Plasma and tissue biopsy have both used for targeting actionable driver gene mutations in lung cancer, whose concordance is imperfect. A reliable method to predict the concordance is urgently needed to ease clinical application.A total of 1012 plasma samples, including 519 with paired‐tissue biopsy samples, derived from lung adenocarcinoma patients were retrospectively enrolled. We assessed the associations of several clinicopathological characteristics and serum tumor markers with the concordance between plasma and tissue biopsies.p = 0.633). The performance of CEA combined with CYFRA21‐1 in predicting the concordance was similar to that of the combination of independent factors derived from the LASSO regression model . CEA  and CYFRA21‐1 levels  were significantly correlated with the maximum variant allele frequency, respectively.When carcinoembryonic antigen (CEA) levels were higher than thresholds of 15.01 ng/ml and 51.15 ng/ml, the positive predictive value of concordance reached 90% and 95%, respectively. When CEA levels were lower than thresholds of 5.19 ng/ml and 3.26 ng/mL, the negative predictive value of concordance reached 45% and 50%. The performance of CYFRA21‐1 in predicting concordance was similar but inferior to CEA (AUC: 0.727 vs. 0.741, CEA combined with CYFRA21‐1 could effectively predict the concordance between plasma and tissue biopsies, which could be used for evaluating the priority of plasma and tissue biopsies for gene testing to timely guide clinical applications in advanced lung adenocarcinoma patients. The aim of this research was to explore a feasible and reliable method for clinicians to quickly and accurately identify patients who are suitable for plasma biopsy for genotyping first. We enrolled a total of 1012 plasma samples, including 519 with paired‐tissue biopsy samples derived from lung adenocarcinoma patients. Our findings indicated that CEA combined with CYFRA21‐1 could effectively predict the concordance between plasma and tissue biopsies based on next generation sequencing, which could be used for evaluating the priority of plasma and tissue biopsies for gene testing. We aimed to provide a feasible tool to accurately identify concordance rate between plasma and tissue biopsy based on NGS in lung adenocarcinoma patients based on serum tumor markers. For the patients who present higher concordance rate, the application of plasma biopsy could be prior to tissue biopsy and therefore avoid the operative risk of tissue biopsy. What is more, for the population with lower concordance rate, tissue biopsy is more recommendable than plasma biopsy, in order to reduce the economic burden caused by unnecessary plasma biopsy.22.1In total, 1012 patients diagnosed with stage III or IV lung adenocarcinoma were retrospectively enrolled at Chongqing Daping Hospital, Hunan Cancer Hospital, and Guangzhou First People's Hospital in China Figure . These p2.2Tissue DNA and Cell‐free DNA (cfDNA) were extracted using QIAamp DNA FFPE Tissue Kit (Qiagen) at the same time. Capture‐based targeted sequencing was performed on tumor and plasma samples from 3 hospitals using a cancer gene panel spanning 237Kb of the human genome (Burning Rock Biotech), which covers the whole exons and selected introns of 168 genes, consisting of 68 lung cancer‐related genes and 100 genes related to other cancers (full description was in Supplementary Methods).2.3Concordance was defined as that a driver alteration could be detected in both plasma and tissue samples. Discordance was defined as that a driver alteration could be detected only in plasma or tissue samples.2.4Within 1 week after plasma biopsy, 3 ml of fasting venous blood was collected in the morning. Then, serum was separated for testing the levels of serum tumor markers by electrochemiluminescence immunoassay. The range of normal serum tumor markers referred to CEA 0–5.0 ng/ml, NSE 0–16.3 ng/ml, CA125 0–35 U/ml, and CYFRA21‐1 0–3.3 ng/ml.2.5t‐test was used for normally distributed continuous variables, and the Mann–Whitney U‐test was performed for continuous non‐normally distributed variables. Categorical variables were analyzed using the chi‐square test. The correlation between two non‐normally distributed continuous variables was assessed by determining Spearman's rank correlation coefficient. Receiver operating characteristic (ROC) curve analyses were performed, and the method of Delong was employed to calculate the standard error of the area under the curve (AUC).p <0.05 were considered statistical significance unless otherwise specified.All data were analyzed using the R software version 4.0.3. Continuous data conforming to the normal distribution were presented as mean ± standard deviation, and those nonconforming were presented as median (interquartile range). Categorical variables were presented as frequencies. Normality was evaluated using the Shapiro–Wilk test. In addition, Student's 33.1p < 0.05, Table EGFR mutation , KRAS mutation , and ALK fusion . For 519 tissue samples, the mutation rate of all driver genes was 79.6% (413/519). The top‐three detected alterations were consistent with plasma‐based biopsy with different ratios, that is, EGFR mutation , KRAS mutation , and ALK fusion . In addition, three cases  of NTRK fusion were detected in tissue samples but not in plasma samples  and 53.2% (538/1012) were males. The proportion of patient with distant metastases was 85.1% (861/1012). Pleura metastasis were the most frequent metastatic sites  (group B). Further univariate analysis showed that the concordance rate was significantly higher in males, M1 stage disease, and stage IV disease than in females, M0 stage disease, and stage III disease, respectively , CYFRA21‐1 , NSE , and CA125  in the concordance group were significantly higher than in the discordance group (Table p < 0.001) and CYFRA21‐1  in group A were significantly higher than in group B  or NPV . We found that CEA thresholds were 15.01 ng/ml and 51.15 ng/ml, and CYFRA21‐1 thresholds were 6.21 ng/ml and 23.72 ng/ml in patients with the PPV of 90% and 95%, respectively Table . Further3.4p = 0.008), M1 stage disease (OR (95% CI): 1.65 , p = 0.079), bone metastasis (OR (95% CI): 3.10 , p < 0.001), increased CEA level : 1.53 , p < 0.001), increased CYFRA21‐1 level : 2.62 , p = 0.001), increased NSE level : 1.29 , p = 0.049) : 1.85 , ) Figure . We then) Figure . The ind3.5EGFR in plasma  and CYFRA21‐1 level  patients. However, only 57.7% (1004/1739) of the tumor sample was left for tumor mutational burden study.In this study, we first used the cancer‐gene NSG panel to analyze 9 lung cancer driver genes in 519 patients with paired plasma and tissue samples and found the concordance of plasma and tissue biopsies was 73.4%, similar to previous studies.Serum tumor markers are abnormally expressed by malignant tumor cells, or derived from the host response to tumor, which can reflect the occurrence and development of tumor and monitor tumor response to treatment.The further exploration of the relationship between the levels of linear changes in serum tumor markers and concordance rates indicates that the concordance rate increased with the increased level of serum tumor marker, especially CEA and CYFRA21‐1. The thresholds of CEA and CYFRA21‐1 in predicting concordance rates of 90% to 95% and discordance of 40% to 50%. We found that CEA and CYFRA21‐1 threshold was 15.01 ng/ml and 7.43 ng/ml in patients having a PPV of 90%, respectively. We also found that a predictive model of CEA combined with CYFRA21‐1 outperformed individuals of CEA, CYFRA21‐1, NES, and CA125, showing similar predictive efficacy to the combination of the four tumor markers and the LASSO model. CEA combined with CYFRA21‐1 was also recommended for assisting the diagnosis of lung adenocarcinoma,EGFR mutation rate in plasma increased with the increment of CEA level, consistent with previous research results in tissues.EGFR mutation rate in the group of plasma‐only samples was comparable with that in the group of paired‐plasma samples at the same CEA level, suggesting that CEA has similar efficacy in predicting concordance in plasma‐only and paired‐plasma samples. Therefore, patients with high levels of CEA should be recommended for detecting EGFR mutation in plasma.Furthermore, we found that the Through a multicenter clinical study with large samples, we found that serum tumor markers, especially CEA combined with CYFRA21‐1, could effectively predict the concordance rates between plasma and tissue biopsies, and provided a predictive model of serum tumor markers for clinicians to quickly and accurately identify patients who are suitable for plasma biopsy for genotyping first. This study had some limitations. First, it was a retrospective real‐world study. Second, only four serum tumor markers, which were used to monitor diseases in the clinical diagnosis and treatment in all three hospitals, were included. Other markers associated with lung cancer were not included in this study such as tissue polypeptide antigen (TPA). Moreover, changes in the predictive efficacy of serum tumor marker during the course of treatment were not further explored. A well‐designed, prospective study is needed to stratify patients according to tumor stage, histological subtype, and treatment line to validate our findings in future work.5EGFR mutations in plasma. Our study provides a nomogram model based on CEA and CYFRA21‐1 as a convenient tool for individualized prediction of the concordance of plasma and tumor biopsies, to optimize the utility of plasma biopsy in genotyping for advanced lung adenocarcinoma patients.In summary, CEA combined with CYFRA21‐1 can effectively predict the concordance rate of plasma and tumor biopsies in advanced lung adenocarcinoma patients. Elevated CEA levels also could predict the presence of Chenlong Zhao: Conceptualization ; formal analysis ; methodology ; visualization ; writing – original draft (lead). JiangHua Li: Conceptualization ; formal analysis ; investigation ; software ; writing – original draft . Yongchang Zhang: Data curation ; project administration . Rui Han: Data curation ; formal analysis ; resources . Yubo Wang: Data curation . Li Li: Data curation . Yimin Zhang: Data curation ; investigation . Mengxiao Zhu: Methodology ; validation ; writing – original draft (supporting). Jie Zheng: Data curation . Haiwei Du: Methodology ; writing – review and editing . Chen Hu: Data curation . Nong Yang: Data curation ; project administration . Chengzhi Zhou: Data curation ; project administration . Shangli Cai: Writing – review and editing . Yong He: Conceptualization ; methodology ; project administration ; resources ; writing – review and editing .This work was supported by the National Natural Science Foundation of China , the Chongqing Science Technology Commission (cstc2021jcyj‐msxmX0014), Key Technology Project for Prevention and Control of Major Diseases in Chongqing (2019ZX002), Clinical medical technology innovation ability training program (2019CXLCA003), Clinical Medical Research Talent Training Program of Army Medical University (2018XLC1015).The authors declare that they have no competing interests.Tissue and plasma samples of patient were obtained with informed consent, under the protocol approved by the Research Ethics Committee of Daping Hospital.All patients involved in our study obtained written consent for publication.Figure S1Click here for additional data file.Figure S2Click here for additional data file.Figure S3Click here for additional data file.Figure S4Click here for additional data file.Figure S5Click here for additional data file.Figure S6Click here for additional data file.Figure S7Click here for additional data file.Figure S8Click here for additional data file.Figure S9Click here for additional data file.Figure S10Click here for additional data file.Appendix S1Click here for additional data file.Table S1Table S2Click here for additional data file.


This study was to examine the protective effects of curcumin/cyclodextrin polymer inclusion complex (CUR/CDP) on ethanol‐induced liver injury in mice and to explore its potential mechanisms. In the ethanol‐induced acute injury mouse model, the effects of pretreatment with silymarin, cyclodextrin polymer (CDP), curcumin (CUR) and CUR/CDP at low, middle, and high doses were evaluated by biochemical and histopathological examination. The liver index, alanine aminotransferase (ALT), aspartate aminotransferase (AST), and lactate dehydrogenase (LDH) levels in serum of the mice were measured. The superoxide dismutase (SOD), glutathione peroxidase (GSH‐PX) activities, and malondialdehyde (MDA) level in liver tissue were assessed by assay kits. Moreover, hematoxylin–eosin (HE) staining was carried out to observe pathological changes of liver. Western blotting was performed for determining the changes in the expressions of DNA damage‐associated proteins. The results showed that compared with the control group, the liver index and the levels of ALT, AST, LDH, and MDA in the ethanol treatment group were significantly increased and the activities of GSH‐Px and SOD were obviously decreased. However, pretreatment with silymarin, CUR, and CUR/CDP reversed the change of above indicators except CDP. Moreover, CUR/CDP at high dose further weakened the liver index, inhibited the biochemical indexes, and enhanced the activities of antioxidant enzymes to a greater extent than silymarin and CUR. Western blot analysis indicated that CUR/CDP significantly down‐regulated the expressions of DNA damage‐related proteins including p‐ATM, γ‐H2AX, p‐p53, and p‐p38MAPK, which inhibited ethanol‐induced the G2/M arrest and ultimately prevented liver function from oxidative stress injury. These results indicated that CUR/CDP possessed good protective effect on mice liver damage in vivo by increasing the activities of GSH‐Px and SOD to suppress DNA damage. CUR/CDP exhibited excellent protective effect on ethanol‐induced liver injury in mice in a dose‐dependent manner, which was attributed to its antioxidant activity that alleviated oxidative stress by improvement activities of antioxidant enzymes and inhibition of lipid peroxidation, thereby avoiding DNA damage‐induced G2/M arrest. CUR/CDP had the potential to be used as a novel drug or supplement for the prevention of alcoholic liver injury. The animal experiments were carried out following the guidelines of the Guangdong Ocean University Ethics Committee (IACUC No.: GDOU‐LAE‐2020‐007). The experimental design was shown in Figure 2.3g for 10 min, and the serum was collected. The activities of ALT, AST, and LDH in the serum were determined using respective assay kits according to the manufacturer's protocols.The blood samples were centrifuged at 3000 2.4g for 15 min, the supernatant was collected and the activities of SOD and GSH‐PX and content of MDA in the liver homogenate were measured using respective kits according to the manufacturer's instructions.The equivalent part of the liver tissue from each mouse was accurately weighed and then homogenized with normal saline in an ice‐water bath according to a mass/volume ratio of 1:9. Following centrifugation at 3000 2.5The equivalent part of the liver tissue from each mouse was fixed with 4% paraformaldehyde solution and then paraffin‐embedded sections were made. The sections were stained with hematoxylin–eosin and observed under a light microscope.2.6Whole protein was extracted from liver tissue homogenate by RIPA lysis buffer. The protein concentration was determined using the BCA kit. Equal amounts of individual protein samples were separated by electrophoretic in SDS‐PAGE. After electrophoresis, proteins were transferred to a PVDF membrane and blocked with 5% non‐fat milk for 2 h. After incubation with primary antibodies at a dilution of 1:1000 overnight, the membranes were washed with TBST. The membranes were then incubated with secondary HRP‐labeled antibody for 2 h. Protein bands were visualized using a chemiluminescence reagent (ECL). The bands were calculated using Image J software.2.7α = .05  to 44.91%, 61.47% and 82.91% and decreased p21 protein expression from 221.7% (ethanol group) to 163.83%, 154.52%, and 82.23%, respectively. In addition, p21 expression in the CUR/CDP at high‐dose group was lower than that of CUR and CDP groups, and cyclin B1 expression in the CUR/CDP at the high‐dose group was higher than that of CUR and CDP groups. These results indicated that compared with CUR and CDP groups, CUR/CDP showed stronger inhibition effect on ethanol‐induced G2/M arrest.Studies have shown that ethanol induces oxidative DNA damage in human peripheral lymphocytes , Guangdong Ocean University Innovation Program (230419100), Nanhai Youth Scholar Project of Guangdong Ocean University (002029002009), the Project of Science and Technology of Zhanjiang City (2020A01034 and 2022A01045), the Scientific Research Foundation of Guangdong Ocean University (no. R17034), the Innovative Team Program of High Education of Guangdong Province (2021KCXTD021), Excellent Graduate Thesis Cultivation Program  and Guangdong Provincial Key R&D Program (2022B0202040001).There are no conflicts to declare.


The Cockcroft–Gault formula is commonly used as a substitute for glomerular filtration rate (GFR) in Calvert's formula for carboplatin dosing, where adjusting serum creatinine measured using the enzymatic method with 0.2 mg/dL has been suggested in Japan. However, the effects of these adjustments on efficacy in patients with non‐small‐cell lung cancer remain unknown.We conducted a post hoc analysis of the PREDICT1 study (CJLSG1201), a multicenter prospective observational trial of carboplatin–pemetrexed. Glomerular filtration rate values in Calvert's formula were back‐calculated from the administered dosages of carboplatin and the reported value of the target area under the curve. We estimated the serum creatinine adjustments and divided the patients into crude and adjusted groups.N = 169) demonstrated similar efficacy to those in the adjusted group (N = 104) in progression‐free survival (PFS) and overall survival (OS) , with higher grade 3–4 hematologic toxicity. Among patients aged ≥75 years, the crude group (N = 47) showed superior efficacy compared with the adjusted group (N = 17) in PFS and OS .Patients in the crude group (Serum creatinine adjustment may be associated with similar efficacy compared to the crude serum creatinine value. In older patients, the adjustment should be cautiously applied owing to the potential for reduced efficacy. Adjustment of serum creatinine measured using the enzymatic method with 0.2 mg/dL in creatinine clearance calculation using the Cockcroft–Gault formula is associated with similar clinical efficacy and a low incidence of toxicity compared with those of non‐adjusted values in patients with preserved renal function eligible for carboplatin–pemetrexed treatment. However, adjustments should be used with special caution in older patients owing to the potential for reduced efficacy. All responses were assessed according to RECIST criteria. A confirmatory evaluation was required after at least 4 weeks if a complete or partial response was observed. The response was considered a stable disease if it was confirmed and sustained for ≥6 weeks after initiation of chemotherapy. Progression‐free survival (PFS) was defined as the time from the initiation of study treatment to the date of confirmation of progressive disease or date of death from any cause, whichever occurred first. Overall survival (OS) was defined as the time from initiation of chemotherapy until death from any cause. Toxicities were graded according to National Cancer Institute Common Terminology Criteria for Adverse Events version 4.0.2.32 pemetrexed was administered. Both target AUC (5–6) and the method for estimating GFR in Calvert's formula were at the investigator's discretion. In addition, dose reduction was also conducted at the investigator's discretion. The GFR values were back‐calculated based on the actual administered carboplatin dosage and reported value of the target AUC using modifying Calvert's formula, as follows:In the PREDICT1 study,2.4SCr levels were measured using an enzymatic method at all institutions. We hypothesized that carboplatin dosage was determined based on Calvert's formula, where GFR was substituted by CCr calculated using the Cockcroft–Gault formula, as follows:This value was used as crude eCCr. For the adjusted eCCr value, the modified Cockcroft–Gault formula was used by adding 0.2 mg/dL to the reported SCr value as follows:Adjusted eCCrmL/min=140−ageyears×weight2, which is defined as obese according to the World Health Organization criteria,Serum creatinine has intra‐ and inter‐day fluctuations of 5%–10%Patients were divided into crude and adjusted groups according to the above‐described ranges. In clinical practice, truncated‐ or rounded‐off dose of chemotherapeutic agent is often used, which is potentially associated with differences between calculated and administered doses. We also assumed these differences were within this range. If patients were classified into both groups or not classified into either group, they were considered unclassified.The accuracy of the estimation was evaluated using the mean absolute error (MAE), its standard error (MAE ± standard error), and root mean squared error (RMSE), as follows:2.5Because of the nature of primary study, the measured GFR values were not provided; therefore, to evaluate the impact of adjustment on carboplatin AUC, we used the estimated glomerular filtration rate (eGFR)2.6EGFR) mutation/anaplastic lymphoma kinase (ALK) translocation (negative/unknown or positive), and initial AUC (<6 or 6) were used to estimate hazard ratios (HRs) and test for differences in PFS and OS between groups. In the exploratory analysis, we divided patients into non‐older (age < 75 years) and older adult (age ≥ 75 years) subpopulations and developed propensity scores using the same factors except for age in each subpopulation. Cox analyses adjusted for this propensity score were also conducted for each subpopulation between groups. Logistic regression analysis for each adverse event adjusted by group (adjusted or crude) and initial AUC (<6 or 6) were used. A two‐sided p‐value of <0.05 indicated a statistically significant difference. SPSS version 27 (IBM) was used for statistical analyses.Mann–Whitney U and Fisher's exact tests were used to evaluate binary and continuous variables, respectively. Kaplan–Meier estimates were employed for time‐to‐event endpoints, including PFS and OS. Cox proportional hazards models adjusted for age , sex , performance status (PS) (0 or 1/2), smoking history (never or current/ex), clinical stage (III/IV or recurrence), epidermal growth factor receptor (33.1N = 7), inclusion criteria not met (N = 3), change in regimen before tumor evaluation (N = 1), and duplicate registration (N = 1). We estimated SCr adjustment in 338 patients and divided them into two groups: 169 and 104 in the crude and adjusted groups, respectively , below the lower limit of crude eCCr range, and above the upper limit of adjusted eCCr range (N = 30), below the lower limit of adjusted eCCr range (N = 17), and classified into both groups (N = 1). A comparison between bGFR and eCCr levels in each group is shown in Figure Total of 350 chemotherapy‐naïve patients with non‐squamous NSCLC receiving carboplatin–pemetrexed as the first‐line treatment were enrolled at 27 institutions in Japan between July 2012 and June 2017 . The proportion of older people aged ≥75 years in the crude group was also significantly higher than that in the adjusted group . A BMI of ≥30 kg/m2 was observed in one and two patients in the crude and adjusted groups, respectively, and AIBW was used to estimate CCr in these patients. Only one patient in the crude group had a bGFR >125 mL/min. Crude eCCr values were significantly different between both groups . The proportion of patients treated with carboplatin with a target AUC of six was significantly higher in the adjusted group (58.7%) than that in the crude group (41.4%) (p = 0.01). We calculated the percentages of carboplatin dosing in the adjusted group, and the median proportion based on the dose calculated using crude eCCr value was 82.7% . No significant differences in the other clinical characteristics were observed between the groups.Table Among 64 patients aged ≥75 years, the baseline characteristics were similar between groups, except for PS  (Table Dose reduction was conducted in 24 (14.2%) and 14 (13.5%) in the crude and adjusted groups, respectively. In older patients, dose reduction was conducted in 12 (25.5%) and 3 (17.6%) in the crude and adjusted groups, respectively. In non‐older patients, dose reduction was conducted in 12 (9.8%) and 11 (12.6%) in the crude and adjusted groups, respectively.3.2p = 0.559), and disease control rates were 77.9% and 81.1%, respectively (p = 0.537). Unclassified patients showed similar response and disease control rates, except those treated with the lower limit of the adjusted eCCr range    in the crude and adjusted groups were 22.5% and 26.0%, respectively  Figure . The med) Figure .p = 0.525). The median PFS was 4.8 months  in the crude group (N = 47) and 3.2 months  in the adjusted group  and adjusted groups  and 0.43 , respectively, between both groups. Conversely, in non‐older patients, the adjusted HR for PFS and OS were 1.16  and 1.03 , respectively, between groups.Among the 64 patients aged ≥75 years, RR in crude and adjusted groups were 27.7% and 17.6%, respectively (N = 338)   Figure . As esti) Figure . In the ) Figure . Regardi) Figure ; meanwhi) Figure .p = 1.000). Antibodies against programmed cell death‐1 or its ligand were administered to 33 patients (19.5%) in the crude group and 19 patients (18.3%) in the adjusted group (p = 0.578). EGFR or ALK tyrosine kinase inhibitors were administered to 20 patients (11.8%) in the crude group and 15 patients (14.4%) in the adjusted group (p = 0.874).For subsequent therapy, 106 of 169 patients (62.7%) in the crude group and 65 of 104 patients (62.5%) in the adjusted group received at least one therapy (3.3p = 0.049). Similarly, crude eCCr was associated with a higher incidence of Grade 3 or 4 anemia and thrombocytopenia than that with adjusted eCCr . The incidence of Grade 3 or 4 non‐hematologic toxicity was similar in both groups. We conducted a logistic regression analysis adjusted by initial AUC to evaluate the odds ratio between both groups, which showed a significantly higher incidence of Grade 3 or 4 neutropenia in the crude group, with an adjusted odds ratio of 1.91   Table . TreatmeAmong the 64 patients aged ≥75 years, the frequency of Grade 3 or 4 hematologic toxicity in the crude group was similar to that in non‐older patients, whereas older patients in the adjusted group had a lower incidence of Grade 3 or 4 hematologic toxicity than that in the non‐older group Table . Grade 34p = 0.559), PFS , and OS  between both the groups. Regarding toxicity, patients in the adjusted group tended to have a lower incidence of hematologic adverse events than those in the crude group. However, although the number of cases was small, patients aged ≥75 years in the adjusted group had significantly shorter PFS and OS with a considerably lower incidence of hematologic toxicity than those in the crude group, even after propensity score adjustment of patient background . These results indicate that adjustment of SCr measured using the enzymatic method in CCr calculation using the Cockcroft–Gault formula is associated with similar clinical efficacy and a low incidence of toxicity compared with those of non‐adjusted values in patients with preserved renal function eligible for carboplatin–pemetrexed treatment, although should be performed with special caution when administered to older patients.In this post hoc analysis of a multicenter prospective observational trial of carboplatin–pemetrexed treatment in patients with non‐squamous NSCLC, patients were divided into two groups (adjusted and crude groups) by comparing bGFR based on both actual administered carboplatin dosage and reported AUC with eCCr. Similar clinical efficacy was demonstrated in terms of RR . Moreover, the frequency of hematologic toxicity was generally higher in the crude group than in the adjusted group  ; however, the majority were aged <65 years and enrolled patients aged >70 years (N = 59) had poor renal function with a mean CCr of 38 mL/min51Cr‐EDTA clearance in older patients  with mild to moderate kidney disease reported that eCCr calculated using creatinine measured using the Jaffé method tended to underestimate GFR; however, eCCr estimated using one of the enzymatic methods produced the highest GFR estimate compared with the reference 51Cr‐EDTA clearance.In the exploratory analysis for older patients, PFS and OS in the crude group were longer than those in the adjusted group, even after propensity score adjustment of patients' background, including PS, clinical stage, and target AUC, which showed differences between both the groups Figure . The fre) Figure , which wChemotherapy combined with immunotherapy has become the standard therapy for metastatic NSCLC.p = 0.031). The frequency of hematologic toxicities in the crude group was generally higher than that in the adjusted group ; formal analysis (lead); writing – original draft ; writing – review and editing (supporting). Tetsunari Hase: Conceptualization (lead); formal analysis (lead); writing – original draft (lead); writing – review and editing (lead). Toru Hara: Data curation . Tomoki Kimura: Data curation . Eiji Kojima: Data curation . Takashi Abe: Data curation . Yoshitsugu Horio: Data curation . Yasuhiro Goto: Data curation . Naoya Ozawa: Investigation . Naoyuki Yogo: Investigation . Hirofumi Shibata: Investigation . Tomoya Shimokata: Investigation . Tetsuya Oguri: Data curation . Masashi Yamamoto: Data curation . Kiyoshi Yanagisawa: Funding acquisition (lead). Masahiko Ando: Formal analysis . Yuichi Ando: Investigation . Masashi Kondo: Supervision . Makoto Ishii: Supervision . Yoshinori Hasegawa: Supervision .This work was supported by Japan Agency for Medical Research and Development (AMED) (grant number 16cm0106408h0001 [to KY]).T. Hase received research funding from AstraZeneca, Chugai Pharmaceutical Co., Ltd., and Novartis Pharma. K.K. Y. Ando received personal fees from Chugai Pharmaceutical Co. and research funding from Novartis Pharma K.K., Ono Pharmaceutical Co., Ltd., Yakult Honsha Co., Ltd., Chugai Pharmaceutical Co., Ltd., BeiGene Inc., and Geo Holdings Co., Ltd. Y. Hasegawa received personal fees from Novartis Pharma K.K., Boehringer Ingelheim, and AstraZeneca. The grant was paid to each institution. All remaining authors have no conflicts of interest to declare.The primary study protocolWritten informed consent was obtained from all patients.Figure S1.Click here for additional data file.Figure S2.Click here for additional data file.Figure S3.Click here for additional data file.Figure S4.Click here for additional data file.Table S1.Click here for additional data file.LegendsClick here for additional data file.


Inflammatory and oxidative markers, including advanced glycation end products (AGEs), malondialdehyde (MDA), advanced oxidation protein products (AOPP), oxidized low‐density lipoprotein (ox‐LDL), highly sensitive C‐reactive protein (hs‐CRP), tumor necrosis factor‐α (TNF‐α), and ferric reducing ability of plasma (FRAP) were measured at baseline and the end of the trial. In addition, vitamin C tolerance was evaluated. A nutritionist visited all participants for a standard diabetic regimen. Following vitamin C supplementation, the serum levels of MDA  and AGEs  demonstrated a significant decrease after controlling for multiple confounders, including age, blood pressure, waist circumference, HbA1C, TG, and LDL‐C, while no significant changes were observed for AOPP  and ox‐LDL . The FRAP showed an increasing trend as an antioxidant marker but was not statistically significant . The hs‐CRP and TNF‐α had no significant changes . Also, no major adverse events were observed. Vitamin C supplementation may be beneficial in reducing AGEs and MDA in patients with T2D.This study evaluated how daily vitamin C administration impacts systemic oxidative stress and inflammation and its safety in T2D patients. This randomized, double‐blinded, placebo‐controlled, parallel‐arm clinical trial included 70 patients with T2D. They were allocated to receive either 500 mg/day of vitamin C or a matching placebo for 8 weeks. Of the 70 subjects assigned to the trial, 57 were included in the statistical analysis (vitamin C:  This study showed that adding 8 weeks of vitamin C supplementation to hypoglycemic medications had significant beneficial effects on MDA and AGEs, which are long‐lived molecules in patients with type 2 diabetes. DiabeteThere is convincing evidence that oxidative stress has a critical role in the pathogenesis of type 2 diabetes (T2D) and the development of diabetes complications  can reduce insulin resistance and cardiovascular complications in patients with T2D  and inflammation in T2D showed conflicting results. An analysis of the impact of vitamin C on the oxidative parameters in gestational diabetes showed a significant decrease in malondialdehyde (MDA) and an increase in glutathione and superoxide dismutase (SOD) , oxidized low‐density lipoprotein (Ox‐LDL), MDA, and ferritin reducing ability of plasma (FRAP). In addition, inflammatory activity was evaluated by measuring tumor necrosis factor‐α (TNF‐α) and highly sensitive C‐reactive protein (hs‐CRP).22.1α = 0.05; β = 0.2). Participants aged 30–65 years with T2D based on the American diabetes association (ADA) criteria  more than 160 mmHg, diastolic blood pressure (DBP) more than 90 mmHg, use prescription or over‐the‐counter vitamins, smoking history, alcohol use in the past month, aspirin intake in the past year, with or planning pregnancy, history of corticosteroid intake, history of hypersensitivity to vitamin C, and history of acute or chronic inflammation , body mass index (BMI) more than 35 kg/mAppendix .2.2n = 70) were randomized to receive 500 mg of vitamin C daily or a matching placebo added to their hypoglycemic medication. Medication adherence was controlled by counting the tablets consumed and comparing the calculated number with the report of medication intake by the patients. The exact nutritional schedule was considered for both groups, which focused on the same consumption of vegetables and fruits in all participants. Patients were also instructed to continue the lifestyle they had before the study. Patients were asked to inform the research team in case of any unexpected symptoms during the trial period. At the follow‐up visit, all participants were systematically assessed for any adverse effects during the trial, using open‐ended questions followed by a 25‐item checklist of side effects .Before the commencement of the trial, the procedures and purpose were explained, and the participants gave written informed consent before enrollment. The protocol of the current study was in accordance with the Declaration of Helsinki and was approved by the Ethics Committee of the National Institute for Medical Research Development of Iran . This trial was registered in the Iranian Registry of Clinical Trials . The blood pressure was measured three times, 5 min apart, with calibrated Omron M7 digital sphygmomanometers  and arm cuffs covering at least 80% of the arm. The mean blood pressure value was calculated using the second and third records. With the subject standing still on a flat surface, waist circumference (WC) was measured using a nonstretchable measuring tape. WC was measured midway between the costal margins and the iliac crest in the horizontal plane.Trained staff performed examinations. The height measurement was made with rigid tape and a precision of 0.1 cm. Patients were asked to dress in light clothing while their weight was measured using a portable digital scale . According to the Quetelet equation, the body mass index (BMI) was calculated by dividing weight (kg) by height squared  was measured by the glucose oxidase method , Biotechnica), and glycated hemoglobin A1c (HbA1c) by the high‐performance liquid chromatography (HPLC) method . Cholesterol, triglyceride (TG), low‐density lipoprotein cholesterol (LDL‐C), and high‐density lipoprotein cholesterol (HDL‐C), as well as urea, creatinine, and uric acid, were determined using enzymatic methods , Biotechnica). Serum alanine aminotransferase (ALT), aspartate aminotransferase (AST), and alkaline phosphatase (ALP) were assessed using enzymatic photometry , Biotechnica). Insulin was measured by the chemiluminescence method . Homeostasis model assessment of insulin resistance (HOMA‐IR) was calculated as follows: HOMA‐IR = (FBS (mg/dL) × insulin (mu/L)/405)  were measured using the spectrofluorimetric analysis described by Kalousova et al.  (ShimadzAdvanced oxidation protein products (AOPP) was assessed with the spectrophotometric method of Kalousova et al.   concentration was assessed using colorimetric method . The intraassay and the interassay coefficient of variation were 5.5% and 5.9%, respectively.Oxidized low‐density lipoprotein (Ox‐LDL) was assessed using a sandwich ELISA kit . The intra‐ and interassay coefficient of variation were 4% and 7.3%, respectively.Samples were triplicated for AGEs, AOPP, and FRAP. If the concentration was above the calibration range, the samples were assessed with dilution, and the values were multiplied by the dilution coefficient. The measurement was repeated if the concentration was lower than the dynamic calibration range. All samples were assayed in duplicate.TNF‐α serum levels were measured using an ELISA kit . The intra‐ and interassay coefficient of variation were 3.2% and 10.9%, respectively. Serum levels of hs‐CRP were assessed using a two‐site ELISA . The intra‐ and interassay coefficient of variation were 5% and 9.5%, respectively.2.6t‐test and Chi‐square, respectively. Between‐ and within‐group comparisons were performed using independent samples and paired t‐tests. Fisher's exact test was used to compare adverse events between the groups. The effect size in terms of the raw mean difference was provided for each variable. The variables with nonnormal distribution were expressed as median (interquartile range) and compared between groups using the nonparametric Mann–Whitney U‐test. Univariate analysis of covariance (ANCOVA) was applied to evaluate the difference between the effectiveness of the two interventions. The measured markers of the primary outcome were entered as dependent variables, while possible confounding categories and baseline measurements were considered independent variables and covariates, respectively. The ANCOVA model assumes no strong correlation exists between the confounding variables . Therefore, three variables, including HbA1C, HDL, and total cholesterol, were not entered into the same model.All analyses were performed on participants who completed the trial. Probability graphics and the Shapiro–Wilk test were used to test the compliance of variables with normal distribution. Quantitative and qualitative variables were expressed as mean, standard deviation (SD), and percentage, respectively. The baseline level of quantitative and qualitative variables between the intervention and placebo groups were compared using Student's p‐value of lower than .05 was considered statistically significant for all tests.Each analysis calculated the effect size from partial eta squared according to Cohen's recommendations . The SPSS package version 24.0 for Windows  was used for statistical analysis. A 33.1n = 35) or matched placebo (n = 35). As Figure p‐value > .05). Furthermore, the baseline serum levels of inflammatory, oxidative, and antioxidant biomarkers were not significantly different between the two arms of the study  = 7.91, p‐value < .001; AGEs (%): mean difference (95% CI) = 5.16 (2.13–8.20), t (31) = 3.47, p‐value = .002] . Also, there were no significant changes in weight, systolic and diastolic blood pressure, waist circumference, FPG, HbA1c, urea, creatinine, uric acid, cholesterol, TG, LDL‐C, HDL‐C, AST, and ALT .Following 8 weeks of intervention, serum oxidative stress markers, including AGEs and MDA, decreased significantly in the vitamin C group [MDA (μmol/L): mean difference (95% CI) = 0.38 0.28–0.47), 8–0.47, t3.2.2p‐values were .330 and .616, respectively). In addition, there was no significant difference in inflammatory markers (CRP and TNF‐α), FRAP, and ox‐LDL levels in the placebo arm .There were no significant changes in MDA or AGEs levels in the placebo group . Based on the univariate ANCOVA, the difference between the baseline and follow‐up levels of MDA and AGEs remained significant after adjustment for age, BMI, glycemic indicators, and lipid profile. In the final model, after adjusting for the major confounders, 56.0% of the variance observed in MDA and 27.9% in AGEs level were due to vitamin C intake .Other baseline characteristic variables were analyzed after 8 weeks to detect possible confounders, and the ANCOVA modeling was performed, as shown in Table 3.3p‐value = .123; nausea: p‐value = .499). All mild to moderate adverse events were tolerable.Vitamin C supplements led to an upset stomach during or after eating in four patients and nausea in two patients. However, Fisher's exact test analysis showed no significant difference between the two arms  ; formal analysis ; methodology ; writing – original draft . Firouzeh Heidari: Formal analysis . Reza Karimi: Data curation ; investigation . Armin Rajab: Data curation ; writing – original draft . Shahram Rahimi‐Dehgolan: Formal analysis ; methodology . Amirhossein Yadegar: Data curation ; formal analysis (supporting); methodology (supporting); validation ; writing – review and editing (lead). Fatemeh Mohammadi: Data curation ; formal analysis (supporting); methodology (supporting); validation ; writing – review and editing (lead). Hossein Mirmiranpour: Data curation . Alireza Esteghamati: Conceptualization ; visualization . Manouchehr Nakhjavani: Conceptualization ; methodology (lead); project administration (lead); supervision (lead); visualization (lead); writing – original draft (lead); writing – review and editing (lead).This study was supported by a grant from the National Institute for Medical Research Development (NIMAD) under award number 971098.There is no conflict of interest to declare.All procedures were in accordance with the Helsinki Declaration, and all the procedures involving human subjects/patients were approved by the Ethics Committee of the National Institute for Medical Research Development of Iran (Code of Ethics: IR.NIMAD.REC.1397.123). Written informed consent was obtained from all the participants.Appendix S1.Click here for additional data file.Appendix S2.Click here for additional data file.


To investigate the antidiabetic effects and mechanisms of quinoa on type 2 diabetes mellitus (T2DM) mice model. In this context, we induced the T2DM mice model with a high‐fat diet (HFD) combined with streptozotocin (STZ), followed by treatment with a quinoa diet. To explore the impact of quinoa on the intestinal flora, we predicted and validated its potential mechanism of hypoglycemic effect through network pharmacology, molecular docking, western blot, and immunohistochemistry (IHC). We found that quinoa could significantly improve abnormal glucolipid metabolism in T2DM mice. Further analysis showed that quinoa contributed to the improvement of gut microbiota composition positively. Moreover, it could downregulate the expression of TAS1R3 and TRPM5 in the colon. A total of 72 active components were identified by network pharmacology. Among them, TAS1R3 and TRPM5 were successfully docked with the core components of quinoa. These findings confirm that quinoa may exert hypoglycemic effects through gut microbiota and the TAS1R3/TRPM5 taste signaling pathway. The results of this study showed that quinoa has a positive effect on the recovery of intestinal microbiota disorder in type 2 diabetes mice. Healthy 4–5‐week‐old C57BL/6 male mice in this study were purchased from Beijing Vital River Laboratory Animal Technology Limited Company, and all the animals were reared at a standard animal feeding room  in Beijing University of Chinese Medicine (BUCM). All experiments were approved by the animal ethics committee of BUCM. The experiments applied in this study were performed according to animal protection guidelines.2.2Twenty‐four 4–5‐week‐old male mice were given general meals and diet balance for adaptive feeding for 1 week. And six mice were randomly divided into a normal control group to receive an ordinary diet. The other 18 mice were fed HFD for 4 weeks and then fasted for 12 h before intraperitoneal injection of STZ (110–120 mg/kg) to induce the T2DM model. After 3 days, the T2DM mouse model was identified as successful if fasting glucose level was ≥11.1 mmoL/L and was randomly divided into the following three groups: positive control group (the metformin group), quinoa group, and T2DM model group. The metformin group was fed orally gavage once a day (100 mg/kg body weight/day), the quinoa group was fed quinoa (2 g/day) for 16 weeks, and all mice were fed the previous diet during this time. Fasting blood glucose (FBG) levels were recorded at the end of each week. Pancreas and colon tissues were selected, and part of them were fixed with 10% formalin for histological examination. The remainder of colon tissue was immediately stored at −80°C for the following analysis.2.3At the end of the experiment, the eyeball of mice was picked to draw blood for further detection. The blood was stored in evacuated tubes for centrifugation after 2 h stewing at room temperature to take the upper serum supernatant. The levels of triglyceride (TG), glucose (GLU), fasting insulin (FINS), low‐density lipoprotein cholesterol (LDL‐C), high‐density lipoprotein cholesterol (HDL‐C), and total cholesterol (TC) levels were determined using an automatic biochemical analyzer.2.4The pancreas and colon tissues of mice were selected and fixed in 10% formalin for 24 h, dehydrated in a gradient manner, and embedded in paraffin for sectioning, stained with hematoxylin and eosin, and images were captured to evaluate the histopathological changes of pancreas and colon.2.5The paraffin‐embedded colon was sliced at 4–5 μm with a rotary microtome and placed in an oven at 37°C for 12 h, and then degreased with xylene and hydrated with gradient ethanol. Sequentially, slides were incubated with antigen retrieval solution for 8 min, and we then added rabbit anti‐TRPM5 (1:600), rabbit anti‐TAS1R3 (1:600), and rabbit anti‐GLP‐1 (1:500) at 4°C overnight. Afterward, the slides used for IHC staining were incubated with the secondary antibody for 30 min, followed by DAB and hematoxylin staining. The tissues were examined and photographed by laboratory microscopy. Immunohistochemical positive reaction intensity was analyzed by image‐J6.0 software and expressed as an AOD value. Western blotting was used to detect the expression of TRPM5, TAS1R3, and GLP‐1 proteins in the colon. The colonic protein supernatant was extracted using a tissue protein extraction kit (Servicebio Biotechnology) and the protein concentration was determined using a BCA protein assay kit (Thermo Fisher Scientific). Proteins were separated by 10% SDS‐PAGE gel and then transferred to the PVDF membrane. Then, the latter was blocked with 5% (w/v) skim milk for 1.5 h, incubated with rabbit anti‐TRPM5 (1:1000), rabbit anti‐TAS1R3 (1:1000), and rabbit anti‐GLP‐1 (1:1000) at 4°C overnight. The membrane was gently incubated together with the appropriate secondary antibodies and the bands were visualized with an enhanced chemiluminescence (ECL) reagent. The strip density was quantified by Image J software, and the corresponding β‐actin was used as the internal control for normalization.2.6The mice in the four groups were placed in plastic cages separately, and the cages were covered with gauze and the mice were allowed to defecate naturally. The feces of the mice in the four groups at 00 weeks and 16 weeks were taken, and then the feces were quickly loaded into the frozen storage tube with sterilized tweezers. All operating procedures were performed with strict adherence to the principle of sterility. After that, the freeze‐storage tube containing mouse feces was transferred to a −80°C cryogenic refrigerator with a dry ice box for reserve, and then transported to Novogene Co. LTD for preservation and subsequent analysis. 16S rRNA gene sequencing was performed on Illumina MiSeq and data obtained from Illumina MiSeq amplicon sequencing were analyzed using QIIME 2. Sequences with ≥97% similarity were assigned to the same operational taxonomic unit (OTU) by the UCLUST algorithm in QIIME. Tax4Fun was used to generate predicted KEGG annotations. Spearman correlation was used to analyze bacterial community and environmental factors.2.7https://pubchem.ncbi.nlm.nih.gov/pccompound/), Chemsrc (WWW. Chemsrc. Com), Organchem (WWW. Organchem. CSDB. Cn). Then, we imported the obtained formula into the SwissADME platform (http://www.swissadme.ch/), and two or more “Yes” ingredients were identified as potential active ingredients in quinoa based on GI absorption (High) and five drug properties . Then, we import the potential chemical structural formulas of the active ingredients into SwissTargetPrediction (http://www.swisstargetprediction.ch/). Finally, the prediction targets of potential active ingredients were obtained with species as “homo sapiens,” probability >0 as the screening criterion. Ultimately, the potential targets of quinoa were obtained after being standardized by the UniProt database (https://www.uniprot.org/) with the non‐human samples removed. We could obtain therapeutic targets for diabetes from the GeneCard (https://www.genecards.org/), OMIM (https://omim.org/), DisGeNET (http://www.disgenet.org), and DrugBank (https://go.drugbank.com/). Finally, the cross‐network between the quinoa target and diabetes target was analyzed by drawing a Venn diagram , which represents the potential target of quinoa to ameliorate diabetes. The selected common targets were enriched and imported into the Metascape database for data visualization and KEGG pathway analysis. The “component‐target‐pathway” network diagram of related amino acid metabolism was constructed with Cytoscape3.9.1.The chemical constituents of quinoa were collected from PubMed and CNKI. The structural formulas of the components were from PubChem (2.8https://pubchem.ncbi.nlm.nih.gov/). (2) For receptor molecule preparation, the crystal structure of the proteins was obtained through the RCSB PDB website (http://www.rcsb.org/), and then water molecules and small‐molecule ligands were removed using PyMOL software. AutoDockTools software was used to add nonpolar hydrogens, Gasteiger charges were calculated, and the receptors were prepared and saved in PDBQT format. (3) Molecular docking was performed with the help of PyMOL 2.5.2 software plug‐in to find the docking active site, and the processed components were molecularly docked with the target protein using Autodock Vina 1.2.0 software, and the docking results were visualized using PyMOL 2.5.2 software.Molecular docking was performed as follows: (1) For ligand molecule preparation, structural information of quinoa components was obtained through PubChem database  was used to compare differences between groups. When the data conformed to normality and homogeneity of variance, the LSD test was used. When the data conformed to normality but not homogeneity of variance, Dunnett's T3 test was used. When the data were not normally distributed, the nonparametric tests Kruskal–Wallis and Mann–Whitney 33.13.1.1p < .01). After the intervention of metformin and quinoa, the level of FBG presented with a significant decrease compared to that in the T2DM group . The FINS level was decreased in the metformin group statistically; while there was a tendency to be lower in the quinoa group compared to the model group, the difference was not statistically significant .Compared with the control group, the levels of FBG, FINS, and GLU were significantly increased in the T2DM group Figure , p < .01p Figure , p < .01t Figure . Moreovet Figure , p < .013.1.2p < .01, Figure p < .01, Figure Serum biochemical indexes were measured after a 16‐week intervention. The levels of serum TC, LDL‐C, and HDL‐C showed a significantly upregulated tendency in the model group compared with the control group ; Proteobacteria was negatively correlated with TRPM5 and positively with GLP1 (p < .01). We used the same method to analyze at the genus level .In order to explore the effect of quinoa on type 2 diabetes mellitus, we designed an interactive analysis of intestinal microflora and biochemical indices at phylum and genus levels. As shown in Figure l Figure . Dubosie3.3A total of 118 chemical constituents of quinoa were collected through literature search. After intestinal absorption and drug‐like screening, 72 potential active ingredients were obtained. Based on swisstarget prediction platform, 536 potential active ingredient targets of quinoa were predicted. After retrieval from GeneCard, OMIM, DisGeNET, and DrugBank, 17, 346 diabetes‐related targets were obtained as shown in Figure 3.4We chose the top six core potential compounds according to degree, closeness centrality, and betweenness centrality Table  to have 3.53.5.1p < .01), and the expression of GLP‐1 was decreased in the colon of mice with T2DM compared with that in the normal control mice . After the supplementation of metformin and quinoa for 16 weeks, the expression of TAS1R3 and TRPM5 in the colon IHC was decreased (p < .01), and the expression of GLP‐1 was increased (p < .01), and there was no statistical significance between metformin and quinoa groups.We assessed TAS1R3, TRPM5, and GLP‐1 expression in colon IHC, and we could show that the expression of TAS1R3 and TRPM5 was increased in the colon of mice with T2DM Figure ; p < .01e Figure ; p < .013.5.2p < .01). Further analysis of the GLP‐1 in colon revealed that in the T2DM group, the level of GLP‐1 was lower than in the control group, whereas these changes were reversed by metformin and quinoa, and quinoa had a better regulatory effect compared with metformin .It can be observed from Figure n Figure , p < .014Quinoa is considered a pseudocereal for botanical reasons which belongs to the Chenopodiaceae family . This results in the release of GLP‐1 and gastric inhibitory polypeptide (GIP)  in intestinal  quinoa could modulate gut microbiota which is imbalanced by T2DM and regulates glucose metabolism ultimately. (2) Quinoa plays a hypoglycemic role through multi‐aspects. (3) Quinoa can further improve hyperglycemia by regulating amino acids metabolism through nutrient perception.5In our study, quinoa could regulate the microflora disorder in diabetic mice which was induced by HFD combined with STZ, and has the effect of alleviating hyperglycemia. The prediction of network pharmacological results showed that quinoa plays a hypoglycemic role through multi‐target, multi‐pathway, and multi‐component. Therefore, it is expected to promote human health as an auxiliary hypoglycemic food.Chun‐Yan Zheng: Conceptualization ; data curation ; formal analysis ; investigation (lead); methodology (lead); writing – original draft (lead); writing – review and editing . Tian An: Conceptualization (lead); data curation ; formal analysis ; methodology ; writing – original draft . Zheng Ting Liang: Conceptualization ; data curation ; formal analysis (lead); methodology ; writing – original draft . Bo‐Han Lv: Supervision ; writing – review and editing . Yu Tong Liu: Supervision ; writing – review and editing . Xue‐Hong Hu: Conceptualization ; writing – review and editing . Yue‐Lin Zhang: Methodology ; project administration . Nan Nan Liu: Writing – review and editing . Si‐Yu Tao: Writing – review and editing . Ru Xue Deng: Writing – review and editing . Jia‐Xian Liu: Resources (lead); writing – review and editing . Guangjian Jiang: Conceptualization ; funding acquisition (lead); resources ; supervision (lead); writing – review and editing .This work was supported by Grants from the National Natural Science Foundation of China  and Horizontal subject of Zhong Fu Boai Health management (Beijing) Co., Ltd (2180071720024).The authors declare that they have no conflict of interest.The study protocol was approved by the Animal Care and Management Committee of the Beijing University of Chinese Medicine. All manipulations were at the request of the guidelines of the Animal Care Committee.Appendix S1Click here for additional data file.


Two‐dimensional radiotherapy is often used to treat cervical cancer in low‐ and middle‐income countries, but treatment planning can be challenging and time‐consuming. Neural networks offer the potential to greatly decrease planning time through automation, but the impact of the wide range of hyperparameters to be set during training on model accuracy has not been exhaustively investigated. In the current study, we evaluated the effect of several convolutional neural network architectures and hyperparameters on 2D radiotherapy treatment field delineation.Six commonly used deep learning architectures were trained to delineate four‐field box apertures on digitally reconstructed radiographs for cervical cancer radiotherapy. A comprehensive search of optimal hyperparameters for all models was conducted by varying the initial learning rate, image normalization methods, and (when appropriate) convolutional kernel size, the number of learnable parameters via network depth and the number of feature maps per convolution, and nonlinear activation functions. This yielded over 1700 unique models, which were all trained until performance converged and then tested on a separate dataset.Of all hyperparameters, the choice of initial learning rate was most consistently significant for improved performance on the test set, with all top‐performing models using learning rates of 0.0001. The optimal image normalization was not consistent across architectures. High overlap (mean Dice similarity coefficient = 0.98) and surface distance agreement (mean surface distance < 2 mm) were achieved between the treatment field apertures for all architectures using the identified best hyperparameters. Overlap Dice similarity coefficient (DSC) and distance metrics (mean surface distance and Hausdorff distance) indicated that DeepLabv3+ and D‐LinkNet architectures were least sensitive to initial hyperparameter selection.DeepLabv3+ and D‐LinkNet are most robust to initial hyperparameter selection. Learning rate, nonlinear activation function, and kernel size are also important hyperparameters for improving performance. Most new cases occur in low‐ and middle‐income countries (LMICs) where routine cervical cancer screenings are not common clinical practice.To address these problems, Kisling et al. developed the first fully automated treatment planning tool for external‐beam radiotherapy in locally advanced cervical cancers.Recently, deep convolutional neural networks (DCNNs) have become the state of the art for medical imaging segmentation. In the context of radiotherapy treatment planning, DCNNs have been very promising for the automation of various contouring and planning tasks.In addition, many studies focus on architectural novelty. These compare the performance of a specially‐designed approach with a few general‐purpose architectures and do not report the impact of a wide range of hyperparameter choices on their results. While this is typically done for consistency, this creates a lack of information in the literature of the impact of hyperparameter choices on model performance and may lead future researchers to initialize hyperparameters based upon convention rather than empirical results.Therefore, the aims of the current study are twofold. Our primary focus is not architectural novelty but rather identifying the combinations of six classes of hyperparameters that led to the best performance in six commonly‐used 2D DCNN autosegmentation models, and to show which architectures are most robust to initial hyperparameter selection. We additionally identified the best‐performing model for automatically defining radiotherapy treatment fields on DRR images. Because many radiotherapy treatment sites also employ DRR‐defined field apertures  for treatment planning, we expect the findings of the current study to be translatable to other treatment sites using 3D conformal radiotherapy.22.1Simulation CT scans and radiotherapy treatment plans from 310 patients with cervical cancer previously treated at our institution were retrospectively used in this study after institutional review board approval. All patients had physician‐approved four‐field box radiotherapy treatment plans generated by the Radiation Planning Assistant, an automated treatment planning platform.n = 230), validation (n = 25), and final test (n = 55) datasets. The validation set was used to evaluate each model's performance during training, and final results were reported for the final test dataset, which was excluded from any training and evaluation done during the training phase. The DRRs were the image inputs to all models, and one‐hot encodings of the treatment field binary masks were the autosegmentation ground truths. All models were trained on three orthogonal fields  as separate inputs; LL DRRs were excluded as they were simply horizontally mirrored images of the RL treatment fields and therefore do not require autosegmentation to generate.The 310 cases used in the study were randomly split into training . Network depth was explored for all U‐Net–like architectures; for our analysis, network depth was defined as the number of down sampling steps plus the bottleneck layer of the encoder‐decoder architecture. Depth values explored ranged from 3 to 6 levels. Additionally, kernel sizes of 3×3 and 5×5 were evaluated for these architectures, as well as the initial number of filters  as permitted by graphics processing unit (GPU) memory.Six commonly used deep learning architectures were evaluated: 1) DeepLabv3+ with Xception backbone, DeepLabv2 normalization  and image intensity histograms vary widely between patients depending on patient size and other anatomic factors. Three intensity normalization techniques were evaluated in our analysis; these included z‐score normalization Equation , histogrEquation :(1)InI¯) and standard deviation (σI) values, which were calculated using the pixel intensities of all training images or using individual image's pixel intensities, respectively. For histogram stretching, intensity values were normalized such that new intensities were within  . Four combinations of intensity values were chosen for bottomI and topI , and all values > Itop were set to have intensity values equal to Itop. For L2 normalization, we calculated the Euclidean norm (∥I∥2=I12+I22+⋯+In2) using individual DRR pixel intensities. Following the nomenclature previously established, this was a local statistic, and no attempts were made to normalize on a global scale. Combined, a total of 7 intensity normalization schemes were evaluated.For z‐score normalization, both global and local statistics were evaluated using intensity mean (All models were trained on a 16‐GB Nvidia Tesla V100 GPU with Keras 2.2.2 (TensorFlow 1.11.0 backend).2.3A| and |B| are the number of voxels from contoured volumes A and B, respectively; |A∩B| denotes the number of voxels included in the intersection between volumes A and B; A,Bd is a vector containing all minimum Euclidian surface distances from the surface point from volume A to B; and d¯A,B is the average value in the vector d. DSC values range from 0 (no overlap) to 1 (perfect overlap); for both MSD and HD, values closer to zero represent better agreement between two contours’ surfaces. The raw model predictions (no post‐processing was applied) were used to calculate these metrics. Pearson correlation coefficients were calculated to identify trends in quantitative metrics during data analysis.The predicted treatment fields were compared with the clinically defined treatment fields. The Dice similarity coefficient3In total, 1743 of 2527 potential models were trained to exhaustively evaluate individual architectures, architecture‐specific parameters, learning rates, and image normalization approaches ; differences in mean DSC, MSD, and HD values were 0.01, −1.33 mm, and −7.92 mm, respectively. Choosing between concatenating and element‐wise addition of feature maps from residual connections at each convolutional block . Mean DSC, MSD, and HD values improved by 0.03, −1.1 mm, and −4.7 mm, respectively, for AP beam apertures compared with lateral fields, and by 0.03, −1.3 mm, and −5.7 mm, respectively, for PA beam apertures compared with lateral fields.Beam aperture orientation showed differences in automatically defined fields Figure . The per4In the current study, we performed an exhaustive search of optimal architecture and hyperparameter selection to automatically define cervical cancer radiotherapy treatment beam apertures. Our analysis highlighted some hyperparameter adjustments that were associated with better performance, such as increasing the kernel size, using the PReLU over the ReLU activation function, and increasing network depth for U‐Net‐like architectures. On average, the best performing models resulted in DSC values of 0.98 and MSD values of < 2 mm. These results showed that radiotherapy treatment beam apertures can be automatically defined with high agreement to clinically acceptable beams and could potentially be integrated into fully automated treatment planning workflows.−3 or lower, although nonadaptive optimizers such as stochastic gradient descent often do well with a learning rate of 0.01. Furthermore, while a learning rate scheduler was not used in the current study to maintain consistency during training, many studies report improved performance with the use of schedulers.It is worth noting that simply increasing the parameter count is not necessarily enough to improve performance Table . One excPatient features can also affect prediction accuracy, especially the presence of surgical hardware or other high‐density materials such as fecal impaction due to the patient's diet Figure . In thesDirect comparison with the literature is challenging owing to differences in anatomic sites or imaging modalities. Using a similar DRR approach, Han et al. described a technique to predict field apertures for whole‐brain radiotherapyThe current study has a few limitations. As noted, performance on the lateral fields was slightly lower than on the AP and PA fields. This is likely due to the presence of high‐density materials , which frequently created image intensity distributions that were less homogenous than those for the AP or PA fields in the DRR projections. The lower foreground (patient anatomy) to background (air) ratio for RL fields may have similarly contributed to the lower performance. Additionally, as previously noted, the choice of optimizer influences the selection of the learning rate. Although Adam performed better with smaller learning rates, it is important to note that this may not generalize to all optimization methods, particularly nonadaptive methods.GPU memory limitations prevented a subset (784) of U‐Net–like models from training; specifically, Res‐U‐Net with more than 32 filters in the initial convolution. We believe this had little effect on the final result because the best‐performing models used 16 filters in the initial convolution. Similarly, although U‐Net–like architectures performed best when network depth was set to 6 , it is unknown if this trend would have continued because GPU memory did not permit deeper networks to be trained. Because depth implies additional resampling operations and thus allows the network to learn correlating features at additional scales, it is not unreasonable to explore the impact of deeper networks on more capable hardware. However, U‐Net–like architectures often double the number of convolutional filters at each down sampling operation. As previously discussed, increasing parameter count often does not yield better performance; in fact, some studies show comparable performance to state‐of‐the‐art algorithms while significantly restricting parameter count through control of the number of convolutional filters.In conclusion, the current study, which required over 30,000 computing hours to train 1743 models, is to the best of our knowledge the first to report such an exhaustive search for optimal deep learning architectures and hyperparameters for fully automated beam aperture definition. Of the models evaluated, we identified DeepLabv3+ and D‐LinkNet as the most robust to hyperparameter initialization; however, none of the architectures provided statistically significant improvements when optimal hyperparameters were selected. Among the sets of hyperparameters we identified as providing the best performance, learning rate affected performance for all models. Other optimal hyperparameters varied on a per‐architecture basis, although all U‐Net–like architectures benefited from deeper networks. When using the identified best hyperparameters, our approach is capable of integration into a fully automated treatment planning workflow such as the Radiation Planning Assistant.Conceptualization, Methodology, Software, Validation, Formal Analysis, Investigation, Resources, Writing—Original Draft, Writing—Review & Editing, Visualization: Skylar Gay. Formal Analysis, Data Curation, Writing—Review & Editing: Kelly D. Kisling. Formal Analysis, Data Curation, Writing—Review & Editing: Brian M. Anderson. Formal Analysis, Data Curation, Writing—Review & Editing: Lifei Zhang. Formal Analysis, Writing—Review & Editing: Dong Joo Rhee. Formal Analysis, Writing—Review & Editing: Callistus Nguyen. Formal Analysis, Writing—Review & Editing: Tucker Netherton. Formal Analysis, Writing—Review & Editing: Jinzhong Yang. Formal Analysis, Data Curation, Writing—Review & Editing: Kristy Brock. Formal Analysis, Data Curation, Writing—Review & Editing: Anuja Jhingran. Formal Analysis, Data Curation, Writing—Review & Editing: Hannah Simonds. Formal Analysis, Data Curation, Writing—Review & Editing: Ann Klopp. Formal Analysis, Data Curation, Writing—Review & Editing: Beth M. Beadle. Conceptualization, Investigation, Writing—Review & Editing, Supervision, Project administration, Funding acquisition: Laurence E. Court. Conceptualization, Investigation, Methodology, Software, Validation, Formal Analysis, Resources, Writing—Original Draft, Writing—Review & Editing, Project administration: Carlos E. Cardenas.The authors have no conflicts of interest to disclose.


Legumes maintain soil fertility thanks to their associated microbiota but are threatened by climate change that causes soil microbial community structural and functional modifications. The core microbiome associated with different chickpea and lentil genotypes was described after an unexpected climatic event. Results showed that chickpea and lentil bulk soil microbiomes varied significantly between two sampling time points, the first immediately after the rainfall and the second 2 weeks later. Rhizobia were associated with the soil of the more productive chickpea genotypes in terms of flower and fruit number. The root‐associated bacteria and fungi were surveyed in lentil genotypes, considering that several parcels showed disease symptoms. The metabarcoding analysis revealed that reads related to fungal pathogens were significantly associated with one lentil genotype. A lentil core prokaryotic community common to all genotypes was identified as well as a genotype‐specific one. A higher number of specific bacterial taxa and an enhanced tolerance to fungal diseases characterized a lentil landrace compared to the commercial varieties. This outcome supported the hypothesis that locally adapted landraces might have a high recruiting efficiency of beneficial soil microbes. Lens culinaris Medik.) and chickpea (Cicer arietinum L.) are annual pulse crops native to the Mediterranean, Middle East regions , a major rainfall event temporarily flooded the experimental field. Therefore, the effects of the irrigation variable could not be completely addressed. Consequently, the goal of this study changed during the experimental progress, and attention was focused on the description of the core microbiome composition associated with different chickpea and lentil genotypes after a major climatic event.3 of irrigation water over the whole experimental parcel surface, including lentil and chickpea . The used irrigation water was high‐quality groundwater . Cation concentrations within this water were: Ca2+, 1.7; Mg2+, 0.9; Na+, 1.7; K+, 0.06 mmol L−1. The corresponding anion concentrations were: CO32−, trace; HCO3−, 2.6; SO42−, 0.8; and NO3−, 1.32 mmol L−1 . Each parcel consisted of 11 rows, each 3‐m‐long and 30 cm apart with 3 cm between sown seeds. All recommended cultural practices for lentil and chickpea production were adopted. Weeding was carried out by hand. Strips of land 1‐m‐wide acted as buffer areas between the parcels.The open field experiment was conducted at the Azienda Pantanello (ALSIA research center), Southern Italy  using four lentil  and four chickpea genotypes  provided by the INCREASE consortium . Days to 50% flowering were recorded at proper time and fruits are reported at physiological maturity when 90% of the pods in a parcel were golden brown.During the phenological stage corresponding to ‘full blooms to early visible pod’ a non‐destructive measurement of physiological variables was performed by means of steady‐state Licor LI‐600 Porometer/Fluorometer for rapid insight into stomatal conductance (gsw).Two sampling and measurement campaigns were performed (19 July 2021–24 July 2021 and 02 August 2021–05 August 2021). In July  and August  quantities of chickpea and lentil fruits and flowers were classified on a scale from 0 to 2, where 0 indicates the absence of fruits or flowers, 1 indicates the presence of one‐two plants with flowers or fruits and 2 indicates that fruit or flowers were present in more than two plants. Later, for each genotype, the fruit and flower index (Fr.I and Fl.I) was calculated as follows:n,Fr.I. = Frg/n,Fl.I. = Flg/n indicates the replicate number. Therefore, the fruit and flower index value ranged from 0 to 2, where 1 indicates that fruits and flowers were at least present on average in all samples.where Frg indicates the sum of the fruit class value for each genotype, Flg indicates the sum of the flower class value for each genotype and Soil samples from the lentil and chickpea parcels were collected in July (T0) from the 0 to 15 cm layer, pooled according to the water irrigation treatment , air‐dried and sieved through a 2 mm sieve before laboratory analysis. Physical–chemical features, such as texture, pH, and conductivity, were measured and soil chemical properties were determined according to standard analytical procedures  in the first sampling (T0) while, during the second sampling (T1), soils were collected only from the repetition IV. A Dutch soil auger was used to collect soil cores from ground surface depths of 0–15 cm after that any large debris had been removed with a hand trowel. Regarding the first sampling, four replicates for each genotype were pooled inside a treatment  for a total of 24 soil samples (3 water levels x 8 genotypes). The genomic DNA was extracted from about 500 mg of each soil sample through the Fast DNA® Spin Kit for Soil . After the quantification by Qubit® 2.0 fluorometer (Invitrogen), 3 ng of the genomic DNA was amplified using the 16S Metagenomics Kit (Thermo Fischer Scientific). The amplification program was the same used by Sillo et al. . BrieflyLens culinaris root samples  were collected  for a total of 24 parcels initially subjected to diverse irrigation treatments and then differentially affected by the rain (sampling in August 2021). Samples were weighted to a maximum of 250 mg in sterile 2 mL microcentrifuge tubes. Afterwards, a sterile stainless‐steel bead (Ø 5 mm) was added, and the samples were disrupted in liquid nitrogen with a TissueLyser II (Qiagen) at 30 Hz for 30 s. Total DNA was extracted with the DNeasy Plant mini kit (Qiagen) according to the manufacturer's instructions.Total DNA was quantified with a Nanodrop 2000 (ThermoScientific) and the 260/280 260/230 absorbance ratios were calculated. DNA integrity was further analysed by running the samples on a 1% agarose electrophoretic gel.L. culinaris  and thermal cycler conditions were set as described by the Illumina protocol  blocker oligos (Kaneka Eurogentec S.A.), targeted at plant mitochondrial and chloroplast 16S rRNA genes and plant Internal Transcribed Spacer (ITS) region of the rRNA gene, were added to the sequencing reaction. PNA was custom‐designed for Metabarcoding data were analysed with QIIME 2  sequences for taxonomic assignment. The output was elaborated to obtain a relative abundance (%) of each ASV in the total amounts of the entire sample. Illumina reads from metabarcoding of lentil root samples was submitted to NCBI SRA under BioProject PRJNA933539 for bacterial and fungal reads, respectively.The ASV table was used as input for Microbiome Analyst .The annotation tools FUNGuild . After verifying the normal distribution and the homogeneity of variances of the molecular, agronomic, and eco‐physiological data, the ANOVA and Tukey post‐hoc comparisons were used, adopting a probability level of p‐value ≤ 0.05. If normal distribution and homogeneity of variances were not confirmed, data were analysed with Kruskal–Wallis test and Dunn's post‐hoc comparison was used . The data of physical–chemical soil properties were analysed with t student test . The web‐based tool MicrobiomeAnalyst, described above, performed PERMANOVA to analyse the diversity component .Data were analysed with the statistical software Past 4.09 , suggesting an average nutrient availability. A significant increase in soil pH was observed in the three lentil parcels at T1 compared to lentil soils at T0 . The lentil‐associated soil EC and the exchangeable Na (respectively 857 μS/cm and 0.16 g/Kg) were significantly lower compared to lentil bulk soil at T0 .Results from soil physical–chemical analyses are reported in Table The whole experimental period (from 04 June 2021 to 10August 2021) was characterized by an average relative humidity maximum of 81.91%, reached on 19 July 2021, and a minimum of 36.58% on 01 July 2021 Figure . During p‐value: 0.0011, Dunn's post‐hoc comparisons). Similarly, chickpea fruit index in August (T1) was significantly higher for the An.Ca.1586 and Pascià genotypes , compared to Nero Tolve and Sultano . There was no significant difference among chickpea genotypes in the August flowering index, even if the An.Ca.1586 and Sultano genotype flowering indexes  were slightly higher than Nero Tolve and Pascià   was significantly higher in the An.Ca.1586 and Pascià genotypes  compared to Nero Tolve and Sultano . In August, fruit production was exclusively observed in the Colfiorito and Elsa genotypes , while fruits were absent in Eston and Itaca  in July, while flowers were absent in Eston and Itaca. In August, the Colfiorito, Elsa, and Eston genotypes produced significantly more flowers than Itaca , and the experimental field was flooded. As a result, the irrigation treatments were no longer applied, and the effects of the earlier stress application most likely disappeared. Following the rainfall event, the performed physiological and biometric  measurements did not reveal any significant differences among the genotypes of chickpea and lentil when irrigation treatments were considered Figures  and S3.p‐value: 0.0028052; [ANOVA] F‐value: 3.8358; Figure F‐value: 2.499; R‐squared: 0.30426; p‐value: 0.001).Analysis of the chickpea and lentil bulk soil prokaryotic community revealed that alpha diversity was significantly higher in the chickpea genotypes  and Proteobacteria 30%) were the dominant Phyla, followed by Bacteroidetes, Firmicutes, and Gemmatimonadetes (3%) Figure . In T1 t0% were tp‐value: 0.70465; [ANOVA] F‐value: 0.47281; Figure F‐value: 0.82395; R‐squared: 0.11; p‐value: 0.85; Figure F‐value: 5.0843; R‐squared: 0.18772; p‐value: 0.001; Figure Shinella fusca was significantly more represented in the bulk soil of the An.Ca.1586 genotype , compared to the other chickpea genotypes , Acidobacteria , Aquificae , Chlorobi , and Gemmatimonadetes  were observed at T1 , Solirubrobacter , Lysobacter , Arenimonas , and Ilumatobacter   Figure . On the Actinobacteria (from 47% to 52%) and Crenarcheota (from 4% to 5%) compared to T0, and slight decreases in Proteobacteria (from 31% to 28%), Bacterioidetes (from 7% to 5%), and Firmicutes (from 4% to 2%)  and Proteobacteria (29%) were the dominant phyla, followed by the less well‐represented Bacteroidetes (6%), Crenarchaeota (4%), Planctomycetes, and Firmicutes (3% and 3%) Figure . At T1 t Figures . The OTUp‐value: 0.83626; [ANOVA] F‐value: 0.28411), and a slight, but not statistically significant, reduction in the alpha diversity at T1 was displayed  , Gaiellaceae , Geobacteraceae , and Clostridiaceae     Figure . At T1, ) Figure . On the ) Figure . These c) Figure . Convers) Figure . At the ) Figure .p‐value: 0.99728; [ANOVA] F‐value: 0.015016)   Figure . Regardi) Figure .Since many lentil parcels were suffering and showed disease symptoms Figure  probablyp‐value: 0.01043; Dunn's post‐hoc test Bonferroni corrected, Figure The fungal functional annotation tool FUNGuild highlighted that saprotrophs were the most abundant inhabitants of all the lentil root samples Figure , indepenRhizoctonia solani was detected in Itaca, Elsa, and Eston roots, while no R. solani reads were recorded in the Colfiorito genotype. In Figure R. solani reads found in each sample were visually compared. Among fungal pathogens, Macrophomina phaseolina was present in all studied lentil genotypes, but only in one Eston sample. Fusicolla septimanifiniscientiae was identified in all lentil genotypes but only in one Colfiorito sample. Fusarium spp. were  abundant in roots of all lentil genotypes. For instance, Fusarium oxysporum was detected in all genotypes, with a maximum of 5195 reads in one Eston root sample (Eston IV RG2), Fusarium falciforme was very abundant in all lentil roots, showing up to 29,719 reads in one Elsa sample (Elsa I RG3), Fusarium croci reads were detected in all lentil genotypes, with a maximum of 2069 reads in a Colfiorito sample (Colfiorito IV RG3).Notably, The prokaryotic functional annotation tool FAPROTAX revealed that among the lentil genotypes, no significant patterns in the prokaryotic ecological functions were observed Figure . HoweverA Venn diagram representing the common and specific prokaryotic families of the lentil roots and of the bulk soil at T0 and T1 was generated Figure . In totaAciditerrimonas, Alteromonadaceae, Leuconostocaceae, Microthrixaceae, and Nitriliruptoraceae.Five prokaryotic families were specifically associated to bulk soil at T0 , while 78 families were shared between bulk soil at T0 and T1 . Instead, 12 prokaryotic families were specifically found in bulk soil at T1, such as On the other hand, 54 prokaryotic families were common to all studied compartments (roots and bulk soil). Some examples are Bacillaceae, Burkholderiaceae, Chitinophagaceae, Hyphomicrobiaceae, Pseudomonadaceae, Rhizobiaceae, and Xanthomonadaceae.An additional Venn diagram comparing the prokaryotic families present in root samples from each lentil genotype with the bulk soil samples at T1 highlighted the presence of 149 prokaryotic families in the bulk soil (T1), 126 families in Colfiorito samples, 122 families in Elsa samples, 102 families in Eston samples and 119 families in Itaca samples Figure . Eleven An increase in intense weather events, such as heavy rain and flooding, as well as rarefaction of precipitation events are affecting our lives in both direct and indirect ways  compared to the others. Shinella and Mesorhizobium are known to establish a symbiosis with legumes , they are ubiquitous in the environment and have wide metabolic capabilities, being able to degrade recalcitrant organic carbon sources  were showing chlorotic leaves and necrotic roots, and many plants in some parcels died. It could be hypothesized that these symptoms could be due to a fungal or bacterial disease. Therefore, a metabarcoding analysis on fungal and bacterial DNA isolated from lentil roots was performed. The fungal functional annotation analysis revealed that the experimental lentil field was affected by fungal pathogens, but the Itaca genotype showed substantially more reads corresponding to them than the rest of the field. In particular, the presence of Rhizoctonia solani was not the only phytopathogenic fungus inhabiting the experimental field. Not surprisingly, there was a high abundance of Fusarium spp. reads. The Ascomycete Fusarium spp. is widely distributed in soil, as it can survive for many years with a saprotrophic behaviour  , allowing their use for pest management practices  compared to the other lentil genotypes. Colfiorito is an Italian lentil landrace, that is, a traditional local variety, characterized by a high level of genetic diversity ; formal analysis ; investigation ; methodology ; writing – original draft ; writing – review and editing . Fabiano Sillo: Data curation ; formal analysis ; investigation ; methodology ; writing – original draft ; writing – review and editing . Elisabetta Franchi: Investigation ; methodology ; writing – review and editing (supporting). Ilaria Pietrini: Data curation ; formal analysis ; investigation ; writing – review and editing (supporting). Vincenzo Montesano: Investigation (supporting); writing – review and editing (supporting). Giovanni Marino: Formal analysis ; investigation ; writing – review and editing (supporting). Matthew Haworth: Formal analysis ; investigation ; writing – review and editing (supporting). Elisa Zampieri: Investigation ; writing – review and editing (supporting). Danilo Fusini: Formal analysis ; investigation ; writing – review and editing (supporting). Martino Schillaci: Investigation (supporting); writing – review and editing (supporting). Roberto Papa: Resources (lead); writing – review and editing (supporting). Chiara Santamarina: Resources (supporting); writing – review and editing (supporting). Federico Vita: Investigation (supporting); writing – review and editing (supporting). Walter Chitarra: Methodology ; writing – review and editing (supporting). Luca Nerva: Methodology ; writing – review and editing (supporting). Giannantonio Petruzzelli: Investigation ; writing – review and editing (supporting). Carmelo Mennone: Methodology (supporting); writing – review and editing (supporting). Mauro Centritto: Conceptualization (supporting); funding acquisition (supporting); methodology ; writing – review and editing (supporting). Raffaella Balestrini: Conceptualization (lead); funding acquisition (lead); investigation ; methodology ; writing – review and editing (lead).The authors have no conflict of interest to declare.Table S1. Climatic data are reported for the period from 01 June 2021 to 30 September 2021. The sowing date was at 04 June 2021. For each day (DD/MM/YYYY), the rain (mm), the average direct solar radiation (Wm−2), the minimum, maximum, and average air temperature (°C), the average relative humidity (%), the wind speed (m s−1) and the reference evapotranspiration  calculated with FAO Penman–Monteith method and Hargreaves method are reported.Click here for additional data file.Table S2. The operational taxonomic unit (OTU) of chickpea bulk soil at T0 and T1 is reported.Table S3. The operational taxonomic unit (OTU) table of lentil bulk soil samples is reported.Table S4. The amplicon sequence variant (ASV) table of lentil root samples referring to fungal sequences is reported.Table S5. The amplicon sequence variant (ASV) table of lentil root samples referring to bacterial sequences is reported.Click here for additional data file.Table S6. List of prokaryotic families specific and shared among lentil roots, bulk soil at T0 (July), and bulk soil at T1 (August). The ‘Compartment(s)’ column indicates the specific or shared sample type, the ‘Number of elements’ column indicates the number of prokaryotic families found in each compartment, and in the ‘Families’ column the prokaryotic families are listed. The Venn diagram table was obtained through the https://bioinformatics.psb.ugent.be/ web‐based tool.Click here for additional data file.Table S7. List of prokaryotic families specific and shared among lentil root samples, bulk soil at T1 (August). The ‘Compartment(s)’ column indicates the specific or shared sample type, the ‘Number of elements’ column indicates the number of prokaryotic families found in each compartment, and in the ‘Families’ column the prokaryotic families are listed. The Venn diagram table was obtained through the https://bioinformatics.psb.ugent.be/ web‐based tool.Click here for additional data file.Figure S1. Meteorological data. In the graph (a) the mm of rain for each day of the experimental period (from 04 June 2021 to 10 August 2021) are reported. In the graph (b) the minimum, maximum, and average air temperature (°C), as well as the average relative humidity (%), are displayed for each day of the experimental period.Figure S2. Scatterplots of chickpea agronomic and eco‐physiological measures. Chickpea genotype  heights (cm) were measured in July and August 2021  (a)–(d). Chickpea genotype stomatal conductance (gsw) was measured at T0 and T1 (e)–(h). Irrigation treatments (RG) are reported in different colours as 100% water, not stressed (RG1), 50% water (RG2), and 25% water (RG3).Figure S3. Scatterplots of lentil height. Lentil genotype  heights (cm) were measured in July and August 2021 . Irrigation treatments (RG) are reported in different colours as 100% water, not stressed (RG1), 50% water (RG2), and 25% water (RG3).Figure S4. Heat tree and pie charts of chickpea bulk soil microbiome. The heat tree of the prokaryotic phyla present in chickpea bulk soil (a) and the pie charts (b)–(d) were generated with the web‐based tool Microbiome Analyst  shows the taxonomic abundance of chickpea bulk soil in both time points (T0 and T1). The pie charts (c), (d) show the taxonomic abundance of chickpea bulk soil at T0  and at T1 .Figure S5. Univariate analysis at feature level of chickpea bulk soil microbiome in July. The univariate analysis of the four chickpea genotypes  was evaluated for the first time point in July  and for the second time point in August  with the web‐based tool Microbiome Analyst  and for the genus Mesorhizobium (b).Figure S6. Univariate analysis at phylum level of chickpea bulk soil microbiome. The univariate analysis of the chickpea bulk soil was evaluated comparing the first  and the second time point  with the web‐based tool Microbiome Analyst  and the second  time point with the web‐based tool Microbiome Analyst  and the second  time point with the web‐based tool Microbiome Analyst , Solirubrobacter (b), Lysobacter (c), Arenimonas (d), Ilumatobacter (e) and Glycomyces (f).Figure S9. Heat tree and pie charts of lentil bulk soil microbiome. The heat tree (a) depicts the hierarchical structure of phyla and the relative abundance of lentil bulk soil microbial communities. The colour gradient and the size of node, edge, and label are based on the log2 ratio of median abundance. The pie chart (b) shows the taxonomic abundance of lentil bulk soil. The pie charts (c), (d) show the taxonomic abundance of lentil bulk soil at T0  and at T1 . The graphs were generated with the web‐based tool Microbiome Analyst  and the second time point  with the web‐based tool Microbiome Analyst  and the second time point  with the web‐based tool Microbiome Analyst  and at the second time point  with the Chao1 index, and significant differences were evaluated with ANOVA. The beta diversity among the lentil genotypes  was evaluated at T0 (c) with the Jaccard index, and significant differences were evaluated with PERMANOVA. The web‐based tool Microbiome Analyst was used (Dhariwal et al., Figure S13. Lentil parcel showing disease symptoms. In the picture, a suffering lentil parcel of Itaca genotype is reported. Most of the plants in the parcel were dead, the others were suffering, displaying chlorotic leaves.Click here for additional data file.