Our study summarized the existing evidence through a systematic review and meta-analysis, and found that larger tumor size is associated with an increased risk of overall and cancer-specific mortality, and disease recurrence in upper tract urothelial carcinoma patients. The role of tumor size in predicting prognosis in upper tract urothelial carcinoma (UTUC) patients remains poorly defined.To assess the prognostic value of tumor size in patients with UTUC through a systematic review and meta-analysis.A comprehensive literature search of the PubMed and Embase databases were performed to identify all relevant articles published up to December 2021 according to the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) statement. Available hazard ratios (HRs) and corresponding 95% confidence intervals (95% CIs) were analyzed to evaluate the association between tumor size and survival outcomes.p < 0.10 and I2 generally >50%. Subgroup analyses illustrated that the association of tumor size with adverse prognosis in UTUC patients is not affected by treatment modalities. Segmental resection of ureter, whether receiving lymph node dissection, cutoff of tumor size, and region of population were potential sources of heterogeneity. The funnel plot test indicated no significant publication bias in the meta-analysis of survival outcomes.A total of 35 articles representing 32 292 patients met the eligibility criteria and were finally included for the meta-analysis. Tumor size was significantly associated with poor outcomes in terms of overall survival , cancer-specific survival , recurrence-free survival , and intravesical recurrence . There was between-study heterogeneity in the effect of tumor size on all these meta-analyses, with This study shows that larger tumor size is associated with an increased risk of overall and cancer-specific mortality, and disease recurrence in UTUC. Integration of tumor size with other prognostic indicators may help in risk stratification and individualized treatment of UTUC.Through a systematic review and meta-analysis, this study found that larger tumor size is associated with an increased risk of overall and cancer-specific mortality, and disease recurrence in patients with upper tract urothelial carcinoma. Our inclusion criteria were as follows: (1) the histological type of the tumors was confirmed as UTUC; (2) oncological outcomes of different tumor sizes were reported; and (3) prognostic value  for tumor size were reported. Studies were excluded if those met one of the following criteria: (1) articles not published in English; (2) nonoriginal articles, such as review articles, commentaries, meeting abstracts, letters to the editor, or case reports; (3) laboratory studies, such as studies on cancer cell lines or animal models; and (4) studies that did not provide information on survival or could not offer sufficient data to acquire HRs and 95% CIs. When there was an overlap of patient cohorts between studies, the most recent or complete article was included in the analysis to avoid duplication of the same datasets.2.3Two investigators independently extracted the following information from the included articles: the first author’s name, publication year, recruitment country, period of patient recruitment, number of patients, study design, age, gender, tumor size, treatment methods, adjuvant therapy, follow-up duration, and oncological outcome. Subsequently, the HRs and 95% CIs of tumor size associated with each of the outcomes were retrieved. All discrepancies regarding data extraction were resolved by an independent third investigator.2.4The quality of the included studies was assessed using the Newcastle-Ottawa Scale (NOS) 2.52 statistic. The random-effect model was applied to calculate the pooled HRs and 95% CIs if there was significant heterogeneity among the enrolled studies (I2 > 50% or p < 0.10). Alternatively, we used the fixed-effect model to perform cumulative analyses when no significant heterogeneity was found (I2 < 50% or p > 0.10). In addition, subgroup analyses, stratified by different study features, were conducted to evaluate the potential factors contributing to heterogeneity. Sensitivity analyses were performed by excluding studies with an NOS score of <7 or with unadjusted key confounding factors to assess the stability of the core results. The presence of publication bias was evaluated using the funnel plots. Statistical analyses were performed using Review Manager (Revman) 5.4 . All p values were two sided, and p < 0.05 was considered statistically significant.The endpoints of the present meta-analysis were OS, CSS, RFS, and IVR in UTUC patients. We extracted and combined HRs with the corresponding 95% CIs from every eligible study to analyze the prognostic value of tumor size. Heterogeneity between the studies was evaluated by Cochran’s Q test and I33.1Following an initial electronic search, we identified 597 potentially eligible articles (171 in PubMed and 426 in Embase); finally, 35 articles published from 2006 to 2021, which met all the inclusion and exclusion criteria, were enrolled in this meta-analysis. 3.2The main characteristics of the 35 eligible studies are summarized in 3.3All studies we analyzed scored from 6 to 9 using a nine-point scoring system. The result showed that most studies had good performance in sample selection and outcome, but failed in comparability. Assessment of study-specific quality scores from the NOS system is summarized in 3.43.4.1p < 0.00001) and I2 test (91%) revealed significant heterogeneity.A total of 14 studies with 19 834 patients provided data on the association between tumor size and OS in patients with UTUC. The forest plot A showed 3.4.2p < 0.00001) and I2 test (74%) revealed significant heterogeneity.A total of 18 studies with 18 137 patients provided data on the association between tumor size and CSS in patients with UTUC. The forest plot B showed 3.4.3p < 0.00001) and I2 test (84%) revealed significant heterogeneity.A total of 12 studies including 4546 patients provided data on the association between tumor size and RFS in patients with UTUC. The forest plot C showed 3.4.4p = 0.02) and I2 test (54%) revealed significant heterogeneity.A total of seven studies with 2334 patients provided data on the association between tumor size and IVR in patients with UTUC. The forest plot D showed 3.5As statistically significant heterogeneity was detected among studies, further subgroup analyses were performed in the following cohorts: (1) cohorts receiving RNU, and not nephron-sparing surgery; (2) those receiving segmental resection of ureter; (3) those receiving adjuvant therapy including adjuvant chemotherapy and/or adjuvant radiotherapy; (4) those with negative lymph node; (5) those receiving no lymph node dissections; (6) those comparing tumor size ≥3 versus <3 cm, not treating tumor size as continuous variables; and (7) those in the regions of Asia, and Europe and America. The results suggested that segmental resection of ureter, whether receiving lymph node dissection, cutoff of tumor size, and region of cohorts might be the origin of potential heterogeneity as there was an obvious decrease of heterogeneity in these subgroups compared with the whole cohort . In addi3.6Publication bias was evaluated using the funnel plots in our study. As presented in 3.7Compared with bladder cancer, UTUC is usually in a more advanced stage at diagnosis and is associated with higher recurrence and progression rates p < 0.00001), CSS , RFS , and IVR . To the best of our knowledge, the present study is the first meta-analysis to systematically evaluate these associations. The sample size of the current investigation is the largest to date in studies focusing on survival outcomes of UTUC, given the rarity of the disease. Our study is beneficial to clarify the controversial results about the prognostic value of tumor size in UTUC.The results of our study indicated that larger tumor size was significantly associated with poorer OS  are usually not indicative of tumor depth either. Thus, tumor size could serve as an ideal surrogate in preoperative risk stratification of UTUC, which is solidly supported by our results. More specifically, since tumor size impacts oncological outcomes of patients tremendously, it needs to be considered in the selection of treatment modalities. For example, although lymph node dissection is considered to be important in UTUC treatment, there has been no definite consensus regarding the clinical indications yet. Tumor size has the potential to be deemed as an objective variable for identifying lymph node dissection candidates in UTUC The current study has several limitations that need to be acknowledged. First, all enrolled studies were retrospective in nature. The data extracted from those studies may lead to an inherent bias potentially. Second, most populations included in this meta-analysis were of Asian ethnicity, which might result in an ethnicity bias and limit the generalization of the results. Although we performed subgroup analyses based on the regional distribution of cohorts, the results showed inconsistency between the subgroup of Asia and the subgroup of Europe and America. Therefore, additional populations from other ethnicities are required to further validate the impact of tumor size on UTUC prognosis. Third, obvious heterogeneity among studies was observed, which limits the value of the results. Although the random-effect model takes into account the heterogeneity among studies, the conclusions reached in our meta-analysis should be interpreted with caution. Fourth, the vast majority of studies included only patients receiving RNU, which might lead to a selection bias as patients may be candidate to RNU for reasons of, namely, high-grade disease at biopsy and disease multifocality other than tumor size. Finally, in the case of UTUC, an increasing risk of poor prognosis with increasing tumor size is not necessarily controversial in any quality studies. It is more important to determine the cutoff size of high versus low risk, but our study could not answer the question. In addition, tumor size measurement is particularly inaccurate in UTUC, regardless of whether performed endoscopically or radiographically, and the cutoff for determining risk at 3 cm remains arbitrary. It is also unclear whether these sizes are of the index lesion or represent the cumulative size of all tumors in cases of multiple lesions. Therefore, further studies are supposed to be designed for determining the optimum cutoff of tumor size, which can then contribute to risk stratification guidelines.In spite of these potential limitations, this meta-analysis has its own advantages. First, the sample size adopted was significantly larger than that in any individual study. The massive study population enhanced the statistical capabilities and ensured accurate risk estimations. In addition, all articles included in the final analysis were of high quality by NOS scores, which increase the reliability of the pooled results.4In summary, this meta-analysis revealed that tumor size is associated with an increased risk of overall and cancer-specific mortality and disease recurrence in UTUC. Integration of tumor size with other prognostic indicators may help in risk stratification and individualized treatment options. However, given the study limitations including heterogeneity and retrospective nature of the primary data, these results need to be confirmed further by adequately designed prospective studies with larger populations to provide a better conclusion.Author contributions: Jian Lu had full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.Study concept and design: Lu, L. Ma, Huang.Acquisition of data: R. Ma, Liu, Cheng, Zhou, Pan, Yang, Xia, Zhu, J. He, W. He.Analysis and interpretation of data: Cheng, Tao.Drafting of the manuscript: Liu.Critical revision of the manuscript for important intellectual content: R. Ma, Lu.Statistical analysis: Cheng, Tao.Obtaining funding: None.Administrative, technical, or material support: None.Supervision: Bi, Yang, Xia, Zhu, J. He, W. He, Wang, Huang.Other: None.Financial disclosures: Jian Lu certifies that all conflicts of interest, including specific financial interests and relationships and affiliations relevant to the subject matter or materials discussed in the manuscript , are the following: None.Funding/Support and role of the sponsor: None.


Since the initial COVID-19 cases were identified in the United States in February 2020, the United States has experienced a high incidence of the disease. Understanding the risk factors for severe outcomes identifies the most vulnerable populations and helps in decision-making.This study aims to assess the factors associated with COVID-19–related deaths from a large, national, individual-level data set.A cohort study was conducted using data from the Optum de-identified COVID-19 electronic health record (EHR) data set; 1,271,033 adult participants were observed from February 1, 2020, to August 31, 2020, until their deaths due to COVID-19, deaths due to other reasons, or the end of the study. Cox proportional hazards models were constructed to evaluate the risks for each patient characteristic.2: HR 1.71, 95% CI 1.50-1.96), race , region , chronic respiratory disease , cardiac disease , diabetes , recent diagnosis of lung cancer , severely reduced kidney function , stroke or dementia , other neurological diseases , organ transplant , and other immunosuppressive conditions .A total of 1,271,033 participants  were included in the study, and 3315 (0.26%) deaths were attributed to COVID-19. Factors associated with COVID-19–related death included older age , male sex , obesity (BMI ≥40 vs <30 kg/mThis is one of the largest national cohort studies in the United States; we identified several patient characteristics associated with COVID-19–related deaths, and the results can serve as the basis for policy making. The study also offered directions for future studies, including the effect of other socioeconomic factors on the increased risk for minority groups. The COVID-19 pandemic has brought an unprecedented crisis in global public health since it first appeared in late 2019. By the end of 2020, 19,943,605 confirmed cases and 344,497 deaths were reported in the United States [Many studies have been conducted on this topic in the United States. For example, a study of 2215 adult COVID-19 patients admitted to intensive care units showed that patients older than 80 years had a much higher risk of COVID-19–related death. Men also were at an increased risk . Pre-exiIn 2020, a UK team used National Health Service data to build the OpenSAFELY platform and to conduct a cohort study of 17 million people to investigate factors associated with COVID-19–related deaths in England . The finTo understand whether these factors proposed in the OpenSAFELY study, including age, sex, and other comorbidities, were also linked to higher risks of COVID-19–related deaths among the US population during the similar time window, we conducted a study that expands upon the UK study through the analysis of the Optum de-identified COVID-19 electronic health record (EHR) data set and compared findings with the aforementioned N3C cohort with over 1 million patient records between February 1, 2020, and August 31, 2020, in the United States.This study was designed to replicate the UK OpenSAFELY  study wiThe Optum COVID-19 data set was provided by Optum to the University of Texas (UT) Center for Health Care Data, University of Texas Health Science Center  School of Public Health, and UTHealth School of Biomedical Informatics (SBMI) Data Service. The data set accessed throughout the study was locally hosted by SBMI. It comprised longitudinal EHR data derived from a network of health care provider organizations across the United States. The data were certified as de-identified by an independent statistical expert following Health Insurance Portability and Accountability Act statistical de-identification rules and managed according to Optum customer data use agreements. Clinical and other medical administrative data were obtained from both inpatient and ambulatory EHRs, practice management systems, and numerous other internal systems. Information was processed, normalized, and standardized across the continuum of care from both acute inpatient stays and outpatient visits. Optum data elements included demographics, medications prescribed and administered, lab results, vital signs, other observable measurements, clinical and inpatient stay administrative data, and coded diagnoses and procedures.All authors were authorized to access the Optum COVID-19 data set and were compliant with the data use agreements.Considering many of the risk factors investigated in this study are chronic health conditions that are more commonly present in adult patients, the study population included only adult men and women aged 18 years or older on or before February 1, 2020. To be included in the study, participants must have had at least 1 year of prior observation before the study start date in order to adequately capture their baseline characteristics. Participants also were required to have EHRs for the prior year to be considered eligible for inclusion. In addition, participants with missing demographics, including sex, age, and region, were excluded from the study. Eligible participants were followed from February 1, 2020, until their deaths due to COVID-19, deaths due to other causes, or the end of the study . The Optum data set used in this study was delivered on September 3, 2020, which contained some death data for early September. However, we elected to end the study period a few days earlier to account for possible delays in data delivery.Data for this study were provided by Optum and remain on the servers of the Biomedical Informatics Group-the Analytics Research Center, SBMI, UTHealth. No individually identifiable information was provided, and no participants were contacted by the investigators directly. The secondary analysis of this de-identified data was approved by the Committee for the Protection of Human Subjects, University of Texas Health Science Center at Houston  under protocol HSC-SBMI-20-1194.The outcome of interest was COVID-19–related deaths. All non-COVID-19–related deaths or surviving patients were censored at the time of death or the end of the study, respectively. Due to the de-identification of data, only the death year and month were available. Neither the exact death day nor the cause of death was provided. We, therefore, used an indirect way to define COVID-19–related deaths: If the month of a patient’s last COVID-19 diagnosis  matched or was within 1 month after the death month and any of the other most recent recorded dates  was the same as or within 1 month after the death month, the patient was considered to have experienced a COVID-19–related death. The extra 1-month window was included to account for the possible delay of data entry. For example, if a patient had a positive COVID-19 diagnosis on April 4 and died in April, with any of the aforementioned dates falling in April, the patient was considered to have died from COVID-19. However, if the patient had a positive diagnosis in February but died in April, the patient would be considered to have died from other causes. To determine the death day, we defined the most recent recorded date among the aforementioned dates that matched the death month as the presumptive date of death. For those without matching records on the death month, the presumptive date of death was set to the 15th of the death month as the midpoint of possible death dates.2, not obese; ≥30 and <35 kg/m2, class I obesity; ≥35 and <40 kg/m2, class II obesity; ≥40 kg/m2, class III obesity [Potential risk factors and their categorizations in this study generally followed those used in the OpenSAFELY study . Age was obesity . Smoking1C) measured within the past 15 months, diabetes was grouped into uncontrolled (HbA1C ≥58 mmol/mol), controlled (HbA1C <58 mmol/mol), or without recent HbA1C records. Cancers were grouped based on the first diagnosis date . The most recent creatinine value was used to calculate estimated glomerular filtration rate (eGFR) according to the CKD-EPI equation [2. Chronic respiratory disease other than asthma included chronic obstructive pulmonary disease, bronchiectasis, cystic fibrosis, and interstitial lung fibrosis. Cardiac disease included ischemic heart disease and congestive heart failure. Hypertension or high blood pressure was defined as either a prior diagnosis of hypertension or most recent systolic or diastolic blood pressure ≥140 mm Hg or ≥90 mm Hg, respectively. Chronic liver disease included chronic viral hepatitis, cirrhosis, and primary genetic liver disease. Stroke or dementia included hemorrhagic stroke and dementia that were related to cardiovascular etiology. Besides stroke or dementia, other neurological diseases included motor neuron disease, myasthenia gravis, multiple sclerosis, Parkinson disease, cerebral palsy, quadriplegia or hemiplegia, and progressive cerebellar disease. Organ transplant included both solid organ and bone marrow transplant. Autoimmune disease indicated rheumatoid arthritis, systemic lupus erythematosus, and psoriasis. Other immunosuppressive conditions included HIV, permanent immunodeficiency ever diagnosed, as well as aplastic anemia and temporary immunodeficiency diagnosed within the last year.Based on glycated hemoglobin , inconsistent or missing information on death , age less than 18 years , and missing information on age, sex, or geographic region , leaving an analytic sample of 1,271,033 persons who were tested for COVID-19 between February 1, 2020, and August 31, 2020 . Among tKaplan-Meier curves showed that men had a higher cumulative probability of death from COVID-19 in every age group compared with women. In addition, mortality rose as age increased for both sexes .The HR of COVID-19 deaths for each characteristic is shown in In this study, except when reporting the HRs for age groups , age was otherwise modeled as a restricted cubic spline. With that approach, the relationship between log HRs and age was approximately log-linear . The ris1c <58 mmol/mol during the past 15 months) diminished the risk to 1.11 (1.00-1.23) compared with that of 1.67 (1.46-1.91) in patients with poorer diabetic control or 1.92 (1.75-2.10) in those without recent assessment of control. Other chronic conditions including cardiac disease, severely reduced kidney function (eGFR <30 mL/min/1.73 m2), chronic respiratory disease, stroke or dementia, other neurological diseases, organ transplant, and other immunosuppressive conditions were associated with elevated risks of COVID-19–related death. The effects of hematological and lung cancers were investigated separately from other cancers due to their direct impact on the immune system and the sites of the COVID-19 infection, respectively. Other cancers did not increase the risk, whereas having lung cancer diagnosed within the prior year raised the risk of COVID-19–related death. Patients diagnosed with hematological cancer within the prior year had a higher but statistically nonsignificant risk elevation.The risk increased with rising BMI. Patients with diabetes showed elevated risks. However, well-controlled diabetes . Similarly, the apparent impact of chronic liver disease was decreased with the adjustment for diabetes. Asthma and autoimmune diseases did not show increased risks of COVID-19–related death in either the age-sex adjusted model or the fully adjusted model.In the primary analysis, all the eligible participants were included regardless of SARS-CoV-2 test results. To investigate if the risk factors for death among persons with COVID-19 differed from mortality in the overall cohort, separate Cox proportional hazard models using only the lab-confirmed COVID-19 cases were fit  and 4. MIn the primary analysis, participants with missing BMI, smoking status, and eGFR were treated as non-obese, never-smoker, and normal kidney function. To determine whether this affected the results, we fit a separate Cox proportional hazard model using only the participants with complete information on all 3 factors. The HRs were similar to those from the primary analysis, suggesting robustness of the model to missing values .P<.001). Checking the plots of the scaled Schoenfeld residuals versus time, however, revealed no non-zero slopes in any of the factors. The apparent violation of the proportional hazards assumption, therefore, could be due to the large sample size of the study. The C-statistic of the primary model was 0.87, demonstrating a satisfactory discriminative ability in identifying the risks of COVID-19–related death.Similarly, due to about 18% of the participants missing a designation of race, the primary multivariable model did not include race. The HRs reported in the primary analysis  for raceThis study, using individual-level EHR data from a large population, is one of the largest cohort studies published on this topic in the United States. The results are complementary to those reported from the N3C—the largest US-based cohort to date.The inclusion of all populations in the primary analysis may raise the doubt of assessing risks for being infected and for death after infection. Our analysis among COVID-19 patients eliminates the concern by showing no apparent differences between the 2 groups.Our results regarding increasing risks for demographic factors and comorbidities were consistent with various studies ,8,15,16.The elevated risk observed in the Northeast was likely due to the fact that, at the earliest stage of the pandemic, it was the disease hot zone where hospital capacity was stretched and evidence-based disease management protocols were not yet developed. Studies that have investigated the relationship between smoking and COVID-19 prevalence or mortality have generated conflicting results. Some suggested that smoking was not associated with COVID-19 ,18, whilMany studies have reported a higher risk of severe outcomes of COVID-19 in cancer patients ,8,20-22.The prevalence of the comorbidities examined in our study was slightly higher than the National Health Interview Survey 2017 data, as shown in a recent study on a similar topic . This waThe current study has some important limitations that must be considered. Unlike the UK’s National Health Service, the fragmented health care system in the United States limits the ability to aggregate health records for the general US population. Patients switching to different insurance companies or providers may experience gaps in their electronic medical records, and those without health insurance are much less likely to be seen by the providers contributing to this cohort. The geographic distribution of the participating providers also can affect the representativeness of this sample.To correctly capture patients’ baseline health conditions, we imposed the restriction that only participants with at least 1 year of prior health care engagement were eligible to be included in this study. However, this approach may introduce some selection biases. Those who did not meet this criterion may have different outcomes from COVID-19 infection and therefore, could reduce the generalizability of our study findings.To protect the identities of individual patients, the exact death date and the cause of death were not included in the data set. Although we have developed a reasonable criterion to define the 2 features (see the Methods section), misestimations are still possible, impacting the risk assessments. In addition, at the early stage of the pandemic, testing and diagnosis coding standards were not fully established, so some persons with COVID-19 may not have been detected. Such misclassification, to the extent that it occurred, would tend to be random and only serve to reduce the associations that were observed.Identifying patient characteristics associated with increased risks of COVID-19–related death has been an important topic since the start of the pandemic. Using over 1 million patient EHRs to conduct a large cohort survival analysis, we found that age, gender, race, region, and comorbidities including obesity, diabetes, recently diagnosed lung cancer, reduced kidney function, chronic respiratory disease, cardiac disease, stroke or dementia, other neurological diseases, organ transplant, and other immunosuppressive conditions were associated with elevated risks of COVID-19–related death, while smoking, other cancers, asthma, and certain autoimmune diseases were not. Our large and geographically diversified individual-level data provide comprehensive and reliable results on this topic. This study can also serve as a foundation for future policy making about the protection of vulnerable populations, the distribution of vaccines, and other considerations.


PIP scores generated for 36,511 patients were assessed according to the results of PGx multigene panel testing. PIP scores versus the proportion of patients in whom at least one EADGI was found were 22.4% vs. 22.4% (p = 1.000), 23.5% vs. 23.4% (p = 0.6895), 30.9% vs. 29.4% (p = 0.0667), and 27.3% vs. 26.4% (p = 0.3583) for patients tested with a minimum of 3-, 5-, 14-, and 25-gene panels, respectively. These data suggest a striking concordance between the PIP scores and the EAGDIs found by gene panel testing. The ability to identify patients most likely to benefit from PGx testing has the potential to reduce health care costs, enable patient access to personalized medicine, and ultimately improve drug efficacy and safety.Utilizing pharmacogenomic (PGx) testing and integrating evidence-based guidance in drug therapy enables an improved treatment response and decreases the occurrence of adverse drug events. We conducted a retrospective analysis to validate the YouScript With the introduction of the Right Drug Dose Now Act in the United States Congress , in ordeAutomated algorithms that identify potential EADGI risks have shown a utility in determining which patients would benefit most from PGx testing ,12,13. IAs the incidence of complex diseases that require polypharmacy is increasing, assessing, and mitigating PGx risk can reduce the length of hospital stays, decrease the healthcare costs, and improve patient outcomes. The aim of this study is to further validate the accuracy of the PIP score in predicting patients who may have EADGIs and would benefit most from PGx testing. This retrospective analysis of approximately 36,000 patients compared the pretest PIP scores with the PGx multigene panel test results and highlighted the common risks detected.CYP2C19, CYP2C9, and CYP2D6 through Invitae  from May 2013 to March 2022, were included in the study. The specific PGx genes tested varied, ranging from 3 to 25 genes based on provider ordering preferences and the tests that were available at the lab. At the time of testing, patients must have had at least one medication reported on their requisition form or have had a current medication list sourced from the attached electronic health record, in order to allow for the calculation of PIP scores based on the medication regimens. A complete list of the drugs used to calculate the PIP scores in this study, and their respective clinical areas is available in the Individuals residing in the United States, who underwent provider-ordered PGx testing, at minimum for All patient data were de-identified and recorded in a Health Insurance Portability and Accountability Act (HIPAA)-compliant electronic database. Review and analysis of de-identified and aggregated data were approved for waiver of authorization by WCG IRB . PIP scores were calculated for each patient as previously described ,16. BrieCertain interactions were not included in the PIP score calculations, including interactions that required monitoring and interactions that would not increase the severity of an already detected drug–drug interaction because the added information from PGx testing would not change the clinical management. Importantly, phenoconversion, predicted by incorporating published evidence in changes in the area under the curve (AUC) for DGGIs and DDGIs, was accounted for in PIP scores. Another YouScript algorithm was used to determine the likelihood and severity of all potential EADGIs  plus additional drug–drug interactions (DDIs) and drug–drug–drug interactions (DDDIs). For each individual in the cohort, two PIP scores were calculated. First, a 25-gene PIP score was calculated using the medications listed for the patient at the time of testing. However, because the 25-gene PIP score did not necessarily reflect the genes selected by the clinicians, or the genes available at the time of testing, the second PIP score was an adjusted PIP score, as determined using only the PGx genes for which the patient received a laboratory test. After PGx testing, interpreted phenotypes and the presence of EADGIs were determined for each patient. CYP3A4 and CYP3A5 ordered by their clinicians. In the 14-gene group, patients had to have had at least the genes in the 5-gene panel plus CYP2B6, CYP4F2, SLCO1B1, TPMT, DPYD, HLA-B*57:01, IFNL3, UGT1A1, and VKORC1 ordered. The 25-gene panel included all genes in the 14-gene panel plus ADRA2A, COMT, CYP1A2, F2, F5, GRIK4, HTR2A, HTR2C, MTHFR, NAT2, and OPRM1. Of note, the PIP scores and presence of EADGIs were based on all the genes ordered from the 25 gene list and the listed medications, instead of being solely based on the minimum set of genes. To validate the performance of the PIP algorithm, the adjusted PIP scores were compared with the proportion of patients who were determined, by PGx testing, to have at least one EADGI . This comparison was made in the overall cohort  and in the subgroups of patients who had testing with a minimum set of 5, 14, or 25 genes. In the 5-gene group, patients had to have had at least the three base genes plus For the entire cohort, the EADGIs determined by PGx testing were categorized according to the interaction level, estimated change in AUC, and the primary clinical area. The AUC change was based on the appropriate published literature for any given interaction. The primary clinical area was assigned according to the most commonly used indication for each drug, as determined by Invitae’s clinical PGx pharmacist team, utilizing patient-friendly headings with the Anatomical Therapeutic Chemical (ATC) classification system as a guide . The nump-value of less than 0.05 indicated statistical significance, and a greater value indicated that no evidence of a significant difference was found. The statistical significance of a single proportion, compared to a population estimate, was determined using the z-test with a two-sided alpha level of 5% . A p-valAmong the 36,511 patients included in the cohort, 56.3% were female, 47.2% were white, and the mean age was 61 years  . The meaAmong all the patients, 30,471 (83%) were taking at least one high-impact PGx medication that could result in an EADGI . Almost p = 1.0000) were determined by PGx testing to have had at least one EADGI , the mean PIP and the mean adjusted PIP scores were 32.6% and 27.3%, respectively, and 27.3% of the patients (p = 1.0000) were determined by PGx testing to have at least one EADGI. Notably, 11,861 patients (32.5%) had an adjusted PIP score of 0.0%, which indicated that they were taking no medications with a high-evidence PGx impact at the time of testing. When these patients were appropriately removed from the analysis, the mean PIP score among the remaining 24,650 patients rose to 33.2%, and 33.2% of the patients (p = 1.0000) were found to have had at least one EADGI, resulting in an NNT of 3.0. A total of 17,703 patients had an adjusted PIP score of >25%, which based on the previous studies, indicated a moderate (26–50%) to high (>50%) likelihood of an EADGI being detected [Among the entire cohort (with a minimum of three genes tested), the mean PIP and the mean adjusted PIP scores were 26.4% and 22.4%, respectively, and 22.4% of patients  having at least one EADGI. A total of 9804 EADGIs were detected in 9134 patients; 2444 (24.9%) of these were major or contraindicated interactions. Medications across multiple clinical areas were typically the cause of DDGIs involving a cumulative impact on the AUC , thus caMedications found to have EADGIs were ranked according to the frequency with which they were prescribed within a calendar year, as determined by the 2019 ClinCalc DrugStats Database [The results from this study, of more than 36,000 patients, demonstrate that the PIP score is an accurate predictor of how frequently EADGIs are detected, based on the comparison with PGx testing results. Importantly, the accuracy of the adjusted PIP scores to PGx test results was comparable regardless of the number of minimum genes tested. Taken together, these data demonstrate that the use of a CDST, with an embedded PIP score calculator, to identify patients who would benefit most from PGx testing is a practicable alternative to universal preemptive testing. PGx-guided medication management has the potential to improve the safety and efficacy of drugs and reduce inappropriate use of multiple medications for all patients . In factThe findings from this study also demonstrate the importance of implementing PGx-guided medication management with a more global approach. Traditionally, reporting has been limited to a single or a few genes, based on a test order , to a panel targeted to a specific clinical area, such as behavioral health, or to limited conditions, such as major depressive disorders. This trend is consistent in this cohort, with only 8.7% and 5.7% of patients having the 14- or 25-gene panel ordered, respectively. However, many PGx testing laboratories typically run multigene panels, as high-throughput assays can reduce the overall cost and turnaround times without compromising sensitivity and specificity . UnfortuFor the 25% of EADGIs detected in our study that were major or contraindicated, a different medication would have been recommended because the potential risk outweighed the benefit. The the top five medications, with major or contraindicated EADGIs, were es , clopidogrel, tramadol, and codeine. The PIP score for a patient simultaneously taking es , clopidogrel, and codeine/tramadol was 62%, meaning an EADGI would be identified more often than not at the time of testing. CYP2C19 has established EADGIs with 21 medications used in behavioral health, cardiology, gastroenterology, infectious disease, neurology, pain management, and reproductive and sexual health. This includes es   and clopidogrel . It was reported that patients with the most extreme CYP2C19 metabolizer phenotypes were increased by approximately 9% in suicide victims taking es  when compared with a control population [CYP2C19 genotyping, to guide clopidogrel prescribing over a one-year period, 20 deaths due to cardiovascular disease would be averted—a 27% decrease, or one life saved for every 50 patients tested [CYP2C19 variants [CYP2C19 to inform the prescribing of behavioral health medications, without considering other interactions, which could have an impact on medications prescribed (by other providers) for other indications . A recent study of the impact of a behavioral health-focused PGx panel test in veterans showed a faster time to remission of major depressive disorders and a reduction in the medications that would cause a known EADGI in behavioral health treatments; however, no mention was made of applying the information for EADGIs outside of that clinical area of behavioral health [CYP2D6. However, CYP2D6 currently has 51 EADGIs for medications used in behavioral health, cardiology, gastroenterology, oncology and hematology, infectious disease, neurology, reproductive and sexual health, and urology. In 2017, the U.S. President officially declared the opioid crisis a public health emergency [CYP2D6 [CYP2D6 poor or ultrarapid metabolizers, an approximately three-fold higher frequency than in a typical U.S. population [Importantly, many of these medications have interactions with the same gene but are used to treat across different clinical areas. For example, pulation . Anotherpulation . Both ils tested . Despitemergency , and in pulation ,31. A unWith a mean medication count of 9.4 in the cohort, co-medication is also of concern. Importantly, PIP scores account for phenoconversions, specifically those caused when pharmacokinetic drug interactions amplify or attenuate an individual’s inherent genetic ability to metabolize medications ,34. DrugCYP2C19 genotype-guided antiplatelet therapy was found to likely reduce healthcare spending by an estimated USD 8525 in costs per patient [CYP2D6 was cost-effective with an ICER of around EUR 47,000 per QALY [CYP2C19 or CYP2D6 [The utilization of PGx testing has also been shown to reduce the costs associated with medication management and any adverse reactions for the medications, which are highlighted in this study with the most major or contraindicated EADGIs. For example, implementation of PGx testing and management in opioid prescribing could save an estimated USD 14,000 per patient annually . Further patient . Given t patient . Statin  patient ,41. A reper QALY . A recenr CYP2D6 . These sThere is a financial and clinical case to be made for each of these examples, but when the implications are considered more broadly, the case for PGx panels becomes increasingly relevant. Since PGx test results are longitudinally constant, results can be preemptively utilized with appropriate CDSTs at the point of care for the lifetime of the patient. This will be further facilitated if Congressional efforts are successful in incentivizing updates to electronic health record systems with the goal of ensuring that healthcare providers are alerted to EADGIs when making prescribing decisions , similarAnalysis was limited to data from the United States because the interpreted phenotype distribution used relied on North American data  and the This is the largest study to date to demonstrate the validity of a probability-driven algorithm that incorporates phenoconversion risk, specifically Invitae’s YouScript PIP score, to identify patients who would most likely benefit from PGx testing. The advantages of using this prescreening tool include automated incorporation of new and updated guidelines and a more efficient use of health care resources in order to help optimize medications across the entire patient care team. Prior studies have shown that the overall impact of using PGx testing and YouScript includes reduced hospitalizations, emergency department visits, and healthcare costs. Further validation of PIP scores must include the clinical impact in addition to quantifying comparability with testing results. Future work will also include additional validation in a broader population, randomized controlled trials, and coverage of more clinical areas and crossovers.


Alcohol-associated liver disease (ALD) is an intricate disease that results in a broad spectrum of liver damage. The presentation of ALD can include simple steatosis, steatohepatitis, liver fibrosis, cirrhosis, and even hepatocellular carcinoma (HCC). Effective prevention and treatment strategies are urgently required for ALD patients. In previous decades, numerous rodent models were established to investigate the mechanisms of alcohol-associated liver disease and explore therapeutic targets. This review provides a summary of the latest developments in rodent models, including those that involve EtOH administration, which will help us to understand the characteristics and causes of ALD at different stages. In addition, we discuss the pathogenesis of ALD and summarize the existing in vitro models. We analyse the pros and cons of these models and their translational relevance and summarize the insights that have been gained regarding the mechanisms of alcoholic liver injury. Alcohol-associated liver disease (ALD) is a major cause of chronic liver disease worldwide . Accordi4, and LPS, and when most of these “second hits” are combined with alcohol, they can induce the occurrence of alcoholic hepatitis and liver fibrosis in experimental animals . Th. Th11].  animals ,16,17,18We have summarized various models according to the different stages of ALD. This includes the advantages, limitations, and translational relevance of the pure alcohol model, which is currently the most commonly used ALD animal model, as well as the research progress of the second hit model, which has shown great potential. In addition, we also discuss the pathogenesis of ALD and summarize the results of liver injury  and common in vitro models of ALD . This wiThe pathophysiology of ALD is complex, and the direct effects of ethanol and its metabolites  on the liver and other organs, as well as immune cell activation and inflammation triggered by alcohol intake, are drivers of the ALD course ,8. Programmed cell death (PCD) in ALD is thought to play a central role in the progression of liver injury. The pathways involved in PCD include the apoptosis, necroptosis, pyroptosis, autophagy, and ferroptosis pathways . ApoptosAdipose tissue is primarily considered a major storage organ, and alcohol intake can stimulate liver fat decomposition and promote liver fat regeneration, causing more lipids to enter the liver, resulting in steatosis, inflammation, and even adipocyte death ,47,48,49Enterococci, Bifidobacteria, Eubacterium g23, Oscillibacter, and Clostridiales [Bacillus and Veillonella were increased in the faeces of patients with severe alcoholic hepatitis relative to healthy subjects [Enterobacteriaceae were 27 times more abundant in the faeces of patients with alcoholic cirrhosis than in healthy volunteers, and Enterobacteriaceae were the most common hepatic translocating bacteria in patients with cirrhosis [Streptococcus, Shuttleworthia, Rothia, and Nubsella in the duodenal mucosa is associated with the early stages of progressive ALD [Recent evidence indicates that the gut is a key site for ALD . Drinkinridiales ,66. Baciirrhosis . Long-teirrhosis ,69. Whenirrhosis . In the irrhosis ,72,73. Birrhosis ,74,75. Ssive ALD . In addisive ALD ,78. LPS sive ALD ,78.The infiltration of inflammatory cells such as macrophages and neutrophils is a prominent feature of the early staging of ALD and ASH ,80. Hepa2O2), superoxide anion (O2−), and hydroxyethyl (·OH), and can react with acetaldehyde, leading to the formation of acetaldehyde protein adducts [In addition to the aforementioned mediators, several other factors have also been shown to play important roles in the pathogenesis of alcohol-associated liver disease. Notably, oxidative stress in the liver has been recognised as a hallmark feature of ALD, with highly reactive free radicals produced by ethanol and its metabolites promoting liver tissue damage . After b adducts ,106,107. adducts . Hepatic adducts . Additio adducts ,110. TheAnimal models can provide profound experimental strategies that are difficult to complete in clinical studies and can help to reveal the pathogenesis of human diseases. Rodent models have become the preferred experimental models for the preclinical studies of human diseases in many fields, including in ALD, due to their relatively low cost, shorter gestation time, shorter fecundity, and, perhaps most importantly, ease of handling of gene manipulation.w/v). After the acclimatization feeding is completed, feeding with the LDE diet is continued for 4–12 weeks [w/v) for a long period of time, the blood alcohol concentration (BAC) of rodents reached 100~150 mg/dL and the plasma ALT and AST levels of the animals were significantly increased, which was accompanied by a six-fold increase in liver triglyceride levels, causing mild liver damage [The Lieber–DeCarli model is one of the most frequently used rodent experimental models in the study of early ALD . The con12 weeks . When anr damage ,113. In r damage ,114. Ther damage . As a miIt is worth noting that the drinking pattern of this model is different from human drinking habits because the rodents are forced to drink alcohol when they are hungry and thirsty and they switch from eating solid food and water regularly to a liquid diet, thus inducing some changes in physiological phenomena ,115. In The Tsukamoto–French model overcomes the rodents’ natural aversion to alcohol and allows for the better control of alcohol consumption in animals, resulting in more severe liver damage . In thisIn conclusion, the TF model induces a liver injury process similar to human ALD in rodents, including progressive steatosis, fibrosis, and liver cirrhosis characterized by focal necrosis and immune cell infiltration ,120. Whiv/v) by gavage [v/v) by in-gastric administration [v/v, 3 doses, 12 h apart), the improved model increased the animals’ BAC levels, ALT and AST levels, inflammation, and neutrophil infiltration [v/v, 3 gavages, 12 h apart) [The original chronic plus binge model (known as “Gao-Binge” or the NIAAA model) was developed by Gao Bin’s team . The paty gavage ,141. They gavage ,141. In stration . In 2015stration . Comparestration . In receltration . In addiltration ,142. In h apart) ,125,142.h apart) ,126.In the chronic plus binge model, alcohol binge can accelerate the metabolism of alcohol and the development of its metabolite acetaldehyde in the liver within 2–3 h . EarlierThe chronic plus binge drinking model reproduces the drinking behaviour and acute/chronic liver injury of ALD patients and has been widely used to study the pathogenesis of ASH and mild AH ,115. In If the pathology of human ALD is to be reproduced more accurately, it is necessary to provide a second hit in an animal model. In current research, well-known second hits include nutritional modification, an agonistic/xenobiotic second hit, viral infection, and a genetic second hit .Nutritional modification is one of the most common environmental secondary attack methods. Both a high-fat diet and excessive alcohol consumption initially contribute to the development of hepatic steatosis, and combining a high-fat diet (HFD) with alcohol consumption is one of the most basic nutritional regulation approaches ,130. In w/v in the first week and 0.25% w/v after the second week) to a high-fat diet  combined with an ethanol  diet, after 16 weeks of feeding, the serum levels of ALT and AST in rats rose to 2–3 times those of rats fed a normal diet, resulting in moderate to severe fatty liver as well as focal lobular central necrosis and inflammation, with some animals developing liver fibrosis and even cirrhosis [In addition, the liver plays an important role in maintaining iron homeostasis in the body . In the irrhosis . As thisirrhosis . However4, acetaldehyde compounds, acetaminophen (APAP), and N-Nitrosodiethylamine (DEN). The original LPS plus ethanol model was developed by Bhagwandeen et al. Rats were fed an LDE diet for 10 weeks and then injected intravenously with LPS (10 mg/kg). Chronic ethanol combined with low-dose LPS can cause hepatocyte necrosis and neutrophil infiltration in rats with severe steatosis. The final BAC level of rats was approximately 90 mg/dL [v/v) and an intraperitoneal injection of LPS (2 mg/kg). Mouse liver sections also showed lipid droplet accumulation and enhanced liver damage, with distinct areas of necrosis and inflammatory cell infiltration [Common exogenous stimuli for the second hit include LPS, CCl90 mg/dL . In rece90 mg/dL . In anotltration . In concltration . Moreove4 model for alcoholic liver fibrosis is considered to be a classic model. The mice are injected with CCl4  in the intraperitoneal cavity within eight weeks, and this is combined with an LDE diet [4 model, liver damage was caused in mice through the inhalation of CCl4 (once a week for the first 4 weeks and twice a week for the following 3 weeks), and ethanol was added to the drinking water . The ethanol plus CClLDE diet ,133. ThiLDE diet . In a re4 can cause significant fibrosis within 4 weeks and a strong proinflammatory reaction within 7 weeks [4. Severe liver fibrosis was first induced in mice by the intraperitoneal injection of CCl4  for 18 weeks, followed by a combination of an LDE diet (4% v/v) and CCl4 injection for a further 10 weeks. After 28 weeks of alcohol plus CCl4 administration, liver histopathology in mice showed steatosis, inflammation, fibrosis, the ballooning of hepatocytes, and tumour nodules [4 model successfully reproduces most of the disease course and liver pathology of ALD, and although it is time-consuming, it is simple to operate and inexpensive to care for and is a common model for alcoholic liver fibrosis.The inhalation of CCl 7 weeks . The mod 7 weeks . In addi nodules . The ethv/v) for 15 days (including 5 days of acclimatization feeding) and on the last day were given the corresponding EtOH gavage  and an intraperitoneal injection of APAP (200 mg/kg). The study showed that this model increased inflammatory secretion, lipid accumulation, and oxidative stress in mice, accompanied by a significant increase in ALT and AST levels [In addition, in the ethanol plus APAP model, mice were gavaged with ethanol (4 g/kg of ethanol every 12 h for 2.5 d), and 12 h after the last binge, they were injected with APAP (150 and 300 mg/kg) by oral gavage . In anotT levels . This etDEN is an alkylating agent of DNA bases that induces gene expression patterns in mouse tumours that resemble the poor prognosis subclass of human liver cancer ,149. TheThe genetic second hit includes two aspects: enhancing the function of suspected pathogenic genes to promote the damage mechanism and the reduction in or loss of the function of protective genes that make the liver sensitive to the harmful effects of alcohol . At presv/v) for 4 weeks. The ethanol content of the liquid diet was then adjusted to 7% v/v for the next 4 weeks. Eight weeks of feeding resulted in increased lipid droplets in the liver tissue of the mice, accompanied by increased triglyceride and cholesterol levels. It was shown that HBV and alcohol diet synergistically induced abnormal hepatic lipid metabolism in mice, leading to alcoholic fatty liver [Correspondingly, HBV infection is also a risk factor for hepatocellular carcinoma and liver-related death in patients with ALD . HBV-Tg ty liver .Although second-hit models are well suited to induce early- and late-stage ALD in animals, there are differences in the pathogenesis of liver injury caused by the combination of ethanol and a second liver-damaging substance, compared to liver injury caused by ethanol alone . It is pw/v) and water for 22 h per day for 24 months [Nonhuman primates (monkeys and apes) have long been regarded as important laboratory animals for the study of biomedical disease processes . Because4 months . In this4 months ,167,168.4 months ,167. How4 months .v/v), replacing the ethanol-containing water in the incubator every morning. After 4 weeks, histological analysis revealed ballooned hepatocytes and mild fibrosis in low-dose ethanol-treated zebrafish, accompanied by high ALT levels (200 IU/L) and high triglyceride levels (300 mg/dL) and elevated BAC levels to 0.175% [In addition, the zebrafish has emerged as a powerful vertebrate model for studying liver-related diseases. Although the structure of the zebrafish liver is different from that of mammals, it is functionally similar to 70% of human disease genes and exhibits very similar basic physiological processes, genetic mutations, and pathogenic responses to environmental damage consistent with humans . Zhou eto 0.175% ,171. Como 0.175% ,172. Howo 0.175% ,174.Most of what we currently know about liver disease in humans comes from animal models. However, animal models are not able to fully reproduce human ALD process, highlighting an area where appropriate in vitro models can complement ALD models to further understand key developmental and pathological mechanisms.−1) are added to the culture medium to treat the cells and are left for 24 h in vitro to imitate the HFD plus EtOH model. In this model, EtOH promotes PA-induced hepatic lipid accumulation and inflammation in rat primary hepatocytes by activating the ER stress response [In the past 50 years, two-dimensional (2D) monolayer cell cultures have commonly been used in in vitro models to study cell biology . The comresponse . With the increasing demand for in vitro models, cell culture methods are developing towards more realistic imitation. A 2D sandwich structure is a major innovation of 2D culture technology. Embedding primary human hepatocytes (PHH) into two layers of extracellular matrix (ECM) proteins provides a stable scaffold that allows hepatocytes to improve cell-to-cell contact by interacting with two layers of ECM proteins . HoweverThe 2D monolayer cell culture is inexpensive and easy to maintain and is the most widely used method for the in vitro modelling of the liver as an alternative to animal models for drug safety and screening for early drug candidates . NeverthThe complex structure of the liver cannot be completely simulated by monolayer cell culture systems. Therefore, combining different cell types in a functional three-dimensional (3D) structure can overcome the shortcomings observed when liver cells are cultured in 2D structures . This isIn a recent study, a new in vitro ALD model was successfully constructed on a chip. The chip contained cocultured spheres of primary rat hepatocytes and hematopoietic stem cells. The spheroids were exposed to a medium containing ethanol (60 mM) for 48 h to demonstrate the damage caused by alcohol . In receIn previous decades, the development of ALD animal models allowed for significant progress to be made in ALD research . DiffereFurthermore, although most alcohol-associated liver disease can be achieved in animals by altering stimulating factors and experimental animals, it is currently difficult to establish animal models of alcohol-triggered HCC that can reproduce pathophysiological processes similar to those of human HCC ,207. As Another point worth noting is that the development of AFL into inflammation is a prerequisite for early ALD to develop into intermediate and late ALD  . After iFurthermore, the species, gender, age, and environment of the animals may have an impact on the construction of the model ,142,221.


Carya cathayensis) kernel is rich in powerful bioactive flavonoids, which can remove excess free radicals in the human body and play an important role in regulating the physiological metabolism of the plant. This study investigated the changes of flavonoids in hickory exocarp and embryo during development. In this study, 72 DEGs involved in the regulation of flavonoid biosynthesis in fruits were identified, and TT4, CCoAOMT1, UGT71D1, C4H, F3H, TT8, FLS1, and LDOX were identified as the core genes of flavonoid biosynthesis. A total of 144 flavonoid-related metabolites were detected by metabolite analysis. Transcriptome and metabolome analysis combined to construct the flavonoid biosynthesis regulatory pathway in the development stage of hickory fruit. Our results provide a theoretical basis for the exploration and regulation of functional genes related to flavonoid biosynthesis and metabolism in hickory and other plants and the breeding of new walnut varieties.Hickory ( Carya cathayensis) is an important and widely planted nut tree species in Zhejiang and Anhui provinces of China. Because of its high nutritional value and unique flavor, Hickory has become more and more popular in southern China in recent years. Flavonoids produced by plants are important dietary components in animal systems. Flavonoids and phenolic compounds have a wide range of properties, including antibacterial, antifungal, antiviral, and anticancer activities , key enzyme  genes, and transcription factors  in a single direction.The studies on the metabolic pathway of flavonoids and related enzymes, genes, and regulatory factors in hickory are still unclear. This study used the transcriptome and metabolome sequencing techniques to analyze the gene expression profiles and metabolites in the exocarp and embryo of hickory at different stages after flowering and pollination. The dynamic changes of gene transcription in the process of flavonoid synthesis in hickory were analyzed in-depth, and the key enzymes, genes, and transcription factors of the flavonoid synthesis pathway in hickory were explored to provide more information for the study of flavonoid synthesis in hickory and other plants and the mining of related functional genes. At the same time, it laid a theoretical foundation for the research on the flavonoid proteome and translation group and the breeding of hickory varieties.C. cathayensis) in the fruit garden  of Donghu Campus of Zhejiang A & F University, which was a 17-year-old tree with the whole-genome sequencing strain (ZAFU-1). After pollination, 100 fruits were randomly picked from east, south, west and north of the middle and upper part of the tree crown at 85d , 91d , and 127d , respectively. The samples were frozen quickly in liquid nitrogen and stored in a refrigerator at −80°C after sampling (separation of exocarp and embryo). In 2020, three test trees were planted at 5 m spacing in Zhi’nan Village , Taihuyuan Town, Lin’an District, Hangzhou City, Zhejiang Province. Three trees are approximately 30 years old, with similar tree potential and a growing environment. One hundred fruits at each stage and each tree were randomly picked from the east, south, west, and north of the middle and upper part of the tree crown at 85d , 100d , 109d , and 127d  after pollination. The samples were frozen quickly in liquid nitrogen and stored in a refrigerator at −80°C after sampling (separation of exocarp and embryo).The trials were carried out in 2016 and 2020. In 2016, the material was collected from a hickory tree . Clustering the expression patterns of all the different genes was carried out, and then, the gene sets conforming to certain biological characteristics were selected for trend analysis. Cluster Profiler , and reads with low quality (reads with Qphred ≤20 bases accounting for more than 50% of the entire read length) were removed. The content of Q20, Q30, and GC of clean data was calculated. The reference genome and gene model annotation files were directly downloaded from the Chinese hickory genome website.Profiler ; 3.4.4 sPPI analysis of DEGs was based on the STRING database, using target genes to construct a network. Otherwise, Diamond ; 0.9.13 GO/KEGG was performed for each gene module. In addition, the gene regulatory network map obtained Hub genes, which are at a pivotal position in the regulatory network of flavonoid metabolism in the exocarp and embryo.® 6,500+ mass spectrometry platform. The mass spectrometry detection process was divided into Blank sample (BLANK), IS standard mixed sample (IS mixed standard), quality control sample (QC), and experimental sample on computer detection. The SCIEXOSV1.4 software was used to open the offline mass spectrometry file (.wiff), and the integration and calibration of chromatographic peaks were carried out. The quality control of the QC sample data ensured the accuracy and reliability of the data results.Target-like metabolomics studies were conducted using liquid mass spectrometry (LC-MS) based on the highly sensitive SCIEX QTRAPp < 0. 05. Z-score analysis was performed to measure the relative quantitative value of metabolites on the same level. Finally, KEGG enrichment analysis of differential metabolites was conducted.Then, the metabolites were analyzed by multivariate statistical analysis: hierarchical clustering (HCA), correlation analysis of metabolites, and functional analysis of metabolic pathways. The experimental samples were detected and qualitatively analyzed, and the triple quadrupole MRM model quantitatively analyzed the compounds. Pathway annotation and classification of identified metabolites were performed. Major databases such as KEGG, HMDB, and lipid map were used to label the identified metabolites. Correlation analysis of different metabolites was conducted. A statistical significance test was conducted for correlation analysis of metabolites, and the threshold was set as a value of K-means clustering method was used to determine the optimal clustering k value of 5 , with three biological replicates. The clean reads obtained from each library accounted for more than 90% of the total reads. Approximately 95% ~ 97% of clean reads in each expression profile data could be mapped to the reference genome . And morStringtie assembled 3,505 transcripts, and more than 90% of the transcripts were less than 5 KB in length. GO and KEGG were used to annotate the functions of these transcripts. The results showed that 966 transcripts were mapped to different GO functional nodes. The top 10 GO terms were catalytic activity, heterocyclic compound binding, organic cyclic compound binding, ion binding, organic substance metabolic process, primary metabolic process, cellular metabolic process, macromolecule metabolic process, nitrogen compound metabolic process, and small molecule binding. KEGG pathway enrichment showed that 125 transcripts were enriched into 72 pathways. The top 10 pathways were Biosynthesis of secondary metabolites, Ribosome, Carbon metabolism, Phenylpropanoid biosynthesis, Endocytosis, Nucleotide excision repair, MAPK signaling pathway, Oxidative phosphorylation, and Spliceosome and Butanoate metabolism –C.PCA showed that the exocarp and embryos among different samples during the same development period were clustered in compact groups, indicating good repeatability. However, between the exocarp and embryo, and between the exocarp and embryo were separated at different development stages, showing a good specificity . The cor2FC | > 1. The expression of DEGs in various combinations is shown in According to the sample relationship analysis result, DEGs were further screened under the condition of filter FDR < 0.05 and | logFour gene expression patterns were generated through clustering  and trenKEGG functional enrichment analysis was performed on the two trends of ClusterI-II with significant differential genes in exocarp and embryo at four developmental stages of hickory to obtain the related pathways of flavonoid synthesis during fruit development . First, C4H, BAN, and CCoAOMT7. And 60 DEGs related to flavonoid biosynthesis were significantly enriched in Cluster II, including F3H, TT4/7/8, CCoAOMT1, LDOX, and BEN1. Hence, the activity of flavonoid synthesis in exocarp was significantly stronger than that in the embryo.Seventy-two DEGs were further mapped to two expression trends  value is 9  and CCOAOMT1 (CCA0670S0080). Similarly, the hub genes were UGT71D1 (CCA1700S0011) in BROWN block; C4H (CCA0524S0150) in TURQUOISE module block; and F3H (CCA0705S0076), TT8 (CCA0510S0065), FLS1 (CCA0533S0160), and LDOX (CCA1505S0029) in YELLOW block  among all QC samples was generally higher than 0.95 , and theWith the fruit development, the upregulated metabolites decreased gradually, while the downregulated metabolites increased gradually in the embryo. Upregulated metabolites increased initially and then decreased in the exocarp, while downregulated metabolites gradually decreased . The expPAL2 in the embryo was higher than that in the outer exocarp. It gradually decreased as the fruit developed to maturity. The expression level of C4H in the embryo was higher than that in the outer exocarp and decreased with the development of hickory fruit. The expression level of TT4 in the outer exocarp was significantly higher than that in the embryo and decreased gradually with fruit development to maturity. Upstream substrates catalyzed by TT4 produce isoliquiritigenin and naringeninchalcone, which will initiate two isoflavone biosynthesis pathways. As can be seen from the FPKM value, the TT4 gene is related to these two isoflavone biosynthesis pathways. Hence, their downstream enzyme CHIL expression level in exocarp is significantly higher than that in the embryo. It indicates that isoflavones are synthesized through these two pathways.To further explore the relationship between DEGs and differentially expressed metabolites involved in flavonoid synthesis, this paper systematically constructed the flavonoid biosynthetic pathway during the development of hickory fruit . The patOMT1 gene in exocarp was higher than that in the embryo. The expression level increased gradually with the development of hickory fruit, mainly in the cotyledon completes and fruit maturity stages. In the other step, Naringenin was converted to Dihydrokaempferol catalyzed by F3H, then to Kaempferol catalyzed by FLS, and then to Kaempferide catalyzed by 4′OMT. As can be seen from the FPKM value, the expression levels of F3H and FLS genes in exocarpium were significantly higher than those in the embryo. With fruit development to maturity, the expression levels of F3H and FLS genes gradually decreased, mainly in the middle and late cotyledons. The third step is that Naringenin is transformed into Eriodictyol under F3′5′H and TT7 synergistic catalysis and transformed into Taxifolin after F3H catalysis. Meanwhile, dihydrokaempferol of the second step can also be converted into Taxifolin under the co-catalysis of F3′5′H and TT7 and then into Quercetin under the catalysis of FLS. The second step product kaempferol can also obtain quercetin through F3′5′H and TT7 synergistic catalysis. Quercetin is then converted to Myricetin with the catalysis of F3′5′H. As can be seen from the FPKM value, the expression levels of F3H, TT7, and FLS in exocarpium were significantly higher than those in the embryo. With fruit development to maturity, the expression levels of F3H, TT7, and FLS were gradually decreased, mainly in the middle and late cotyledon. In the third step of the biosynthesis of flavonoids and flavonols, the intermediate taxifolin was converted into leucocyanidin under DFR catalysis, and the anthocyanins biosynthesis pathway was initiated. Leucocyanidin was converted to Cyanidin catalyzed by ANS and then to anthocyanins by enzymes such as UGT, GT, and RT. As seen from the FPKM value, the expression levels of DFR, GT, and UGT in exocarp were significantly higher than those in embryo. Their expression levels tended to decrease with fruit development to maturity. However, among the genes encoding UGT enzyme, some DEGs, such as UGT74E2, UGT91C1, and UGT89B1, were expressed in the embryo relatively higher than in the outer peel. Some DEGs, such as UGT73B1, UGT76E1, and UGT85A4, the expression of these genes in the exocarp was higher than that in the embryo -Maackiain3-O-glucoside. The other pathway is the conversion of Naringenin chalcone into Naringenin under the cooperative catalysis of TT5 and CHIL and then into Sissotrin after a few steps of catalytic transformation. Naringin, an intermediate in the isoflavone biosynthesis pathway, will initiate the three-step flavonoid and flavonol biosynthesis pathway. The first step is the conversion of Naringenin to Apigenin catalyzed by FNS1, then to Luteolin catalyzed by F3′H and F3′5′H, and then to Chrysoeriol catalyzed by OMT1. According to the FPKM value, the expression level of the e embryo .The above results showed that the expression levels of C4H, UGT74E2, UGT91C1, UGT89B1, and UGT79B3 in the embryo were higher than those in the outer pericardium. Still, the expression levels of these genes decreased gradually with the development of the fruit, mainly in the middle and late cotyledons F3H, PAL2, TT4, TT7, DFR, CHIL, OMT1, GT72B1, FLS1, UGT73B1, UGT76E1, etc. In general, the expression levels of these genes in the outer peel were significantly higher than those in the embryo, and the expression levels decreased gradually as the fruit developed to maturity.PAL, CHS/TT4, 4CL, FLS, and F3H. In this study, 72 DEGs were directly involved in flavonoid biosynthesis during fruit development. Gene expression in exocarp and embryo could be divided into four categories, Cluster I-IV. Cluster I was involved in synthesizing flavonoids in embryo, and 12 DEGs were found, including C4H, BAN, CCoAOMT7, UGT74E2, UGT89B1, and UGT91C1. Cluster II was involved in synthesizing flavonoids in exocarp, and there were 60 DEGs, including F3H, TT4, TT7, TT8, CCoAOMT1, LDOX, and Ben1. In this paper, WGCNA was used to screen the hub genes of flavonoid biosynthesis in fruits. The hub genes involved in flavonoid synthesis in fruits were TT4 (CCA1250S0092), CCOAOMT1 (CCA0670S0080), UGT71D1 (CCA1700S0011), C4H (CCA0524S0150), F3H (CCA0705S0076), TT8 (CCA0510S0065), FLS1 (CCA0533S0160), and LDOX (CCA1505S0029). These genes played an important role in regulating flavonoid biosynthesis and metabolism in hickory.Many studies have shown that flavonoid metabolism in plants is a complex network-regulated process involving many genes. Transcriptomics analysis showed that flavonoid metabolism in pomegranate , grape , kiwifruThe formation of polyphenolic compounds is initiated by the phenylpropanoid biosynthetic pathway, known as the starting point for synthesizing a large family of low molecular weight plants secondary metabolites such as lignin, phenolic acids, flavonoids, stilbenes, and lignans . In thisJiang et al. identified 53 unigenes encodings 15 enzymes to reveal the flavonoid biosynthesis metabolic pathway in Semen Ziziphi Spinosae. These unigenes were identified as candidate genes involved in flavonoid biosynthesis in SZS. PAL, C4H, 4CL, CHS, CHI, FLS, FNS, ANS, ANR, LAR, and UFGT genes were all downregulated in T3 than in T1. The expression levels of these genes influence the accumulation of flavonoids in SZS .As an intermediate of flavane-3-alcohol biosynthesis in the flavonoid metabolic pathway, F3H can transform flavanones into dihydroflavonols, a key enzyme regulating flavonoid accumulation in plants . A largeFurthermore, naringenin can be converted to dihydroflavonols, dihydrokaempferol, dihydromyricetin, and dihydroquercetin by flavanone-3-hydroxylase (F3H) activity. Dihydroflavonols can be reduced to respective flavonols by FLS enzyme and finally glycosylated, for example, quercetin to rutin, by flavonoid-3O-glucosyltransferase (UFGT) activity .Studies have shown that drought enhanced the transcription levels of PAL, C4H, and F3H, genes encoding a key enzyme in enzyme-catalyzed phenylpropanoid biosynthesis, leading to an increase in the flavonoid levels .Julgans sigillata Dode). The possible reason is that in the early stage of fruit development, the young exocarp grows vigorously, the flavonoid metabolism is greater than catabolism, the content of auxin in the vigorous growing organs is higher, and the flavonoid compounds in the storage organs are transported quickly  can be found at National Center for Biotechnology Information (NCBI) BioProject database under accession number PRJNA793525.J-HC: visualization, methodology, formal analysis, data curation, conceptualization, writing—original draft, and writing—review and editing. NH: conceptualization, methodology, and writing—review and editing. XX: conceptualization, methodology, investigation resource, data curation, and writing—review and editing. DZ: data curation, visualization, methodology, and writing—review and editing. T-QF: data curation, visualization, and methodology. Q-XZ: writing—review and editing. Y-JH: conceptualization, supervision, writing—review and editing, project administration, and funding acquisition. All authors contributed to the article and approved the submitted version.The study was financially supported by the National Natural Science Foundation of China ; the Key Research and Development Project of Zhejiang Province, China (no. 2020C02005); the Natural Science Foundation of Zhejiang Province (LY18C150002); the Zhejiang Key Research and Development project 2021C02037; the Bridging Project for the Enterprises and the Local walnut R&D Groups in Guizhou Province (grant number [2015] 4010); and the Guizhou Kehe platform talent [2019]5643.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.https://www.frontiersin.org/articles/10.3389/fpls.2022.896421/full#supplementary-materialThe Supplementary Material for this article can be found online at: Click here for additional data file.


Digital scanning of the master die with and without a fit checker followed by image superimposition, and analysis was performed to evaluate internal and marginal adaptation in four areas . ISO 12836:2015 standard was followed for precision and trueness evaluation. Statistical analysis was achieved using a t-test at α = 0.05. Internal fit and marginal adaptation revealed no significant difference between the two test groups (p > 0.05). The significant difference in trueness (p < 0.05) was found between the two groups in three areas . The best and worst trueness values were seen with 3D-printed crowns at occlusal (8.77 ± 0.89 µm) and Intaglio (23.90 ± 1.60 µm), respectively. The overall precision was statistically better (p < 0.05) in the 3D-printed crowns (9.59 ± 0.75 µm) than the milled (17.31 ± 3.39 µm). 3D-printed and milled zirconia crowns were comparable to each other in terms of internal fit and marginal adaptation. The trueness of the occlusal and axial surfaces of 3D-printed crowns was better, whereas the trueness of fitting surface of milled crowns was better. 3D-printed crowns provided a higher level of precision than milled crowns. Although the internal and marginal fit of both production techniques were comparable, 3D printing of zirconia produced more precise crowns.Precise fit of a crown and accurate reproduction of the digital design are paramount for successful treatment outcomes and preservation of clinician and technician time. The study aimed to compare the internal fit, marginal adaptation, precision, and trueness of 3D-printed zirconia crowns compared to their milled counterpart. A total of 20 monolithic 3 mol% yttria stabilized-zirconia crowns ( Today, monolithic zirconia is a popular material for the fabrication of single crowns, short- and long-span fixed dental prostheses, and full-arch restorations ,5,6,7,8.Zirconia restorations are conventionally produced by a subtractive method (SM), i.e., milling from a blank utilizing computer-aided designing/computer-aided manufacturing (CAD/CAM) . ComputeA recent advancement in the manufacturing process of dental restorations involves building the object in thin layers of raw material followed by postprocessing curing and finishing steps. This technique is generally called additive manufacturing (AM) or 3D-printing, and it involves different techniques such as thermoplastic extruded material, vat photopolymerization, binder and material jetting, powder bed fusion, and others ,16. In dAM has brought a number of advantages to the production field, such as mass production, minimal material waste, the ability to form complex geometries, minimal residual stresses, and the elimination of tool wear ,17,18. HInternal adaptation and marginal fit of AM or SM restorations are crucial factors for the success of the final dental restoration, just like conventionally made restorations ,24,25,26One adequate and recommended gap-measuring technique has not been established yet. A variety of options exist, each with its own advantages and limitations. These include ,29,30,31On the other hand, trueness refers to the deviations/approximation of the measured values from the intended or planned values, while precision refers to the repeatability of measurement . A greatBecause the AM (3D-printing) of ceramic dental restorations is still in its early stages, there is only a limited number of studies evaluating the restoration adaptation, precision, and trueness of AM full-contour monolithic zirconia crowns. Therefore, this study aimed to investigate and compare the above-mentioned properties of additively manufactured 3Y-TZP crowns compared to the subtractively made counterparts. The null hypothesis stated that internal fit, marginal adaptation/gap, level of trueness, and precision between milled and 3D-printed monolithic zirconia single crowns would be similar.Based on previous studies ,15,20,37A dentate mandibular typodont  was used to prepare a mandibular left first molar tooth to receive a monolithic zirconia crown. The preparation configuration and dimensions are listed in ® Zri zirconia disc, shade A1  and to a commercial lab for the additive production of crowns using ceramic 3D-printer  and zirconia paste . In total, 20 crowns were produced (10 per manufacturing process) and sintered following the manufacturer’s directions. Once fired, they were analyzed as received without further treatment.Additionally, the STL file of this 3D acquisition was used to design a monolithic crown using CAD software . The parameters of the crown are shown in The master die with the four reference markers was solely scanned initially using 3Shape TRIOS 3 scanner , and the resulting file was denoted as a “reference scan.” A micro-brush was used to apply a small amount of polyvinyl siloxane impression adhesive to the occlusal surface of the master die. Following that, the intaglio surface of the crown was lightly lubricated and filled halfway with fit checker material . The crowns were loaded with (49.05 N) until the material set. Then, the excess fit checker was removed with a sharp #15 blade. The crown was retrieved, leaving the fit checker attached to the master die. The master die was scanned again with the fit checker using the same scanner, and the resulting file was denoted “fit scan.” The STL files of the “fit scan” and “reference scan” were imported into Geomagic Control X software, V 2018, . The “reference scan” was divided into four comparison areas: occlusal, axial , marginal , and overall A. The prThe ISO standard #12836:2015  was follFor the trueness evaluation, the “measured data” scan of each crown was overlayed on the CAD design to measure the deviation from the plan in different areas as follows: occlusal, axial, marginal, intaglio, and overall B. The RoFor the precision evaluation, the “measured data” scan of the first crown of each experimental group was superimposed on the “measured data” scans of the remaining crowns. The RMS value represents the deviation of the “measured data” from one crown to another. Similarly, the higher the RMS value, the less precise the production method is.® Software  was used for statistical analysis. T-test was employed to compare differences in means between the two test groups (additive & subtractive) in terms of internal fit and marginal adaptation, precision, and trueness. The level of significance was set to 0.05.JMPp > 0.05) between 3D-printed and milled crowns , axial (p = 0.0021), and intaglio (p = 0.0130)). The trueness in the occlusal and axial surfaces was better in the 3D-printed crowns, while the trueness of the intaglio surface was better in the milled crowns. No trueness difference was detected in the marginal and overall comparisons (p > 0.05), With regards to trueness evaluation, descriptive statistics are summarized in p < 0.001)), with 3D-printed crowns revealing better precision compared to the milled ones, Descriptive statistics for precision evaluation are summarized in This study investigated the 3-dimensional internal fit and marginal adaptation of monolithic 3D-printed and milled zirconia crowns. Additionally, the study evaluated the level of trueness and precision of the produced crowns compared to the original design and to each other. The results of the study suggest partial acceptance of the null hypothesis with regard to adaptation, while it was rejected in relation to trueness and precision.In this study, the specimens were made using two manufacturing techniques (additive and subtractive) using the same STL file to ensure consistency and standardization of the produced crowns. Additionally, the digital method (3D-superimposition) to analyze the fit and adaptation allows complete visualization of the intaglio surface and measurement of a huge number of points (making up the whole surface area of the intaglio side) compared to selected point measurements.Crown fit and marginal adaptation are affected by a number of factors, including the manufacturing system and technique, processing parameters, the raw material, preparation design, shrinkage upon sintering, and the cement used, among others ,40. In tThe findings of this study concurred with other reports in the literature regardless of the variation in materials used, measuring time, location, and technique. In a systematic review  of CAD/C2, 3DCeram) and printer  matched those used in our study but the measuring technique involved the use of silicon replica.As compared to milled zirconia, fewer studies exist on the use of 3D-printed zirconia in dentistry ,50,51,52High trueness and precision of the final crown as compared to the designed restoration is not only important for adequate crown fit on the prepared tooth but also to minimize chair-side corrections and modifications of the restoration . The truDental restorations must be produced with high precision to ensure proper fit and adequate biological response. The precision of a printed object is dependent on multiple factors, including the printing technique, the number, location, and size of supports, and the digitizing method/scanner . The autAmong the limitations of the study is the use of a single brand/material in each manufacturing technique. Moreover, the inconsistency between the published literature with regard to the distribution and extent of measured areas  made the comparison with previous studies more challenging. Additionally, specimens were tested as sintered and were not subjected to cyclic fatigue that could show different types of marginal adaptation . TherefoAdditive and subtractive manufacturing techniques produced zirconia crowns with comparable internal fit and marginal adaptation.The 3D-printed crowns had better occlusal and axial trueness, while milled crowns showed better intaglio trueness.3D printing produced more precise restorations than milling.Further in vitro and in vivo studies are needed to evaluate other aspects of 3D-printed zirconia crowns, such as mechanical and optical properties.Within the limitations of this in vitro study, it can be concluded that: