Cytokines are small molecules that modify the response of the human body to external signals. Tumor Necrosis Factor was found in 1975 and its main activity was assumed to be lethal for cancer cells and to be able to kill tumors, hence the name. Research over the last 35 years has shown that this is not really the case. This review illuminates what caused this misunderstanding and answers the question if there is any connection at all between the cytokine Tumor Necrosis Factor and cancer.Tumor Necrosis Factor was one of the first cytokines described in the literature as a soluble mediator of cytotoxicity to tumors. Over the years, more extensive research that tried to employ Tumor Necrosis Factor in cancer treatments showed nevertheless that it mainly functioned as a proinflammatory cytokine. However, this did not stop the search for the holy grail of cancer research: A cytokine that could act as a one-stop treatment for solid tumors and lymphomas. This review will summarize the long experimental history of Tumor Necrosis Factor that caused the initial observations of a tumor necrotizing cytokine that could serve as a potential cancer treatment and discuss the current state of research into this side of the activities of Tumor Necrosis Factor. The cytokine Tumor Necrosis Factor is a central immune mediator with a wide variety of functions. It has been shown to be essential in the early, inflammatory aspect of the immune response  but alsoTumor Necrosis Factor (TNF) can be dated back to experiments published by Carswell, Old et al. in 1975, who showed that in Bacille Calmette–Guerin (BCG)-infected and subsequently LPS-challenged rabbits, a tumor-necrotizing agent existed that was transferable [The name sferable , which asferable , and in sferable  and Fransferable .The treatment was controversial, and reports of its effectiveness as reviewed 40 years later by William Coley’s daughter Helen Coley Nauts were only based on anecdotal evidence. It was, however, still available until 1990 when its production ceased because it missed the approval of regulatory authorities, but it can to this day be obtained as a specialized fever therapy for cancer (Fieberterapie) in Germany.The contemporary view of TNF is that it is a proinflammatory cytokine. Generally, TNF is expressed in large amounts early in the immune response, predominantly by macrophages  but alsoA vast spectrum of functions and activities in host defense and the initiation and maintenance of inflammation was demonstrated  and “TNF2 × LTβ bound to a new receptor termed LTβR [After Coley’s original observation that solid tumors treated with bacterial components could develop hemorrhagic necrosis, clinical advances in cancer treatment caused the interest in this line of research to subside. However, various groups persistently tried to identify the factor that caused the necrotizing of tumors. In 1968, a group was successful in isolating a serum component that was cytotoxic for a transformed cell line . A few ymed LTβR . HistoriGene-deficient mouse models of both receptors, p55 ,39 and pFurthermore, the TNF signaling pathways were previously analyzed in detail and summarized . While iHowever, even without a specifically defined mechanism, the assumption that TNF could act as a cytotoxic molecule was central to the hypothesis that this cytokine was effective as an anti-tumor agent. In fact, its cytotoxic role had been investigated for many years before the death domain of TNFR-1 was identified. In this context, early experiments identified TNF as a molecule expressed by activated lymphocyte blasts that was cytotoxic to L cells, transforming mouse fibroblasts . TNF wasWhile previous experiments showed that TNF by itself was a weak cytotoxic agent with limited cytotoxic activity and a central involvement of TNF in the inflammatory response became clear very early in the investigation ,11,57,58Initial in vivo experiments were carried out in experimental settings such as in rodent models using murine or human TNF, and indeed, it was shown that high doses of TNF resulted in the selective necrosis of tumors. However, it also became clear that while mouse experiments that used high doses of human TNF were somewhat promising, using murine TNF in this context caused side effects that were comparable to inoculation with endotoxins, seemingly contradicting the earlier experience with Coley’s toxin  and thusThis problem to directly use TNF was underlined in early clinical trials using the cytokine as a monotherapy for various, mostly advanced cancers, which revealed very strong, dose-dependent side effects of TNF resembling endotoxic shock syndrome, such as fever, nausea, hypotension, and others ,61,62,63While the use of TNF in therapy has been hampered by side effects and a lack of anti-tumor activity, attempts have been made to both reduce toxicity by means of the molecular modification of the cytokine and to increase tumoricidal activity by way of directly delivering TNF to the tissues. Decreasing toxicity was achieved by combining TNF with polyethylene glycol, which reduced the detrimental effects of the conjugated protein in vivo in a recurring tumor model in dogs . While oA very different approach to modulate the side effects of TNF, while still efficiently fighting soft tissue sarcomas and melanomas, has been developed that targets the delivery of the cytokine using isolated limb perfusion (ILP). After the vasculature of an extremity was isolated to avoid unwanted systemic effects, peripheral tissues were directly inoculated with high doses of TNF, mostly in combination with melphalan and sometimes IFN-γ ,73,74,75In summary, while mouse experiments still supported the tumor-modifying action of TNF, the notion of a direct strong cytotoxic function had to be abandoned. Furthermore, a transfer to the human condition was not straightforward due to significant off-target effects and could only be used as a predominantly palliative measure to treat unresectable tumors using ILP.Beyond primary cytotoxicity, a plethora of indirect actions of TNF must be considered as potential mechanisms that could modulate tumor growth, since many TNF functions are strongly context-dependent. In fact, after it was shown that TNF is predominantly produced by macrophages after activation ,13, it bOne of the earliest activities of TNF that was elucidated was the activation of vascular endothelial cells . This reThe systemic treatment of mice with SA1 sarcoma with high doses of TNF was reliable at causing a tumor-hemorrhagic reaction that resulted in the destruction of greater than 75% of the tumor’s center in 24 h with the development of numerous hemorrhages in the tumor’s vascular bed .Counterintuitively, in models of angiogenesis in rat corneas and developing chick chorioallantoic membranes, low doses of TNF induced blood vessel formation. It was conclusively demonstrated that activated macrophages were the source of this angiogenic effect . This acThis surprising influence of TNF on macrophage polarization was confirmed in infection models .The strong proinflammatory activity of TNF was recognized early , as was The first anti-TNF biological available for clinical use was infliximab (trade name Remicade), which was based on the chimeric mouse–human antibody cA2 . This chExtensive investigations into the binding signature of cA2 and the blocked activities showed that it bound and neutralized soluble and membrane TNF but not lymphotoxins and inhibited cachexia and TNF-induced mortality. It also bound Fc receptors and could induce antibody-dependent cellular and complement-dependent cytotoxicity ,104. ConAt the same time that infliximab was developed, a TNF-R-2 immunoglobulin Fc-domain fusion protein was generated by Bruce Beutler’s laboratory at the Howard Hughes Medical Institute, University of Texas Southwestern Medical Center, Dallas . The fusEarly trials of etanercept in patients with RA showed that the drug had a strong anti-inflammatory action, especially in combination with methotrexate . The fusThe anti-TNF blocker adalimumab (trade name Humira) was developed in a collaboration between BASF Bioresearch Corporation and Cambridge Antibody Technology using phage-display technology to derive human antibodies for clinical development and therapy from gene libraries . AdalimuThe affinities, avidities, and Fc-dependent complement activation of infliximab, etanercept, and adalimumab were quantified and compared in a comprehensive study . While tAnti-TNF drugs have been meanwhile approved for the treatment of various chronic inflammatory conditions of autoimmune origin, such as RA, polyarticular juvenile idiopathic arthritis, psoriatic arthritis, ankylosing spondylitis, and plaque psoriasis. In contrast to the TNFR-2-based anti-TNF fusion protein etanercept, the antibody-based drugs infliximab and adalimumab have been additionally approved for the treatment of Crohn’s disease.M. tuberculosis infection was dependent on the interaction of TNF with TNFR-1 [The high efficacy of these drugs against inflammatory conditions caused a fast increase of usage, but it became clear that there were unexpected adverse side effects. It was shown early in the investigation of the various roles of TNF that the maintenance of the protective granulomas in h TNFR-1 . This wah TNFR-1 . Four yeh TNFR-1 . In 2004h TNFR-1 . Furtherh TNFR-1 , treatmeh TNFR-1 .M. tuberculosis while signaling through the LTβR [A comprehensive concluding study that involved advanced computer modeling yielded three important mechanistical observations that could explain the differences. First, a TNF inhibitor (TNFi) that binds to membrane TNF, such as infliximab, significantly reduces granuloma formation. Second, the antibody-based inhibitor shows strong differences in binding kinetics to TNF and tissue permeability causing reactivation, while apoptotic, cytolytic, and pharmacokinetic changes are not essential. Third, host-specific factors such as TNF-induced NF-κB activation that maintains granuloma function is still supported during etanercept but not infliximab treatment . Finallythe LTβR .While the increase in human granulomatous diseases after TNF inhibition was identified and analyzed in mouse models with relative ease, the answer to the question if the neutralization of TNF increases the chance to receive a cancer diagnosis was originally based on anecdotal evidence and the historically perceived role of TNF. Despite a large number of investigations, the evidence is still controversially discussed. While infliximab and adalimumab are based on anti-TNF antibodies and bind only to TNF, etanercept is based on a TNFR-2-Ig fusion protein that can bind TNF and LTα , which cBecause TNFi was first approved for the treatment of RA, the majority of initial investigations encompassed only small numbers of patients with short treatment phases. The first analysis of these data pointed to an increase in the incidence of lymphomas . These pSimilarly, two Swedish-population-based studies that were linked with the Swedish Nationwide Cancer and Census Registers, and differentiated between solid cancers  and hemaA more recent study analyzed the consequences of the treatment of 18,000 RA patients with TNFi and rituximab (RTX), and specifically the influence on 425 previous malignancies . To compFinally, a study published in 2021 showed a relatively weak correlation with non-melanoma skin cancer and non-Hodgkin lymphoma and recommended in increased frequency of skin checks in older patients that used TNFi treatment regimens .In the first meta-analysis, 144 potentially interesting publications that used TNFi to treat RA were analyzed. Because of various reasons, such as the use of TNFI based on TNFR-2 or the lack of a randomized control trial design, the number was reduced to 15. A duration of the treatment that was considered too short excluded another six trials. Nine clinical trials encompassing 5014 patients who had been randomized into treatment or control groups were analyzed . This stThe research journey into the role of TNF in immunology in general and cancer immunology in particular started with huge expectations . This we


TNF-α) gene single-nucleotide polymorphisms (SNPs) TNF-863A/C (rs1800630), TNF-308A/G (rs1800629), TNF-238A/G (rs361525), and TNF-α serum concentration with age-related macular degeneration. Analysis of TNF-α rs1800630, rs1800629, and rs361525 polymorphisms showed that the TNF-α rs1800630 A allele was statistically significantly more frequent in the exudative AMD group compared to the control group (p = 0.029). Additionally, the TNF-α rs1800630 A allele was more frequent in females with exudative AMD than in the control group of healthy females (p = 0.027). The TNF-α rs1800630 A allele was more frequent in females with exudative AMD than in females with early AMD (p = 0.014). TNF-α rs1800630, rs1800629, and rs361525 haplotype A-A-G were associated with decreased odds of exudative AMD (p < 0.0001), and haplotype A-G-G was associated with 24-fold increased exudative AMD occurrence (p < 0.0001). TNF-α protein levels were lower in subjects with exudative AMD compared to the control group (p < 0.001). The study showed significant associations between inflammatory cytokine TNF-α single-nucleotide polymorphisms and serum level with AMD pathogenesis. Analysis of TNF-α genotypes and serum concentration may be helpful for the AMD diagnosis.Age-related macular degeneration (AMD) is a neurodegenerative disease leading to irreversible central vision loss among the elderly in developed countries. While the disease accounts for 9% of all cases of vision loss, the prevalence of AMD is likely to increase due to the exponential aging of the population. Due to this reason, our study aimed to determine the associations of tumor necrosis factor-alpha ( Aging-related disorders can be defined as the progressive occurrence of several defective cellular mechanisms or metabolic pathways, resulting in degeneration . Age-relVarious pathologies, including a focal detachment of the RPE, outer retinal atrophy, and new blood vessel growth between Bruch’s membrane and the retina, can progress into either geographic atrophy (GA) or choroidal neovascularization (CNV) AMD, which are also known as ‘dry’ or ‘wet’ AMD, respectively . The priCFH) and age-related maculopathy susceptibility 2 (ARMS2) [AMD is a multifactorial disease caused by various genetic variants. Each has a modest effect on the risk and is also influenced by nongenetic/environmental factors  ,11. The  (ARMS2) . However (ARMS2) .TNF-α gene is genetically regulated, and polymorphisms in the promoter region may alter TNF-α production [Recent studies have shown the immune system’s role in AMD development and progression. Increased concentrations of several inflammatory cytokines have been found both in serum and locally in ocular tissues or fluids in patients with AMD . An examoduction .TNF-α) gene single-nucleotide polymorphisms (SNPs) TNF-863A/C (rs1800630), TNF-308A/G (rs1800629), TNF-238A/G (rs361525), and TNF-α serum concentration with AMD.Due to the vital role of inflammatory molecules in the pathogenesis of AMD, we aimed to determine the associations of tumor necrosis factor-alpha .The study included subjects admitted to the Hospital of Lithuanian University of Health Sciences Ophthalmology Department for preventive ophthalmological evaluation. In total, 1078 participants were enrolled in our study. Polymorphisms were determined by dividing the subjects into three groups. The first group consisted of patients with early AMD (n = 330) ranging in age from 42 to 94 years. This group included 227 (68.8%) females and 103 (31.2%) males. The second group consisted of patients with exudative AMD (n = 393) ranging in age from 49 to 95 years. The group included 254 (64.6%) females and 139 (35.4%) males. Moreover, the third group consisted of ophthalmologically healthy individuals (n = 355) aged 51 to 94 years. The group included 222 (62.5%) females and 133 (37.5%) males. TNF-α serum concentration was determined in subjects divided into two groups: subjects with exudative AMD (n = 18) and ophthalmologically healthy individuals corresponding to the subjects by age and gender (n = 20).The AMD group consisted of subjects who underwent ophthalmological evaluation and were diagnosed with early or exudative AMD. Present study subjects were evaluated by slit-lamp biomicroscopy. Additionally, all AMD patients underwent optical coherence tomography (OCT), and optical coherence tomography angiography (OCT-A) was performed to confirm the exudative AMD after the OCT examination.The Age-Related Eye Disease Study (AREDS) classification system was used for AMD diagnosis and classification .Early AMD consists of a combination of multiple small drusen (protein and lipid deposit) formation between the RPE and BrM  and seveThe intermediate form is described as a presence of at least one large (≥125 μm in diameter) drusen, numerous medium-sized drusen, or GA without extension to the center of the macula with mild symptoms, as mild blurriness in their central vision or trouble seeing in low lighting or may not cause any symptoms.Dry/atrophic AMD with the GA of the RPE;Neovascular or exudative AMD, which is diagnosed when choroidal neovascularization with detachments in the RPE hemorrhages and/or scars appear and cause progressive blurring or other central vision impairments .The advanced AMD is divided into:AMD exclusion criteriaUnrelated eye disorders, e.g., high refractive error, cloudy cornea, lens opacity  except minor opacities, keratitis, acute or chronic uveitis, glaucoma, or diseases of the optic nerve.Any other inflammatory diseases.Systemic illnesses, e.g., diabetes mellitus, malignant tumors, systemic connective tissue disorders, chronic infectious and noninfectious diseases, hypertension, coronary artery disease, stroke or conditions following organ or tissue transplantation.Ungraded color fundus photographs resulting from obscuring the ocular optic system or because of fundus photograph quality.Use of antiepileptic or sedative drugs.The salting-out method performed the extraction of deoxyribonucleic acid (DNA). Blood was collected in vacuum tubes with the anticoagulant EDTA (ethylenediaminetetraacetate) to prevent microspheres’ formation and protect the DNA from degradation. The DNA used in the study was isolated from peripheral venous white blood cells. The salting-out method is based on collecting cells by centrifugation, their suspension in a buffer solution, the degradation of cell membranes with detergents, the hydrolysis of proteins by proteinase K, the deproteinization of chloroform, and the precipitation of DNA with ethanol. It is known that about 250 μg of DNA can be obtained from 10 mL of blood and is used in assays such as real-time polymerase chain reaction (RT-PCR). In the first stages of DNA extraction, it is vital to inactivate nucleases (enzymes that destroy DNA or RNA) using different buffers. It is also recommended that all DNA extraction steps be performed at a low temperature, and extracted DNA be stored at −70 °C and unfroze before use.For further research using DNA, it is necessary to measure the concentration of extracted DNA. It was measured using an Agilent Technologies Cary 60 UV-Vis spectrophotometer, which can analyze minimal amounts of samples (1–2 µL) without using cuvettes or capillaries. By measuring the absorbance  of the solution at a certain ultraviolet (UV) wavelength, the DNA purity and the amount of remaining protein were determined along with the DNA concentration. 260 nm UV light waves are absorbed by nucleic acids (DNA and RNA) and 280 nm by proteins. The ratio between DNA and protein absorption (260/280) should be 1.8.TNF-α gene SNPs: TNF-863A/C (rs1800630), TNF-308A/G (rs1800629), TNF-238A/G (rs361525) were studied by RT-PCR. A series of three steps achieve RT-PCR amplifications: (1) denaturation, in which double-stranded DNA templates are heated to separate the strands; (2) annealing, in which short DNA molecules called primers bind to flanking regions of the target DNA; and (3) elongation, in which DNA polymerase extends the 3′ end of each primer along the template strands. These steps are repeated to produce exact copies of the target DNA exponentially. Samples of 1078 subjects were genotyped with the StepOnePlus RT-PCR amplifier . Applied Biosystems and Thermo Fisher Scientific  developed primers and molecular markers for genotyping.ELISA is an enzyme-linked immunosorbent assay for detecting and quantifying peptides, secreted proteins, hormones, and cytokines. The most crucial element of an ELISA is a particular antibody–antigen interaction. A commercial TNF-alpha Human ELISA Kit ) was used to measure serum TNF-α levels in subjects. TNFα solid-phase sandwich ELISA is designed to measure the amount of the target bound between a matched antibody pair. A target-specific antibody has been precoated in the wells of the microplate. Samples and standards were then added into wells and bound to the immobilized antibody. The sandwich was formed by adding the second (detector) antibody. The enzyme activity was measured using a substrate that changes color when modified by the enzyme.http://www.oege.org/software/hwe-mr-calc.shtml accessed on 11 March 2022). The Pearson χ2 test using two-way alternatives compared the distribution of polymorphism genotypes and alleles between patients with early and exudative AMD and the control group. Based on genetic models, a binary logistic regression analysis was performed to assess AMD’s odds ratios (OR). This analysis was performed for individual AMD groups (early and exudative), indicating OR with a 95% confidence interval (CI). Logistic regression analysis for exudative AMD was performed with age adjustment, as subjects in the exudative AMD group were statistically significantly older than the control group. The best genetic model selection was based on the Akaike Information Criterion (AIC). Therefore, the best genetic models were those with the lowest AIC values. Haplotype analysis was performed using the online program SNPStats (https://www.snpstats.net/start.htm accessed on 1 February 2022). Linkage disequilibrium (LD) analysis was assessed by D′ (deviation between the expected haplotype frequency and the observed frequency) and r2 (square of the haplotype frequency correlation coefficient) measures. Associations of haplotypes with early and exudative AMD were assessed by logistic regression, indicating OR with a 95% CI. Statistically significant differences were observed when p < 0.05.Statistical analysis was performed using the IBM SPSS Statistics 27.0 software. Data are presented as absolute numbers with percentages. Descriptive statistical characteristics were used for non-normally distributed data: median, minimum, and maximum (min. and max.) values. Mann–Whitney U test was used to determine the differences in age and TNF-α concentration between two independent groups. The control group’s distribution of TNF-α rs1800630, rs1800629, and rs361525 polymorphisms were evaluated using the Hardy–Weinberg equilibrium . Additionally, any differences were found comparing the age of the control and early AMD groups (p = 0.266). However, the patients with exudative AMD were statistically significantly older than controls (p < 0.001). Thus, further exudative AMD analyses were adjusted by age.Our study enrolled 1078 subjects, who were divided into three groups: the control group (n = 355), patients with early AMD (n = 330), and patients with exudative AMD (n = 393). The demographics are shown in TNF-α rs1800630, rs1800629, and rs361525 genotypes in the control group using the Hardy–Weinberg equilibrium (HWE). The analysis showed that the SNPs were in HWE (p > 0.01)  .TNF-α rs1800630, rs1800629, and rs361525 revealed that the TNF-α rs1800630 A allele was statistically significantly more frequent in the exudative AMD group compared to the control group   . No statD groups .TNF-α rs1800630, rs1800629, and rs361525 polymorphisms in the early AMD and control groups and the exudative AMD and control groups did not show statistically significant results (Binary logistic regression of  results .2) was estimated. Data are presented in We performed the haplotype analysis of TNF-863A/C (rs1800630), TNF-308A/G (rs1800629) and TNF-238A/G (rs361525) polymorphisms. The deviation between the predicted haplotype frequency and the observed frequency (D′) was calculated, and the square of the correlation coefficient  is associated with the decreased odds of exudative AMD development , and the haplotype A-G-G of TNF-α  is associated with increased odds of exudative AMD development   .TNF-α rs1800630, rs1800629, rs361525) with the predisposition to early and exudative AMD occurrence in males and females separately.Continuing the analysis of the study, we evaluated the associations of single-nucleotide polymorphisms   .p > 0.05)   . Analysi= 0.027) .p > 0.05)   . Allele = 0.014) .TNF-α rs1800630, rs1800629, and rs361525 polymorphisms and the early or exudative AMD . The results are shown in TNF-α protein serum levels were measured in patients with exudative AMD who had not been treated with anti-VEGF injections (n = 18) and controls (n = 20). We found a statistically significant difference between study groups: TNF-α serum levels were lower in patients with exudative AMD than in the control group (mean ± standard deviation (SD): 16.182 pg/mL ± 6.094 vs. 30.652 pg/mL ± 6.322; TNF-α rs1800630, rs1800629, and rs361525. Due to the small sample size, we formed two groups for comparison: heterozygotes and homozygotes according to a rarer allele and homozygotes according to a more frequent allele. No statistically significant differences were found  and only for exudative AMD patients and only for the control group, according to the genotypes of re found .TNF-α locus are involved in high TNF-α production [TNF-α rs3615225 has been associated with gastric cancer and sepsis risk, particularly in eastern populations [TNF-α rs1800629 polymorphism was significantly associated with the risk of chronic periodontitis, type 2 diabetes mellitus, and celiac disease [TNF-α rs1800630 polymorphism was significantly related to the increased birth weight (g) within preterm-birth (PTB) patients [Recent studies of the Russian population have shown that promoter polymorphisms at -238 rs361525), -308 (rs1800629), and -863 (rs1800630) positions of its gene could regulate TNF-α production. These genetic variants may have implications for AMD pathogenesis due to inflammatory processes imbalance caused by TNF-α production dysregulation . It has oduction . In thisulations . TNF-α r, -308 . Additionally, the TNF-α rs1800630 A allele was statistically significantly more frequent in females with exudative AMD than in the control group of healthy females . Moreover, we found statistically significant differences in the TNF-α rs1800630 polymorphism allele frequencies between females with primary AMD and females with exudative AMD. The A allele is more frequent in females with exudative AMD compared to females with early AMD .Analysis of Studies show that the frequency distribution of allele/genotype varies among different ethnic and racial groups. The prevalence of the TNF–308 A allele was 7% in China and Korea, 15% in Germany, and 11% in normal Italian subjects. The Portuguese, Finnish, and US populations have 13% and 14%, and 15% of TNF–308 A allele frequencies. In India, the –308 A allele frequency falls between the values for China/Korea and Italy . The grop < 0.0001), and haplotype A-G-G with the increased odds of exudative AMD . Behniafard et al. reported that the GG haplotype at TNF-α  was seen in 92.7% of the patients with atopic dermatitis, which was significantly higher than the controls (p < 0.001), while a negative haplotypic association with atopic dermatitis was seen for TNF-α  AG and GA (p < 0.01) [The haplotype frequencies of TNF-863A/C rs1800630), TNF-308A/G (rs1800629), and TNF-238A/G (rs361525) SNPs were not involved in previous AMD studies. Our results revealed that the haplotype A-A-G of TNF-863A/C (rs1800630), TNF-308A/G (rs1800629), and TNF-238A/G (rs361525) polymorphisms was associated with the decreased odds of exudative AMD . The expression of the TNF gene in human macrophages is strongly downregulated when they are exposed to phagocytose retinal pigment epithelial cells in vitro [In our study, TNF-α serum levels were measured in patients with exudative AMD before the treatment of anti-VEGF and in the control groups. TNF-α protein level was lower in subjects with exudative AMD than in controls  [p < 0.05). Additionally, decreased serum TNF-α level in patients with chronic schizophrenia was correlated with an increased likelihood of having psychopathological symptoms [According to studies by other researchers, a decreased TNF-α protein serum level correlates with bodyweight loss. In a study of 27 obese subjects with a body mass index (BMI) of 36.3 ± 5.7, a significant decrease in TNF-α serum level was observed comparing subjects before and after weight loss treatment  . Fan et symptoms .TNF-α promoter polymorphisms  could be potential diagnostic biomarkers of the exudative AMD. Changes in TNF-α level may also be a significant risk factor for exudative AMD.Thus, our study demonstrated that the TNF-α rs1800630 A allele was more frequent in the exudative AMD patients than in the controls. The haplotype A-A-G of the TNF-863A/C (rs1800630), TNF-308A/G (rs1800629), and TNF-238A/G (rs361525) polymorphisms were associated with the decreased odds of exudative AMD and the haplotype A-G-G with the increased odds of exudative AMD occurrence. The TNF-α rs1800630 A allele was statistically more frequent in females with exudative AMD than in the ophthalmologically healthy females. Additionally, the TNF-α rs1800630 polymorphism A allele was more frequent in females with exudative AMD than in females with primary AMD. TNF-α serum level was lower in patients with exudative AMD than in healthy controls.


Cerebral ischemia-reperfusion injury (CIRI) mostly occurs in the treatment stage of ischemic diseases and aggravate brain tissue damage. Although studies have demonstrated that miR-489-3p is closely related to CIRI, the effects of miR-489-3p on neural function in CIRI have not been directly studied. The transient middle cerebral artery occlusion (tMCAO) model was established by suture method, and the corresponding plasmids that interfered with the expression of miR-489-3p or Sirtuin1 (SIRT1) were injected into the model mice, and the behavioral changes of the mice were observed. Then the concentration of serum neuronal injury markers and oxidative stress indices were examined. Next, the pathological conditions, neuronal loss and apoptosis of brain tissue were observed by hematoxylin-eosin staining, Nissl staining, and Transferase-mediated deoxyuridine triphosphate-biotin nick end labeling staining. Finally, the hemoglobin content and cerebral edema in the mouse brain were determined. In addition, the expression levels of miR-489-3p and SIRT1 were detected by reverse transcription quantitative polymerase chain reaction or Western blot, and the targeting relationship between miR-489-3p and SIRT1 was verified by bioinformatics analysis and luciferase reporter assay. The experimental results found that in tMCAO mice, miR-489-3p in brain tissue was up-regulated and SIRT1 was down-regulated. Down-regulating miR-489-3p or up-regulating SIRT1 ameliorated behavioral dysfunction, neuronal damage and apoptosis, oxidative stress and brain histopathology. miR-489-3p targeted the regulation of SIRT1 expression, and down-regulating SIRT1 can reverse the protective effect of silenced miR-489-3p on brain injury. Taken together, by targeting SIRT1, elevated miR-489-3p aggravates CIRI-induced neuronal apoptosis and oxidative stress.  In tMCAO mice, miR-489-3p in brain tissue is up-regulated, and SIRT1 is down-regulated.Down-regulating miR-489-3p can improve tMCAO-induced injury to mice;miR-489-3p targets the regulation of SIRT1 expression;Down-regulating SIRT1 can reverse the protective effect of silenced miR-489-3p on tMCAO;miR-489-3p aggravates tMCAO-induced injury by targeting SIRT1.Cerebral ischemia and reperfusion injury (CIRI) is an irreversible brain injury. Every year, about one-third of patients with CIRI die, and more than one-third are permanently disabled . CIRI pamicroRNAs (miRs) could regulate gene expression by inhibiting protein translation and targeting mRNA destabilization/degradation . A recenSirtuin1 (SIRT1) is a nicotinamide adenine dinucleotide-dependent deacetylase, playing an important role in DNA damage response, glucose and lipid metabolism and transcription . It can Therefore, our study aims to explore the mechanism of miR-489-3p in CIRI with the hypothesis that miR-489-3p aggravates CIRI through targeted regulation of SIRT1 expression and hopes to provide a theoretical basis for exploring targets in the treatment of CIRI.This experiment passed the review and supervision of the Animal Ethics Committee of The Sixth People’s Hospital of Nantong City .Forty-eight male C57/BL6J mice  were purchased from Shanghai SLAC Laboratory Animal Co., Ltd. . Under a 12-hour light/dark cycle, the mice were housed in a specific pathogen-free environment at 21 ± 1°C and 40–60% humidity.After intraperitoneal injection with 1% pentobarbital sodium (0.06 g/kg), a 1-cm longitudinal median incision was made from the mandible to the sternum, the proximal end of the common carotid artery was ligated, and the internal carotid artery was blocked by a arterial clip. The thread (0.23 mm × 0.18 mm) was inserted into the common carotid artery, and the external carotid artery ostium was sutured with 5–0 silk suture. After removing the arterial clip, the thread passed through the internal carotid artery to the middle cerebral artery (12.00 mm deep). It was observed that the signal in the blood flow monitor dropped to about 20%. After occlusion for 1 h, the thread was taken out, the external carotid artery was ligated, the internal carotid artery was bifurcated, and the common carotid artery was released. When the signal in the blood flow monitor returned to 100%, the skin was sutured along the incision. The mice with sham operation did not perform thread insertion. As the body temperature of the mice reached equilibrium, the mice could drink and eat after the operation.Mice were randomly divided groups, each group with 6 mice: (1) Sham group; (2) Model group; (3) miR-489-3p antagomir group: injection of miR-489-3p antagomir; (4) antagomir NC group: injection of antagomir NC; (5) oe-SIRT1 group: injection of SIRT1 overexpression vector; (6) oe-NC: injection of SIRT1 overexpression vector NC; (7) miR-489-3p antagomir + si-SIRT1 group: injection of miR-489-3p antagomir and SIRT1 interference vector; (8) miR-489-3p antagomir + si-NC group: injection of miR-489-3p antagomir and SIRT1 interference vector NC.Injection: anterior fontanel is at coordinate 0, 1.5 mm to the right, 1.2 mm back, and 4.5 mm deep. The plasmid  was injected into the right lateral ventricle of mice at 0.3 µL/min 24 h before modeling.After 24 h of transient middle cerebral artery occlusion (tMCAO), neurological assessment was performed using Zea-Longa scoring. 0 point = no neurological deficit; 1 point = left extension disorder (mild); 2 points = moved to the left side while crawling (moderate); 3 points = turned to the hemiplegic side while walking (severe); 4 points = not able to walk alone (loss of consciousness). A double-blind method was used during the assessment.After 24 h of tMCAO, behavioral testing (rotation experiment) was carried out for 3 d. Two plates (30 cm × 20 cm × 1 cm) were placed to form an angle of 30°, leaving a small seam between them. The mice were placed in the seam, and tuned to the left or right when they entered the corner. The probability of normal mice turning left or right is basically the same, while mice with one brain injury turn around to the brain injury side. After 10 repeated testings, with an interval of 1 min, the number of times to the brain injury side was recorded.Markers of neuronal damage, neuron-specific enolase (NSE), S100B, brain-derived neurotrophic factor (BDNF), superoxide dismutase (SOD) and malondialdehyde (MDA) in serum were tested using ELISA kits .After behavioral testing, mice were euthanized by inhalation of carbon dioxide. The obtained brain tissue was fixed in 4% paraformaldehyde, dehydrated in gradient ethanol and cleared in xylene. The sections were prepared to paraffin slices (5-µm).Paraffin slices were deparaffinized with xylene and treated with 3-min hematoxylin staining and 1-min eosin staining. Afterward, the stained slices were dehydrated by gradient ethanol  and permeabilized with xylene . Finally, the neutral gum-sealed slices were observed with an optical microscope.Paraffin slices were performed 1-h tar violet staining and differentiated in 70–80-95% alcohol (10 s each time). After the slices were dehydrated by absolute ethanol and cleared with xylene, they were fixed with neutral resin and observed under a microscope .2O2 for 15–20 min, and digested with proteinase K (Sigma-Aldrich). Citrate was added for 30 min and the sections were incubated with TdT enzyme reaction solution (50 μL) for 1 h and with horseradish peroxidase-labeled anti-digoxigenin (50 μL) for 30 min. Sections were finally developed with diaminobenzidine  for 10 min and photographed under a microscope  after counterstaining with hematoxylin. TUNEL-positive cells are apoptotic cells, with pyknotic nuclei, brownish-yellow granules, shrunk cell bodies, and irregular shapes. Five fields of view were randomly selected, and apoptosis rate was calculated .The apoptosis of mouse brain cortex was observed using TUNEL kit (Beyotime). The paraffin sections were dehydrated, incubated with 3% HThe brain tissue was frozen at −20°C for 30 min, cut in the coronal position to make 2-mm brain tissue sections for reaction with TTC staining kit . The brain tissues were imaged (the white part is the infarct area). The proportion of infarct area to total area was calculated.Thoroughly-grounded brain tissues were centrifuged at 4°C, from which the supernatant (50 μL) was collected and incubated with hemoglobin detection reagent . A microplate reader  was utilized to examine absorbance at 400 nm.The olfactory bulb and brainstem were deprived, and the brain tissue was weighed (wet weight). Then, dry weight was measured after the brain tissue was baked at 100°C for 48 h. Brain water content (%) = (wet weight-dry weight)/wet weight.−ΔΔCt was the method to calculate gene levels. The primers were provided by GenePharma . The cDNA synthesized by TaqMan MicroRNA Reverse Transcription Kit  was subjected to quantitative PCR, during which ABI 7300 real-time PCR system  was used. U6 and glyceraldehyde-3-phosphate dehydrogenase (GAPDH) were the normalized controls and 2nePharma .Table 1Total protein collected by lysis buffer (protease inhibitor) was tested by bicinchoninic acid kit (Thermo Fisher Scientific) to examine protein concentration. The protein was denatured in 100°C boiling water, separated by sodium dodecyl sulfate polyacrylamide gel electrophoresis, and transferred to a polyvinylidene fluoride membrane . Then, the 10% milk powder-blocked membrane was incubated with SIRT1 and GAPDH primary antibody (1:1000) and with the secondary antibody (1:5000). Enhanced chemiluminescence detection reagent  and ChemiDoc instrument  were employed for immunoblotting, and a biological image analysis system (Bio-Rad) was for analyzing gel bands.miR-489-3p and SIRT1 binding region was predicted by starBase. The wild-type (WT) and mutant type (MUT) of SIRT1 3'UTR containing miR-489-3p binding region were cloned into pCMV6  to form pCMV6/SIRT1-WT and pCMV6/SIRT1-MUT, respectively. The formed vector, along with miR-489-3p mimic or mimic NC was co-transfected into HEK293T cells, after which dual luciferase analysis kit  was employed to examine luciferase activity.P < 0.05.Data were analyzed using SPSS 21.0 . Measurement data were expressed as mean ± standard deviation, and evaluated by independent sample t test if followed a normal distribution. Statistical difference was significant when Suture method-established tMCAO mouse model was evaluated. First, Zea-Longa scoring assessed that after tMCAO, the neurological deficit score of mice was increased ; behavioRT-qPCR examination found that tMCAO mice had higher miR-489-3p expression . Mice weThe impairment of SIRT1 mRNA and protein expression was examined in tMCAO mice . TherefoMice were injected with oe-SIRT1 (oe-NC as a control) before modeling, leading to SIRT1 up-regulation in tMCAO mice . For tMCIn determining the effects of miR-489-3p and SIRT1 on CIRI, rescue test was initiated with mice injecting with miR-489-3p antagomir + si-NC or miR-489-3p antagomir + si-SIRT1 before modeling. The observations displayed that miR-489-3p antagomir-mediated protection against tMCAO-induced brain injury was reduced by si-SIRT1 in mice . It concMultifactorial pathogenic mechanisms including calcium overload, oxidative/nitrosative stress, endoplasmic reticulum stress, mitochondrial dysfunction, apoptosis and autophagy pathway activation, protein kinases, epigenetic changes, inflammation, protein cleavage products and other degradation products, no reflux and genomic/metabolomic insights help CIRI evade precise treatment . Till noIn different eukaryotic lineages, miRs bind to 3'UTR of mRNA, thereby post-transcriptionally regulating mRNA . An impoIn addition, our study found that SIRT1 expression in tMACO mice was inhibited, and SIRT1 deficiency reduced the inhibitory effect of silenced miR-489-3p on tMACO mice, indicating that miR-489-3p mediates CIRI progression by regulating SIRT1 in part. As mentioned earlier, the pathophysiological changes of tMCAO are related to the changes of certain biomarkers. S100B expression in ischemic brain injury will increase . Serum pIn summary, our study provides evidence that by targeting SIRT1, elevated miR-489-3p aggravates neuronal apoptosis and oxidative stress induced by CIRI. Our research may be potentially important for the treatment of CIRI.


The manner of m6A in HFpEF and HFpEF + EXT hearts was explored via m6A-specific methylated RNA immunoprecipitation followed by high-throughput and RNA sequencing methods. A total amount of 3992 novel m6A peaks were spotted in HFpEF + EXT, and 426 differently methylated sites, including 371 hypermethylated and 55 hypomethylated m6A sites, were singled out for further analysis . According to gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses, unique m6A-modified transcripts in HFpEF + EXT were associated with apoptosis-related pathway and myocardial energy metabolism. HFpEF + EXT had higher total m6A levels and downregulated fat mass and obesity-related (FTO) protein levels. Overexpression of FTO cancels out the benefits of exercise in HFpEF + EXT mice by promoting myocyte apoptosis, myocardial fibrosis and myocyte hypertrophy. Totally, m6A is a significant alternation of epitranscriptomic processes, which is also a potentially meaningful therapeutic target.Heart failure with preserved ejection fraction (HFpEF) shows complicated and not clearly defined etiology and pathogenesis. Although no pharmacotherapeutics have improved the survival rate in HFpEF, exercise training has become an efficient intervention to improve functional outcomes. Here, we investigated N6-methyladenosine (m Heart failure (HF) with preserved ejection fraction (HFpEF) is a complicated clinical syndrome associated with poor quality of life, extensive utilization of medical resources and premature death . HFpEF a2, 6 min walk, cardiorespiratory fitness, diastolic function, quality of life, and general health (Recent studies have shown that exercise training (EXT) is an important means of nonpharmacological intervention and prevention of cardiovascular diseases. EXT can improve exercise ability and quality of life, and is related to reducing the risk of hospitalization and cardiovascular death . Howeverl health . Anotherl health . Neverthl health , which s6A) methylation is the most common internal mRNA alternation, which affects the metabolism of RNA throughout its life cycle  mice and give evidence of highly diverse m6A-modified patterns in both groups. It is displayed that abnormal m6A RNA alternations in HFpEF + EXT samples are possible for modulating myocardial apoptosis and myocardial energy metabolism, close to the cardiac aging phenotype. Our outcomes offer evidence that exercise-induced m6A alternation is closely related to the pathogenesis of HFpEF in the new model and will promote further research on the potential drug targets of m6A alternation in the treatment of HFpEF.The modification of mnd HFpEF . Neverthω-nitro-l-arginine methyl ester (l-NAME)  was added in the water, and the pH of the mixture was adjusted to 7.4. Mice in the control group were fed unlimited normal food and water. At the eighth week, mice began to do moderate-intensity running on a treadmill at a 10° gradient for 8 weeks . After the mice were euthanized, we collected hearts with 1.5 ml RNase-free centrifuge tubes, and stored them immediately at 4°C for 12 h. Samples were finally placed at −80°C for long-term storage to prevent RNA degradation.The contents of this study had been reviewed and approved by the ethics committee of Capital Institute of Pediatrics, whose approved license number was DWLL2019003. This study’s routines were all in accordance with corresponding ethical standards. C57BL/6J male mice were adopted for wild-type studies. All mice used in the trials were 8 weeks old  and keptAt 28 weeks of age, cardiac function by echocardiography in all mice in a scrambled order was assessed with a VEVO 3100 instrument, by dedicated personnel. Anesthesia mice were induced by continuous inhalation using 2% isoflurane +2 L/min air flow rate. The probe was placed in the center of the heart to observe the short axis of the heart. Observe the long axis of the heart during the slitting and adjust the heart rate to maintain 415–460 beats/min. Coefficients gathered included: left ventricular end-diastolic diameter, left ventricular end-systolic diameter, heart rate, left ventricular shortening, LVEF, peak doppler flow velocity in early mitral valve diastolic, peak doppler flow velocity across late mitral valve diastolic. All measurements were averages of at least three beats.6A .Our western blotting assays were performed strictly following the standard protocols of our laboratory. Firstly, mouse heart tissues were ground using the tissue crusher with the tissue lysate, and lysates were prepared with RIPA lysis buffer from Beyotime Biotechnology, where protease inhibitor were added. Blots were screened using specific antibodies: GAPDH , Mettl3 , FTO , ALKBH5 , Mettl14 , and mMouse hearts were fixed with freshly made 4% paraformaldehyde overnight, dehydrated by a dehydrator, embedded in paraffin, sliced by a paraffin slicer, 7 μm for each section, and stained with hematoxylin and eosin. Furthermore, we previously sectioned frozen heart tissue samples from mice at 7 μm slices, followed by incubation with wheat germ agglutinin (1:100) for 1 h in the dark, and finally washed three times with PBS. Cell nuclei were stained with DAPI  and washed thrice with PBS. Firstly, we dropped a water-soluble antifade mounting medium on the samples, then we covered the samples with glass cover slips, and finally we investigated the samples with a confocal microscope (Leica Sp8). Using the Image-Pro Plus 6.0 software to calculate the cross-sectional areas of cardiomyocytes. To detect fibrosis in the murine cardiac, we stained heart sections with the usual Masson’s trichrome procedure.cTNT promoter with an EGFP tag. The virus titer we used to do the experiment was approximately 1 × 1012 V g/ml. At 8 weeks, we injected 120 μL of AAV-EV and AAV-FTO viruses via tail vein for each mouse.Empty adeno-associated virus (AAV-EV) and adeno-associated virus expressing FTO (AAV-FTO) were manufactured by Hanbio Biotechnology Ltd. . The viruses were under the control of a heart-specific 6A antibody (1: 500 in PBS with 0.01% Tween-20) at 4°C for 12 h after blocking with 5% skim milk (in PBS with 0.01% Tween-20) for 1 h. We used horseradish peroxidase-conjugated anti-rabbit IgG secondary antibody to treat membranes for 1 h at room temperature, and rinsed 4 times for 10 min each with PBS followed by chemiluminescence development. To ensure that repeated dots contained the same amount of total RNA, methylene blue staining was utilized.We extracted total RNA from experimental mouse hearts with Trizol (Invitrogen). Total RNA was diluted to 1000 ng/μL and denaturated for 3 min at 95°C to break the RNA secondary structure into single strands. Immediately placed on ice to prevent the re-formation of RNA secondary structures. 2 μL RNA was directly dropped onto the Hybond-N+ membrane  and incubated at 80°C using an 80°C hybridizer for 1 h. Uncrosslinked RNA was washed with PBS with 0.01% Tween-20 for 5 min. We incubated membranes with anti-m6A antibody (Sigma-Aldrich: ABE572), protein A-magnetic beads (Invitrogen: 10002D), protein G-magnetic beads (Invitrogen: 10004D); mixed; and incubated overnight. The mixture was subjected to magnetic separation. The supernatant was collected and added with 5× precipitation buffer and RNA enzyme inhibitor, and the mixture was reacted at 4°C for 1–3 h, washed 2–3 times with low-salt precipitation buffer, and washed 2–3 times with high-salt buffer. The RNA was extracted from chloroform lysate to obtain the purified product. Ribosomal RNA removed the obtained products, and the SMART principle synthesized the first-strand cDNA. We amplified enriched library fragments by PCR and purified the library fragments by magnetic beads with DNA to obtain an RNA methylation m6A detection library. Finally, we employed the bioptic qsep100 analyzer to quality check the libraries. The NovaSeq high-throughput sequencing platform and PE150 sequencing mode were adopted for sequencing.After three mice in HFpEF and HFpEF + EXT groups were euthanized, the total RNA samples were extracted from heart tissue specimens and quantitatively analyzed applying the NanoDrop ND-1000 . Next, 20 μg total RNA was collected for interruption and purification to obtain the product after fragmentation treatment, and the Zymo RNA Clean and Concentrator-5 kit was used for the purification and recovery of the fragmented RNA. The resulting product was added with anti-m6A peaks. We used the clusterProfiler R package (v3.6.0) for GO and KEGG analyses. And we used the Guitar R package (v1.16.0) to visualize m6A-RNA-related genomic features. We subjected m6A peaks with p value <0.05 for the de novo motif analysis with homer (v4.10.4).Adapters and filters were trimed with the Cutadapt (v2.5) for sequences, and the Hisat2 aligner (v2.1.0) was adopted to align remaining reads to the human Ensemble genome GRCh38 (mouse Ensemble genome GRCm38). The exomePeak R package was adopted to identify differential mTotal RNA samples were taken from heart tissue specimens after three mice in the HFpEF and HFpEF + EXT groups were euthanized. Trimming adapters and filtering for sequences was done with Cutadapt (v2.5), and the Hisat2 aligner (v2.1.0) was adopted to align the remaining reads to the human Ensemble genome GRCh38 (mouse Ensemble genome GRCm38). The feature Counts were used to determine the reads that mapped the genome (v1.6.3). Differential gene expression analysis was made with the DESeq2 R-package.6A levels, as previously described (The Single-base elongation and ligation-based qPCR amplification method (SELECT) assay was used to monitor site-specific mescribed . Brieflyescribed . RNA freWe picked 10 genes with differentially methylated sites based on MeRIP-seq, and tested these genes with Real-time PCR using SYBR-green and standard amplification protocols. The ΔΔCt technique was used to calculate expression levels. After 3 days of treadmill adaptation, experimental mice were tested for fatigue. Mice ran upward (20°) on a treadmill at a warm-up rate of 5 m/min for 4 min, which was grown to 14 m/min for 2 min. Subsequently, the speed was grown by 2 m/min every 2 min until the mice were exhausted. Exhaustion was defined as the inability of the mouse to resume running within 10 s of direct contact with the electrical stimulus net. While measuring the running time, the running distance was calculated.In Situ Cell Death Detection Kit . The tissue section was fixed with fixation solution  for 20 min at +15°C to +25°C and washed for 30 min with PBS. The 2-minute incubation of slides was made in permeabilization solution  on ice. Next, slides were added with 50 μL TUNEL reaction mixture and incubated in a humidified atmosphere for 60 min at +37°C in the dark. Finally, samples were directly analyzed under a confocal microscope.TUNEL experiments were made with the Mouse hearts were fixed in 2.5% cacodylate buffer with glutaraldehyde, embedded with 2% agarose, fixed in 1% osmium tetroxide buffer, stained in 2% uranyl acetate, dehydrated with ethanol, embedded in resin, sliced by an ultrafine slicing machine, and stained with 2% uranyl acetate and lead citrate. Images for experimental use were obtained on the FEI Tecnai G2 Spirit electron microscope equipped with LaB6.p < 0.05 were of statistical significance.All data for all experiments are from three or more independent experimental manipulations and are shown as mean ± SEM. The GraphPad Prism 6.0 program was used to conduct the statistical analysis. Differences with two groups were used two tailed unpaired Student’s t-test analysis and three or more groups were analyzed with a one- or two-way analysis of variance. Differences with a significance level of n = 6, p = 0.0005; n = 6, p < 0.0001; n = 6, p < 0.0001; n = 6, ns; n = 6, p < 0.0001; n = 6, p < 0.0001; n = 6, p < 0.0001; In our study, HFpEF + EXT mice evidently ameliorated myocardial fibrosis and myocardial hypertrophy compared with the control  and had 6A level is an important feature of m6A modification. Therefore, the global m6A levels of mouse hearts in different groups were detected. Dot blot examination of cardiac samples from separate groups revealed that HFpEF + EXT mice had higher total m6A levels than HFpEF mice. (p = 0.0019). Next, we performed transcriptome-wide MeRIP-seq and mRNA-Seq to analyze the effect of EXT on the m6A methylation of HFpEF.The global m6A alternation patterns which were different from those of HFpEF mouse heart samples. We found 26401 m6A peaks, corresponding to 13832 gene transcripts in the HFpEF + EXT group by the model-based analysis through exomePeak v2.13.2   .6A peak in the HEpEF + EXT group was mainly increased in the 5′ untranslated region (5′ UTR) and decreased in the 3′ untranslated region (3′ UTR) compared with the HFpEF group. , stop codon, and 3′ UTR. Our results showed that the enrichment of m6A peak was mainly concentrated in the region near CDS, 3′ UTR, and stop codon vicinity regions  and phospholipid phosphatase-1 (Plpp1) genes, which were the sites with reduced and grown m6A levels, respectively.Differential methylation transcripts were identified and analyzed using GO and KEGG pathway analyses. By comparing the abundance of m6A methylation changes in HFpEF, we performed the GO analysis of differentially methylated RNAs. Compared with those in HFpEF mice, our results revealed that hypermethylated and hypomethylated RNAs in HFpEF + EXT mice were especially related to energy metabolism terms, e.g., regulation of carbohydrate catalog, regulation of glycolytic process and carbohydrate catabolic process, suggesting that differentially methylated RNAs were mainly concentrated in energy metabolism  indicated no difference in gene expression between samples . RNA-Seq6A, target genes were idenfieid by the conjoint analysis of m6A-seq and RNA-Seq data. Through bioinformatics analysis, we confirmed 308 hypermethylated m6A peaks in mRNA transcripts, of which 103 and 205 were significantly upregulated and downregulated respectively  to stain cardiac pathological sections. The representative fluorescence photos of TUNEL positive nuclei in the HFpEF heart after exercise are shown in In order to verify whether exercise can exert its cardioprotective function by maintaining the integrity and function of mitochondria, we measured and detected the mitochondrial structure of the heart of control group and HFpEF mice with or without exercise using an electron microscope. The ultrastructure of heart sections of control group and HFpEF mice with or without exercise was analyzed by an electron microscope to determine the integrity of mitochondria . In the p = 0.001; n = 6 per group; 6A modification, including Mettl3, Mettl14, and ALKBH5. Mettl3 was increased, and no significant difference was observed in Mettl4 and ALKBH5 . Furthermore, the overexpression of FTO efficiently augmented cardiomyocyte hypertrophy, as demonstrated by the increase in cross-sectional area of cardiomyocyte in HFpEF + EXT mouse hearts (p < 0.0001). Moreover, the overexpression of FTO accelerated apoptosis in myocytes and increased the proportion of TUNEL-positive cells in the myocardium (p = 0.0306). The overexpression of FTO significantly destroyed mitochondrial integrity and function by disorganized mitochondrial arrays and aggregates of swollen mitochondria with slight lysis of the cristae (n = 6, p = 0.0444 and p < 0.0001; Overexpression of FTO cancels out the benefits of exercise in HFpEF + EXT mice by promoting myocyte apoptosis, myocardial fibrosis and myocyte hypertrophy. At the age of 16 weeks, we injected the AAV vector encoding FTO into the HFpEF + EXT mice through the tail vein, and detected the expression of FTO after 8 weeks. After injection of AAV-FTO for 8 weeks, expression of myocardial FTO increased by approximately 7.5-fold in HFpEF + EXT mouse hearts . After 8 cristae . Further cristae . The ove6A writers Mettl3 and Mettl4; m6A eraser FTO; and reader YTHDF2 are elevated in patients with HFpEF (6A within transcriptome-wide in HFpEF + EXT samples is still mostly unknown. Our research showed that m6A RNA methylation is changed in HFpEF + EXT mice, indicating specific m6A alternation patterns on the transcriptome-wide and gene-specific scales that were distinct from those of in HFpEF mice. GO and KEGG analyses demonstrated that m6A RNA methylation differential genes between HFpEF + EXT and HFpEF mice are specifically associated with myocardial energy metabolism. HFpEF + EXT mice are observed with downregulated FTO compared with HFpEF mice, and the overexpression of FTO cancels out the benefits of exercise in HFpEF + EXT mice by promoting myocyte apoptosis, myocardial fibrosis and myocyte hypertrophy. FTO is a key m6A demethylase that may regulate cardiomyocyte function through catalyzing the demethylation of m6A on particular mRNAs, such as Fas, Jun, Tnnt2, Mef2a and Agtr1a.HFpEF is an aging disease characterized by cardiac hypertrophy, myocardial interstitial fibers, and cardiac diastolic dysfunction . Due to th HFpEF . In HFpEth HFpEF . In eldeth HFpEF . However6A profile of HFpEF, and these differential modification genes are closely associated with myocardial energy metabolism. The pattern of m6A modification in HFpEF + EXT mice was different from those of HFpEF mice on both transcriptome-wide and gene-specific scales. 22 409 m6A peaks in HFpEF + EXT mice were determined, indicating that m6A is a widespread post transcriptional RNA alternation in various heart disorders. In addition, we investigated that differentially methylated m6A peaks in HFpEF + EXT mice are primarily present in 5′UTR, which may promote mRNA translation. Importantly, in our research, we found that in HFpEF + EXT mice, the Fas m6A-modified site is primarily present in the 5′UTR. Therefore, we speculated that the differential methylation of RNAs in HFpEF + EXT might affect post-transcriptional translation levels and translation efficiency of RNA. We found that genes with differential m6A methylation and mRNA expression in HFpEF + EXT mice were mainly enriched in processes and pathways related to myocardial energy metabolism and apoptosis pathway, such as MAPK signaling pathway, insulin signaling pathway, sphingolipid metabolism, pyruvate metabolism, apoptosis, and regulation of intrinsic apoptotic signaling pathway processes (Exercise modifies the mrocesses . HFpEF irocesses . Recent rocesses . However6A methylation expression levels are upregulated, but the mRNA and protein expression levels are downregulated, indicating that m6A modification of Fas may influence mRNA degradation.Exercise improves mitochondrial function in HFpEF and affects the myocardial energy metabolism phenotype . Post-exFTO gene was identified in a type Ⅱ diabetes genome-wide relationship research (6A levels in RNA and impaired cardiomyocyte contractile performance, according to recent investigations (6A demethylase FTO improves cardiac function via controlling glucose absorption and glycolysis (FTO is downregulated in HFpEF + EXT samples, and overexpression of FTO cancels out the benefits of exercise in HFpEF + EXT mice by promoting myocyte apoptosis, myocardial fibrosis and myocyte hypertrophy. In 2007, the research . Furtherresearch . Howeverigations . Additioycolysis . In this6A modification in the pathogenesis of HFpEF. Secondly, although we found the target genes were modified by m6A, the mechanism of how the binding proteins control the target genes has not been explored. Next, how binding proteins affect target gene stability, translational efficiency, or degradation will be explored. Thirdly, although we demonstrated that overexpressing FTO in the heart worsened cardiac function, future research with cardiac specific knockout FTO mice and HFpEF models will investigate the actual mechanism by which FTO mediates HFpEF.The present study has limitations. Firstly, we did not find human heart samples to validate our experimental results. Therefore, we will next seek human heart samples to validate the important role of m6A RNA methylation was modified in HFpEF + EXT mice, who displayed distinct m6A alternation patterns at the transcriptome and gene levels compared to HFpEF mice. Differentially methylated genes were found to be linked with myocardial fibrosis, myocyte hypertrophy, myocardial apoptosis, and myocardial energy metabolism in GO and KEGG studies. Exercise improved mitochondrial function in HFpEF and myocardial energy metabolism phenotype to reverse the myocardial phenotypes. FTO was downregulated in HFpEF + EXT mice compared with that in HFpEF mice, and the overexpression of FTO cancels out the benefits of exercise in HFpEF + EXT mice by promoting myocyte apoptosis, myocardial fibrosis and myocyte hypertrophy. FTO is a key m6A demethylase that may regulate cardiomyocyte function through catalyzing the demethylation of m6A on particular mRNAs, such as Fas, Jun, Tnnt2, Mef2a and Agtr1a.Consequently, m


In this paper, we classify scientific articles in the domain of natural language processing (NLP) and machine learning (ML), as core subfields of artificial intelligence (AI), into whether (i) they extend the current state-of-the-art by the introduction of novel techniques which beat existing models or whether (ii) they mainly criticize the existing state-of-the-art, i.e. that it is deficient with respect to some property . We refer to contributions under (i) as having a ‘positive stance’ and contributions under (ii) as having a ‘negative stance’ (to related work). We annotate over 1.5 k papers from NLP and ML to train a SciBERT-based model to automatically predict the stance of a paper based on its title and abstract. We then analyse large-scale trends on over 41 k papers from the last approximately 35 years in NLP and ML, finding that papers have become substantially more positive over time, but negative papers also got more negative and we observe considerably more negative papers in recent years. Negative papers are also more influential in terms of citations they receive. In particular, deep learning has led to unprecedented performance gains on a large number of NLP and ML tasks, including machine translation , image cOn the other hand, there has seemingly also been a recent surge of papers highlighting limitations of (deep learning) approaches, including claims about models exploiting dataset biases , flawed Indeed, from a historic perspective, deep learning – formerly known under the name of ‘artificial neural networks’ – is a prime exemplar of a technology that has received very mixed assessments over time, ranging from initial hype to negative and positive appraisal in repeating cycles ,9.stance of a paper (with respect to its related work).1 We take a prototypical paper of negative stance to be one that concludes that related work is basically flawed, mistaken and potentially based on false assumptions , extends it and sets a new state-of-the-art. Positive papers could also be referred to as extension papers, which build upon and extend current knowledge.2 Motivated especially by (recent) observations of negative assessment of individual papers regarding the existing literature and its claims , we defiions cf. ; negativWe hold this task important in order to be able to analyse trends in science and its evolution , which c —we define the new task of stance detection for scientific literature; —we provide a human-annotated dataset of over 1.5 k scientific papers, labelled for their stance; —we provide a large-scale trend analysis on over 41 k papers from the NLP and ML community in the past approximately 35 years; —we address various trend questions including (i) whether negativity has recently increased, (ii) whether positive/negative papers are more influential, and (iii) whether positive/negative papers are more likely to be accepted.We point out that ‘negativity’, which is a focus of our work, plays a central role in various contexts: in the social sciences, signed social networks are networks in which agents have positive and negative relations to each other, potentially explaining phenomena such as long-term disagreement  <title> [SEP] <abstract>. We set the maximum token length to 300, which is sufficient for most papers and a good compromise for efficient memory usage. We add a fully connected layer with one output neuron and linear activation on top of the pooled output to obtain a single prediction for the stance of a paper. Since we define stance as a value between −1 and +1, we clip the prediction to the desired range.For all our experiments, we use SciBERT . We feedslanted triangular learning rate [−5, 2 × 10−5 or 5 × 10−5, a warm-up ratio of 0.06, and linear decay. We train for 2, 3 or 4 epochs and optimize using Adam [−6, β1 of 0.9, β2 of 0.999 and the mean squared error (MSE) as the loss function.The model is fine-tuned with the following hyperparameters: a batch size of 8 or 16, a ing rate  with a ming Adam  with an For our experiments in §6, we train 18 models by performing a full grid search over the specified hyperparameters and keep the best model based on the MSE on the dev set. We repeat this five times and calculate performance metrics (cf. §6.1) as the average score of those models.. 6cross-domain and in-domain performance and compare it with several baselines. Once the quality of the model is assured, we apply it large-scale to determine trends over time and venues in §7.In the following, we first verify the reliability of our stance detection model described in §5. To do so, we assess its . 6.1. 6.1.1R2)coefficient of determination , see above. We also calculate the F1 score for the labels individually. The natural macro F1 score samples papers according to the natural distribution of the data (i.e. mostly positive), as predicted by our best performing model. To do this, we draw the test sets randomly (from the existing test sets) according to the true distribution17 and then calculate the macro F1 score on the three labels. Since randomness is involved, we repeat this 1 k times and average the scores.We use various metrics to evaluate our models. The  the MSE . A model. 6.1.2POS: always predict a positive stance +1; ZERO: always predict a neutral stance 0; NEG: always predict a negative stance −1; AVG: always predict the average of manual annotations.We compare our models with simple baselines. . 6.1.3source data, we set the train–dev split ratio to 0.7 and 0.3, and we use the whole annotated data for the target data.Due to the exponential growth of science, our model is mostly trained on more recent data. However, we also want to make sure that we obtain reliable predictions, e.g. for past papers. As a consequence, we first evaluate our model in a cross-domain setting. In this, we train our model on papers from different time stamps or domains and evaluate on a respective out-of-domain test set. For the . 6.1.4NLP). We set the train–dev–test ratio to 0.6/0.1/0.3.We also perform in-domain tests where we train and test on the same domain of data . The ML dataset consists of 726 annotated abstracts . The NLP portion of the Hist dataset has 194 annotated samples ; the ML portion of the Hist dataset contains 52 papers .As mentioned in §4.2, we divided our human-annotated dataset into three groups. The human-annotated . 6.2Hist is lowest, which is not surprising as this dataset is smallest in size and assumedly has largest divergence to the modern datasets, due to temporal divergence [Hist portion of our data, which is why we use it for the analysis below.Results are shown in vergence . On aver. 6.3We further assess the quality of our best performing combined model by comparing the human annotated and predicted stance in the test set using the confusion matrix shown in In appendix we propose. One pattern for the misclassified papers is strong criticism followed by the release of a new dataset indicated in the last sentence. In some of the error cases, the gold standard is also incorrect or too extreme, e.g. too strongly negative.We further conduct a manual analysis on papers that have a large discrepancy between predicted and human stance. In these cases, we find that the model often predicts very negative papers as very positive. This may be related to positive papers being the majority class. In several cases, the model was also seemingly led astray by small positive contributions, especially in last sentences, and by superficial cues indicating positive contributions such as . 6.418 a text processing library, and (iv) a valence/arousal lexicon [We compare the stance predictions of our model with the predictions of several sentiment analysis tools to assess how similar our concept of ‘stance’ and the concept of ‘sentiment’ are. We test (i) a RoBERTa  model fo lexicon . We eval. 719 We further use Welch’s t-test [H-test [We analyse large-scale trends from the combined model’s predictions and smooth the graphs with Gaussian blurs.s t-test  and the  [H-test  to detecOur first questions connect to the recent paper of Bowman , who obs. 7.1a. It shows that most papers have a positive stance and that the more negative the stance gets, the fewer papers there are. Less than 4% of all papers have a negative stance and more than 3/4 of all papers have a stance of greater than or equal to 0.6.The histogram of stance values predicted by the model, aggregated over all venues and years, is visualized in figure 5. 7.2t-test and the H-test, show with a significance level of 0.01% that the distribution of predicted stance values differs between NLP and ML. Figure 5b shows the histogram of the predicted stance values, aggregated over all years, for both datasets. The distribution is similar to the overall trend, but ML has more papers with stance values between 0.5 and 0.8, whereas NLP has more papers with a stance of 1.0. Overall, 3.9% of all NLP papers and 2.3% of all ML papers exhibit a negative stance, which makes NLP more negative than ML.Both hypothesis tests, the . 7.3average stance value over time in NLP in the 1980s. When our ML dataset started in 1989 it was more positive than NLP . In the late 1990s, the stance of papers in NLP and ML came closer together, and in the 2000s  NLP took over and became slightly more positive than ML . The positiveness reached its peak around 2015 with an average stance above 0.80 for NLP. It then started to get more negative for NLP which means that the field of NLP got less positive recently. The ML domain, however, got more positive with a maximum stance of 0.81 in 2021.We analyse the development of the ML and NLP increased substantially from the mid-1980s to the 2020s. This means that papers got more positive on average, i.e. build upon another and report better and better results (i.e. new state-of-the-art performances), confirming the observation that ML and NLP have become ‘rapid discovery sciences’ [Overall, the average stance of both ciences’ .NLP comes from more positive/negative papers overall or from positive/negative papers getting less/more positive/negative. NLP and ML, respectively. There was then a continuous downward trend in negativity until the 2010s. In the recent years, negativity has increased for both domains, but considerably more sharply so for NLP, from 2% of all papers in 2015 to 4%.We further analyse whether the increase in positiveness from 1990 to 2010 and the decrease in positiveness in the most recent years for average stance value of all positive papers and the average stance value of all negative papers over time. The development of the average positive stance value is very similar to the development of the average stance value overall. By contrast, the average negative stance value has a decreasing trend for both datasets which means that negative papers have become more negative over time.p-value 0.01%, t-test); we note that it is also the only journal in our dataset besides TACL.Finally, we analyse trend curves for individual venues, visualized in . 7.4p-value 3%, t-test); very negative papers receive even more citations. By contrast, a paper with a positive stance receives less citations on average , but very positive papers with a stance value of more than 0.8 receive slightly more citations .NLP and ML, separately, which is shown in ML is more extreme than NLP in that negative papers with a stance of −0.5 or less receive even more citations  and positive papers with stance values from 0.1 to 0.7 even fewer .Similar results can be found by analysing the individual domains, et al. [Overall, this shows that papers of negative stance seem to attract more citations than papers of neutral or positive stance, indicating that they receive more attention and have larger effect on the community. Together with Catalini et al. , this meet al. .h-indices) tend to be over-represented in papers of negative stance; high-quality authors, in turn, may attract more citations , which could explain the link between citations and stance. To analyse the relationship between stance and citations in more depth, we performed a linear regression similar to Sienkiewicz & Altmann [length , (b) complexity  for papers of positive, negative, and neutral stance. We standardize all factors per venue and show the mean of each factor together with the 95% confidence interval in z-score, i.e. high complexity, tend to be more common in very negative papers. Very negative papers have higher arousal and neutral papers have lowest arousal. Author information separates positive, negative and neutral papers best. In contrast to our initial hypothesis expressed above, very negative papers have authors with lower (current) h-indices.However, we acknowledge that also other factors may be at work here, e.g. that stance may only be a confounding variable. For example, it could be that high-prestige authors , (c) se/arousal ) and (d). 7.521 and from ACL, EMNLP, NAACL, NeurIPS, AAAI, ICML and ICLR over the years 2007–2017 as collected by Kang et al. [t-test and the H-test show with a significance level of 0.01% that the distribution of predicted stance values differs between accepted and rejected papers. p-value 1%, t-test). The acceptance rate for papers with stance values between −0.6 and 0.8 is lower than the overall acceptance rate , but for very positive papers, the acceptance rate is slightly higher again .We use 5453 accepted and 14 974 rejected papers from the ICLR conferences in 2013 and 2017–2021, collected from OpenReview,t-test and the H-test show that the acceptance chances are different in the two time spans with a significance level of 0.01%. For the most recent years 2015–2021, the trend is similar to the overall trend, including that papers with a very positive stance of more than 0.8 are more likely to be accepted , but those papers do not have much higher chances. However, the acceptance rates in earlier years 2007–2014 were different. Papers with a very positive stance of 0.8 or more used to have better acceptance chances than very positive papers in 2015–2021 . This is consistent with the trend over time, which shows that fewer papers were negative in the years 2007–2014 than in 2015–2021, implying a bias: positive papers were more popular back then and therefore more positive papers were more likely to be accepted.We also calculate the acceptance rates for two separate time spans, 2007–2014 and 2015–2021, as shown in . 8We analysed stance in abstracts of scientific publications, where authors position themselves positively or negatively (with respect to related work). We annotated over 1.5 k abstracts from ML and NLP venues and trained a SciBERT model on a subset of the annotated abstracts, verifying that the model is of sufficiently high quality for the task. We then used this model to automatically predict the stance of a paper based on its title and abstract. We applied the model large-scale to a collection of 41 k scientific publications in the domain of NLP and ML from the years 1984 to 2021 to enable large-scale analysis.The analysis revealed that the majority of papers in the past and today have a positive stance, that the average stance has substantially increased over time, yielding support for the hypothesis that ML and NLP have become ‘rapid discovery sciences’, and that the ML domain is more positive than the NLP domain. Scientific publications used to have a more negative stance in the early days, then became very positive until they started to get more negative again recently. Overall, publications also got more extreme over time, which means that positive papers became more positive and papers with a negative stance more negative. We found (very) negative papers to be more influential than (mildly) positive ones in terms of citations they receive and more likely to be accepted to NLP/ML venues.We believe that NLP/ML turned more positive when the fields became more statistically solid, starting from the 1990s, and when authors started to build on the existing literature, with a peak of positivity in the mid-2010s, the beginning of the deep learning revolution. This hype has apparently also led to a recent increase in negativity, when papers started to challenge the validity of some of the claims , when isOur results also inform the recent work of Bowman , who war22 annotate word-level rationales for our sentence-level scores, assess the correlation between stance and socio-demographic factors  and analyse how negative papers may potentially transform a field.Future work should address other scientific disciplines beyond NLP and ML for a broader scientific trend analysis, examine the correlation between overall stance of a paper and individual (negative) citations in its related work sections,23We release our data, code and model on GitHub.


Age was a positive predictor for the composite endpoint of DCM-HFpEF patients . Conclusion: DCM-HFpEF is a disparate entity from DCM-HFrEF. Additional phenomic studies are needed to explore the molecular mechanisms and develop targeted therapies.Aims: Diabetic cardiomyopathy (DCM) is an ill-defined entity. This study aims to explore the clinical characteristics and prognosis of diabetic patients that disparately develop heart failure (HF) with preserved ejection fraction (HFpEF) other than HF with reduced ejection fraction (HFrEF). Patients and Methods: A total of 911 patients diagnosed with diabetes mellitus were identified in the ChiHFpEF cohort (NCT05278026). DCM was defined as diabetic patients diagnosed with HF, absent from flow obstructive coronary artery disease (CAD), uncontrolled refractory hypertension and hemodynamics significant heart valvular diseases, arrhythmia and congenital heart diseases. The primary endpoint was a composite of all-cause death and rehospitalization due to HF. Results: As compared to DCM-HFrEF patients, DCM-HFpEF patients had a longer duration of diabetes, were older and more noticeable in hypertension and non-obstructive CAD. After a median follow-up of 45.5 months, survival analysis showed that DCM-HFpEF patients had a better composite endpoint. Cox regression implicated that non-obstructive CAD was a negative (HR 0.101, 95% CI 0.028–0.373,  Diabetes mellitus (DM) is a metabolic disorder afflicting multiple systems and organs. It is considered as the seventh leading cause of death. Cardiovascular diseases (CVDs) are the main cause of deaths in DM patients. Even though advances in prevention and treatment of CVDs have greatly improved the prognosis of CVD patients, morbidity and mortality of CVD patients comorbid with DM remain high . AdditioHeart failure with preserved ejection fraction (HFpEF) and heart failure with reduced ejection fraction (HFrEF) are the two most common types of HF according to the updated classification of HF. However, clinical research of DCM-HFpEF versus DCM-HFrEF is lacking. It is deemed that in the early stage of DCM, left ventricular (LV) concentric remodeling, LV hypertrophy or diastolic dysfunction along with slightly decreased global longitudinal strain tend to be the primary abnormal manifestations . These aStudies comparing the clinical prognosis of HFrEF and HFpEF are controversial. Gregory et al. reported similar adjusted incidence rates of rehospitalization, but those with HFrEF had an increased risk of mortality at 30 days . Others The ChiHFpEF cohort is a prospective study of HFpEF in 2967 Han Chinese patients with documented CVDs in Nanjing First Hospital NCT05278026). After screening according to the inclusion and exclusion criteria, 911 patients from January 2014 to June 2022 with DM were included in the final analysis . Patient026. AfteHF was defined as the presence of at least one of the symptoms (dyspnea and fatigue) or signs  related to HF; NT pro-BNP ≥ 125 pg/mL with cardiac structural and/or functional dysfunction adjudicated by echocardiography. DCM was defined as diabetic patients with HF, who were free from flow obstructive CAD, uncontrolled refractory hypertension and hemodynamics significant heart valvular diseases, arrhythmia and congenital heart diseases. Taking HF with midrange ejection fraction  as a mild form of HFrEF, we classified LVEF < 50% as HFrEF in the current analysis, while HFpEF was defined as LVEF ≥ 50% in accordance with 2021 European Society of Cardiology guidelines . All diaThe study protocol and informed consent were approved by the institutional review committee of Nanjing First Hospital. Written informed consent for participation was obtained from all enrolled patients.The primary endpoint of this study was a composite of all-cause death and rehospitalization due to HF during the follow-up period. The secondary endpoints were (1) all-cause death and (2) rehospitalization due to HF.Patients’ fasting blood samples were collected rightly on the next day within 24 h after admission for further routine measurements of hematology, clinical chemistry, biomarkers of HF and myocardial injuries.The detailed collection and processing of echocardiographic data have been described before. In accordance with the internationally accepted guidelines, each patient included in the study underwent transthoracic echocardiography (TTE) with color flow Doppler and tissue Doppler by board-certified cardiologists trained in echocardiography. t-test or Mann–Whitney U test. Categorical variables were analyzed by the Chi-square test or Fisher’s exact test. All statistical analyses were performed with SPSS 25.0 . In this study, DCM-HFpEF was considered as a reference group. Therefore, the primary analyses were performed in two approaches: (1) the non-DCM versus the DCM-HFpEF group and (2) the DCM-HFrEF versus the DCM-HFpEF group. Continuous values were expressed as mean ± standard deviation (SD) for data of normal distribution, or medians and the 25th to 75th interquartile range (IQR) for data of non-normal distribution. Categorical variables were expressed as numbers and percentages. Differences in numerical variables were analyzed using the Student’s The cumulative hazard ratios of all-cause death, rehospitalization due to HF and composite endpoint in the DCM-HFpEF or DCM-HFrEF groups were estimated by Kaplan–Meier analysis and compared using the log-rank test. Cox proportional regression analyses were conducted to estimate the influence of risk factors on the outcomes of DCM-HFpEF or DCM-HFrEF. In the analysis of outcome predictors for HFrEF, we included age, gender, hypertension, non-obstructive CAD, duration of diabetes and heart rate (HR) at admission. In the analysis of the outcome predictors for HFpEF, age, hypertension, AF, pulmonary hypertension (PH), SBP and alcohol habit were included. The variables included in the Cox regression models for the primary endpoint  and secondary endpoints were chosen based on between-group univariate analysis. Statistical significance was a two-sided significance level of 0.05.The follow-up process was performed by outpatient visit or by telephone inquiry.No patient was involved in the design of this study.p < 0.001). The gender differences between the two groups were not obvious. However, DCM-HFpEF patients had a longer diabetes duration, a higher proportion of PH and were more likely to be treated with insulin and hemodialysis , and more likely to be female . Interestingly, the disease course of diabetes was longer in DCM-HFpEF patients than that of DCM-HFrEF patients, 480 (192–696) weeks versus 240 (48–480) weeks, p < 0.001, indicating DCM-HFpEF is unlikely to be the transitional state between non-DCM to DCM-HFrEF. Furthermore, DCM-HFpEF patients had a higher proportion of hypertension . They also had a slower HR and higher SBP at the time of admission. In addition, there was a higher proportion of non-obstructive CAD  and a lower proportion of AF  in DCM-HFpEF versus DCM-HFrEF patients. Compared with DCM-HFrEF, DCM-HFpEF patients were older  versus 558 (326–1152), p < 0.001 and LVDs: 54 mm versus 32 mm, p < 0.001. Consistently, LA enlargement was more salient in DCM-HFpEF versus DCM-HFrEF, 49 mm versus 42 mm, p < 0.001. This was in accordance with a higher incidence of atrial fibrillation in DCM-HFrEF patients. Systolic function was largely preserved in DCM-HFpEF patients in comparison to DCM-HFrEF patients, 33% versus 64%, p < 0.001. The main differences in echocardiographic parameters are visualized in Compared with DCM-HFrEF, LV enlargement was not significant in DCM-HFpEF patients, showing as LVDd: 64 mm versus 48 mm, p = 0.2074). A total of 25 (5.3%) rehospitalizations due to HF occurred in DCM-HFpEF patients and 9 (2.9%) occurred in non-DCM groups . The composite endpoints occurred in 41 (8.8%) DCM-HFpEF patients versus 10 (3.2%) non-DCM patients . After a median follow-up of 45.5 months (interquartile range 23.2–62.3), 122 patients (13.4%) reached the clinical endpoints presented in p = 0.0061), compared with DCM-HFpEF patients. A total of 66 (49.6%) rehospitalizations due to HF occurred in DCM-HFrEF patients , compared with DCM-HFpEF patients. The composite endpoints occurred in 71 (53.4%) DCM-HFrEF patients  as compared to DCM-HFpEF patients. In patients of DCM-HFrEF, 16 (12.0%) deaths occurred  and rehospitalization due to HF  of DCM-HFrEF patients (p = 0.017) (p = 0.018) and death  of DCM-HFpEF patients  . Age waspatients .In this study, we conducted a prospective, single-center cohort study of DCM according to the different phenotypes of HFrEF versus HFpEF, in order to explore the potential clinical features related to the heterogeneity of DCM. The main findings are (1) compared with DCM-HFrEF patients, DCM-HFpEF patients were older and more likely to be female, suggesting the disparate demographic characteristics of DCM-HFrEF versus DCM-HFpEF patients; (2) DCM-HFpEF patients had longer disease courses of diabetes versus non-DCM and DCM-HFrEF patients, indicating DCM-HFpEF is not a transitional state between non-DCM and DCM-HFrEF; (3) All-cause death and rehospitalization due to HF were more frequent in DCM-HFrEF versus DCM-HFpEF patients; (4) age was a positive predictor for a composite endpoint of DCM-HFpEF versus non-DCM patients; (5) non-obstructive CAD was a negative predictor for both a composite endpoint and rehospitalization due to HF of DCM-HFrEF patients. Hypertension was also a negative predictor for death of DCM-HFrEF patients; (6) age was a positive predictor for both a composite endpoint and death.The classification of HF according to left ventricular ejection fraction (LVEF) was recommended recently . HFpEF iDCM is primarily caused by detrimental mechanisms in the myocardium by elevated serum glucose, hyperinsulinemia and glycosylated proteins, leading to capillary damage, myocardial hypertrophy with mitochondrial dysfunction and myocardial fibrosis ,16. It iIn our study, the prevalence of AF, PH, use of insulin and hemodialysis history were higher in DCM-HFpEF and DCM-HFrEF patients as compared to non-DCM patients. Renal dysfunction has been confirmed to accelerate the progression of HF and to increase the risk of hospitalization, rehospitalization, intensive condition and death . In thisMoreover, significant negative cardiac remodeling was noticed in the DCM-HFrEF group as compared to the DCM-HFpEF group. We previously reported NT-proBNP was not associated with diastolic dysfunction in HFpEF patients . HoweverIt remains unknown whether the comorbidities and traditional risk factors such as hypertension, CAD and aging are merely concurrent or contribute to the disparate development of DCM-HFpEF versus DCM-HFrEF phenotypes. Mechanistically, metabolic inflammation underlying hyperglycemia and insulin resistance could lead to a direct detrimental effect on cardiomyocytes and cause impaired calcium homeostasis, oxidative stress, mitochondrial dysfunction, apoptosis, myocardial fibrosis and myocardial remodeling ,26,27. RIt is of interest that there is a much lower incidence of hypertension and non-obstructive CAD in DCM-HFrEF versus DCM-HFpEF patients in this study. Furthermore, non-obstructive CAD was a protective factor as to composite endpoints for DCM-HFrEF, largely owing to reducing rehospitalization due to HF. Although it needs to be confirmed in a large clinical cohort of DCM, we showed for the first time the lower incidence of hypertension and non-obstructive CAD in DCM-HFrEF versus DCM-HFpEF patients. Our observation of different incidences of CAD and hypertension might shed insight into the mechanisms of differential development of DCM-HFrEF versus DCM-HFpEF. HFpEF was mainly caused by aging, endothelial and microcirculatory dysfunction, as well as low-grade inflammation but not a severe hit of myocardial ischemia. In our study, we depicted the clinical facts such as non-obstructive CAD and hypertension that correlate to DCM-HFpEF and DCM-HFrEF. We did not explore the genetic susceptibilities of DCM-HFpEF and DCM-HFrEF which may play an important part in explaining why non-obstructive CAD and hypertension have a major role in HFrEF but not in HFpEF.In a study of HFpEF phenogroups, the phenogroup with a high incident of DM had the comorbidities of obesity and a high level of renin. Renin and the prorenin receptor participate in blood pressure regulation, adipogenesis, glucose and insulin resistance and lipid homeostasis ,30,31. PThere are some limitations of the study. To begin with, the study findings should be interpreted with caution due to the small sample size and an unclear risk of selection bias. Next, the samples were not evenly grouped. Meanwhile, residual and uncontrolled confounding were unavoidable in this study. Therefore, the conclusion drawn in this study needs to be confirmed by multi-center, large-scale studies.Factors related to macrovascular atherosclerosis, hypertension and gender might drive the development of DCM-HFrEF; age might be a predictor of DCM-HFpEF. As to their clinical outcome, DCM-HFrEF patients had the highest mortality, followed by DCM-HFpEF patients.


Micro/Nanofluidic and lab-on-a-chip devices have been increasingly used in biomedical research . BecauseGiven the multidisciplinary of this Special Issue (SI), papers on different subjects were published making a total of 14 contributions, 10 original research papers, and 4 review papers. The review paper of Ko et al. , a thoroIn the following, a brief summary of the research papers published in this SI will be presented, with organs-on-a-chip, microfluidic devices for detection, and device optimization having been identified as the main topics. Some researchers focused on the development of advanced microfluidic devices which are devised to reconstruct the tissue architecture of organs biochemically and biophysically. For instance, Kuriu and co-workers  worked oSchistosoma mansoni DNA. Despite additional tests being needed, this technique requires less hands-on time and does not need an additional control reaction like current methods do. A different detection system was proposed by Saha and co-workers [Other authors have focused on the use of microfluidic devices for detection purposes. Li et al.  designed-workers , but in The optimization of microfluidic devices has also been explored by some authors. Grigorev et al.  investigTo conclude, we would like to acknowledge all the authors for their contribution to the success of this SI as well as the reviewers whose feedback helped to improve the quality of the published papers. We would also like to recognize Ms. Min Su from the Micromachines publishing office for her endless assistance and help in disseminating this SI.