), insulin-like growth factor 1 (IGF1), insulin-like growth factor-binding protein 3 (IGFBP3), epidermal growth factor receptor (EGFR), adenylate cyclase-activating polypeptide 1 (ADCYAP1), albumin (ALB), angiotensin I converting enzyme (ACE), aquaporin 4 (AQP4), C-reactive protein (CRP), prion protein (PRNP), serpin family A member 1 (SERPINA1), serpin family E member 1 (SERPINE1), and sirtuin 1 (SIRT1). According to the hypergeometric distribution, the genes associated with hypoglycemia were overrepresented in the cognitive decline network .At the next step, we matched the gene network of hypoglycemia with the network of cognitive decline and AD. The gene network of cognitive decline is presented in p-value 10−45).A growing body of evidence indicates a link between impaired glucose metabolism in the central nervous system and AD ,64,65,66Analysis of the overrepresentation of GO processes associated with AD network  revealedADRB2 [PSMB9 [HK1, IGF1R, INSR, NR3C1, SIRT6, and WWOX were associated with hypoglycemia [CCL2, CD40, CDKN1A, CYP3A4, FOS, HCRT, IGFBP2, IL6, PARK7, SERPINA1, and VEGFA) were up-regulated by hypoglycemia and were found to be also up-regulated in AD. Another group of genes  was down-regulated by hypoglycemia and also down-regulated in AD.It was interesting to match the relations of hypoglycemia and AD with associated genes . GeneticADRB2 ,68, and 2 [PSMB9 ,70. Mutaglycemia ,74,75,76glycemia ,80,81,82ADIPOQ [AQP4 [CCL2 [CRP [EIF2AK3 [EPO [IGF1 [IGF1R [IGF2 [IGFBP2 [IL6 [NFE2L2 [NOS1 [NPY [PARK7 [SERPINE1 [SIRT1 [SIRT6 [SLC2A1 [TNF [VEGF and VEGFR [A number of analyzed genes were suggested as potential targets for AD treatment, including ADIPOQ , AQP4 [8OQ [AQP4 , CCL2 [8P4 [CCL2 , CRP [86[EIF2AK3 , EPO [88PO [IGF1 , IGF1R [1 [IGF1R ,91, IGF21R [IGF2 ,93, IGFB [IGFBP2 , IL6 [95 [NFE2L2 ,97, NOS1L2 [NOS1 , NPY [99OS1 [NPY , PARK7 [Y [PARK7 , SERPINESERPINE1 , SIRT1 [1 [SIRT1 , SIRT6 [1 [SIRT6 ,104, SLC [SLC2A1 ,106, TNF2A1 [TNF ,108, VEGnd VEGFR . The rolThus, the obtained results demonstrate significant similarity in the gene networks of hypoglycemia, cardiovascular disease, diabetic microvascular complications and AD. This is consistent with clinical evidence that cognitive dysfunction is associated with severe hypoglycemia and the presence of micro- and/or macrovascular diseases in subjects with diabetes . The revOur study is not without limitations. As the ANDSystem utilizes an automatic text mining-based approach for network reconstruction, we cannot exclude that some relevant information has been missed. The study is a hypothesis-generating one. The role of identified genes/proteins, as well as biological processes, in hypoglycemia and associated events, needs further experimental testing.http://www-bionet.sscc.ru/and/cell/ (accessed on 8 September 2021). The main modules of ANDSystem are the knowledge extraction module, the ANDCell knowledge base and the user interface ANDVisio. The knowledge extraction module is based on text-mining technology utilizing the dictionaries of object names and semantic templates. The preparation of dictionaries is based on the automatic extraction of names and synonyms of biological objects from external databases and the texts of scientific publications. The semantic templates are the structured records listing the object types, dictionaries, regular expressions for text analysis and descriptions of the interaction semantics. As a result of the triggering of a linguistic template, interactions between objects from dictionaries are revealed. Linguistic templates generalize interactions by 24 types , and also define the organism in which this interaction is found. The knowledge extraction module allows the filling of the ANDCell knowledge base. It is a prebuild knowledge base which contains information about more than 20 million interactions between biological objects. The update of the information stored in the ANDCell is performed annually. Both the knowledge extraction module and the ANDCell knowledge base are located on a server. The ANDVisio is a client module allowing the user to query the ANDCell knowledge base. Based on the user queries the molecular-genetic networks could be reconstructed, analyzed and visualized as bipartite graphs. Biological objects are shown as nodes and interactions between them are represented as edges of the graph. The ANDSystem could be used for building in an automatic manner the associative molecular (gene) networks describing phenotypes, diseases and biological processes important for bio-medical tasks [The reconstruction of gene networks was performed using the ANDSystem, version: 20.0413b646_2021 . The ANDSystem is available online: al tasks ,23,24.i/Mi, where Ki is a number of links that the i gene has in the analyzed gene network; Mi is a number of links that the i gene has in the global human gene network of ANDSystem [As the ANDVisio allows to analyze the molecular-genetic networks it was applied to find the node connectivity and betweenness centrality coefficients of nodes in the hypoglycemia gene network. These parameters were calculated with function “Statistics” of the “Analysis” section of ANDVisio. The cross-talk specificity (CTS) values were calculated by ANDVisio function “Intelligent Filtration.” CTS was calculated according to the formula: CTS=KNDSystem ,23,24.The gene set enrichment analysis is broadly used to identify groups of genes that are over-/under-represented in a large gene set and that can possibly be associated with studied conditions based on statistical approaches, for example, using the hypergeometric distribution.https://david.ncifcrf.gov/home.jsp (accessed on 31 August 2021)), version 6.8  [Homo sapiens”; Gene_Ontology,“GOTERM_BP_DIRECT.” The statistically significant enrichment of a Gene Ontology biological process was considered when the p-values with Bonferroni correction were lower than 0.01.The gene set enrichment analysis web-tool DAVID   was usedhttps://bgee.org/ (accessed on 27 April 2021)) function TopAnat [The enrichment analysis of anatomical structures mapped to genes by expression patterns was performed by Bgee ) stores information on gene molecular function. It was queried to check the molecular function of identified genes associated with hypoglycemia.The GeneCardshttp://geneontology.org/ (accessed on 4 September 2021)) [The AmiGO2 database (Available online: r 2021))  is a webhttp://bioinformatics.psb.ugent.be/webtools/Venn/ (accessed on 7 September 2021).The Venn diagram demonstrating the interactions of hypoglycemia-related genes from the gene networks of cardiovascular disease, diabetic nephropathy, diabetic retinopathy, diabetic neuropathy, and Alzheimer’s disease was made by the “Bioinformatics & Evolutionary Genomics” resource available online: Hypoglycemia is a trigger for a number of complications and comorbidities in diabetes, including cardiovascular events, microvascular diabetic complications, cognitive dysfunction, and AD. In this work, we reconstructed and matched to each other the gene networks of hypoglycemia and the above-mentioned disorders using the ANDSystem that operates text-mining technology.INS, IL6, LEP, TNF, IL1B, EGFR, and FOS were the principal central hubs, meanwhile, GPR142, MBOAT4, SLC5A4, IGFBP6, PPY, G6PC1, SLC2A2, GYS2, GCGR and AQP7 were the most specific, according to the CTS criterion. The enrichment analysis of GO biological processes showed that regulation of insulin secretion, glucose homeostasis, apoptosis, nitric oxide biosynthesis and cell signaling are significantly enriched for hypoglycemia. The anatomical structures that are overrepresented among those associated with hypoglycemia genes are the central nervous system, muscles, aorta, connective tissue, and others.There were 141 genes/proteins in the hypoglycemia-associated network. Among them, ADIPOQ, CRP, EDN1, EPO, GLP1R, IGF1, IL1B, IL6, INS, INSR, NFE2L2, NPY, TNF, and VEGFA) can significantly contribute to the development of hypoglycemia comorbidities. It turned out that genes associated with hypoglycemia, macro- and microvascular diabetes complications and Alzheimer’s disease are involved in nitric oxide biosynthesis, glucose homeostasis, ERK1 and ERK2 cascade, smooth muscle cell proliferation, and some others. In AD, hypoglycemia also regulates the neuron death process. Among the genes associated with both AD and hypoglycemia, we have identified those that are promising for further study as drug targets .In the next step, we built the gene networks of diabetic complications and comorbidities for which hypoglycemia is considered a trigger. A step-by-step comparison of the hypoglycemic gene network with that for cardiovascular diseases, diabetic retinopathy, diabetic nephropathy, diabetic neuropathy, cognitive decline and AD showed that hypoglycemia-related genes are overrepresented for all hypoglycemia-triggered conditions according to the hypergeometric distribution. It was suggested that 14 genes (The obtained results expand the understanding of the molecular mechanisms of the deteriorating effect of hypoglycemia on the targeted organs in diabetes. Influencing the expression of many genes and intensity of physiological processes, hypoglycemia can play an important role in the promotion of diabetes-associated vascular disease and cognitive dysfunction.


Centella asiatica (ECa 233), which has anti-inflammatory properties, on the local expression of the transient receptor potential vanilloid 1 (TRPV1), the acid-sensing ion channel subunit 3 (ASIC3), and the calcitonin gene-related peptide (CGRP) in the temporomandibular joint (TMJ) structure 21 days after injecting the TMJ with complete Freund’s adjuvant (CFA). To investigate the effect of a standardized extract of  A mouse model was induced by analyzing the CFA-injected TMJ on days 7, 14, and 21. We assessed TMJ histology by the osteoarthritis cartilage grade score. Then, we observed the effect of different ECa 233 concentrations  and of 140 mg/kg ibuprofen doses on TRPV1, ASIC3, and CGRP local expression on day 21. Osteoarthritis cartilage scores were 1.17±0.37 and 3.83±0.68 on days 14 and 21, respectively, in the CFA group (n=5). On day 21, TRPV1, ASIC3, and CGRP expression significantly increased in the CFA group. In the ibuprofen-treated group, TRPV1 expression significantly decreased, but ASIC3 and CGRP showed no significant difference. All ECa 233 doses reduced TRPV1 expression, but the 100 mg/kg ECa 233 dose significantly decreased ASIC3 expression. TRPV1, ASIC3, and CGRP expression increased in mice with TMJ-OA on day 21. All ECa 233 and ibuprofen doses inhibited pathogenesis by modulating the local expression of TRPV1 and ASIC3. Therefore, ECa 233 was more effective than ibuprofen. It occurs at every age, but especially in people above 40 years of age, and is usually detected in patients who require dental treatment for TMJ-OA pain.3 The structure of the TMJ is composed of synovial tissue, bones, nerves, and blood vessels. Thus, TMJ injuries damage several tissues.Temporomandibular joint osteoarthritis (TMJ-OA) is a degenerative joint disease characterized by varying degrees of inflammation, destruction of the articular cartilage, and resorption of the subchondral bone4 It is commonly expressed in sensory neurons innervating joints, especially in articular tissues.5 A previous study reported that the total protein levels of TRPV1 in osteoarthritis (OA) were higher than those found in healthy joints.6TRPV1 is a polymodal, nonselective cation channel and a member of the transient receptor potential vanilloid family.7 In a hind-paw osteoarthritis model, ASIC3 was expressed in dorsal root ganglia after 21 days.8 Furthermore, ASIC3 can effectively transduce the prolonged tissue acidosis occurring in osteoarthritis pathology.9ASIC3 is a sodium-selective ion channel activated by low extracellular pH and a member of the degenerin sodium channel superfamily. It is a pain modulator expressed in the knee joint, strongly correlated with weight-bearing pain and secondary hyperalgesia.9Moreover, CGRP also promotes TRPV1 upregulation10 and ASIC3 expression.10Synthesis of the calcitonin gene-related peptide (CGRP) is activated by nerve damage and enhanced by inflammatory mediators, such as bradykinin, serotonin, and interleukins (IL) — released after tissue injury—especially in the cell membrane of chondrocytes.4 CGRP also directly enhances both TRPV1 and ASIC3. In this study, we investigated whether the local expression of these three proteins in TMJ-OA could be used as a marker for target treatment.TRPV1 and ASIC3 are inflammatory markers triggered by compression injuries, which are the main causes of TMJ-OA.11 Therefore, alternative drugs are necessary and should be investigated.Although nonsteroidal anti-inflammatory drugs (NSAIDs) effectively relieve pain and minimize inflammation in TMJ-OA, they may induce peptic ulcers in the gastric epithelium and increase the risk of cardiovascular diseases when used for long periods.12 Therefore, condylar bone deterioration starts on day 21 and can then be diagnosed as TMJ-OA.Our previous study showed that inflammation, which led to TMJ-OA, 21 days after we injected complete Freund’s adjuvant (CFA) in the TMJ of mice. Microcomputed tomography showed significant anatomical changes to the condylar head of the joint, indicating bone deformation.Centella asiatica is a herb that has been used as a medicine for decades in many countries, such as India, China, and even Thailand. C. asiatica has many interesting pharmacological properties, such as rapid wound-healing, antioxidant, and anti-inflammatory properties. Previous studies analyzed C. asiatica as a standardized pure extract called ECa 233 and found it to be minimally toxic. Its active ingredient is triterpenoid glycoside (85%), divided into madecassoside (53%) and asiaticoside (32%); in a ratio of 1.5:0.5.13 ECa 233 can suppress many inflammatory mediators, including reactive oxygen species, nitric oxide, inducible nitric oxide synthase, cyclooxygenase (COX)-2, tumor necrosis factor (TNF)-α, and IL-1β.14 Bobade, et al.15 (2015) reported the therapeutic effect of asiaticoside, which can decrease bradykinin levels by 5HT1A/B agonist action.15 Bradykinin is one of the mediators that can elevate CGRP , TRPV1, and ASIC3 expression levels.16 Therefore, the asiaticoside in ECa 233 indirectly inhibits CGRP, TRPV1, and ASIC3 expression via bradykinin reduction. However, ECa 233 protective effects against the local inflammatory mediators CGRP, ASIC3, and TRPV1—strongly associated with CFA-induced TMJ-OA —remain unreported.Therefore, this study aims to investigate the effect of ECa 233 on the local expression of TRPV1, ASIC3, and CGRP in the TMJ 21 days after CFA-induced TMJ-OA.In Vivo Experiments guidelines for the use of animals in research. Sample size aimed at 80% statistical power (1-β) with a 95% confidence interval (α=0.05).12 The mice were divided into six groups: sham, CFA, ibuprofen (140 mg/kg), and groups treated with different ECa 233 concentrations . Five animals were required for each group. Adult male mice (six week-olds weighing between 28 and 32 grams each) from the Institute of Cancer Research mice (ICR mice) were used, like previous studies on the CFA-induced TMJ-OA model. Five mice were housed in controlled temperature and humidity  cages under a 12-hour light/dark cycle.This study was approved by the Animal Care and Use Committee of the Faculty of Medicine Siriraj Hospital at the Mahidol University (SI-ACUP014/2561), complying with the Animal Research: Reporting of 17 The hair around the TMJ were trimmed in order to identify them by palpation. Then, a 30-gauge needle was inserted through the facial skin, the edge of the arch, and into the TMJ space.12Each mouse was intraperitoneally anesthetized with sodium pentobarbital (60 mg/kg). To induce TMJ-OA, 10 µl of 1 mg/ml CFA  were dissolved in a normal saline solution (1:1) and the final solution was injected into the right TMJ of mice. The anatomical landmark of the injection was prescribed in previous studies.ECa 233 was kindly supplied by Siam Herbal Innovation Co. Ltd. (Lot No. ARA051401). The sham and CFA groups were given 0.3 ml of 0.5% carboxymethyl cellulose. The ECa 233-treated group received a 0.3 ml dose of ECa 233 (dissolved in 0.5% carboxymethyl cellulose) at 30, 100, or 300 mg/kg concentrations via oral gavage every day after the CFA induction for 21 consecutive days.18On days 7, 14, and 21, the sham and CFA groups were deeply anesthetized and transcardially perfused with 250 ml of ice-cold phosphate-buffered saline (pH 7.4). Then, they were decapitated. The skulls were immersed for 7 days in 4% paraformaldehyde in 0.1 M phosphate-buffered saline solution (pH 7.4). Then, the skin of the skulls was removed and the cervical bones were open. Thereafter, the right TMJ was dissected and decalcified in 10% formic acid for 7 days before being embedded in paraffin. Using a microtome, the TMJ structures were sagittally sliced; including the condylar head, articular disk, and temporal bone into 10 µm sections (three sections for each mice) and stained them with hematoxylin and eosin.21 intact surface/intact cartilage morphology =0, intact surface with superficial abrasion =1, surface discontinuity =2, vertical fissures or clefts =3, surface erosion =4, sclerotic bone within a denuded surface =5, and deformation =6.19The TMJ sections were collected at intervals of approximately one every 10, so that the surface changes to the TMJ by hematoxylin/eosin staining could be observed. Furthermore, the degree of joint degeneration was assessed and graded individually via the following criteria of the osteoarthritis cartilage histopathology assessment system:th section of TMJ were processed to immunohistochemically examine ASIC3, CGRP, and TRPV1 expression. Since positive and negative controls were used in our staining to validate the specificity of the primary antibody; sections were immunolabeled with rabbit polyclonal TRPV1, ASIC3, and CGRP diluted to 1:200 , subjected to Dako EnVision and Peroxidase , and examined by light microscopy . After immunohistochemistry staining, every sample was randomly distributed to the investigators. Thus, we received blind samples (single-blind technique) to eliminate data analysis biases. Our data were analyzed by the ImageJ software.Every 10The appropriate location to analyze the quantities of molecules were selected, in order to detect proteins in the TMJ tissues. The condylar cartilage was analyze, since chondrocytes and nerve endings have exposed ASIC3 and TRPV1 on their surfaces. CGRP was also secreted from nerve endings, inducing vasodilation and immune cell extravasation.area indicated the distribution of ASIC3, CGRP, and TRPV1 in the condylar cartilage. The percentage of the stained area was determined by totaling the proportion of the protein-positive staining area and the condylar cartilage area, and multiplying them by 100.Having detected TRPV1, ASIC3, and CGRP on our immunohistochemistry staining, the exact location of the condylar cartilage should be determined by comparing the immunohistochemistry-stained pictures to gross anatomy pictures. The parameter After finding the protein-positive stained area, it was necessary to evaluate the color value of the picture background. Therefore, the values were measured to randomly select three non-stained areas on the “articular disk,” those values were recorded, and used to calculate the mean value. Then, the area whose color value was higher than the background value was selected. Thus, reliable percent values of the protein-positive stained area were obtained.t-test. We compared the differences between groups by the one-way analysis of variance (ANOVA) and the Dunnett’s test (post hoc test). A p value of less than 0.05 indicated statistical significance.All statistical data were analyzed by the Statistical Package for the Social Sciences version 26.0 . Data are expressed as the mean ± standard error. Normal data distribution was examined by the Kolmogorov-Smirnov test. The sham and CFA groups were compared via the independent t-test, n=5, p<0.05). Thus, the expression of these proteins was associated with TMJ-OA evolution . The 100 mg/kg ECa 233 group showed the protective effect of the extract against ASIC3  = 17.56, p≤0.001). Meanwhile, the ibuprofen group and all ECa 233-treated groups showed no effect on CGRP expression  = 25.39, p≤0.001)  = 297.24, p≤0.001) .The inflammatory mediators TRPV1, ASIC3, and CGRP showed increased expression in mice with TMJ-OA on day 21. ECa 233 at doses of 30, 100 and 300 mg/kg prevented this pathogenesis by modulating the local expression of TRPV1, ASIC3, and CGRP. In this study, ECa 233 treated TMJ-OA more effectively than ibuprofen.21 Subsequently, sensitization, and upregulation of TRPV1, ASIC3, and CGRP. Our experiments confirmed that the TRPV1, ASIC3, and CGRP levels significantly increased on day 21 after injecting CFA in the TMJ.The upregulation of inflammatory mediators, such as bradykinin, TNF-α, nerve growth factor, and IL-1β was increase in the joint inflammation. Furthermore, joint inflammation increases temperature, acidity in the inflamed area, and the development of osteoarthritis.22 Ibuprofen can also inhibit TRPV1 via serotonin conjugation.23 Although its effect on ASIC3 is marginal, it suppresses ASIC1a— another ASIC subunit—because the ibuprofen-binding site in ASIC3 has different amino acids than ASIC1a.9 Moreover, TRPV1 and ASIC3 are the expressed receptors detected on the chondrocyte, whereas CGRP is a neuropeptide secreted and bound to the CGRP receptor at the subchondral area. In mice, ibuprofen can significantly decrease the serum levels of CGRP from endotoxicosis.20 However, studies evaluating CGRP expression directly on the subchondral area are still unavailable.24 In our study, both ibuprofen and ECa 233 failed to downregulate CGRP levels on subchondral areas. This may have happened because CGRP—which is detected on the subchondral bone—is a molecule secreted from several locations, such as nerve axons and endothelial cells, which could have traveled into the synovial fluid and bound to the receptor in the chondrocyte.Moreover, our study showed that both ibuprofen and different concentrations of ECa 233  failed to affect the anatomical changes in the TMJ. However, ibuprofen only significantly inhibited TRPV1. Ibuprofen inhibits inflammation via nonselective COX inhibitors. Therefore, as the inflammation decreases, TRPV1 also decreases.25 Moreover, TRPV1 knockout mice showed severe alveolar bone loss after seven days; but when TRPV1 activation was prolonged, CGRP expression decreased in the alveolar bone10. An in vitro study on CGRP showed that osteoclastogenesis inhibition suppresses bone loss.25 Therefore, ECa 233 failed to significantly change CGRP levels in this area; though this result requires further investigation.TRPV1 activation enhanced bone resorption in mice tibias and augmented C-fiber innervation in the bone.13 Thus, ECa 233 can reduce TRPV1 levels by this pathway. Moreover, a concentration of 100 mg/kg of ECa 233 significantly downregulated ASIC3 expression. According to another research, ECa 233 can inhibit ASIC3 by suppressing bradykinin. ECa 233 can also reduce inflammation, thereby lowering both temperature and acidity.14 Therefore, administering ECa 233 can reduce the expression of TRPV1 and ASIC3.According to our results, ECa 233 significantly inhibited TRPV1. Another research suggests that ECa 233 can inhibit the production of lipopolysaccharide-stimulated pro-inflammatory mediators, including TNF-α and IL-1β without cytotoxicity. Such production is related to the sensitization and upregulation of TRPV1.26 This could be because a lower concentration of ECa 233 (30 mg/kg) is insufficient to effectively inhibit ASIC3 expression, whereas a higher concentration of ECa 233 (300 mg/kg) may activate other biological pathways and no longer inhibit ASIC3 expression. Another possible reason for the latter result was the increased activity of gamma-aminobutyric acid (GABA), which reduces progressive osteoarthritis.27 However, GABA expression is not directly related to inflammatory markers such as ASIC3.28Our study also showed that while a concentration of 100 mg/kg of ECa 233 significantly downregulated ASIC3 expression, the higher concentration (300 mg/kg) showed no beneficial effect. Similarly, recent research showed that ECa 233 treatment shows an inverted U-shaped dose-response relationship.12 Our study showed that the condylar head of the TMJ changed after CFA induction. The evolution of TMJ-OA caused the progressive degeneration of the TMJ, represented by a graded score up to day 21 of the experiment. Therefore, our study confirms this model by grading the TMJ diagnosed with TMJ-OA via the osteoarthritis cartilage histopathology assessment system, which is consistent with previous studies.19Furthermore, a CFA-induced TMJ-OA mouse model has been proposed.29 These drugs can prohibit the local inflammatory mediators located at the cartilage of TMJ, thereby preventing progressive TMJ-OA. However, these drugs cause side effects and are contraindicated in patients with cardiovascular diseases.30 Interestingly, both acute and sub-chronic toxicity tests in animal and phase I human studies showed that ECa 233 is safe.32 ECa 233 neither induced or inhibited the activities of drug-metabolizing enzymes in hepatocytes and showed no sign of toxicity in the cardiovascular system.32 Therefore, ECa 233 may be a potential drug of choice for TMJ-OA. However, other ECa 233 mechanisms in treating TMJ-Oa need further investigation.Physicians prescribe anti-inflammatory drugs such as ibuprofen and corticosteroids as the standard clinical treatment of TMJ-OA.The 100 mg/kg concentration of Eca 233 showed a protective effect against TMJ-OA by reducing the local expression of TRPV1 and ASIC3. Due to its safe profile, it can be used as a new treatment of choice for TMJ-OA, thereby substituting NSAIDs.


The Corona Virus Disease 2019 (COVID-19) pandemic represents an ongoing worldwide challenge. The present large study sought to understand independent and overlapping metabolic features of samples from acutely ill patients (n = 831) that tested positive (n = 543) or negative (n = 288) for COVID-19. High-throughput metabolomics analyses were complemented with antigen and enzymatic activity assays on plasma from acutely ill patients collected while in the emergency department, at admission, or during hospitalization. Lipidomics analyses were also performed on COVID-19-positive or -negative subjects with the lowest and highest body mass index (n = 60/group). Significant changes in amino acid and fatty acid/acylcarnitine metabolism emerged as highly relevant markers of disease severity, progression, and prognosis as a function of biological and clinical variables in these patients. Further, machine learning models were trained by entering all metabolomics and clinical data from half of the COVID-19 patient cohort and then tested on the other half, yielding ~78% prediction accuracy. Finally, the extensive amount of information accumulated in this large, prospective, observational study provides a foundation for mechanistic follow-up studies and data sharing opportunities, which will advance our understanding of the characteristics of the plasma metabolism in COVID-19 and other acute critical illnesses. Our original study aimed to identify metabolic signatures that could help inform prognosis and guide treatment early after the onset of COVID-19, the disease caused by SARS-CoV-2 infection. Indeed, small-molecule metabolites provide the building blocks that fuel viral replication, from nucleic acids to proteins and membrane lipids. SARS-CoV-2 [On 28 April 2020, we began performing one of the earliest investigations on the impact of SARS-CoV-2 infection on the circulating metabolome . At thatRS-CoV-2 ,4,5, likRS-CoV-2 , was fouRS-CoV-2 ,8, a tarDespite public health interventions and the advent of multiple vaccination strategies, SARS-CoV-2 remains a serious global threat. This may be explained by multiple factors, including (i) vaccination rates lagging behind the percentage required to reach herd immunity; (ii) reopening too early, while discontinuing public health mandates; (iii) the emergence of variants with more efficient transmission ,10, whicTo this end, herein we prospectively collected a large independent data set, including patient characteristics, clinical information, and routine and specialized clinical laboratory results of acutely ill patients that were admitted to one of two large university hospitals in New York City, who tested positive or negative for SARS-CoV-2. This large patient group allowed for the verification and expansion of previously published metabolomics studies in COVID-19 and other acute critical illnesses. Patients: This study was approved by the Institutional Review Board of Columbia University Irving Medical Center (CUIMC) (Protocol Number AAAT0680). The laboratory information system (Cerner Millennium) was used to identify all patients from 1 March 2020 through 31 May 2020 who had antithrombin (AT) testing ordered. It was then determined if these patients had been tested for the presence of SARS-CoV-2 based upon the availability of SARS-CoV-2 PCR results in the patient’s electronic medical record. All patients with AT and SARS-CoV-2 testing during the aforementioned 3-month time window were included in this study. The frozen, residual, discarded sample from AT testing was used for metabolomics and lipidomics analysis. Data were obtained from 831 patients who were admitted or seen in the Emergency Department from 14 April 2020 through 31 May 2020 in one of two large hospitals in New York City . The patients were stratified based on SARS-CoV-2 RT-PCR positivity status . As part of routine care, hemostasis was evaluated on STAR Evolution and STAR Max analyzers , hematology testing by Sysmex XN900 , and chemistry testing by Roche Cobas c502 . Laboratory values, including antithrombin (AT), prothrombin time (PT)/international normalized ratio (INR), activated partial thromboplastin time aPTT, fibrinogen, d-dimer, white blood cell count (WBC), absolute neutrophil count (ANC), absolute lymphocyte count (ALC), absolute monocyte count (AMC), hemoglobin, red blood cell count (RBC), RBC distribution width (RDW), reticulocyte count, platelet count, IL-6, lactate dehydrogenase (LDH), lactic acid, procalcitonin, troponin, blood urea nitrogen (BUN), creatinine, glucose, total-, direct-, and indirect bilirubin, aspartate amino transferase (AST), alanine amino transferase (ALT), albumin, total protein, ferritin, C-reactive protein (CRP), erythrocyte sedimentation rate (ESR), creatine kinase (CK), triglycerides, and blood type, were collected. Laboratory data were obtained from the Clinical Data Warehouse at Columbia University Irving Medical Center (CUIMC) after approval from the Tripartite Request Assessment Committee. Clinical and demographic data, including sex, age, race, ethnicity, weight, body mass index, comorbidities , intubation/ventilator requirement, continuous veno-venous hemofiltration (CVVH) requirement, radiographically confirmed thrombotic complications , clotting of CVVH, hospitalization course , mortality, and date of death were collected manually by reviewing the electronic medical record. Data were collected retrospectively for patients treated at two New York Presbyterian Hospital campuses . Residual platelet-poor plasma samples were collected for subsequent analyses.Sample processing and metabolite extraction: Plasma samples were extracted via a modified Folch method (chloroform/methanol/water 8:4:3 v/v/v), which completely inactivates other coronaviruses, such as MERS-CoV [MERS-CoV . BrieflyUltra-High-Pressure Liquid Chromatography–Mass Spectrometry metabolomics and lipidomics: Analyses were performed using a Vanquish UHPLC coupled online to a Q Exactive mass spectrometer . Samples were analyzed using a 5 and 17 min gradient as described [escribed ,19. Solvescribed ,19. Metabolomics: UHPLC-MS metabolomics analyses were performed as described in method [2, 1.7 µm; Phenomenex, Torrance, CA, USA) at 45°C. A volume of 10 uL of sample extracts from the aqueous phase of the Folch extraction was injected into the UHPLC-MS. Each sample was injected and run four times with two different chromatographic and MS conditions as follows: (1) using a 5 min gradient at 450 µL/minute from 5–95% B  and the MS was operated in positive mode and (2) using a 5 min gradient at 450 µL/minute from 5–95% B  and the MS was operated in negative ion mode. The UHPLC system was coupled online with a Q Exactive  scanning in Full MS mode at 70,000 resolution in the 60–900 m/z range, 4 kV spray voltage, 15 sheath gas, and 5 auxiliary gas, operated in negative or positive ion mode (separate runs). These chromatographic and MS conditions were applied for both relative and targeted quantitative metabolomics measurements, with the differences that for the latter targeted quantitative post hoc analyses were performed on the basis of the stable isotope-labeled internal standards used as a reference quantitative measurement, as detailed below.n method ,18,19 ann method , using aLipidomics: Samples were resolved as described, 4–6, [2, 1.8 µm particle size  using an aqueous phase (A) of 25% acetonitrile and 5 mM ammonium acetate and a mobile phase (B) of 50% isopropanol, 45% acetonitrile, and 5 mM ammonium acetate. Samples were eluted from the column using either the solvent gradient: 0–1 min 25% B and 0.3 mL/min, 1–2 min 25–50% B and 0.3 mL/min, 2–8 min 50–90% B and 0.3 mL/min, 8–10 min 90–99% B and 0.3 mL/min, 10–14 min hold at 99% B and 0.3 mL/min, 14–14.1 min 99–25% B and 0.3 mL/min, 14.1–16.9 min hold at 25% B and 0.4 mL/min, 16.9–17 min hold at 25% B and resume flow of 0.3 mL/min isocratic elution of 5% B flowed at 250 µL/min and 25 °C or a gradient from 0–5% B over 0.5 min, 5–95% B over 0.6 min, hold at 95% B for 1.65 min, 95–5% B over 0.25 min, hold at 5% B for 2 min, flowed at 450 µL/min and 35 °C [m/z at 70,000 resolution, with 4 kV spray voltage, 45 sheath gas, and 15 auxiliary gas. ed, 4–6,  over an nd 35 °C . The Q EMS2 analyses for untargeted metabolomics: For discovery mode untargeted metabolomics, dd-MS2 was performed at 17,500 resolution, AGC target = 1 × 105, maximum IT = 50 ms, and stepped NCE of 25, 35 for positive mode, and 20, 24, and 28 for negative mode, as described in Stefeanoni et al. [i et al. , and appi et al. .Quality control and data processing: Calibration was performed prior to analysis using the Pierce ESI Positive and Negative Ion Calibration Solutions (Thermo Fisher Scientific). Acquired data were then converted from raw to .mzXML file format using Mass Matrix . Samples were analyzed in randomized order with a technical mixture  injected every 10 runs to qualify instrument performance. This technical mixture was also injected three times per polarity mode and analyzed with the parameters above, except CID fragmentation was included for unknown compound identification .Metabolite assignment and relative quantitation: Metabolite assignments, isotopologue distributions, and correction for expected natural abundances of deuterium, 13C, and 15N isotopes were performed using MAVEN  [®® LIPIDOMIX®® Mass Spec Standard, Avanti Lipids) and in-house libraries of 3000 unlabeled  and labeled standards (see below for the latter). Discovery mode analysis was performed with standard workflows using Compound Discoverer 2.1 SP1 . From these analyses, metabolite IDs or unique chemical formulae were determined from high-resolution accurate intact mass, isotopic patterns, identification of eventual adducts  and MS2 fragmentation spectra against the KEGG pathway, HMDB, ChEBI, and ChEMBL databases. Additional untargeted lipidomics analyses were performed with the software LipidSearch . NJ, USA) , againstSimultaneous thrombin and plasmin generation assay (STPGA): Simultaneous evaluation of thrombin and plasmin generation  was performed as described previously [eviously . Brieflyeviously VWF, FVIII, and ADAMTS13 activity and antigen quantitation: The antigen and activity measurement of VWF and ADAMTS13 was performed by using commercial ELISA kits. VWF antigen and collagen-binding activity levels were measured by using Human Von Willebrand Factor ELISA Kit  and TECHNOZYM®® vWF:CBA ELISA Kit , respectively. ADAMTS13 antigen and activity levels were measured by using Human ADAMTS13 ELISA Kit  and TECHNOZYM®® ADAMTS13 Activity ELISA , respectively. FVIII antigen levels were measured by using Human Factor VIII total antigen assay ELISA kit . All assays were performed following manufacturer’s recommendations with additional dilution of plasma samples as required.Statistical Analysis: Graphs and statistical analyses (either t-test or repeated measures ANOVA) were prepared with GraphPad Prism 5.0 , GENE E , and MetaboAnalyst 4.0. In MetaboAnalyst, relative quant data (but not for abs quant), raw values for integrated peak areas for each metabolite, were normalized on a pool of day 0 controls and auto-scaled for each species independently prior to margining all the data for multivariate analysis. Analyses through MetaboAnalyst included principal component analysis, partial least square discriminant analysis, hierarchical clustering analyses , calculation of receiver operating characteristic (ROC) curves, correlation analyses (Spearman), and machine learning analyses . Metabolomics analyses performed for 543 samples from hospitalized acutely ill COVID-19-positive patients were compared with those performed for 288 samples from acutely ill COVID-19-negative patients A. Raw dap < 0.0001; r2 = 0.871 Spearman; The metabolic phenotypes of these COVID-19-negative patients partially overlapped with those who were COVID-19-positive B. Figurep < 0.0001). However, no significant differences in ADAMTS-13 levels or activity were observed; thus, VWF antigen: ADAMTS13 activity ratios were increased (p < 0.0001), favoring high-molecular-weight VWF oligomers and increased thrombotic potential. Overall, these results demonstrate significant up-regulation of creatine metabolism, accompanied by dysregulation of arginine catabolism to proline, polyamines, and citrulline J; also aDespite widespread decreases in most amino acids, circulating levels of kynurenine, a tryptophan catabolite (and other kynurenine pathway intermediates) were confirmed ,33,34,35Given the role of obesity in COVID-19 outcomes , we evalBecause older male COVID-19 patients have a poorer prognosis, as compared to young females ,40,41, aMale patients, both with and without COVID-19, had higher RBC counts and hemoglobin levels, lower citrulline and creatine levels, and lower levels of highly unsaturated fatty acids  identified clinical and metabolic correlates to coagulation parameters, including D-dimer, APTT, INR , FVIII, TG, VWF:AG, and VWF:collagen-binding activity A–F. IL-6Similarly, correlating metabolites and clinical parameters to markers of tissue damage , inflammation (CRP), liver damage , and proteolysis/hemodilution  identified a strong interaction with arginine/proline metabolism, ferritin/hemoglobin/RBC counts, and tryptophan/kynurenine metabolism G–L. InteLeveraging the manually curated clinical records for the enrolled patients, we identified clinical and metabolic markers correlating with mechanical ventilation , stroke Pre-existing conditions, including obesity, cardiovascular disease, kidney disease, cancer, and diabetes, are all associated with poorer prognosis in COVID-19 . Meta-anSampling at admission allowed us to collect longitudinal samples from some patients. The results from three severe COVID-19 cases, only two of whom recovered, are presented here. The present study provides the most extensive metabolomics analysis of COVID-19 patients to date, including 831 samples at admission from hospitalized patients with and without COVID-19 and 59 longitudinal samples from three case studies. Previous metabolomics studies on COVID-19 were not powered to characterize the effects of other variables critical for disease severity and prognosis. As examples, evaluations of biological  controls with manually curated clinical records, novel metabolic correlates to biological variables and patient characteristics were identified in this study; these results confirm and significantly enhance previous efforts in this disease ,59. For Aging was also accompanied by increased markers of hypoxia , indicative of progressive mitochondrial dysfunction . Given tIn contrast, aging, especially in COVID-19 patients, was accompanied by altered levels of free fatty acids and acylcarnitines. The former may fuel viral membrane synthesis through increased ATP production, which may be sustained by lipid mobilization from adipose tissue and other sources, similar to observations in trauma patients  and follThese metabolic observations of aging and obesity were exacerbated in COVID-19 patients and were consistent with disease severity, as indicated by clinical records and clinical measurements of markers of inflammation , coagulopathy , and renal dysfunction . Metabolic correlates of these clinical parameters are provided in this study, as part of the efforts aimed at compiling an encyclopedic characterization of metabolism in health and disease. For example, we found strong negative correlations between kidney dysfunction and circulating amino acid levels, as possible indicators of decreased renal reabsorption ,30,68 anBesides aging and inflammation, other factors are also associated with poor outcomes in COVID-19. For example, the expression levels of angiotensin-converting enzyme 2 (ACE2) receptor in enterocytes modulate disease severity, in that viral entry into cells is mediated by pairing of ACE2 with the viral spike protein . NotablyEndothelial dysfunction is a common finding in patient populations most at risk for severe disease . The endothelium is directly affected by SARS-CoV-2 infection, resulting in damage to the vasculature ,78. The de novo nicotinamide synthesis are associated with aging and inflammation [Indole metabolites of microbial origin  were alsammation , suggestammation . Depleting tryptophan to promote kynurenine synthesis may also lead to serotonin depletion, a key component of platelet-dense granules with a role in platelet activation . This isAltered lipid metabolism is a common finding in patients with COVID-19 ,87. ActiA recent study comparing healthy controls to COVID-19 patients suggest increased secretion of monosialodihexosylganglioside (GM3)-enriched exosomes (increased levels of sphinomyelins (SMs) and reduced DAGs), altering CD4+ T-cell activation, resulting in immunosuppression . In the Finally, as a proof-of-principle, we entered admission data  into machine learning algorithms, randomly selecting approximately half of the COVID-19 patient cohort as a training set and the other half as a test set. The resulting model exhibited high specificity (>95%), but moderate sensitivity (~70%). The prediction accuracy of these models may be affected by clinical contributors to the metabolic heterogeneity of hospitalized patients, such as elements of their medical history. Nonetheless, we report here for the first time that metabolic phenotypes of COVID-19 patients were most extreme in patients presenting with a history of hypertension, chronic kidney disease, cancer, coronary artery disease, or lung disease. Future research integrating metabolomics with microbiomics datasets could enhance our understanding on how the gut flora contributes to the pathology and disease severity in patients infected with SARS-CoV-2.There are several limitations due to the inherent nature of the study design. Metabolomic and lipidomic analysis was performed on leftover, discarded plasma used for PT analysis. Although extreme care was taken to freeze the residual sample as rapidly as possible, there was variability in the amount of time the specimen was stored at 4 C. Additionally, the time of day as well as fasting status was not standardized, which could increase variability in the observed metabolite concentrations ,92,93. BTaken together, the extensively detailed information in this large, prospective, observational study will support future mechanistic studies and data sharing opportunities to enhance understanding of the plasma metabolism in COVID-19 and other acute critical illnesses.


American Heart Association’s (AHA) Life’s Simple 7 (LS7), an index of cardiovascular health risks, has been associated with worse brain outcomes but few examined this relationship in midlife. We examined whether LS7 scores at midlife were associated with brain morphometry in early old age. Participants were 471 men who participated in the Vietnam Era Twin Study of Aging. The LS7 index was assessed at mean age 62 (range 55-66) and 68 (range 61-71) and included smoking, physical activity, diet, body mass index, cholesterol, glucose, and blood pressure. Each factor was coded, per AHA criteria, on a 3-point scale  and summed to create a composite score (0-14). At mean age 68, participants underwent structural magnetic resonance imaging, which was used to create the previously validated brain measures. Scores included: the ratio of abnormal white matter to white matter, and two Alzheimer’s disease brain signatures  signature). Analyses controlled for age, education, income, ethnicity, and APOE genotype. Concurrently at mean age 68, the LS7 was associated with cortical thickness/volume , MD  signatures and abnormal white matter ratio . Prospectively, the LS7 at mean 62 was significantly associated with age 68 cortical thickness/volume  and MD  signatures but not with abnormal white matter ratio. These results suggest that prevention strategies that promote heart healthy behaviors could have implications for healthy brain aging.


Galleria mellonella larvae infected with Candida albicans. A single injection of F11A and D5A derivatives, in contrast with T11F, led to a significant increase in survival of larvae injected with a lethal inoculum of C. albicans cells, in comparison with infected animals treated with saline. Peptide modulation of host immunity upon C. albicans infection was determined by hemocyte analysis and larval histology, highlighting a different immune stimulation by the studied peptides. F11A, particularly, was the most active in eliciting nodule formation, melanization and fat body activation, leading to a better control of yeast infection. Overall, the obtained data suggest a double role for F11A, able to simultaneously target the fungus and the host immune system, resulting in a more efficient pathogen clearance.The synthetic peptide T11F (TCRVDHRGLTF), with sequence identical to a fragment of the constant region of human IgM, and most of its alanine-substituted derivatives proved to possess a significant candidacidal activity in vitro. In this study, the therapeutic efficacy of T11F, D5A, the derivative most active in vitro, and F11A, characterized by a different conformation, was investigated in  Fungi can cause a wide range of infections in humans, representing a serious threat to human health. It is estimated that more than one billion people suffer from fungal infections worldwide ,2,3. TheHost defense peptides (HDPs) show a wide spectrum of biological activities and are emerging as a group of promising candidates for overcoming antimicrobial resistance due to their rapid and unique antimicrobial action ,15,16. SCandida spp., have been described, derived from microorganisms, plants, invertebrate and vertebrate animals [Numerous peptides with antifungal activity, particularly against  animals ,19,20,22 animals ,29,30,31 animals ,33.C. albicans in comparison to the parental peptide. In particular, the replacement of positively charged residues or cysteine caused a significant reduction in candidacidal activity, while substitution of the negatively charged residue, aspartic acid, resulted in increased candidacidal activity [1-helix conformation (PPII helix) in aqueous solution, but phenylalanine substitution (peptide F11A) caused a loss of the organized structure resulting in a random coil conformation [For many years, our research group focused on antibody (Ab)-derived peptides with antifungal activity ,35. Amonactivity . T11F anormation .Candida activity and the different structural conformation in aqueous solution [C. albicans experimental systemic infection in the greater wax moth Galleria mellonella. Indeed, G. mellonella larvae have emerged as an alternative model host for human pathogens, including fungi [In this work, T11F peptide and two of its derivatives, D5A and F11A, respectively, chosen for the improved in vitro anti-solution ,37, wereng fungi . In adding fungi .G. mellonella infection by C. albicans, likely related to its prominent ability to modulate the host’s immune system.We investigated both the direct antifungal activity and the immunomodulatory properties of the selected peptides. F11A derivative exhibited a more significant therapeutic effect, compared to D5A, in C. albicans cells treated with T11F, SEM observation previously showed the presence of blebs and a leakage of cellular material that was also visible by confocal microscopy [Candida effect of D5A and F11A derivatives. While untreated fungal cells (control) presented a smooth surface and a compact structure  or with the peptides, thus assessing that the peptides were not toxic in this experimental model .C. albicans SC5314. The therapeutic effect was evaluated in larvae infected with a lethal inoculum of G. mellonella involves different hemocytes responsible for pathogen phagocytosis, nodulation and encapsulation (granulocytes and plasmatocytes) with melanin deposition (oenocytoides) [Cellular immune response to fungi in ytoides) .Cytospin analysis performed at 24 h post-inoculation showed a peptide-specific host immunomodulation in both uninfected and infected larvae . In uninC. albicans SC5314, treatment with T11F and D5A induced small monodimensional cell aggregates, mainly composed of activated granulocytes and plasmatocytes, and intracytoplasmic weak melanization around fungal hyphae  were processed for histology. In control larvae tissues were well preserved; at 24 h the fat body showed activation and a slight increase in circulating hemocytes was observed A, while In the last decades there has been an increase in the number of individuals prone to fungal infections, such as immunocompromised individuals, transplant recipients and otherwise debilitated persons, while the availability of effective antifungal drugs for the treatment of mycoses has not increased at the same pace. The emergence of drug-resistant fungal strains made even more urgent the need for new antifungal therapies .Candida species are the most common cause of mycoses. In particular, C. albicans represents a human pathobiont, being part of the commensal microbiota, and infections can arise from a derangement of the immune response and/or can be sustained or worsened by a host inflammatory milieu [C. albicans-triggered diseases constitute an interesting example of an altered balance between pathogen tolerance and immune system response, targeting simultaneously infection and inflammation has been suggested as a useful antifungal strategy [Among fungal pathogens, y milieu . Since Cstrategy ,44. Morestrategy ,45,46,47strategy ,17,47,4850) values against the reference C. albicans SC5314 strain were 1.54 µmol/L for T11F, 0.39 µmol/L for D5A and 2.52 µmol/L for F11A [Our attention was focused on a previously described Ab-derived peptide, T11F, that showed a fungicidal activity in vitro against several pathogenic yeasts at micromolar concentration, and two of its alanine-substituted derivatives, D5A and F11A, both presenting a candidacidal activity in vitro, increased and decreased, respectively, compared to the candidacidal activity of the parent peptide. The half maximal effective concentration  and its derivatives were performed at CRIBI-Peptide Facility  using the fluoren-9-ylmethoxycarbonyl (Fmoc) solid-phase synthesis chemistry, as previously described ,37,52. PC. albicans SC5314 cell suspensions, prepared as previously described [2 glass slides, fixed with glutaraldehyde in sodium cacodylate, then prepared for microscopic examination by Philips 501 scanning electron microscope (15 kV), as previously described [For SEM studies, escribed , were inescribed .G. mellonella model was exploited for in vivo studies, according to previously described procedures [C. albicans SC5314 suspension (approximately 5 × 105 cells/larva) and, 30 min after, were injected with the peptides (10 µmol/kg) or saline (control). Larvae were incubated at 37 °C in the dark for 9 days and scored daily for survival. Survival curves of peptide-treated and control animals were compared by the Mantel-Cox log-rank test. A value of p < 0.05 was considered significant.The ocedures . For theC. albicans SC5314 challenged-larvae, peptide-treated or mock-untreated  were included. 24 h post-inoculation, 3 randomly selected larvae for each experimental condition were bled and the hemolymph was pooled into a microcentrifuge tube containing cold saline. For cytology, 50 μL of hemolymph was diluted 1:1 in saline prior to cyto-centrifugation at 600 rpm for 5 min. Cytospin slides were fixed with Cytofix and stained with hematoxylin and eosin. Image acquisition was performed by the NanoZoomer-XR C12000 series . Data were confirmed in three independent experiments, and representative images are shown.Larvae were inoculated as above described (paragraph 4.3). Both uninfected and C. albicans filamentation and the effect on cellular immunity in vivo, two time points were used, 24 and 48 h. Data were confirmed in three independent experiments and representative images are shown.Larvae were processed for histology as previously described . Briefly