Aortic aneurysm and dissection (AAD) are a group of insidious and lethal cardiovascular diseases that characterized by seriously threatening the life and health of people, but lack effective nonsurgical interventions. Alterations in metabolites are increasingly recognized as universal features of AAD because metabolic abnormalities have been identified not only in arterial tissue but also in blood and vascular cells from both patients and animal models with this disease. Over the past few decades, studies have further supported this notion by linking AAD to various types of metabolites such as those derived from gut microbiota or involved in TCA cycle or lipid metabolism. Many of these altered metabolites may contribute to the pathogenesis of AAD. This review aims to illustrate the close association between body metabolism and the occurrence and development of AAD, as well as summarize the significance of metabolites correlated with the pathological process of AAD. This provides valuable insight for developing new therapeutic agents for AAD. Therefore, we present a brief overview of metabolism in AAD biology, including signaling pathways involved in these processes and current clinical studies targeting AAD metabolisms. It is necessary to understand the metabolic mechanisms underlying AAD to provides significant knowledge for AAD diagnosis and new therapeutics for treatment. Aortic disease is a large group of vascular diseases with high mortality rates, in addition to coronary heart disease (CHD) and peripheral artery diseaseAlthough drug therapy, such as β-adrenergic receptor antagonists and angiotensin receptor type I antagonists, can alleviate the enlargement of aneurysms, no effective medication has been found to prevent the occurrence and progression development of AAD in clinical practiceTo date, numerous studies have illustrated that metabolites that are involved in vascular injury and repair by interacting with AAD risk factors are the most prominent factors driving AAD. In this context, the potential for reducing AAD mortality and improving prognosis through the use of metabolic therapy is great. Although an increasing number of metabolomics studies, an emerging analysis technique, have linked AAD to different low-molecular-weight-metabolites (LMWMs) Metabolomics is a burgeoning field in the life sciences, the emergence of which is inevitable for life science researchLMWMs in the metabolome contain both endogenous sources from genetics and exogenous sources which mainly including nutrition, drugs, and environment. Endogenous metabolites are highly conserved and represent the collections of similar LMWMs, albeit at different concentrations, in different speciesRecent studies have shown that metabolites do not only maintain normal physiological functions of living organisms, they can serve as regulators of pathological processes in diseases such as cardiovascular disease (CVD)The term 'microbiota', a hidden endocrine organ, contributes to 150 times more genetic information than the entire human genome, which could date back to early 1900sIn a healthy state, the human intestine barrier serves as a defense to protect against the translocation of microorganisms or microbial products into the bloodstream. It has been maintained by the tight junctions of epithelial cells, mucus production, mucosal immunity, and the gut-vascular barrierConsequently, the constituent species of the intestinal flora and their relative abundance are factors that may modify susceptibility to the development of AAD. Although there are few studies clarifying the specific underlying mechanism of intestinal flora changes affecting AAD, which cannot fully clarify the crucial cause-effect relationships, it still suggests the potential of gut microbiota as therapeutic applications target for AAD.Diet plays a crucial role in shaping the compositional and functional characteristics of gut microbiota in both humans and animals. Various studies have shown that the absorption of nutrients from food is influenced in part by the composition of an individual's gut microbiota, which in turn is shaped by foodThe gut microbiota participate in the uptake of nutrition and energy, and producing multifarious metabolites to maintain physiological function. There are a number of microbiota-derived metabolites, including short-chain fatty acid (SCFA), bile acid (BA), TMAO, amino acid derivatives, vitamins, lipopolysaccharide (LPS), antioxidants, and hormones in the gutThe intestinal flora produces trimethylamine (TMA) from metabolism of choline, lecithin and L-carnitine by the fermentation of dairy products such as eggs, fish and meat. Then TMA is converted to TMAO via the flavin-containing monooxygenase 3 (FMO3) enzyme in the liverGut microbiota participates in bile acid (BA) metabolism, generating associated unconjugated and secondary BAsLPS is an endotoxin originating from Gram-negative bacteria, especially Escherichia generaSCFAs are produced by intestinal flora and play a role in the fermentation of resistant dietary carbohydrates, which can then enter the circulation and regulate host physiological processes-/- mice showed that polyamines and asymmetric dimethylarginine (ADMA) were elevated in mouse aortic tissue. Furthermore, decreases in glutamine, glycine, taurine, and carnitine concentrations and increases in branched-chain amino acid (BCAA) concentrations were observed in the blood of AA patientsThe metabolism of amino acids has been established to be critical for AAD. Raman microspectroscopy analysis of aortic tissue from mice and humans revealed that alterations in amino acid metabolites, including phenylalanine, tyrosine, tryptophan, cysteine, aspartate, and glutamate, occur in aortic tissue from ascending thoracic aortic aneurysms (aTAA)Homocysteine (Hcy) is one of the most investigated amino acids in AAD. It is known that the only origin of Hcy in the human body is the dietary protein methionine (Met), one of the eight essential amino acidsHyperhomocysteinemia (HHcy), elevated concentration of Hcy in the blood, has been confirmed the role in AAD. We identified 7 clinical studies aimed at shedding light on the association between HHcy and patients with AAD compared to normal participants Table . We iden-/- mice supplemented with excess Hcy in drinking water exaggerated inflammation due to the activation of nicotinamide adenine dinucleotide phosphate (NADPH) oxidase 4, which increased the incidence of AAAConsistent with these studies, studies conducted in animal models suggested that Hcy act as a risk factor to worsen the prognosis of AAD in experimental models. Primarily, excessive Met which is the only source of Hcy exacerbates the development of AAA in RatsArginine a semi-essential amino acid that serves as a substrate for nitric oxide synthase (NOS) and arginase (ARG), the prominent NOS isoform is endothelial-type nitric oxide synthase (eNOS) in aortic vessels, which converts ARG to nitric oxide (NO)The main product of classically activated macrophages (M1) is NO, which requires the infusion of arginine and oxygen. The products of the inducible nitric oxide synthase (iNOS) reaction are citrulline and NO. Nevertheless, alternatively activated macrophages (M2) do not produce NO but instead utilize arginase 1 (ARG1) to hydrolyze large amounts of imported arginineArginine is synthetic from citrulline via the cytosolic enzymes argininosuccinate synthase (ASS) and argininosuccinate lyase (ASL). Citrulline can be produced from a variety of sources: (1) from arginine via NOS catalysis, (2) Substitution from ornithine by decomposition of proline or glutamine/glutamic acid, (3) from ADMA, it is catalyzed by dimethylarginine dimethylamine hydrolase (DDAH)The recoupling of eNOS alleviated expansion of aortic roots and abdominal aortas, then animal models of endothelial dysfunction and eNOS uncoupling increased the rate of AAA formation and increased eNOS augmented the risk of aortic dissection. The increase of eNOS-derived ROS and the decrease of NO production may be the main causes of endothelial dysfunction in AADTryptophan (Trp) is another essential amino acid that can be obtained from dietary consumption, which has been proven to influence AAD. Trp is essential for protein synthesis and therefore for normal cellular homeostasis. There are two pathways that process Trp into other metabolites, including the 5-hydroxytryptamine (5-HT) pathway and the kynurenine (Kyn) pathway. Hepatic tryptophan-2,3-dioxygenase (TDO) maintains physiological concentrations of Trp and Kyn at modulated levels via the kynurenine pathway (KP)-/- mice and low density lipoprotein receptor-deficient mice fed with high fat dietDuring tryptophan catabolism, tryptophan is further metabolized to L-kynurenine (Kyn) via indoleamine 2,3-dioxygenases 1 and 2 (IDO1 and IDO2) and TDO to produce N-formylkynurenine. Kyn is further catabolized to the active metabolites 3-hydroxykynurenine (3HK), 3-hydroxyaminobromic acid (3-HAA), kynurenine acid (KA), quinolinic acid (QA), and the essential pyridine nucleotide end product nicotinamide adenine dinucleotide+ (NAD+)Taurine, mainly derived from seafood such as shellfish, is a non-protein amino acid with a distinctive sulfonic acid compositionResearch has identified a correlation between taurine and vascular dysfunction. Vascular dysfunction was attenuated in type 2 diabetes patients after taurine supplementation in a clinical study, indicating that taurine therapy reverses endothelial dysfunction, which is the central pathway for aortic aneurysms and aortic dissection. Supplementation with taurine is proved to alleviate myeloperoxidase-mediated oxidative stress, leading to the mitigation of AAA formation in miceFatty acids are the major components of dietary fat, which is composed mainly of saturated fatty acids (SFA) and polyunsaturated fatty acids (PUFA)PUFAs are further divided into the omega-6 PUFAs and the omega-3 PUFAs according to the location and function of the double bond. Linoleic acid and arachidonic acid (AA) belong to the ω-6 PUFAs, while linolenic acid, docosahexaenoic acid (DHA) and eicosapentaenoic acid (EPA) belong to the ω-3 PUFAsAAD are chronic inflammatory diseases, which are initiated by the accumulated macrophages, release of inflammatory factors, and oxidative stress. In consequence, pro-inflammatory AA seems to play an important role in AAD. The pro-inflammatory response observed in AAD could be due to the lack of anti-inflammatory AA resultsAA is one of the most abundant PUFAs in the human body and serves as a component of cell membrane phospholipid bilayers, an inflammatory mediator and a vasodilator or constrictor. Endogenous AA is mainly involved in the production of various bioactive derivatives of arachidonic acid, which can be metabolized by three enzyme systems, COX, cytochrome P450 (CYP) enzymes and lipoxygenase (LOX)COX-2 can catalyse converting of AA to prostaglandins (PGs), modulating the expression of pro-inflammatory chemokines to regulate cell functions. COXs and PGs are considerable inflammatory mediators which are associated with AADInterest in omega-3 (ω-3) PUFAs has escalated in recent years because of their various roles in health promotion and disease risk reduction. ω-3 PUFAs include EPA, DHA, α-linolenic acid (ALA), stearidonic acid (SDA), and docosapentaenoic acid (DPA). ω-3 PUFAs with health benefits ascribed mainly to EPA and DHA, which are primarily obtained from the diet such as the body lipids of fatty fish and the liver of white lean fish-/- mice with a diet high in n-3 PUFA content protected the mice against pro-inflammatory and oxidative stress responses following short-term infusion with Ang IIA randomized controlled trial demonstrated that erythrocytes from patients with AAA exhibit an alternating FA profile characterized by an anomalous ratio of n-6 FAs with inflammatory state, reflecting the effects of systemic inflammation and oxidative stress. However, the inflammatory parameters and n-6 fatty acid status were significantly improved afterω-3 PUFA supplementation, indicating that dietary fatty acids conduct a potential therapeutic intervention for AAAEPA plays a protective role in abdominal aortic aneurysm, animal models and macrophages by multiple pathways. In the patients with surgical repair of AAA, EPA/AA ratio was lower than those in healthy Japanese subject, which was negatively correlated with the maximum AAA diameter. Low serum EPA levels and EPA/AA ratio were associated with the size and growth rate of AAANitro-fatty acids (NO2-FAs), electrophilic lipid molecules, are endogenously synthesized via the reaction of unsaturated fatty acids (USFAs) with NO or nitrite-derived nitrogen dioxide free radicalsIt is well established that oxidative stress and inflammation are the two main mechanisms leading to AAD, and the modification of cell membrane phospholipids has been proved in AADCellular metabolism mainly involves the utilization of carbohydrates, proteins, and fats for energy synthesis, and when metabolic processes are used to produce energy, the final step is oxidative phosphorylationR/R aortas compared with Fibulin-4+/+, whereas Complex V had no significant change. In Fibulin-4R/R mice, the extracellular matrix protein Fibulin-4 was significantly reduced, which results in decreased blood ketone and hepatic fatty acid levels, indicating dysregulated metabolism to promote thoracic aortic aneurysm formation-/- mice. Notably, knockout of p53 promoted Ang II-induced AAA via inhibiting the expression of cytochrome C oxidase assembly protein 2 (SCO2) which is a component of the complex IV.There has been evidence that ETC is defective in AAD. A microarray study identified 47 significantly differentially expressed genes in AAA patients compared with controls. GO functional analysis revealed that these genes play important roles in electron transport chainThe TCA cycle, also known as the citric acid cycle or the Krebs cycle, is a series of reactions in a closed loop that forms a metabolic engine within cells Figure . While tHypertension is an important risk factor for aortic aneurysm and aortic dissection. When the body is under the level of hypertension for a long time, it will lead to the change of blood flow of the patient, which in turn can cause the change of the ratio of elastic fibers to collagen and morphology of the aortic wall, increase the shear stress of the blood vessel, increase the stiffness of the blood vessel wall, and eventually cause AADIt is well established that in addition to the AAD-associated metabolites we have described above, there are numerous additional metabolites that play a crucial role in the pathogenesis of AAD.Sex is a prominent risk factor for AAA, and Ang II induces AAA formation to a greater degree in male than in female mice. It has been found that a cytochrome P450 1B1generated metabolite of testosterone, 6βhydroxytestosterone (6βOHT), contributed to Ang II-induced AAA development in male mice, which provides a bridge between sex and metabolites in AAD pathogenesisUric acid (UA) is a purine derivative and a well-established CVD risk marker. Serum uric acid is associated with AD in Chinese menPolyphenols are naturally occurring compounds found largely in fruits, vegetables, cereals, and beverages. Numerous polyphenols including resveratrol and curcumin have been shown to be associated with AAD. Resveratrol attenuated AD by increasing endothelial barrier function through the SIRT1 pathwayPrevious studies have shown that the metabolic profile of AAD patients is significantly different from that of healthy participants or patients with other diseases. Therefore, we summarized the metabolic profile of AAD patients to contribute to provide potential targets for AAD clinical treatment Table . OverallIn addition to the role of metabolites as biomarkers in the diagnosis of AAD, the metabolic changes of AAD suggest a novel metabolic pathway for the treatment of AAD. Targeting metabolism has been proposed in the treatment of AAD and shows some achievement. Chronic inflammation of the aorta has been reported to underlie AAA pathology, with infiltrating leukocytes producing cytokines and proteases that ultimately lead to deterioration of the vessel wallBased on previous studies on the relationship between lipid metabolism and AAA, it is possible to develop drugs for the prevention and treatment of AAA from the perspective of regulating lipid metabolism. High density lipoprotein (HDL) enhanced the level of annexin A1 (Anxa1) to inhibit endothelial cell inflammation, while Anxa1 has been shown to restrain the occurrence of acute aortic dissection by inhibiting the phenotypic transformation of vascular smooth muscle cells, suggesting that there may be drug targets for the treatment of AAD that target the inflammation response to HDLTherefore, the risk of AAD formation can be assessed clinically by detecting the levels of corresponding metabolites in plasma or serum. For patients with a high risk of AAD, such as the elderly and hypertension, dietary advice can also be provided to achieve the purpose of early prevention. In addition, although the research on the recognized therapeutic drugs for AAD is still a wastland to be reclaimed, the metabolism-related experimental animal studies have provided numerous new clues for potential drug targets.Increasing evidence indicates a link between the metabolomics and AAD incidence. Multiple metabolites interact with the host through a variety of pathways and play an indispensable role in the occurrence and development of diseases. Altered metabolite composition or different metabolite concentrations might be responsible for AAD risk and its associated pathological changes. Thus, novel therapeutic targets for preventing and treating AAD have been developed harnessing the potential use of metabolites.Metabolic abnormalities are found in human AAD and animal models of AAD. The metabolism of gut microbiota-derived secondary metabolites is characterized by increased circulating levels of TMAO and LPS, decreased levels of bile acids including UDCA and TUDCA, and decreased short-chain fatty acids such as butyrate. The above characteristics indicate that the differential expression of secondary metabolites caused by intestinal flora disorder are engaged in the pathological process of AAD, which makes it possible to target AAD therapy against intestinal flora. In addition, the differentially expressed primary metabolites were observed to be mainly involved in amino acids, phosphatidylcholines, galactose, ceramide, and polyunsaturated FAs metabolic pathways in AAD,The existing research suggests that efforts are being made to advance the potential application of metabolites in CVD. However, limitations exist in studying metabolites in AAD. First of all, gut microbiota plays a crucial role in regulating gut microbiota-derived secondary metabolites. Identifying specific bacteria present in AAD can help clarify the contribution of gut microbiota-derived metabolites to disease progression. Nevertheless, research on the gut microbiota profile of AAD remains limited. Secondly, while most current studies focus on exploring the metabolic profile of AAD, including differential metabolites and changes in metabolite concentrations, they lack elucidation of the mechanism by which metabolites cause AAD. Future studies should therefore prioritize investigating signaling pathways mediated by metabolites and their downstream functional consequences. Thirdly, there is an urgent need for personalized metabolic profiling. The analysis of spatial metabolic biomarkers in individual AAD patients, as opposed to other cardiovascular diseases, may significantly contribute to this endeavor. Fourthly, the clinical application of metabolites in the treatment of AAD is hindered by a lack of safe and effective conversion pathways. It seems that inhibitors targeting metabolite production and dietary supplementation are effective ways to implement, but large-scale population validation is lacking. Finally, most studies focus on the effects of various metabolites on AA, and there are few studies in this field of AD, and we speculated that it may be caused by the characteristics of acute onset and elevated mortality of AD. However, it is of great significance to strengthen the study of metabolomics of AD to improve the metabolic profile of aortic diseases.The focus of future studies is to figure out how metabolites lead to AAD at the mechanistic level. This involves characterizing metabolites, microbes, and microbial-host interactions within complex microbiota ecosystems. Recognizing the role of gut microbiota and metabolites in both health maintenance and disease susceptibility offers a novel perspective that broadens our approach to AAD research and therapeutic interventions. Additionally, it is important to investigate potential genetic factors that may influence susceptibility to disease in combination with specific nutrient depletion/excess and the functional capacity of the microbiota. Finally, prospective cohort studies can bring metabolite research and dietary interventions into clinical practice. By measuring metabolic profiles in blood or urine to guide appropriate dietary recommendations and deliver targeted interventions, more translational findings will be achieved. These interventions may take various forms in the future, including dietary advice, inhibitors or agonists of metabolites, nonlethal microbial inhibitors or probiotics. Another alternative approach is to modulate gut microbiota to achieve changes in gut microbiota-derived metabolites, which is an active area of research.Based on the above limitations and perspectives, it is beneficial to address the question of how metabolic abnormalities develop in AAD in the future, thus determining which pathways are ideal therapeutic targets. Fully understanding metabolic abnormalities of AAD over the course of the disease is the essential next step for developing therapies.


With the proliferation of digital mental health interventions (DMHIs) guided by relational agents, little is known about the behavioral, cognitive, and affective engagement components associated with symptom improvement over time. Obtaining a better understanding could lend clues about recommended use for particular subgroups of the population, the potency of different intervention components, and the mechanisms underlying the intervention’s success.This exploratory study applied clustering techniques to a range of engagement indicators, which were mapped to the intervention’s active components and the connect, attend, participate, and enact (CAPE) model, to examine the prevalence and characterization of each identified cluster among users of a relational agent-guided DMHI.We invited adults aged 18 years or older who were interested in using digital support to help with mood management or stress reduction through social media to participate in an 8-week DMHI guided by a natural language processing–supported relational agent, Woebot. Users completed assessments of affective and cognitive engagement, working alliance as measured by goal and task working alliance subscale scores, and enactment . The app passively collected data on behavioral engagement . We applied agglomerative hierarchical clustering analysis to the engagement indicators to identify the number of clusters that provided the best fit to the data collected, characterized the clusters, and then examined associations with baseline demographic and clinical characteristics as well as mental health outcomes at week 8.P<.001) and greater declines in depressive symptoms (P=.01) and stress (P=.01) from baseline to week 8 compared to typical utilizers. Significant differences across clusters were found by age, gender identity, race and ethnicity, sexual orientation, education, and insurance coverage. The main analytic findings remained robust in sensitivity analyses.Exploratory analyses (n=202) supported 3 clusters: (1) “typical utilizers” , who had intermediate levels of behavioral engagement; (2) “early utilizers” , who had the nominally highest levels of behavioral engagement in week 1; and (3) “efficient engagers” , who had significantly higher levels of affective and cognitive engagement but the lowest level of behavioral engagement. With respect to mental health baseline and outcome measures, efficient engagers had significantly higher levels of baseline resilience (There were 3 distinct engagement clusters found, each with distinct baseline demographic and clinical traits and mental health outcomes. Additional research is needed to inform fine-grained recommendations regarding optimal engagement and to determine the best sequence of particular intervention components with known potency. The findings represent an important first step in disentangling the complex interplay between different affective, cognitive, and behavioral engagement indicators and outcomes associated with use of a DMHI incorporating a natural language processing–supported relational agent.ClinicalTrials.gov NCT05672745; https://classic.clinicaltrials.gov/ct2/show/NCT05672745 Mental health problems continue to increase in prevalence despite the availability of effective treatment . In 2019Amplified by pandemic-related widespread lockdowns and other restrictions, the undiminished need for mental health services has trended toward increased uptake of web-based psychotherapeutic interventions , also knDespite their documented benefits, real-world engagement   with DMHResearchers therefore continue to explore the impact of engagement on mental health outcomes with a view to determining optimal use recommendations . Several studies have used machine learning  or clustThe association between engagement and outcomes, and thus optimal use recommendations, also might vary by demographic and clinical characteristics, given differences found in engagement in general. For example, women tend to have higher levels of engagement with DMHIs ,25 as doIn general, models of user engagement patterns are only as good as the variables that comprise them. Thus, researchers continue to consider new theoretical models of engagement, as well as a wider array of both passively and actively collected objective and subjective engagement indicators ,27 that Using an integrated approach focused on both objective utilization and subjective engagement metrics might be a particularly important application for DMHIs that incorporate working alliance  into theThe creation of relational agents, otherwise known as conversational agents or chatbots, has provided an innovative mental health care pathway within the class of DMHIs to extend the efficacy of face-to-face therapies. Relational agents typically guide the user through different components of the intervention, including psychoeducation, CBT-based exercises, mood tracking, journaling, and real-time conversational interactions with the agent itself. Relational agents have shown promising preliminary efficacy findings ,35 and eFew studies have investigated the engagement patterns of relational agents, much less how patterns of use of specific components of the intervention relate to favorable outcomes . This isAs a first step toward understanding engagement patterns, demographic or clinical characteristics suggesting distinctive phenotypes, and associated mental health outcomes, we designed an exploratory study using data from a previously reported single-arm trial . We examIdentify the number, prevalence, and characteristics of clusters derived from the analysis of app-measured or self-reported behavioral, cognitive, and affective engagement metrics based on interactions with the intervention components and responses to items that map onto the CAPE theoretical model.Examine differences in baseline demographics and clinical characteristics of users across engagement clusters.Examine whether changes in mental health outcomes such as depressive symptoms, anxiety symptoms, stress, and resilience after 8 weeks of intervention use differ by engagement cluster.Participants in an 8-week, exploratory, single-armed trial of the WB-LIFE DMHI provided data for this study at baseline, and 3 days, 4 weeks, and 8 weeks  between May 11 and July 20, 2022.Recruitment through social media platform screens yielded 256 enrolled adult participants aged 18 years or older who responded to a social media advertisement to participate in a mental wellness study testing a digital tool used for emotional support and mood management. Inclusion criteria required residence in the United States, owning a smartphone, and having English literacy, while exclusion criteria prohibited those who had used Woebot previously and those with lifetime bipolar disorder, lifetime psychosis (including schizophrenia or schizoaffective disorder), a past-year suicide attempt, or current suicidal ideation with a plan or intent to act. All participants signed informed consent before participating. See Chiauzzi et al  for a COWB-LIFE is a DMHI incorporating Woebot, an NLP-supported relational agent that provides an interactional platform to guide users in managing their mood using evidence-based practices such as CBT, interpersonal therapy, and dialectical behavioral therapy. Mood tracking, artificial intelligence–supported text-based conversations driven by the user’s goals, and various tools rooted in evidence-based theoretical constructs and psychoeducational stories can be selected by users. For additional details about the intervention, please refer to Chiauzzi et al .Behavioral, cognitive, and affective engagement measures of interest were selected based on WB-LIFE’s underlying theoretical constructs and in alignment with the CAPE model of engagement outlined above . Models The collected app data included the following four constructs (32 variables) of objective utilization:A (attend) of the CAPE model and accounts for the variance not ascribed to the other metricsWeekly (1-8) sums of the number of days opening the app, which maps to the P (participate) of the CAPE model as well as the CBT exercises completed2. Weekly (1-8) sums of the number of tools completed, which represents the Weekly (1-8) sums of the number of stories completed, which represents completing psychoeducation components of the interventionWeekly (1-8) sums of the number of messages exchanged with Woebot, which represents the relational agent connection experienced by the userC (connect) of the CAPE model.Because all study participants were by nature already recruited and enrolled in the study, the engagement model could not include the therapist was changed in the assessment to Woebot to enable the user to rate the working alliance felt with the relational agent itself. These data provided 6 variables representing three constructs in our utilization and engagement models, as follows:Study participants actively provided self-report data to quantify 6 variables representing 3 subjective measures of affective engagement. The factor structure of the 12-item Working Alliance Inventory-Short Report (WAI-SR) represents 3 subscales  that map onto Bordin’s  theory ogoal subscale scores at 3 days and 8 weeks to capture user ratings of the agreement between the user and Woebot on the goals of the intervention,task subscale score at 3 days and 8 weeks to measure user ratings of the agreement between the user and Woebot on the plan to accomplish the goals, andbond subscale score at 3 days and 8 weeks to assess the user’s rating of the personal bond felt with Woebot.E (enact) of the CAPE model. The responses were transformed to a numeric Likert scale (1-5) for inclusion in the clustering models.The final construct representing both cognitive and behavioral aspects of engagement consisted of a single question, asking participants “To what extent did you apply Woebot suggestions in your day to day life?” Responses ranged from “not at all” to “a very large extent” to measure the Analyses focused on 4 mental health outcome variables of interest. Because only about half of the sample had clinically significant levels of depressive or anxiety symptoms at baseline, 2 more general measures of mental health wellness were selected for analysis: stress and resilience. Baseline variables were examined as clinical characteristics and compared across clusters; change scores of each mental health outcome between baseline and week 8 were compared across clusters as a preliminary, exploratory analysis.The PHQ-8 contains 8 self-reported variables that measure depressive symptoms experienced in the past 2 weeks . ResponsThe GAD-7 contains 7 self-reported items that assess anxiety symptoms experienced in the past 2 weeks . ResponsStudy participants completed the Perceived Stress Scale (PSS) at baseline and 8 weeks as a way to measure how often they felt life was unpredictable, uncontrollable, and overwhelming in the past month . The PSSThe Brief Resilience Scale (BRS) includes 6 items to assess an individual’s perceived ability to bounce back or recover from stress or a setback . The 6 iBaseline assessments contained questions regarding the age of the participant, gender identity, sexual orientation, race and ethnicity, education, employment, marital status, insurance coverage, and concurrent mental health treatment (defined as currently seeing a therapist or taking medication for a psychiatric condition at either the baseline or 8-week assessment). Average baseline values for each mental health outcome of interest were also compared across clusters.cluster package [Analyses were conducted in R  using the  package . The codAgglomerative hierarchical cluster analysis was run by entering the 39 engagement variables described above to determine if meaningful groups could be detected . AgglomeThe characterization and naming of each cluster required an examination of the cognitive, behavioral, and affective engagement variables across each cluster. ANOVA compared mean utilization and engagement scores across clusters, with α set to .05. The Fisher exact test was used to compare proportions of participants in each cluster who responded to each of the 5 response categories of the enactment question.P values were adjusted using the Holm correction.We did not have sufficient power to assess baseline predictors of clustering assignment using multinomial logistic regression; however, we examined baseline differences in clinical and demographic characteristics by cluster using chi-square, Fisher exact, and ANOVA tests, as appropriate. Given the exploratory nature of these analyses, we did not account for multiple comparisons other than in post hoc pairwise comparisons, for which Western International Review Board-Copernicus Group approved the study protocol on January 20, 2022 (#20216751). This study was an additional, exploratory analysis of the sample collected for and reported in Chiauzzi et al , which wP<.001; efficient engagers vs early utilizers, P=.003). Specifically, efficient engagers reported applying what they learned outside of the app more than others.A total of 3 clusters were identified and named “typical utilizers,” “early utilizers,” and “efficient engagers.” Sensitivity analyses that incorporated only the behavioral engagement variables into the clustering analyses identified the same 3 clusters and are presented in P<.001, efficient engager vs early utilizer P=.003, early utilizer vs typical utilizer P=.67).As seen in P=.01; stress: P=.01). Early utilizers did not significantly differ from the other 2 clusters on change in depression  or change in stress .This exploratory study of behavioral, affective, and cognitive engagement patterns of relational agent users enrolled in a single-arm trial yielded 3 clusters that we labeled “typical utilizers,” “early utilizers,” and “efficient engagers.” Typical utilizers had intermediate levels of behavioral engagement. Early utilizers had the nominally highest levels of initial behavioral engagement measured in-app. The groups were most differentiated by the efficient engagers, who had significantly higher levels of day 3 working alliance as compared to users in the other 2 groups, as well as significantly greater enactment in daily life of concepts learned and practiced in the app.Several patterns emerged with respect to the demographic and clinical characteristics of study participants that differentiated the clusters. The 3 clusters significantly differed with respect to the proportion of study participants’ age, gender identity, race and ethnicity, sexual orientation, education, and insurance status. Although pairwise comparisons were not done, efficient engagers had the highest proportions of men, non-Hispanic Black, heterosexual, educated, younger, and uninsured, or users not reporting insurance coverage. Because several cells had small sample sizes, these findings should be interpreted with caution until additional investigation can be done with greater numbers of enrolled participants. Finally, besides efficient engagers having higher baseline levels of resilience than users in other clusters, baseline levels of depressive symptoms, anxiety symptoms, or stress did not differ across the clusters. Resilience has long been associated with positive health states , acting With respect to mental health outcomes, efficient engagers had significantly greater declines in both depressive symptoms and stress in comparison to typical utilizers. It should be noted, however, that the efficient engagers had the lowest levels of in-app measured behavioral engagement variables, which underscores the importance of not treating these measures alone as a proxy for “good outcomes.” Perhaps efficient engagers were able to get what they needed from the app because they approached the intervention with a clear idea of what they wanted to get out of app interactions  and how they were going to interact with the app to accomplish their goals (task). Beyond the scope of this study but ripe for additional investigation is the determination of specific app components that efficient engagers accessed to “get what they needed,” also referred to as “e-attainment” . The ideThis study demonstrated higher than expected levels of behavioral engagement among study participants enrolled in a single-arm trial. For example, in this study, approximately three-quarters of users opened the app in at least 50% of the study weeks . In contrast, a review by Fleming et al  determinOur findings of patterns of engagement among relational agent study participants extend the work presented by several others to include important self-reported measures of affective and cognitive and behavioral engagement that capture aspects of the user’s attention, interest, and affect in an effort to focus on process rather than product ,56. For To the authors’ knowledge, this study was the first of its kind to investigate patterns of engagement using a comprehensive set of cognitive, behavioral, and affective measures among users of a DMHI guided by an NLP-supported relational agent. We used a rigorous analytical approach assessing the theorized “active ingredients” of the intervention and the previously described CAPE framework. The identified clusters were robust in that they withstood sensitivity analyses. A key finding was that engagement and improvement in outcomes may not be directly proportional to each other, aligning with an emerging body of work in DMHI research that may seem counterintuitive to a traditional approach to mental health treatment that emphasizes adherence to treatment over time to obtain expected benefit. Additional strengths of this investigation were the relative diversity of the sample, high use, and survey completion rates.C) to the intervention with enrollment in the trial. To test this in a future study, one could consider a design in which a clinician determines eligibility for using the DMHI app based on predefined criteria, recommends the app to suitable potential participants, and analyzes the percentage of these referrals that convert to app registrations. Third, in this study, a single question measured enactment (E). Additional studies need to include a more specific measure of enactment with items that query the user’s sense of mastery over content learned during the intervention as well as the perceived effectiveness of the enactment itself, potentially at multiple time points. Fourth, the analyses focused on very few sociodemographic or clinical characteristics of potential differentiation across clusters. For example, potentially unmeasured characteristics like previous experience with mental health interventions might have increased the likelihood of setting goals and establishing tasks to accomplish them, which then affected outcomes. The direction of causation cannot be determined from this single-armed trial, so optimal use recommendations might not generalize across populations with varying unmeasured characteristics. Additional research using such approaches as directed acyclic graphs could be used to better inform the direction of causation [The study findings require careful consideration of limitations. First, the study did not have a control group, so any improvement in outcomes might be due to regression to the mean, a digital placebo effect, or spontaneous remission ,58 ratheausation  in studiausation , the higausation  were exaausation . In addiThe findings, if validated across other data sets and in studies using different experimental designs in future work—ideally randomized controlled trials to test patterns found in this study versus those experienced by users of other types of digital interventions that include longer-term follow-up periods to study the stability of symptom improvements over time—have important implications for recommendations of the frequency, duration, and length of time of optimal use for DMHIs incorporating relational agents to achieve favorable outcomes. This study determined that the best outcomes are not always realized by those with the most in-app measures of behavioral engagement. It is important not to mislabel efficient engagers who achieve substantial symptom reductions but do not participate in long periods of the intervention as “dropouts.” Aspects of engagement that measure attention, interest, and affect, as well as characteristics like baseline resilience, likely matter more than the time spent on using the intervention itself, particularly, perhaps, when using relational agents designed to build and maintain relationships with users. Given the findings of this study, helping users determine goals for the intervention aligned with the capabilities of the intervention and methods to achieve those goals may warrant greater attention, particularly given previous work showing greater consensus and collaboration enhances outcomes . RegardiWe identified 3 engagement clusters of users of a DMHI incorporating an NLP-supported relational agent that emerged from our preliminary analyses of cognitive, affective, and behavioral engagement metrics. The clusters differed with respect to several mental health outcomes as well as demographic and clinical characteristics. Additional analyses with larger sample sizes and more rigorous study designs are needed to replicate findings among participants of varying baseline levels of symptomatology who seek help with their mood and anxiety, and to inform more fine-grained recommendations regarding optimal use, and to determine the best sequence of specific intervention components for each individual. Nevertheless, the findings represent an important first step in disentangling the complex interplay between aspects of engagement and outcomes associated with the use of a DMHI guided by an NLP-supported relational agent.


Long noncoding RNAs (lncRNAs) are a large and diverse class of genes in eukaryotic genomes that contribute to a variety of regulatory processes. Functionally characterized lncRNAs play critical roles in plants, ranging from regulating flowering to controlling lateral root formation. However, findings from the past decade have revealed that thousands of lncRNAs are present in plant transcriptomes, and characterization has lagged far behind identification. In this setting, distinguishing function from noise is challenging. However, the plant community has been at the forefront of discovery in lncRNA biology, providing many functional and mechanistic insights that have increased our understanding of this gene class. In this review, we examine the key discoveries and insights made in plant lncRNA biology over the past two and a half decades. We describe how discoveries made in the pregenomics era have informed efforts to identify and functionally characterize lncRNAs in the subsequent decades. We provide an overview of the functional archetypes into which characterized plant lncRNAs fit and speculate on new avenues of research that may uncover yet more archetypes. Finally, this review discusses the challenges facing the field and some exciting new molecular and computational approaches that may help inform lncRNA comparative and functional analyses. The basic definition of a long noncoding RNA (lncRNA) dictates that the RNA transcript must be 200 nucleotides or longer and not be translated into a protein. Traditional lncRNA definitions also exclude housekeeping RNAs, such as ribosomal (rRNA), transfer (tRNA), and small nuclear or nucleolar  genome in 2000 (ENOD40 (EARLY NODULIN 40) in Medicago truncatula, CR20 (CYTOKININ REPRESSED 20) in cucumber (Crocus sativus), GUT15 (GENE WITH UNSTABLE TRANSCRIPT 15) in tobacco (Nicotiana tabacum) and Arabidopsis, MT4 in M. truncatula, and TPSI1 (TOMATO PHOSPHATE STARVATION INDUCED 1) in tomato (Solanum lycopersicum). ENOD40, independently discovered in both M. truncatula and soybean (Glycine max), was the first lncRNA discovered in plants and plays a role in root nodulation in legumes  or in trans (complementary sequences at separate loci). Other groups, such as APOLO and ALTERNATIVE SPLICING COMPETITOR (ASCO) lncRNAs (discussed below) were both annotated as lncRNAs by After these initial studies and during the following decade, our view of the lncRNA portion of the Arabidopsis transcriptome became clearer. Numerous groups, using varying scopes, technologies, and computational methodologies, identified suites of Arabidopsis lncRNAs . Most oflncCOBRA1, FLORE, DRIR, and AGAMOUS (AG)-incRNA4 (As transcriptomic technologies improved in the 2010s (e.g. higher density tiling arrays and next generation RNA-sequencing), it became cheaper and easier to perform more comprehensive lncRNA identification efforts that spanned numerous tissues or conditions. One of the first of these studies came from -incRNA4 . In addiZea mays) by Boerner and McGinnis where they identified ∼2,500 lncRNAs using a public dataset of full-length cDNA sequences , abundance , similarity to known genes , whereas others do not  , were deIn efforts to streamline lncRNA discovery and evolutionary analysis across large datasets, pipelines, such as Evolinc , have beThe increasing number of novel algorithms and computational pipelines for annotating lncRNAs has invited challenging discourse around “best” identification practices. Some pressing questions at the moment are: How do users determine which method is best suited to their data and how does the community compare lncRNAs identified using different methods? One potential solution would be for each research group annotating a novel lncRNA, or group of RNAs, to utilize multiple approaches and take the intersection of those approaches (i.e. predictions shared between those approaches). However, selecting seemingly disparate algorithms that utilize different lncRNA features for identification raises additional concerns about which is most appropriate. In addition, there are potentially species-specific tradeoffs to consider, particularly when annotating lncRNAs in nonmodel plant systems lacking reference genomes. Possible solutions would be for the plant community to (1) develop a common test dataset for benchmarking purposes and (2) have multiple groups collaborate to perform this benchmarking on extant algorithms to compare performance and accuracy.The many attempts at annotating lncRNAs in plant transcriptomes have revealed a number of key properties that have made their identification problematic. One of these issues pertains to sequencing resolution and appropriate supporting data at cellular, tissue, and organismal scales. For example, even though most model plant species have sufficient transcriptomic data across all major plant organs and developmental stages, very few concerted efforts have been undertaken to utilize these data to annotate lncRNAs. In addition, companion data that would provide greater support for lncRNA annotation, such as information about transcription start sites ; MAS , a Pol-III transcript whose discovery relied on surveying ribosomal depleted RNA-seq libraries  pools of RNA. However, polyA-focused studies may overlook a significant fraction of the ncRNA transcriptome and thus ignore biologically significant lncRNAs. In animals, there are thousands of non-polyA noncoding transcripts which do not fall into the classical definition of housekeeping RNAs . For the sake of clarity, we have separated these mechanisms below, with an additional section for lncRNAs whose function falls outside of regulating gene expression. Each of the described lncRNAs, their functional archetype, and when and where they were identified, can be found in Many of the functionally characterized plant lncRNAs are predominantly nuclear-localized and exhibit some role in transcriptional or posttranscriptional gene regulation . For theAPOLO (AUXIN REGULATED PROMOTER LOOP) and its role in regulating the expression patterns of auxin-responsive genes , a protein kinase associated with polar auxin transport (APOLO accumulation encourages the recruitment of RNA polymerase V (Pol-V) and the production of Pol-V APOLO variants. Accumulation of the Pol-II APOLO variant leads to recruitment of the polycomb repressive complex 1 and 2 (PRC1/2) which maintain and deposit the repressive H3K27me3  mark, respectively. This subsequently leads to repression at the locus , a return of the repressive chromatin loop, and loss of PID expression. Interestingly, APOLO is also able to navigate to target sites in trans via sequence complementarity, without a requirement for topological association , a lncRNA transcribed from within the Arabidopsis marneral cluster, also functions as a chromatin topology modulator  gene is a critical regulator of vernalization, the process that initiates flowering after prolonged cold exposure  are both necessary for the proper transcriptional repression of FLC. COOLAIR is a capped, polyadenylated, and alternatively spliced lncRNA that is transcribed from the antisense strand of FLC, initiating near the terminator of FLC. COOLAIR is a component of the vernalization-controlled flowering pathway, in that it is upregulated during cold conditions and contributes to the initial repression of FLC by promoting a repressed chromatin state through removal of active epigenetic marks, namely H3K36me3 and H3K4me1  manner. ASL RNA immunoprecipitation experiments and epigenetic analyses revealed that ASL physically associates with both exosomal proteins and H3K27me3 regions within the FLC locus  is also essential to maintaining proper flowering control in Arabidopsis , a nuclear-localized lncRNA found within the leucine-rich kinase (LRK) gene cluster functions as a positive regulator of the LRK cluster in a PRC2-dependent manner. LAIR directly interacts with the histone modifiers OsMOF and OsWDR5, components of an H4K16 acetyltransferase and associated protein complex , an antisense lncRNA transcribed from the OsSOC1 locus, represents another example by which yield is manipulated by lncRNAs. OsSOC1 is the putative ortholog of the Arabidopsis SUPPRESSOR OF OVEREXPRESSION OF CO1, a floral integrator that is regulated by FLC and other floral regulators . In response to constant red light, HID1, along with unknown protein partners, targets the promoter of PHYTOCHROME INTERACTING FACTOR3 (PIF3), resulting in transcriptional inhibition of PIF3 , which positively regulates plant pathogen defense. Specifically, ELENA1 expression is induced by the bacterial pathogen-associated molecular patterns, elf18 and flg22 (ELENA1 then directly interacts with and enriches the mediator subunit 19A (MED19A) at the distal promoter region of the PATHOGENESIS-RELATED GENE1 (PR1) gene. PR1 expression is induced and subsequently upregulates a suite of genes involved in the biotic stress response (ELENA1 removes FIBRILLARIN 2 (FIB2), a direct interactor of MED19A and transcriptional repressor, from the PR1 promoter. Thus, pretranscriptional regulation of gene expression by lncRNAs can occur through a number of mechanisms, enhancing or repressing depending on context.Another example of lncRNAs targeting regulatory elements comes from the lncRNA nd flg22 . ELENA1 response . In a foSVALKA and antisense-CBF1 transcripts (asCBF1), lncRNAs located in the C-repeat/dehydration-responsive element binding factors (CBFs) gene cluster, function in this fashion (CBF gene cluster occurs (COLD REGULATED (COR) gene family  and mediates alternative splicing of numerous target precursor mRNAs  and SmD1b (Sm ring D1b) and mediates spliceosomal interactions with target mRNAs  can impact the abundance of SEP3 isoforms. SEP3 encodes for a MADS-box transcription factor that is important for flowering time and floral organ identity  transcribed from identity . By formisoforms . FurtherIPS1 (induced by phosphate starvation 1) and other noncoding transcripts within the TPSI1/MT4 family described above  accumulation , SIHDZ34 and SIHDZ45, which are responsible for attenuating jasmonic acid and ethylene responses during biotic infection. The authors showed that lncRNA39896 effectively decoys miRNA-166B, allowing translation of the target mRNAs and regulation of the hormone responses. Mutating the miRNA binding site of lncRNA39896 led to increased miRNA targeting of the SIHDZ34 and SIHDZ45 mRNAs, a higher level of jasmonic acid and ethylene abundance, and increased resistance to Phytophthora infection. Thus, the discovery of endogenous target mimicry has contributed to our understanding of a gene regulatory mechanism with clear biotechnological applications.The functional characterization of ng sites . This meng sites . During nfection . This lnanalysis . The miRPHOSPHATE1; 2  is strongly upregulated during phosphate starvation and leads to the increase of PHO1; 2 protein levels  in plants, they will allow researchers to distinguish between novel small ORF containing transcripts and those that regulate translation in a manner similar to the cis-NAT of PHO1; 2, and perhaps identify novel cytoplasmic lncRNA functions outside of translational regulation away from the ribosome.There are very few known instances of lncRNAs directly acting as regulators of mRNA translation. In rice, a cis-NAT of n levels . Importanse RNAs . In a sense RNAs . They idWhile a majority of the mechanistically described plant lncRNAs govern every aspect of the progression from DNA to protein, there are several lncRNAs that function outside of this role. As large, multidomain molecules with complex structures, lncRNAs are ideal platforms on which other molecules (proteins or RNA) can dock. This innate ability to interact with other molecules has been alluded to above in the context of gene expression, but also occurs in other contexts in both the nucleus and the cytoplasm.TR, the RNA component of a ribonucleoprotein complex called telomerase which is essential for maintaining chromosome ends. Despite its characterization in ciliates, yeast, and vertebrates in the 20th century , a nuclear speckle RNA-binding protein  are lacking, restricting the development of functional clues when a lncRNA is identified in a researcher's RNA-seq data. While we expect functional annotation to become easier as more lncRNAs are described, there are currently only a few bioinformatic methods for assigning putative functions to lncRNAs, including integrating lncRNAs into co-expression and gene regulatory networks as well as motif enrichment analyses that we will describe below.To better understand the function of lncRNAs, clustering lncRNAs with highly correlated expression to protein-coding genes using weighted gene co-expression networks, often referred to as “Guilt-by-Association” (GBA), is gaining traction in both plants and animals . Given sWhen paired with additional types of data, GBA approaches can be quite informative and help pinpoint functional lncRNAs. For instance, a functional annotation of chromatin-enriched ncRNAs in rice incorporated expression, chromatin-interaction data, and phenotypic information to identify lncRNAs associated with yield . In addiCYRANO  occur. These domains, or motifs, are often short (6 to 12 nt) and may occur multiple times within the same RNA to increase binding affinity or number of interactions . As a reCYRANO . The Cyrn plants , and LncKCNQ1OT1 and AIRN) that exhibited similar k-mer profiles to the XIST lncRNA, a well-known lncRNA involved in the epigenetic silencing of the x chromosome through interactions with the polycomb repressor complex  and plant genome evolution (LncLOOM). However, in silico functional predictions and inferences of orthology are likely to only get stronger as more interaction partners and functional domains are identified for plant lncRNAs.Innovations in natural language processing and machine learning methods have made it possible to use functional motifs from characterized lncRNAs to infer function of unknown lncRNAs. In this context, algorithms such as SEquence Evaluation from K-mer Representation (SEEKR) search for the enrichment of particular motifs, or k-mers, within a lncRNA . K-mer s complex . FurtherlncCOBRA1 and FLAIL  gene editing approaches have already demonstrated their utility in plants for assessing gene function in a highly targeted fashion . Howevernd FLAIL . Howevernd FLAIL  and are nd FLAIL . The CRInd FLAIL . This tend FLAIL  and willA lncRNA's subcellular localization can inform in which functional archetype the gene may be involved . NuclearOne avenue of lncRNA subcellular localization that remains untouched in plants is their role in the formation of SGs. SGs are cytoplasmic protein, RNA, and metabolite aggregates that form in response to stress conditions, and are thought to serve as crucial reservoirs of ribosomal machinery, chaperones, and untranslated mRNAs . SGs areIPS1 and other putative miRNA mimic lncRNAs. In support of this finding, IPS1 (AT4) in a pool of mobile mRNAs. Furthermore, these mobile lncRNAs may be important for interspecies communication. A recent study demonstrated that a green peach aphid (Myzus persicae) lncRNA (Ya1) is part of a pool of molecules injected into the aphid's plant host (Arabidopsis) and that this particular lncRNA promotes aphid feeding  near the promoter and 5′ start site along with H3K36me3 (trimethylation of lysine 36 on histone 3) across the gene body and flanking both ends of the transcript start and end sites  has shed light upon lncRNAs that do not contain polyA tails, a facet of lncRNA research that is underexplored. For instance, bidopsis . The bioCOOLAIR's role in regulating the FLC locus by modulating R-loop stability (Finally, every aspect of an RNA's lifecycle, including its shape, interacting partners, and ultimately function, can be influenced by chemical base modifications often referred to as the epitranscriptome . Aside ftability . Given Ctability , considetability . Single-tability , are poiTremendous achievements have been made over the last 25 yr in plant lncRNA biology. Characteristics and mechanisms of lncRNAs first discovered in nonmodel species, then elaborated on in Arabidopsis, are now being used to identify functionally important lncRNAs across the plant lineage. However, at present, the function of the vast majority of annotated plant lncRNAs remains unknown, and traditional molecular investigations into each one of these transcripts would be cost-prohibitive. We anticipate modern-omics technology, in combination with rapid CRISPR-based genetic screening and ML approaches, will close the gap between putative and actual functions for plant lncRNAs.Discoveries in a few key plant species will undoubtedly translate to more and more distant relatives as we begin to understand the rules that govern lncRNA evolution. Moving away from a protein-centric perspective will be critical. Selective pressures at the nucleotide, or even structural level, are likely less important for some lncRNA functions than others. In addition, expression abundance—typically thought of as a sign of functionality in protein-coding genes—may be less important than expression at the right or optimal time or arising from the correct portion of a genome. Developing comparative frameworks that incorporate these types of conservation will be critical in transferring functional information from one species to another.New and exciting tools and techniques are being brought to bear on the identification, functional dissection, and comparative analyses of lncRNAs. As computational algorithms and approaches are specifically developed for lncRNAs, robust sets of functional lncRNAs will be needed for training. Critical thought will need to be placed into which lncRNAs are selected as positive controls to avoid training around non-lncRNA features. To aid in algorithm development, the current plant lncRNA databases need to be harmonized in terms of the criteria each used to identify their lncRNAs.In all of this, the variation in functional archetypes is important to keep in mind. While many lncRNAs likely influence the genome and transcriptome in the nucleus, there is growing evidence that cytoplasmic functions are just as varied. The ability of lncRNAs, alongside other molecules, to translocate between cells or organisms further deepens the mystery of what role these enigmatic transcripts play in plant biology. The functional possibilities seem endless, and the chance for mistaken identities is high, but the long and storied history of lncRNAs in plants makes it clear that their study is worth the effort.


There is an increasing tendency for case reports to reveal anatomical aberrances in mandibular first molars, such as the lateral and accessory canals. Thus, clinicians should pay special attention to anatomic variances when dealing with mandibular first molars requiring endodontic treatment to prevent reinfection within the root canal system, which is responsible for the failure of root canal treatment.This article presents the clinical report and successful root canal treatment of a 24-year-old healthy female patient with an extensive periapical lesion in a 6-canal first mandibular molar. The patient was admitted to the endodontic department because of a periapical abscess found 1 month ago in her left mandibular first molar.Chronic apical periodontitis was diagnosed based on clinical examination coupled with radiographic and cone-beam computed tomography images.The treatment plan was to first perform root canal therapy and then perform clinical observation.During 1-year follow-up period, the treated tooth was asymptomatic, and complete resolution of the extensive apical lesion was eventually achieved, as shown in the postoperative cone-beam computed tomography images and clinical examination.The present case emphasizes the importance of a comprehensive understanding of root canal morphology, especially rare anatomical variations, to ensure successful root canal treatment. Additionally, the case report adds to the library of previously reported cases of extensive periapical lesions with a direct connection to the root canal system, which demonstrates the potential clinical advantages of root canal therapy as a conservative nonsurgical approach in these cases. As mentioned in several clinical studies, large periapical lesions with a direct connection to the root canal system may respond favorably to nonsurgical root canal treatment with sufficient infection control.–7 Therefore, it is of great importance to develop thorough awareness and understanding of root canal morphology for efficient root canal disinfection and successful endodontic treatment.Apical periodontitis occurs as a result of anaerobic polymicrobial infection within the root canal system, which leads to an inflammatory and immune reaction of periapical tissue, followed by destruction of the periodontal ligament and apical bone. It is well established that root canal therapy is the most clinically effective treatment for pulpitis and apical periodontitis to prevent and treat the inflammation and destruction of periradicular tissue by adequately eradicating microbial infection from the root canal system.–11 Moreover, the disto-middle (DM) canal is located between the disto-lingual (DL) and disto-buccal (DB) canals, which are known as the third canal within a distal root, with a prevalence rate of 0.2 % to 3 %.,13 The presence of 3 mesial and 3 distal canals in the mandibular first molar is extremely rare.Growing evidence suggests that a complex root canal morphology is typically observed in mandibular first molars, which normally have 2 canals in the mesial root and 1 or 2 canals in the distal root. Infrequently, an accessory canal between the mesio-lingual (ML) and mesio-buccal (MB) canals in the mesial root is referred to as the mesio-middle (MM) canal a reported incidence ranging from 0.26 % to 46.2 %.In this clinical report, we describe our endodontic treatment of extensive periapical lesions in a mandibular first molar with 6 root canals, including 3 mesial canals and 3 distal canals, to provide new references and evidence for clinical diagnosis, treatment, and prognosis.A 24-year-old female patient was admitted to the endodontic department at the Affiliated Stomatological Hospital of Southern Medical University  because of a periapical abscess found 1 month ago in her left mandibular first molar (tooth 36). The patient had no history of drug/food allergy or hereditary diseases in her family. On clinical examination, dental filling material was detected in the distal portion of the occlusal and proximal surfaces of tooth 36  and 1:100,000 epinephrine , followed by rubber dam isolation . All subsequent procedures were performed using an operating microscope . After removing the pulp chamber roof completely and irrigating the pulp chamber gently with 3 % sodium hypochlorite solution , 6-canal orifices in tooth 36 were uncovered by exposure to microscope, which were identified as MB, ML, MM, DL, DB, and DM canals, respectively Fig. , and posAt the 3-month follow-up visit, the patient was asymptomatic, and clinical examination indicated that there was no tooth mobility and tenderness to vertical percussion in tooth 36. The periodontal probing depth of the diseased tooth was within normal physiological limits, and no fistulae were observed in the soft tissue Fig. . Additio It is well known that the mandibular first molar is the most common teeth to undergo root canal therapy in human dentition, which usually has a mesial root with 2 canals and a distal root with 1 or 2 canals. However, an increasing number of case reports have uncovered mandibular first molars with anatomical aberrances, such as lateral and accessory root canals. Several previous investigations have confirmed that the prevalence rate of additional canals in the mesial root of mandibular molars is reported to be 0.26% to 46.2%, while the incidence of a third distal canal ranges only from 0.2% to 3%. Therefore, special attention should be paid to root canal morphology and anatomical variance of mandibular first molars to avoid reinfection that extends along the root canal system into the apical area, which is one of the critical factors for endodontic treatment failure.,16 The present report highlights an extremely rare case of an extensive periapical lesion in a mandibular first molar with 6 canals, including 3 mesial and 3 distal root canals. Currently, the precise molecular mechanisms involved in the development of periapical periodontitis are not completely understood. Nevertheless, it is generally agreed that adequate chemo-mechanical elimination of the microbial biofilm within the root canal system is the most vital factor responsible for achieving favorable treatment outcomes. Current treatment options available for large periapical lesions include root canal treatment, various surgical managements, or a combination of these options.The ultimate goal of root canal treatment is to eliminate microorganisms thoroughly in the entire pulp cavity via mechanical and chemical cleansing, while avoiding reinfection via complete root canal obturation with an inert filling material.–7 These findings appeared to be mostly associated with root canal infection, which could lead to the development of extensive apical inflammation through direct communication between the root canal system and periapical tissue. Successful root canal therapy completely eradicated pathogenic microorganisms inside the root canal to cut off the source of infection in these cases, thus achieving significant therapeutic efficacy. Additionally, nonsurgical and less invasive treatment options also provide several clinical benefits for patients with large periapical inflammation, including minimum psychological trauma and greater patient acceptance.Root canal therapy was chosen in the present case as an initial treatment option for extensive apical lesions of tooth 36, instead of tooth extraction and periapical surgery. On the 1 hand, the treatment plan was formulated in accordance with a strong patient preference for preservation of the involved tooth, and the young patient age contributed to a favorable functional recovery. On the other hand, several studies have shown significant therapeutic advantages and clinical benefits of root canal treatment in cases of large periapical lesions having a direct connection with the root canal system. In addition, accurate information on the complex internal root morphology was readily available and accessible under microscope and CBCT image guidance, which provided us with the most appropriate approach to detect anatomical variations in tooth 36 with 6 canals.–20 However, it is worth noting that only by accurate interpretation of CBCT images and oral examination can we detect the presence or absence of the third mesial and distal canals in the diseased tooth before and during root canal treatment.In the present case, follow-up visits were scheduled 3 months, 6 months and 1-year following after root canal treatment. Clinical examination and CBCT analyses demonstrated that the treated tooth remained symptom-free, and the apical damage was gradually reduced in size and radiolucency during 1-year follow-up period without any intervention, indicating a satisfactory prognosis for root canal therapy. Gratifyingly, the periradicular lesion spreading to the mesial root of tooth 37 was also completely resolved, as shown on postoperative CBCT images. In addition, calcium deposition within the 2 accessory canals in the mesial and distal roots, due to inflammatory stimuli, hampered root canal preparation, whereas there was no significant influence on the therapeutic effect of endodontic treatment. From a clinical standpoint, it may seem that the excellent treatment outcome in the present case was largely related to complete disinfection of the root canal system via successful root canal therapy and several physiological advantages afforded by the young patient age, such as ample periapical fibroblasts and osteoblasts, rich lymphatic drainage, abundant blood supply, root canal morphology, and dentinal structure.This clinical report represents an extremely rare case of an extensive periapical lesion in a mandibular first molar with 6 canals, including 3 mesial root canals and 3 distal root canals, which reinforces the fact that a comprehensive understanding of not only the normal anatomy but also atypical anatomic variations within the root canal system should be developed to ensure successful endodontic treatment in dental clinical practice. Therefore, multi-angle CBCT images combined with close clinical inspection of the dental pulp cavity at high-magnification are indispensable when dealing with teeth with a high incidence of additional canals. Furthermore, the treatment outcome also confirms previous reports demonstrating the potential advantages of nonsurgical endodontic treatment for clinical implementation in cases of large apical lesions with a direct connection to the root canal system. However, further case reports and studies are recommended to clarify the precise influence of nonsurgical root canal treatment on extensive apical inflammation.Data curation: Xin Li.Investigation: Shuyu Sun.Validation: Tengyi Zheng.Writing – original draft: Xin Li, Tengyi Zheng.Writing – review & editing: Tengyi Zheng.


Arabidopsis, the FT/TFL1 gene family has been shown to integrate signals from various flowering pathways and to play a key role in the transition from flower production to seed development. Studies in several plant species of the FT/TFL1 gene family have revealed it harbors functional diversity in the regulation of flowering. Here, we review the functional evolution of the FT/TFL1 gene family in horticulture plants and its unique regulatory mechanisms; in addition, the FT/TFL1 family of genes as an important potential breeding target is explored.Flowering is an important process in higher plants and is regulated by a variety of factors, including light, temperature, and phytohormones. Flowering restriction has a considerable impact on the commodity value and production cost of many horticultural crops. In  Flowering is an important stage in the life history of higher plants that includes the processes of flower bud differentiation, development, and the opening of flowers  after photoperiod induction; it was designated ‘florigen’ (Chailakhyan and Krikorian Arabidopsis as a product of the FLOWERING LOCUS T (FT) gene  of the FT/TFL1 family of proteins has been identified as a local floral inhibitor expressed in the SAM  , QUIRKY (QKY), and SYNTAXIN OF PLANTS121 (SYP121)  and FRUITFULL (FUL)  , SVP, FLOWERING LOCUS M (FLM), and MADS AFFECTING FLOWERING (MAF), can inhibit transcription by binding to the first intron of FT at low temperatures or before vernalization , APETALA1 (AP1), and CAULIFLOWER (CAL), which maintains meristem indeterminacy  both repress this conifer tree’s growth, and their heterologous expression in Arabidopsis also delays the onset of flowering  has the function of complementing the late-flowering phenotype of the Arabidopsis ft-10 mutant, and CmFTL1 can induce flowering in C. morifolium ‘Yuuka’  in chrysanthemum leaves and promotes flowering  were isolated and characterized from the Dendrobium huoshanense genome   is the second most globally important vegetable crop (after potato), whose flowering time is jointly controlled by the flowering inducer SINGLE FLOWER TRUSS (SFT) and suppressor SELF PRUNING (SP)  conditions and regulates tomato flowering by promoting the expression of SFT (Song Fragaria × ananassa) FvFT1 and FvTFL1 have antagonistic functions for inducing flowering. Interestingly, owing to a base deletion in FvTFL1, strawberry has since become a permanent flowering plant  contains two FT-like genes (PtFT1/PtFT2)   function antagonistically in flowering. Under non-vernalized or SD conditions, the flowering inhibitor BvFT1 inhibits flowering by limiting the expression of the flowering-inducing factor BvFT2 , the structural types in the upstream region (UR) of CsFT have differential effects on flowering induction; the ‘short-1’ UR CsFT and ‘short-2’ UR CsFT accelerate the onset of flowering, whereas the ‘long’ UR CsFT delays flowering  and both StSP5G and StSP5G-like in potato are also able to inhibit flowering : TgFT1, TgFT2, and TgFT3. Overexpression of TgFT2 in Arabidopsis resulted in an early-flowering phenotype, while TgFT1 and TgFT3 overexpression resulted in a late-flowering phenotype  can also repress their flowering  conditions. When SDs switch to LDs before the involucre-forming stage, those plants do not initiate florets on the apical receptacle, or their capitulum development is strongly suppressed  is also induced by low-temperature conditions and is not regulated by photoperiod. In lily, LlFT, as a flowering activator, is significantly induced by a period of low-temperature treatment and this promoted flowering; however, without the vernalization treatment, overexpression of LlFT also led to a bloom, indicating that LlFT is the main regulatory factor controlling flowering in the vernalization pathway , which promotes the expression of the downstream LFY homolog gene NLF and induces flowering  juvenile scions because of their low accumulation of total FT protein  in rose was found to be immobile, precluding its transmission via grafting experiments , which is conducive to the accumulation of photosynthetic products (sugar and starch) in plants. This increase in CO2 is apt to cause changes in tissues’ sugar status or directly drives FT/TFL1 to regulate flowering (Jagadish et al., FT can mediate nitrogen’s control of flowering, its regulatory mechanism is still not well understood (Gras et al., FT/TFL1 family genes may respond to various environmental and endogenous stimuli would promote the development of an efficient and energy-saving approach to regulate flowering. Due to the rapid adoption and spread of genomic sequencing technology applied to horticultural plants, genomic resources are becoming increasingly available. This combined with other techniques, namely high-throughput phenotyping, genomic selection, and gene function analysis, will enable us to obtain detailed knowledge of the FT/TFL1 gene family, so as to modify their action to meet the increasing demand for horticultural products in the future.Because


Drug resistance remains one of the important clinical challenges, making cancer one of the leading causes of death worldwide. It was originally discovered to be an adaptive reaction that occurs gradually in patients receiving conventional treatments due to the genetic heterogeneity of cancer cells. There is ample evidence that the stress of conventional chemotherapy and radiotherapy can gradually activate drug resistance-related genes in cancer cells as a defense mechanism. Due to the target specificity of new clinical drugs, the genetic diversity of cancer cells also helps them avoid targeted therapies.Growing evidence points to the critical role of the tumor microenvironment in cancer progression, which brings to light its potential on patients with solid cancers based on the success of T cell-based immunotherapy for blood cancers. Unexpectedly, response rates in patients with solid tumors treated with immunotherapy are surprisingly low because the primary resistance mechanisms are unclear. For example, less than 30% of patients with non-small cell lung cancer have a clinical response to PD-1/L1 blockade . TherefoHere, we present a Special Issue in partnership with the International Journal of Molecular Sciences as a platform to share the latest findings and novel ideas in clinical and laboratory settings. Following peer-review evaluation, we are pleased to publish a total of 13 papers, including 11 original studies, 1 case report, and 1 review article, in this Special Issue. These high-quality works are produced by clinical scientists and basic researchers around the world. Their content covers multiple disciplines, ranging from laboratory discoveries in experimental cancer models to clinical observations in cancer patients, and encompassing (1) clinical discovery, (2) resistance mechanisms, and (3) novel therapeutics. We hope this collection of papers will inspire researchers to accelerate the development of effective solutions to this important clinical challenge. Below is a brief overview of each study.Clinical discoveryTargeted therapies are designed to inhibit cancer cells in a specific way, offering patients better results and fewer side effects than traditional chemotherapy and radiation therapy. Paradoxically, their therapeutic effect not only inhibits cancer growth but also accelerates the genetic evolution of cancer cells, creating new populations that are resistant to targeted treatments. In this Special Issue, Basse et al. reported novel resistance mechanisms in patients with metastatic lung cancer, with acquired TP53 mutations and Ki67 overexpression observed in cases that are clinically resistant to novel anti-EGFR therapies [Therefore, recent research has attempted to maximize the beneficial efficiency of targeted therapies by identifying patients with suitable biomarkers. Mutations in SF3B1 (subunit 1 of the splicing factor 3b protein complex) were found to be positively associated with poorer response to RG7388-based MDM2-targeted therapy in patients with chronic lymphocytic leukemia . The serResistance mechanismsA better understanding of the causative and regulatory mechanisms of drug resistance through in vitro studies or in vivo experimental animal models may lead to the discovery of effective strategies to improve clinical outcomes in cancer treatment.Martinez et al. studied docetaxel-resistant cell lines in advanced prostate cancer during anti-androgen therapy and taxane-based chemotherapy, and demonstrated that the glucocorticoid receptor acting on docetaxel through a β-catenin-dependent mechanism plays a key role in drug-resistant cancer cells in vitro . The potWith the advancement of single-cell bioinformatics analysis, new pathogenic mechanisms have been discovered in the tumor microenvironment ,12, whicNovel therapeuticsEncouragingly, many basic-research-driven inventions have shown impressive anticancer efficiency and safety in in vitro and in vivo experimental cancer models . Brandt Gene therapy is also a possible cancer treatment strategy, but its off-target effects on the central nervous system and potential oncogenic activity have largely limited its clinical translation. Kang et al. invented a superparamagnetic iron oxide nanocage, which realized targeted gene therapy based on tumor-specific siRNA and was used to treat osteosarcoma in vivo . Piiper’SummaryTaken together, these papers outline the importance of advances in research into targeted therapies and mechanisms of resistance in cancer and demonstrate the clinical relevance and translational potential of this Special Issue, which may provide insights and inspiration for further research endeavors. Our understanding of cancer drug resistance continues to advance.