Diabetic kidney disease is the leading cause of end-stage kidney disease worldwide; however, the integration of high-dimensional trans-omics data to predict this diabetic complication is rare. We develop artificial intelligence (AI)-assisted models using machine learning algorithms to identify a biomarker signature that predisposes high risk patients with diabetes mellitus (DM) to diabetic kidney disease based on clinical information, untargeted metabolomics, targeted lipidomics and genome-wide single nucleotide polymorphism (SNP) datasets. This involves 618 individuals who are split into training and testing cohorts of 557 and 61 subjects, respectively. Three models are developed. In model 1, the top 20 features selected by AI give an accuracy rate of 0.83 and an area under curve (AUC) of 0.89 when differentiating DM and non-DM individuals. In model 2, among DM patients, a biomarker signature of 10 AI-selected features gives an accuracy rate of 0.70 and an AUC of 0.76 when identifying subjects at high risk of renal impairment. In model 3, among non-DM patients, a biomarker signature of 25 AI-selected features gives an accuracy rate of 0.82 and an AUC of 0.76 when pinpointing subjects at high risk of chronic kidney disease. In addition, the performance of the three models is rigorously verified using an independent validation cohort. Intriguingly, analysis of the protein–protein interaction network of the genes containing the identified SNPs  reveals that, at the molecular level, there seems to be interconnected factors that have an effect on the progression of renal impairment among DM patients. In conclusion, our findings reveal the potential of employing machine learning algorithms to augment traditional methods and our findings suggest what molecular mechanisms may underlie the complex interaction between DM and chronic kidney disease. Moreover, the development of our AI-assisted models will improve precision when diagnosing renal impairment in predisposed patients, both DM and non-DM. Finally, a large prospective cohort study is needed to validate the clinical utility and mechanistic implications of these biomarker signatures. Diabetic kidney disease (DKD) is the leading cause of end-stage kidney disease worldwide4. These conditions are strongly associated with high rates of cardiovascular disease and mortality6, as well as very high medical expenditure7. DKD risk prediction via reliable biomarkers is currently an unmet clinical need. Urinary albumin excretion and serum creatinine are the common clinical biomarkers used for diagnosis and staging of renal impairment among chronic kidney disease (CKD) patients. However, abnormalities in these two parameters often indicate existing kidney damage rather than a predisposition of renal impairment in the future8. Their usefulness when estimating glomerular filtration rate (eGFR) is subject to several limitations due to various potential confounders, such as age, sex, muscle mass, and changes in glomerular hemodynamics due to hyperfiltration that is secondary to the patient’s hyperglycaemia status9. Other biomarkers of kidney injury, including cystatin C, kidney injury molecule 1, neutrophil gelatinase-associated lipocalin, and liver fatty acid-binding protein, are not specific to DKD and also have disadvantages. Furthermore, liver or thyroid dysfunction, an alteration in urine volume, a change in creatinine concentration, and treatment with various medications can also affect the levels of these biomarkers13. An approach that accurately predisposes high risk DM patients to DKD remains urgently needed.Diabetes mellitus (DM) remains a major medical challenge and affects 463 million adults globally14. Given the various hemodynamic and metabolic disarrangements exerted by hyperglycaemia on kidney tissue, metabolomic analysis represents a very useful way of addressing this clinical issue15. In addition, genome-wide association studies (GWAS) have been able to identify a number of potential genes, loci, and single-nucleotide polymorphisms (SNPs) that are associated with DKD, which implies that genetic susceptibility is part of the pathogenesis of DKD18. However, up to the present, the development of biomarkers focusing on the genomic-metabolomic signatures specific to DKD has not taken place.High-throughput omics approaches have revolutionized biomarker research and have helped to advance our understanding of renal progression in DM patientsTo fill this knowledge gap, we conduct a trans-omics study that integrated high dimensional data collected from an extensive clinical information dataset, an untargeted metabolomics dataset and a lipidomics dataset, as well as genome-wide SNP genotyping. We adopt a machine learning (ML) methodology to delineate the complex biological processes associated with four health conditions: control subjects, subjects with DM, subjects with CKD and subjects with DKD. Furthermore, we performed protein connectivity mapping of the genes containing the relevant SNPs in order to determine their potential roles in the molecular pathogenesis of CKD and DKD, specifically their functional connectivity and their protein–protein interaction network.2. The DKD patients were more like to be older, to have hypertension, to have a higher serum triglyceride level, a higher level of calcium, and a higher level of insulin . The analysis consisted of three parts: 1) an analysis of the performance of metabolomics, SNPs and the clinical information data associated with the four groups of subjects; (2) feature selection; and (3) model derivation and validation , six features were selected by AI from all of the groups, namely three metabolites and three annotated SNPs Fig. . The thrp < 0.001; Wald test), the PCDH9 genotype , the LY6D genotype , and the ALDH1L1 genotype . Conversely, a higher level of serine  appeared to be associated with a lower risk of the occurrence of DKD  was present for the occurrence of DKD in relation to resolvin D1 . Conversely, a significant decrease in the frequency of the AG genotype of ADCYAP1 gene was found among non-diabetics CKD patients compared to the other groups , abundance of mannose/inositolI , the FAM53A genotype , the CDS1 genotype , and the CCDC182 genotype . On the other hand, there was a significant decrease in the risk association of non-diabetic CKD with the B3GNTL1 genotype , and the abundance of PC C30:2  , and Model 3 (identification of CKD). Eight of the protein-coding genes identified in Model 1 (RAPTOR and CLPTM1L), Model 2 (PCDH9 and ALDH1L1), and Model 3  are able to be connected to each other and they form an obvious protein–protein interaction network , CKD and non-CKD in DM patients , and CKD and non-CKD in non-DM patients  with good prediction performance. Additionally, we identified a biomarker signature made up of several phospholipids (Phosphatidylcholine and Lysophosphatidylcholine), a lipid mediator (resolvin D1) and a purine/pyrimidine metabolism mediator (pseudouridine) that are associated with DKD. Furthermore, the findings related to specific genotypes of the annotated SNPs associated with CKD or DKD highlight that there seems to be genetic susceptibility in predisposed subjects toward renal impairment. Intriguingly, analysis of the protein–protein interaction network of the genes containing the SNPs identified by the three models revealed that, at the molecular level, there seems to be an overlap between the pathogenesis of DM and the pathogenesis of CKD; it also identified various interconnected factors that may affect the progression of renal impairment among DM patients, namely the occurrence of DKD.21. These methodologies have been able to identify altered lipidomic profiles associated with the risk of renal progression among CKD patients22 and to uncover proteomics and metabolomics biomarkers associated with early vs. advanced stage DKD23. Here, we have utilized AI-based techniques combined with traditional approaches to select subsets of genomic and metabolomic features that give the best performances and avoided overfitting the training data24. Importantly, the performances of the three models built in this study were verified using an independent validation cohort and this cohort gave similar results that are comparable to those obtained from the training cohort. Our findings indicate that an AI-assisted approach is able to considerably improve the precision diagnosis of CKD and DKD and this represents an important tool for providing insights into the potential molecular mechanism(s) that underlies various renal disease states.The ability to generate trans-omics big data makes machine learning approaches suitable for obtaining both biological and translational perspectives related to questions in kidney biology23. Our findings are consistent with previous reports whereby there are perturbations affecting the metabolites associated with glycerol-lipid metabolsm and oxidative stress25, such as glycerol-3-galactoside23 and asymmetric dimethylarginine26, as well as purine and pyrimidine metabolism, namely pseudouridine27, in patients with DKD28. Diacylglycerol can activate protein kinase C, which, in turn, affects various inflammatory and growth-promoting pathways9. Conversely, serine, glutamine and lysophosphatidylcholine C18:2 are associated with a decreased risk of DM29. We also found an increase of serum kynurenine levels in CKD patients. Kynurenine is derived from tryptophan metabolism; it appears to play a role in several biological processes, including energy metabolism, the pro-inflammatory response, atherosclerosis and oxidative stress31. Increased concentrations of kynurenine have been found to be associated with acute kidney injury, CKD, DKD and cardiovascular diseases33. Intriguingly, we also found an increase in the level of resolvin D1 in DKD patients; this molecule is a bioactive lipid mediator derived from omega-3 fatty acid. Previous studies have shown that resolvin D1 seems to be able to promote the resolution of the inflammatory response and has anti-oxidant effects35. Accordingly, it is possible that the increase in resolvin D1 is a compensatory effect in response to the elevated inflammation and increased oxidative stress in DKD patients. Additionally, some of the biomarkers discovered in the present study may serve as mediators of the post-transcriptional and/or post-translational modifications involved in progression of injury-repair processes in the kidney under hyperglycemia.The use of serum metabolites increases accuracy when estimating kidney function during DKD compared to serum proteins36. Previous publications have reported a variety of SNPs that are associated with DKD, including CNDP1, FRMD3, RGMA-MCTP2 (rs12437854), AFF3 (rs7583877), ERBB4 (rs7588550), COL4A3 (rs55703767), and MUC738. However, combination of SNPs does not improve discrimination of DKD or CKD beyond traditional risk factors in a range of ethnic populations41. In this study, several protein-coding genes containing SNPs were selected by AI and used as important features when distinguishing the various groups; this highlights the importance of genetic susceptibility to renal dysfunction among predisposed subjects. Among the SNPs associated with DKD, ALDH1L1 encodes a protein that belongs to the aldehyde dehydrogenase family and is associated with the NADPH pathway. Furthermore, LY6D acts as a specification marker during the earliest stage specification of lymphocytes during B-cell and T-cell development. In addition, PCDH9 is responsible for encoding a potential calcium-dependent cell-adhesion protein. On the other hand, B3GNTL1, CDS1, ADCYAP1 and FAM53A SNPs are associated with CKD among non-DM patients. Among these, B3GNTL1 is a putative glycosyltransferase, CDS1 catalyzes the conversion of phosphatidic acid to diacylglycerol and ADCYAP1 encodes a secreted protein that is involved in the control of glucose homeostasis and the regulation of insulin secretion in pancreatic beta cells42. Intercommunication between these genetic factors, which was revealed by the connectivity map, deserves further functional investigation in order to decipher their roles in the pathogenesis of DM and CKD.Genomics studies have identified several genetic variants associated with kidney disease; however, their functional effects remain poorly understoodThere are several limitations to the present study in spite of the meticulous application by us of a range of ML approaches to leverage the big datasets associated with our multi-omics data in order to gain insights into disease phenotype association. Firstly, we used the levels of circulating biomarkers to identify disease association. Analysis of multiple tissues, for example, kidney, liver, and muscle, needs to be performed to clarify the sources of these biomarkers. Secondly, renal biopsy was not conducted to ascertain the histopathological definition of DKD in a community setting due to its intrusiveness. Nevertheless, various clinical clues that are indicative of superimposed glomerulonephritis, such as hematuria, red cell cast or nephrotic range proteinuria, were minimal among our participants. Thirdly, this is a cross-sectional study derived from a single ethnic group. Although an external validation cohort was used, our findings need to be verified on a larger scale and using a number of ethnically diverse cohorts. Fourthly, questions relating to the cause-and-effect relationship between the genes identified in the present study need to be investigated using transcriptional profiling, a cell platform system and/or an animal model approach. Finally, longitudinal studies are needed to validate the usefulness of our models when distinguishing renal progression; this is essential so that the trans-omics signature can become a biomarker profile that can be used for personalized medicine on DKD patients.In conclusion, our findings reveal the potential of employing ML models to augment traditional methods and to help to identify molecular mechanism(s) underlying the complex interaction between DM and CKD; this was done via protein–protein interaction network analysis. Knowledge of how various SNPs and the interaction of metabolites are associated with the CKD and DKD phenotype provides researchers with insights into possible genetic predispositions for these diseases. Moreover, the development of AI-assisted models in this study will advance the ability to carry out precision diagnosis and the molecular classification of DM, CKD, and DKD; this in turn will help the prevention of these diseases and thus will benefit clinical practice in general.Between August 2013 and November 2019, 618 prospectively recruited participants within the Northeastern Taiwan Community Medicine Research Cohort  were enrolled in this study. Community members who were aged over 30 years old were included in the study after obtaining individual informed consent. Subjects who were pregnant, who were undergoing dialysis therapy, or who had undergone renal transplantation, were excluded. At recruitment, all participants provided a detailed personal history and received a clinical examination. An independent cohort (178 subjects) that was used for external validation was collected from individuals who attended outpatient clinics at Chang Gung Memorial Hospital in 2019 and 2020; the same inclusion and exclusion criteria mentioned above were used for these individuals. Demographic information was collected by questionnaire. Fasting blood and spot urine samples were collected for biochemistry analysis. This study protocol conforms to the ethical guidelines of the 1975 Declaration of Helsinki and was approved by the Institutional Review Board of Chang Gung Medical Foundation . Written informed consent was obtained from all subjects involved in the study.2 or more, which was calculated as weight divided by height2, was defined as obesity43. CKD was defined using the National Kidney Foundation: Kidney Disease Outcomes Quality Initiative classification with a persistent proteinuria or an eGFR of less than 60 mL/min/1.73 m2, as determined by the abbreviated Modification of Diet in Renal Disease equation44. Proteinuria was defined if the individual’s urine protein to creatinine ratio was ≥150 mg/g or the individual’s urine albumin to creatinine ratio was ≥ 30 mg/g. DKD was diagnosed if subjects fulfilled both DM criteria and CKD criteria at the same time. Current smoking status was defined as having smoked more than 100 cigarettes in their lifetime and having smoked in the one month before enrollment.Type 2 DM was defined as a fasting glucose of ≥126 mg/dL, a glycosylated hemoglobin ≥6.5, or the use of hypoglycemic medications. Blood pressure was measured using the average of two seated measurements. Hypertension was defined if the patient was receiving medical therapy for such a condition or if their blood pressure was >140/90 mmHg. A body mass index (BMI) of 30 kg/mPeripheral venous blood was obtained after an overnight fast. After centrifugation in 1000 g, the plasma component of the blood was used for either immediate biochemistry analysis or for storage at −80 °C for the subsequent measurements. Genomic DNA was then isolated from peripheral white blood cells using the phenol/chloroform  DNA extraction method after lysis of red blood cells. Finally, the DNA from each subject was precipitated and washed using 95% isopropanol , followed by 80% alcohol ; the resulting DNA was used as total genomic DNA of each individual. Various clinical parameters were determined, including complete blood cell count, liver biochemical marker levels, renal biochemical marker levels, lipid profile, fasting sugar level intact parathyroid hormone level and total 25 (OH) vitamin D level. Serum creatinine was assessed by spectrophotometric analysis using a modified kinetic Jaffe reaction with standardization of the creatinine calibration by an isotope dilution mass spectrometry reference measurement procedure. Electrolyte levels  and carbon dioxide level were assessed using ion-selective electrode methods. Serum calcium and phosphate were measured by spectrophotometric methods . Serum albumin and uric acid levels were assessed by colorimetric methods . Blood urea nitrogen, was measured conductometry . Serum intact parathyroid hormone and vitamin D were measured by electrochemiluminescence immunoassay . Lipid profiles were obtained by enzymatic methods . Hemoglobin concentrations were obtained by the cyanide-free sodium lauryl sulphate-Hb spectrophotometric method . Urine protein and albumin levels were quantified by colorimetric methods .The plasma samples were collected and then they were extracted using methanol before there use in both the untargeted metabolomics analysis and an analysis by commercially available kit .For the untargeted metabolomics analysis using ultra-high performance liquid chromatography-time-of-fly mass spectrometry (UPLC-TOF/MS), in total, 50 μL plasma and 200 μL of cooled methanol were mixed to precipitate any protein present. After centrifugation at 12,000 g for 15 min, the supernatant after transfer was dried using nitrogen gas. The residue was then dissolved in 200 μL 50% acetonitrile for LC-MS analysis. Liquid chromatographic separation was achieved on an ACQUITY UPLC BEH Amide column  using an ACQUITY TM Ultra Performance Liquid Chromatography (UPLC) system (Waters Corp.). The column was maintained at 45 °C, and the flow rate was set at 0.4 mL/min. The mobile phase A was 0.1% formic acid in water and mobile phase B was acetonitrile containing 0.1% formic acid. Mass spectrometry was performed on a Waters Q Tof-MS  operated in ESI positive and negative ion modes. The scan range was from 50 to 1000 m/z. The desolvation gas flow was 800 L/hr at 500 °C. The source cone voltage was 25 V. The capillary voltage was 2.5 kV in the positive mode and 2 kV in the negative mode. The lock mass was leucine encephalin (m/z 120.0813 and 556.2771 for positive and m/z 236.1035 and 554.2615 for negative).For the targeted lipidomic analysis using ultra-high performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS), the plasma samples were analyzed using a commercially available kit . The targeted 184 metabolites included amino acids, biogenic amines, glycerophospholipids, sphingolipids, acyl carnitines, and hexose and all of these molecules were quantified. The samples were processed as previously described (3). The biogenic amines and amino acids were determined by LC-MS/MS, and the other lipid species were quantified by flow injection analysis coupled with tandem mass spectrometry (FIA-MS/MS). The analysis was performed in positive electrospray ionization mode using a Waters tandem mass spectrometer . Chromatographic separation was performed on an Acquity BEH C8 column  at 50 °C using a linear gradient that ranged from 0.2% formic acid in water to 0.2% formic acid in acetonitrile at a flow rate of 0.9 mL/min. The capillary was set at 3.2 kV. The desolvation gas flow was 1200 L/h at 650 °C. The source temperature was 150 °C and the cone voltage was 10 V. For FIA analysis, 0.03 mL/min was used with a commercial solvent, and the capillary was set at 3.9 kV; the desolvation gas flow was 650 L/h and 350 °C, while the source temperature was 150 °C and the cone voltage was 20 V. All data was processed and analyzed using MetIQ software . During further analysis, metabolites with >10% missing values, as well as values below the limit of detection (LOD), were excluded. Data on a total of 147 metabolites from five compound classes were collected and these consisted of: 15 acylcarnitines, 21 amino acids, 9 biogenic amines, 88 glycerophospholipids and 14 sphingolipids. All values were processed by median normalization and log2 transformation as appropriate.The genomic DNA was collected from peripheral venous blood and each subject was genotyped using AxiomTM Genome-Wide TWB 2.0 array plates . After excluding those with a minor allele frequency rate of 0 or SNPs with a missing rate of more than 10%, a total of 392,885 SNPs were available for further analysis.For the model training and testing, all machine learning analyses were performed with R Version 4.2.1  and Python 3.7.11 (scikit-learn and xgboost packages). First, we defined three models  for differentiating DM and CKD using two-stage model building. Model 1 was used to identify DM based on metabolomes, SNPs and clinical tests. Model 2 was used to identify CKD in DM patients based on metabolomes and SNPs. Model 3 was used to identify CKD in non-DM patients based on metabolomes and SNPs. We carried out the same steps for each model using the following feature selection methods.45 a shrinkage coefficient > 0 using the Least Absolute Shrinkage and Selection Operator (LASSO) approach. Subsequently, the results obtained from these three algorithms were integrated to produce feature importance ranking lists for the three models, namely Model 1, Model 2 and Model 3  or χ2 test  were used to extract the minimum features required for highest performance in terms of AUC and accuracy rate .The ANOVA was used to compare differences between the continuous variables that were derived from more than two groups. The results are presented as medians  or means (standard deviations). The χFurther information on research design is available in the SUPPLEMENTAL MATERIALReporting Summary


Phase I of the Korean Undiagnosed Diseases Program (KUDP), performed for 3 years, has been completed. The Phase I program aimed to solve the problem of undiagnosed patients throughout the country and develop infrastructure, including a data management system and functional core laboratory, for long-term translational research. Herein, we share the clinical experiences of the Phase I program and introduce the activities of the functional core laboratory and data management system.During the program (2018–2020), 458 patients were enrolled and classified into 3 groups according to the following criteria: (I) those with a specific clinical assessment which can be verified by direct testing (32 patients); (II) those with a disease group with genetic and phenotypic heterogeneity (353 patients); and (III) those with atypical presentations or diseases unknown to date (73 patients). All patients underwent individualized diagnostic processes based on the decision of an expert consortium. Confirmative diagnoses were obtained for 242 patients (52.8%). The diagnostic yield was different for each group: 81.3% for Group I, 53.3% for Group II, and 38.4% for Group III. Diagnoses were made by next-generation sequencing for 204 patients (84.3%) and other genetic testing for 35 patients (14.5%). Three patients (1.2%) were diagnosed with nongenetic disorders. The KUDP functional core laboratory, with a group of experts, organized a streamlined research pipeline covering various resources, including animal models, stem cells, structural modeling and metabolic and biochemical approaches. Regular data review was performed to screen for candidate genes among undiagnosed patients, and six different genes were identified for functional research. We also developed a web-based database system that supports clinical cohort management and provides a matchmaker exchange protocol based on a matchbox, likely to reinforce the nationwide clinical network and further international collaboration.The KUDP evaluated the unmet needs of undiagnosed patients and established infrastructure for a data-sharing system and future functional research. The advancement of the KUDP may lead to sustainable bench-to-bedside research in Korea and contribute to ongoing international collaboration.The online version contains supplementary material available at 10.1186/s13023-022-02520-5. Approximately 7000 types of rare diseases (RDs) have been genetically identified to date, and it has become easy for people living with rare disease (PLWRD) to access genetic testing and obtain a diagnosis as standard care , 2. UnliThe Korean Undiagnosed Diseases Program (KUDP) was launched as a pilot project in 2017 to support undiagnosed patients and initiate RD research. We reported the results of a pilot project, which indicated a 38.9% diagnostic yield from clinical protocols suitable for the Korean insurance system . The PhaDemographic and clinical information for the 458 enrolled patients is described in Table Enrolled patients underwent individualized tests by stage. The total numbers of performed tests were as follows: chromosomal microarray , single-gene testing (54 tests), target gene panel sequencing (57 tests), ES 413 tests), and nongenetic tests (4 tests). The results were provided to the referring clinician for clinical validation, and the final diagnosis was made based on the test results, the clinician’s opinion, and further validation if indicated. The overall final diagnostic rate was 52.8% (242 of 458 patients), and the yield was different for each group: 81.3% (26/32) for Group I, 53.3% (188/353) for Group II, and 38.4% (28/73) for Group III , and preclinical science laboratories for metabolomics, stem cells, neuroscience and immunology was organized. All variants from ES data were reviewed based on previously reported findings, such as the function of genes, tissue expression, and previous experimental or clinical data. Variants in 40 genes were discussed at a monthly conference between the clinical expert consortium and functional core laboratories. Eighteen genes have been shared on the international matchmaking exchanger (MME) system to date, and functional studies have been completed or are in progress for 6 genes. Clinically, the KUDP has expanded to 2 independent programs, the child and adult KUDPs, since 2020. The aim of this separation was to focus on age-specific diseases and support underprivileged elderly individuals.In 2020, the KUDP functional core laboratories which consisted of a laboratory for analyzing protein structure and function, two model organism screening centers . A pathological fracture was documented at the age of 2 months, and subsequent hearing loss, short stature, and macrocephaly arose. Clinicians suspected osteogenesis imperfecta type III and administered cyclic pamidronate before his teenage years. However, he did not have bluish sclerae or further fractures after discontinuing pamidronate. The patient also experienced recurrent pleural effusion, ascites, and bilateral hydrocele, requiring repeated aspiration since infancy. He presented short stature , macrocephaly , and scoliosis. He had prognathism and bilateral dysplastic ears. A mild intellectual disability was confirmed. Multidisciplinary medical services, including several diagnostic tests and sustained supportive care, were provided, but he remained undiagnosed for 20 years until KUDP admission. Trio-ES demonstrated a heterozygous missense variant, c.2858G > A in PIEZO1 , the causative gene of the primary lymphatic malformation that was initially identified in 2015. We also identified a heterozygous small deletion  by polymerase chain reaction (PCR) and Sanger sequencing [A 21-year-old man was admitted to the KUDP for recurrent lymphedema, skeletal abnormalities, and dysmorphism. He was born at a gestational age of 35DMD, a targeted gene panel for muscular dystrophy, and ES were conducted, but none of these tests resulted in a diagnosis. A muscle biopsy indicated many degenerating and regenerating fibers with endomysial fibrosis, consistent with muscular dystrophy, but the immunohistochemical study showed normal dystrophin expression. After KUDP admission, the expert consortium reviewed the patient’s entire medical history and noticed rapid deterioration and weakness in the lower extremities within a year, followed by progression to the upper extremities after influenza infection. The patient became dependent on a wheelchair and could not elevate his arms within 2 years of symptom recognition. We suspected a rare form of inflammatory myositis and checked for multiple myositis-specific autoantibodies. As a result, he was diagnosed with anti-signal recognition particle antibody-related inflammatory myositis. He was treated with corticosteroids, immunoglobulin and rituximab after diagnosis and showed improvement in muscle power.The patient was a 6-year-old boy who presented with progressive muscle weakness. He started his diagnostic journey at 4 years old for a motor delay and elevated creatinine kinase levels . Subsequent genetic studies, a multiplex ligation-dependent probe amplification test and sequencing for NF1 was identified by a target gene panel. The patient was diagnosed with neurofibromatosis type 1 (NF1) with atypical NF1 stigmata on brain imaging, and rehabilitation was recommended for abnormal walking. His parents voluntarily visited the KUDP center for further evaluation due to his sustained gait abnormality. The patient showed increased muscle tone and definite upper motor neuron signs in the lower legs, and a review of the MRI suggested a metabolic disorder such as Leigh disease instead of NF1 stigmata, although NF1 was also consistent with the phenotype. We enrolled the patient and performed additional mitochondrial genome sequencing, which revealed the homoplasmy variant, 3697G > A in MT-ND, known as the causative variant for Leigh syndrome [The third patient was 35 months old when he was admitted to the KUDP. He was born at a gestational age of 40 weeks following an uneventful pregnancy. He had multiple café-au-lait spots that spread over time. His developmental milestones were slightly delayed, and he showed tiptoeing followed by left ankle contracture since the age of 30 months. He visited the tertiary center and underwent magnetic resonance imaging (MRI) of the brain and target gene panel sequencing. The brain MRI indicated high signal intensity lesions on the bilateral basal ganglia. The ophthalmological examination demonstrated bilateral Lisch nodules. A pathogenic variant, c.344C > T, in syndrome .BRAT1 were identified within 2 weeks. The patient was confirmed to have rigidity and multifocal seizure syndrome (MIM#614498). The parents withdrew life-sustaining treatment after the diagnosis and comprehensive genetic counseling. Two years later, the couple had a healthy baby after undergoing prenatal genetic testing.A 2-month-old girl who showed intractable seizures, hyperreflexia, and hypertonia since her first day of life was referred to the KUDP. She was administered multiple antiepileptic drugs and muscle relaxants, including continuous intravenous midazolam, and was fully dependent on a mechanical ventilator. Serial brain MRI revealed rapidly progressing brain atrophy. The expert consortium classified her as an emergent case requiring rapid diagnosis and clinical decision-making. Rapid ES was conducted on the family, and compound heterozygous variants, c.1276C > T  and c.1313_1314delAG , from Phase I of the KUDP aimed to solve the unmet needs of PLWRDs and establish infrastructures for long-term research on RDs in Korea. In this study, we identified the remaining unmet needs of rare disease patients. Despite easy access to tertiary hospitals, enrolled patients remained undiagnosed for approximately 5 years. The total diagnostic yield was quite high 52.8%), but the yield varied between groups. Over 80% of Group I patients received a final diagnosis, whereas only one-third of patients from Group III received genetic confirmation. As the number of patients classified as Group I decreased over the years and that classified as Group III increased, the annual diagnostic yield slightly decreased each year . We expect a rise in the number of patients classified as Group III, a more appropriate group for the purposes of the KUDP, and that these patients will be the major population of the KUDP. There are many different national programs for RDs, which have various inclusion criteria and protocols. Their diagnostic yields vary from 28% in the UDN to 67% in SpainUDP %, but th.We discussed the enrollment criteria annually, and the “appropriate test” standard changed as the government insurance policy changed permitted diagnostic tests. CMA and target gene panel sequencing were approved in the second half of 2019. Some single-gene tests became available at official laboratories over time. These tests were frequently conducted for enrolled patients before early 2019 but were rarely performed as they became “appropriate tests” after 2019. The standard will change if ES or other tests officially become available at the clinic. Undiagnosed patients who already underwent ES represent RD patients with a major unmet need. We planned regular clinical follow-ups for the patients and reanalyzed ES data every 6 months to 1 year. We screened and selected patients with the following criteria: (1) patients highly suspected to have a certain genetic disease with characteristic features; (2) patients with a pathogenic or likely pathogenic variant of one allele in the autosomal recessive pattern gene and the matched phenotypes; and (3) patients with a rapidly deteriorating disease course without known causes. Among the 48 reanalyzed cases, 8 patients were definitively diagnosed, and functional validation for one candidate gene is ongoing.The basic information of the patients revealed weaknesses in the Korean medical system. The Korean government provides universal medical insurance that prioritizes easy accessibility to above-average medical services and determines the affordable cost and indication of all medical practices. Patients can choose tertiary hospitals for their first medical service and visit several hospitals at the same time, regardless of the stepwise medical delivery system. Many patients had same tests at different hospitals and waste resources. The current medical system assures patient rights and a certain level of medical services among the general population, but it also has serious problems in the context of PLWRD. For this reason, applications for the KUDP were conducted in two ways: referral from a clinician or a voluntary visit to the coordinating central hospital. The latter accounted for the majority of the total enrollment since the KUDP pilot project . The KUDDYT1 and SMN1). ES was repeated for 150 patients, and 49 patients received an additional diagnosis. Some identified genes were recently documented as causes of Mendelian disorders; however, others were not. Although we could not compare the exact entire dataset because of data unavailability, we suggest that appropriate clinical assessments might allow further diagnoses from the same data. We noted a male patient with severe developmental delay, increased T3 levels, and family history evident of X-linked disorder. He remained undiagnosed after ES but was confirmed to have a pathogenic SLC16A2 variant with low sequence depth. We noted a somatic variant in PIK3CA based on clinical suspicion in an undiagnosed patient with hemihypertrophy, macrocephaly, and intellectual disability. In addition, 2 patients had nongenetic diseases  after a year of diagnostic journey. Detailed phenotyping and clinical experience in ultrarare diseases are the first steps for evaluating PLWRD, indicating the importance of RD experts and such programs. Our findings also highlighted the necessity of qualified data management and planned regular reanalysis in the long term.As only target gene panels consisting of a limited number of genes are permitted in the Korean insurance system to date, further approaches, such as ES, are only performed by research platforms. Such data, however, seemed hard to qualify and manage consistently. The entire proportion of patients who underwent NGS before admission was 38.6% (177/458), and this proportion dramatically increased in 2020  of Seoul National University Hospital (IRB No. 1904-054-102).Patient screening and enrollment were performed by the KUDP expert consortium, comprising clinical experts from various clinical departments, including neurology, immunology, nephrology, cardiology, orthopedics, ophthalmology and laboratory medicine. Enrolled patients were required to meet one of the following criteria: (1) undiagnosed after appropriate tests conducted by experts; (2) suspected to have a medically actionable disease that presented rapid deterioration and an irreversible clinical course; or (3) had a diagnostic journey of more than 5 years despite regular check-ups at second or tertiary centers. Enrolled patients were classified into 3 groups. Group I was defined as patients who suspected to have a specific diagnosis which can be documented by direct testing as a result of clinical perception and basic medical information. Patients diagnosed with certain disease categories, usually with genetic or phenotypic heterogeneity, were defined as Group II . Group III was defined as patients with an uncategorized or atypical disease. Most patients requiring diagnostic tests covered by insurance systems at the time of screening were excluded and recommended to undergo routine diagnostic processes at their referring hospital.The consortium discussed each case, decided which diagnostic tests should be performed, and reviewed the test results.Process decisions and operations were made by the KUDP expert consortium Fig. . All datAdditional file 1. Detailed information of genetically confirmed patients.


SHANK3 gene at terminal chromosome 22 that confers high likelihood of comorbid autism spectrum disorder (ASD). Whereas individuals with idiopathic ASD (iASD) can demonstrate diverse patterns of sensory differences, PMS is mainly characterized by sensory hyporesponsiveness. This study used electrophysiology and a passive auditory habituation paradigm to test for neural markers of hyporesponsiveness. EEG was recorded from 15 individuals with PMS, 15 with iASD, and 16 with neurotypical development (NT) while a series of four consecutive 1,000 Hz tones was repeatedly presented. We found intact N1, P2, and N2 event-related potentials (ERPs) and habituation to simple auditory stimuli, both in individuals with iASD and in those with PMS. Both iASD and PMS groups showed robust responses to the initial tone and decaying responses to each subsequent tone, at levels comparable to the NT control group. However, in PMS greater initial N1 amplitude and habituation were associated with auditory hypersensitivity, and P2 habituation correlated with ASD symptomatology. Additionally, further classification of the PMS cohort into genetic groupings revealed dissociation of initial P2 amplitude and habituation of N1 based on whether the deletions included additional genes beyond solely SHANK3 and those not thought to contribute to phenotype. These results provide preliminary insight into early auditory processing in PMS and suggest that while neural response and habituation is generally preserved in PMS, genotypic and phenotypic characteristics may drive some variability. These initial findings provide early evidence that the robust pattern of behavioral hyporesponsiveness in PMS may be due, at least in audition, to higher order factors.Phelan-McDermid Syndrome (PMS) is a rare genetic disorder caused by deletion or sequence variation in the  SHANK3 either by pathogenic sequence variant or by deletion (Phelan-McDermid syndrome (PMS) is a rare neurodevelopmental disorder caused by haploinsufficiency of deletion . PMS is deletion . Comorbideletion , though deletion . Both PMdeletion . Evidencdeletion . HoweverElectroencephalography (EEG) offers a precise and objective tool to evaluate sensory processing. One method of assessing auditory perception is by measuring habituation, or the decrease in electrophysiological activity in response to repeatedly presented sounds. In neurotypical individuals  and in aFmr1 knockout mice  = 0.32, p = 0.73]. A Chi Square analysis also identified no significant sex differences among groups . In the PMS group, chromosomal microarray or targeted sequencing of the SHANK3 gene validated the genetic diagnosis. Confirmed genetic diagnoses of PMS were defined as having either a deletion encompassing SHANK3 (MIM: 606230) or a pathogenic sequence variant in SHANK3 according to standards established by the American College of Medical Genetics and Genomics and the Association for Molecular Pathology. Microarray results were aligned to the hg19 reference genome and sequence variants to reference transcript NM_033517.1. Of the individuals with PMS, 11 had deletions encompassing SHANK3 and four had pathogenic sequence variants in SHANK3. One of the individuals with a deletion had a ring chromosome 22. Participants in the iASD group had no known pathogenic genetic findings. Neurotypical controls had no psychiatric disorders and no first-degree relatives with iASD.Participants included 15 individuals with PMS , 15 with iASD , and 16 with NT development , all between the ages of 8 and 26. The groups did not differ significantly in age . One iASD participant was missing an IQ score. IQ testing was not done with the NT group, but none had history of learning, psychiatric, or neurodevelopmental disorders or concerns, thus we estimate their IQs to have followed a typical normative distribution and therefore also to be higher than both iASD and PMS groups.Intelligence Quotient (IQ) testing appropriate for age and developmental functioning was administered to the iASD and PMS groups, including the Wechsler Abbreviated Scale of Intelligence – Second Edition , StanforParticipants completed a 16-min auditory habituation task during dense-array EEG, while seated in a chair, booster seat, or caregiver’s lap, as best facilitated their remaining seated and still. Each of 150 trials contained a sequence of four 1,000 Hz, 50 ms tones (generated in Audacity), separated by 618 ms. Trials were separated by a 4,000 ms inter-stimulus-interval. Experimental flow was controlled using E-Prime 2.0. Tones were delivered at 80 dB. Participants were not instructed to attend to the tones, and each watched a silent video of their choice throughout the duration of the experiment.F = 1.97, p = 0.15]. Lastly, ERPs were averaged separately for each of the four tones in the trial’s sequence and baseline corrected using a 100 ms pre-stimulus interval. A low-passed filter of 30 Hz was used to account for noise caused by considerable amounts of movement by some of the PMS and iASD participants. This filter level is higher than  Routine toolbox  with EEGher than  or consiher than  other reher than .N1, P2, and N2 mean amplitudes were calculated as the individual averages of the 10 ms surrounding each peak at electrode Cz at the vertex of the head, where auditory event related potential are easily detected . For eacp-value set at 0.008 for the initial tone analysis and 0.002 for analyses of habituation across tones.Statistical analysis was done using SPSS software and included one-way ANOVAs to compare the effect of group on individual component amplitude and latency within each tone. Additionally, repeated measures ANOVAs were conducted to compare habituation of component amplitude across the four tones by group. Greenhouse-Geiser was used when there were violations in sphericity. When Levene’s Test was significant, the Brown-Forsythe results or the combination of Friedman and Kruskal-Wallis Tests were used, as appropriate. Correlations were run between all clinical and electrophysiological variables to assess relationships. Bonferroni corrections for multiple comparisons were used in the analyses, with a significant p’s > 0.13, all η2p < 0.09). Three one-way ANOVAs were conducted to determine group differences between latency values for each component in response to the initial tone, again with no significant differences among groups .Applying three separate one-way ANOVAs, one for each component, analysis of the ERP response to the initial tone found peak amplitude was not significantly altered across diagnostic groups  = 13.64, p < 0.001, η2p = 0.50] where there was a marked reduction in amplitude between the 1st tone (N11: −2.49 ± 0.44 μV) and the 2nd, 3rd, and 4th tones in the sequence . Further habituation effects were also detected between the response to the N12 tone and the N13 tone , though the amplitude difference between the N12 and N14 amplitudes did not reach significance . There was no significant difference between the N13 and N14 amplitudes , suggesting response to repetition was maximally habituated by the third repetition. The effect of group  and the group by tone position interaction  were not significant.Analysis of the N1 found a significant effect of tone position , with a significantly larger P21 amplitude (4.60 ± 0.37 μV) as compared to the subsequent P22 , P23 , and P24  tones . Unlike for N1, P22 amplitude did not differ from either P23  or P24  tones, but P23 was larger in amplitude compared to P24 . Again, there was no significant effect of group  and no significant tone position by group interaction .Similar to results for the N1 component, P2 amplitude showed a significant effect of tone position . All other comparisons were not significant, with no significant group effect  or group by tone position interaction  for N2.Unlike the previous components, the effect of tone position on the N2 component did not withstand correction for multiple comparisons [Correlation analyses were run for the PMS group with: (a) the mean amplitude and latency values for each component at tone 1, and (b) the difference between each component’s amplitude at tone 1 and tone 2, used as a proxy measure of habituation. For negative components N1 and N2, a greater negative number indicates greater habituation. Correlations were not corrected for multiple comparisons due to the stringent threshold criteria driven by the limited sample size. With so little known about sensory processing in PMS, we believe that applying overly strict standards would obscure potential avenues for future research and slow progression of knowledge in this field. As the first study of auditory electrophysiological correlates of auditory hyposensitivity in a rare and difficult to test population, these data are important to include but should be interpreted cautiously as exploratory findings. See SHANK3, or the combination of SHANK3 with ARSA and/or ACR and RABL2B (n = 11); these final three genes are not thought to contribute to the phenotype of PMS. Class II was comprised of the remaining deletions that did not qualify as Class I deletions, i.e., larger deletions that extended beyond SHANK3 and the three aforementioned genes (n = 4). Deletion size was not normally distributed so equal variance was not assumed.Per the categorization scheme outlined in a recent manuscript on associations between genotype and phenotype in PMS, we divided participants with deletions into two classes . Class I1 amplitude in Class I (5.16 ± 0.89) was greater than Class II  and N11–2 habituation was greater in Class I (−2.30 ± 0.66) compared to Class II . No other electrophysiological measures differed between Classes. See The P2p’s > 0.20).Age did not correlate with any of the metrics specified above (p’s > 0.05). See Non-verbal Developmental Quotient (NVDQ), Verbal Developmental Quotient (VDQ), and Full Scale Developmental Quotient (FSDQ) did not correlate with any ERP response or habituation metric (p’s > 0.09). See There were no significant Pearson correlations between clinical consensus ASD diagnosis and any of the metrics specified above (1 amplitude and ADOS-2 Restricted and Repetitive Behaviors (RRB) score , wherein lower P2 response was associated with higher levels of repetitive behaviors. There was also a significant negative relationship between the P21–2 habituation and the ADOS-2 RRB , Social Affect (SA)  and Total scores , with more ASD symptoms associated with reduced P2 habituation. No other electrophysiological measures were correlated with ADOS-2 scores. See A significant negative Pearson correlation was found in the PMS group between the P21 amplitude  and N11–2 habituation ; greater auditory hypersensitivity was associated with both larger initial N1 response and greater N1 habituation. See The total SAND scores as well as the three subdomains of the auditory domain  were selected for analysis given our auditory task. The total, auditory hyposensitivity, and auditory seeking scores did not correlate with any of the ERP response or habituation metrics. However, the auditory hypersensitivity domain correlated with both the N1Shank3 animal models showing reduced auditory startle , it could be a contributor to reduced social engagement reported in PMS . With reFinally, our findings of similar early processing of auditory habituation in groups with iASD as compared to both PMS and NT controls despite notably different sensory phenotypes imply that the level at which auditory processing diverges in these group is likely not in the most basic response and habituation to repeated simple tones. Recent studies of auditory habituation in ASD continue to yield inconsistent results, with some finding reduced habituation  while otSeveral limitations of this study stem from inherent issues with studying a rare genetic disorder, which makes recruitment, matching subjects, as well as obtaining large sample size and/or developmentally narrow age cohorts and high power results difficult. Although this research utilized a respectable sample size for an experimental study involving a rare disorder, a broader age range than is typical of most EEG studies also was needed to adequately sample the PMS population. The number of participants also remains small relative to typical EEG study samples. This small sample size also meant that we could not correct for multiple comparisons in correlations analyses, as overly stringent significance criteria in a sample of our size would be at high risk for obscuring small but meaningful results. As such, we report and discuss nominally significant findings to inform avenues for future work; replication in larger, independent samples is certainly needed.Additional limitations stem from the complexity of our cross-group comparisons. First, groups varied considerably in terms of sex distribution; given limited research on the effect of sex on sensory processing , sex difFinally, our PMS group was somewhat unusual in that only thirty three percent of our PMS sample also had an ASD diagnosis. This percentage is low given the higher prevalence typically reported in the literature , though This study is among the first to explore the neurocognitive basis of the PMS sensory phenotype and serves as the basis for future auditory neurophysiology work in this population. The preserved initial electrophysiological functioning shown here suggests that alterations in downstream processing of sounds, which deals more with extracting the relevance and meaning of stimuli and guiding behavioral response, may drive the hyporesponsive phenotype in PMS. Relations between the electrophysiological measures and ASD diagnosis, symptoms, and 22q13 deletion size in PMS point toward individual variability and genotype-phenotype relationships. Future studies that probe both early and late auditory processing in larger samples of people with PMS would help elucidate the source of these individual differences and identify where in the auditory perceptual pathway the breakdown in processing occurs. This knowledge may aide in the development of targeted therapeutics that reduce the negative consequences of auditory hyporesponsiveness in this group.The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.The studies involving human participants were reviewed and approved by the Icahn School of Medicine Program for the Protection of Human Subjects. Written informed consent to participate in this study was provided by the participant or the participant’s legal guardian/next of kin, as appropriate. Verbal assent was also provided by participants with capacity.EI and JF-F designed the study and wrote the manuscript. EI, HG, SG, YZ, SB, and CM participated in data collection and processing. EI, SG, TL, and JF-F contributed to data analysis. DH, PS, AK, and JF-F characterized the participants and provided clinical supervision. SG, TL, PS, JB, and AK provided substantial revisions. All authors read and approved the manuscript.The Sensory Assessment for Neurodevelopmental Disorders (SAND) was developed by PS and was licensed by Mount Sinai to Stoelting Co. AK received research support from AMO Pharma and consults to Ovid Therapeutics, GW Pharmaceuticals, Acadia, Alkermes, Ritrova Therapeutics, Jaguar Therapeutics, Neuren Pharmaceuticals, Clinilabs Drug Development Corporation, and Scioto Biosciences. JB consults to BridgeBio, holds a patent for IGF-1 for the treatment of Phelan-McDermid syndrome, holds an honorary professorship from Aarhus University Denmark, receives research support from Takeda and Oryzon, and is a journal editor for Springer Nature. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Rubus idaeus L.) growers to be interested in rapid sap tests to provide real-time results to guide immediate nutrient management practices. However, sap analysis has never been conducted in raspberry. The present work aimed to evaluate the relationship of petiole sap nitrate (NO3–), potassium (K+), and calcium (Ca2+) concentrations measured using compact ion meters and leaf tissue total nitrogen (TN), potassium (K), and calcium (Ca) concentrations measured using conventional tissue nutrient analysis. The relationship of petiole sap NO3– and leaf tissue TN concentrations with plant growth and production variables was also explored. Fertilizer treatments of urea were surface applied to raised beds of established “Meeker” floricane red raspberry plots at control, low, medium, and high rates  in 2019 and 2020. The experiment was arranged in a randomized complete block design with three replications. Whole leaves were collected from representative primocanes in mid- and late- July and August 2019 and 2020 . At each sampling time point, a subsample of leaves was used for petiole sap analyses of NO3–, K+, and Ca2+ concentrations using compact ion meters, and conventional tissue testing of leaf tissue TN, K, and Ca concentrations, respectively. There were no interactions between N fertilizer rate and year nor between N fertilizer rate and sampling time. No significant differences were found due to N fertilizer rate for petiole sap NO3–, K+, Ca2+ nor leaf tissue TN, K, Ca concentrations. However, significant year and sampling time effects occurred in measured petiole sap and leaf tissue nutrient concentrations. Overall, the correlations between petiole sap NO3– and leaf tissue TN, petiole sap Ca2+ and leaf tissue Ca, petiole sap K+ and leaf tissue K concentrations were non-strong and inconsistent. Future research is warranted as the interpretation of correlations between raspberry petiole sap and leaf tissue nutrient concentrations were inconclusive.The time delay in receiving conventional tissue nutrient analysis results caused red raspberry ( Rubus idaeus L.) production in the United States of America (USA) with a 3-year average of approximately 28,470 tons of fruit produced mostly in the northwest of the state in 2019–2021. Most of the raspberry production in Washington is from the floricane type and had an average annual value of $70.7 million in 2019–2021  and leaf tissue total nitrogen (TN) concentrations in tomato (Solanum lycopersicum L.) grown for 4, 6, 8, 10, and 12 weeks after transplanting at two different locations in Florida, United States, with the highest r ranging 0.6–0.8 for 6–8 weeks after transplanting. Another study carried out in Florida, United States reported a non-significant correlation between tomato petiole sap NO3-N and leaf tissue TN concentration 14 weeks after transplanting  between petiole sap NO3-N and leaf tissue TN concentrations for potato  grown for 65, 75, 85, and 95 days after planting in Bosnia and Herzegovina with the highest r = 0.80 at 65 days after planting, which corresponds with the growth stage of early tuber initiation. During this growth stage, rapid nutrient uptake occurs, and nutrients are mainly translocated or stored in vegetative leaf tissues  cultivated in west Tennessee, United States varied by growth stage and the coefficients of determination (R2) were between 0.42 and 0.71 with R2 = 0.71 observed at mid-bloom. A case study in Inner Mongolia, China reported a strong relationship (r = 0.92) between potato (cv. Kexin-1) petiole sap potassium (K+) and shoot tissue potassium (defined as K hereafter) concentrations across all sampling dates from 30 to 75 days after emergence during the vegetative growth stage (r < 0.99) between petiole sap K+ and leaf tissue K concentrations in the vegetative growth stage of pak choi (Brassica rapa L. var. Chinensis group “Bonsai”) planted in a greenhouse for 5 weeks in Hawaii, United States.Alternative nutrient analysis methods, such as rapid petiole sap analysis, are often compared to the standard conventional tissue nutrient analysis method to test its potential utility. Some studies have correlated sap to conventional tissue nutrient analysis results to test the sap analysis for assessing plant nutrient status. In annual crops, planting . Tomato planting  utilizesfruiting . At thes tissues , thus, ad tubers . Therefoth stage . GangaiaVitis vinifera L. cv. Red Globe) petiole sap NO3– concentration and plant N content in leaf blades and roots with R2 = 0.74 at full bloom. In apple , petiole sap Ca2+ concentration correlated well with leaf tissue Ca concentration 28–147 days after full bloom with R2 = 0.83  in Mount Vernon, WA, United States  in 2019 and 2020. The 0.03 ha (37 m in length × 9 m in width) field was planted with raspberry on raised beds  in May 2017. In-row plant spacing was 60 cm and between-row spacing was 3 m. The soil type of the site is a silt loam, characterized as a fine-silty, mixed, superactive, non-acid, mesic Fluvaquentic Endoaquepts . The cha–1, respectively) plus a no fertilizer control (0 kg N ha–1) applied using granular urea (46N-0P-0K). Half of the fertilizer was applied in mid-April (approximately a week before primocane emergence) and the other half in late-May (approximately a month before first harvest) in both 2019 and 2020, which was in accordance with the regional nutrient management guide  were collected from 30 representative primocanes free of disease and damage according to  minimal . Leaf saBaseline soil sampling was done on 22 March 2019. Soil samples consisting of 30 cores were randomly selected from the area 5 m around the experimental field using a standard 2.5 cm wide soil probe to a 30 cm depth. Soil from all cores was combined and mixed thoroughly as one composite sample. Post-harvest comprehensive soil sampling was done on 15 September 2020. Soil samples consisting of 12 cores per plot, with four cores on each side plus four cores in the center of the raised bed in the row, were collected using a standard 2.5 cm wide soil probe to a 30 cm depth. Cores from the same plot were combined and mixed thoroughly as one composite sample.Field-moist soil samples were sent to a commercial laboratory  within 24 h after sampling for soil analysis. Soil organic matter was measured using the loss of ignition method at 360°C for 12 h . Soil es3–, K+, and Ca2+ concentrations using compact ion meters with ion selective electrode sensors . Meters were calibrated before use and recalibrated after every 10 measurements utilizing a standard solution provided by the manufacturer , manually pressed for approximately 10 s using consistent pressure, and approximately 20 drops of sap were collected into a 5 ml beaker. The sap was not diluted and followed approaches outlined in previous studies . Extractfacturer . There wThe remaining five leaves plus petioles were kept intact, dried at 38°C for 5 days until constant weight, and then sent to a commercial laboratory  for plant tissue macro- and micro-nutrient analysis. Leaf tissue TN concentration was measured by the combustion method . Leaf tissue K and Ca concentrations were measured using the nitric acid and hydrogen peroxide digestion method and then tested with inductively coupled plasma mass spectrometry  .Primocane height and number were recorded from three representative plants within the interior of each plot on 15 September 2020. Primocane height was determined by measuring the height of the tallest primocane per plant, starting at the base of the crown and extending to the tallest leaf tip. Primocane number was determined by counting the number of primocanes emerging from the base of the crown per plant.Raspberry fruit was machine harvested  by plot every 3–4 days for a total of 10 times from 29 June to 4 August 2020. No yield data were collected in 2019 due to harvester malfunction. The total yield per plant across the whole harvest season was calculated.Cost comparison for the two plant nutrient testing methods was carried out based on the recommended number of samples that should be collected for each method. Costs for collection and analysis were estimated for each test method based on current labor costs, laboratory testing fees, and costs of supplies. The cost of the two methods was then estimated and compared for the 6-year expected life span of a red raspberry planting in Washington.All data were subjected to analysis of variance with linear mixed-effects models using the lme function in the nlme package built in R . Block was always treated as a random effect. N fertilizer rate, year, and sampling time were fixed effects. The assumptions of normality and homogeneity of variance were checked by visual inspection of residual plots. Soil K data were sine transformed to improve the normality and homogeneity of the residuals. All means are reported in original units.post hoc comparisons at the 5% level of significance to compare treatment means.Both petiole sap and dried leaf tissue nutrient data were first analyzed using two-way factor analysis with N fertilizer rate and year as the factors with repeated measures for the 2-year data. Then, data within each year were analyzed using two-way factor analysis with N fertilizer rate and sampling time as the factors. Lastly, data within each sampling time point were analyzed using one-way factor analysis with N fertilizer rate as the factor. Given measured soil variables, primocane height and number, and yield data were only collected in 2020, these data were analyzed using one-way factor analysis with N fertilizer rate as the factor. A Tukey’s honestly significant difference test in the multcomp package was used for 3– and leaf tissue TN, petiole sap K+ and leaf tissue K, and petiole sap Ca2+ and leaf tissue Ca concentrations were subjected to Pearson correlation and linear regression analyses to determine the direction and strength of the relationship. Exponential, logarithmic, polynomial, and power relationships were also considered before deciding to use linear regression. An autocorrelation ACF function in the nlme package was also performed with the temporal series of petiole sap NO3–, K+, Ca2+ and leaf tissue TN, K, and Ca concentrations. The autocorrelation function is a way to monitor the effectiveness of the regression model to capture the time trend when the data have been collected over time.Additionally, petiole sap NO+ and leaf tissue K and Ca concentrations with petiole K+ and leaf tissue Ca concentrations greater in 2019 than in 2020 whereas leaf tissue K concentrations were greater in 2020 than in 2019  plants, and 76% of the variation of leaf tissue TN concentration was explained by petiole sap NO3– concentration. Petiole sap NO3– concentration was also positively correlated to leaf tissue TN concentration in late-August 2019 and 32% of the variation in leaf tissue TN concentration could be explained by petiole sap NO3– concentration. All other relationships between petiole sap NO3– and leaf tissue TN concentration for other N fertilizer rates and sampling times in 2019 were insignificant. In 2020, petiole sap NO3– concentration was positively correlated to leaf tissue TN concentration only in mid-July and 34% of the variation in leaf tissue TN concentration could be explained by the petiole sap NO3– concentration  plus a $50 packing and shipping fee. Thus, a Washington red raspberry grower would pay approximately $1,145 each year for this sample analysis method. For the rapid sap analyses with compact ion meters, assuming that three ion selective electrode meters are needed to measure sap NO3–, K+, and Ca2+ concentrations, the fixed purchasing cost is approximately $1,120 for all three meters . Additionally, the cost for a plant press and pruner for sample collection is $100, and the cost of additional supplies  is approximately $100 per year. The labor cost is $135 for both sampling and testing the 30 samples at $15 person–1 hr–1 , whereas conventional tissue nutrient analysis is available for all macro- and micro-nutrients. An additional advantage of compact ion meters is that they could also be used to test soil nutrients .For the 6-year expected lifespan of a red raspberry planting in Washington, the total cost of conventional tissue nutrient analysis would be $6,870 whereas the cost of sap analysis with compact ion meters would be $3,530. Thus, sap analysis would provide an estimated $3,340 cost savings over conventional tissue nutrient analysis. More cost savings are anticipated with sap analysis in the long run and for large quantities of samples. However, it is important to note that the cost of sap analysis would increase with the purchase of additional compact ion meters to analyze each additional nutrient, whereas the cost of conventional tissue nutrient analysis would remain the same. Further, sap analysis tests are only possible for a limited number of nutrients, NOAll measured petiole sap and leaf tissue nutrient concentrations did not vary by N fertilizer rate. Plant N status was considered adequate across all N fertilizer rates (including the unfertilized control) given that measured leaf tissue TN concentrations were within the sufficiency range of 2.3–3.0% proposed by 3– and leaf tissue TN response to fertilizer rate was unexpected. In this study, fertilized and unfertilized soil had similar soil organic matter content and estimated nitrogen release  for raspberry production in Washington  treated with different fertilizer regimes in western Serbia  . Plants 3– and leaf tissue TN concentration found at some sampling times in this study was in line with 3– and leaf tissue TN concentration at the first bud, first open flower, second harvest, and third harvest stages. However, a weak positive correlation (r = 0.38) was found between tomato petiole sap NO3– and leaf tissue TN concentration across 2 years . This difference in analytical samples may contribute to the lack of correlation between petiole sap NO3– and leaf tissue TN concentration. Nitrogen taken up during peak uptake is mainly assimilated or stored in vegetative tissues and has not been translocated to other plant tissues; therefore a strong correlation between petiole sap NO3– and leaf tissue TN is often expected during this growth stage  leaf sap K+ and leaf tissue K concentration and between luffa (Luffa cylindrica Roem.) xylem sap K+ and shoot tissue K concentration, respectively. Moreover, + and dried tissue K concentration in pak choi fertilized with different K fertilizer rates. There are some potential limitations to exploring the relationship between petiole sap K+ and leaf tissue K concentration due to the small ranges in petiole sap K+ and leaf tissue K concentrations in this study. The weak and inconsistent relationship for K in the current study may be due to the physiological, environmental, and methodological factors. Potassium is a mobile nutrient that can undergo luxury uptake and is physiologically important to establish cell turgor and maintain cell electroneutrality  from the whole leaf tissue were measured as leaf tissue K concentration.The negative relationship between petiole sap Kutrality . The higon meter . In addi2+ and leaf tissue Ca concentration in this study was in contrast with 2+ and shoot tissue Ca concentration and a strong correlation between coffee (Coffea arabica L.) xylem sap Ca2+ and leaf tissue Ca concentration, respectively. Given that no Ca fertilizers were applied in the current study, there are some potential limitations to exploring the relationship between petiole sap Ca2+ and leaf tissue Ca concentration due to the small ranges of the concentrations. Ca2+ is an immobile nutrient and a constituent of leaf tissue Ca. Leaf tissue Ca includes Ca present in the middle lamella and cell walls, enzyme cofactors, and secondary messengers (2+ is not representative of all the Ca in whole leaf tissues, which also included the petiole, leaflet, and petiolule (2+ can also be absorbed, metabolized, assimilated, and thereby form other compounds containing Ca, however, these changes would not reflect in leaf tissue Ca. Petiole sap Ca2+ and leaf tissue Ca concentrations also had different trends across sampling time, which could be impacted by the environment (2+ concentration with a compact ion meter could be interfered by the presence of other ions in the sap (2+ and leaf tissue Ca concentration.The weak positive correlation between petiole sap Cassengers . Therefoetiolule . Petioleironment . Detecti the sap . These c3– and leaf tissue TN concentration were moderately positively correlated to primocane height and primocane number, which was within expectation as N nutrition is closely associated with raspberry growth (3– and leaf tissue TN concentration with yield in this study was in contrast with 3– and leaf tissue TN concentration was positively correlated to potato tuber yield, and with 3– concentration and marketable tomato yield. The non-significant relationship of both petiole sap NO3– and leaf tissue TN concentration with yield was likely due to the lack of plant response to N fertilizer rates (3– and leaf tissue TN concentration with soil estimated nitrogen release were also lacking. This likely accounted for the lack of plant response to N fertilizer rates as plants might accumulate sufficient N from plant nutrient reserves and mineralization of soil organic matter to meet plant needs in all N fertilizer rate treatment plots (Both petiole sap NOy growth . The inser rates . Correlant plots .Results of the autocorrelation reflected the rapid and random variation of the measured petiole sap and leaf tissue nutrient concentrations throughout the sampling time, which further validated the power of established linear regression techniques to effectively capture time trends. The non-significant autocorrelation over the sampling time might be attributed to the prompt plant self-regulation of accumulated ions in the vacuole of storage tissues. The significant autocorrelation over the sampling time for both leaf tissue K and Ca concentrations reflected the limited ability of plants to regulate the corresponding nutrient concentrations in response to biotic and abiotic impacts in the sampled plant organ during the sampling period .There were significant expected cost savings with sap analysis compared to conventional tissue nutrient analysis over the 6-year expected lifespan of a red raspberry planting. Based on the success of sap analysis for plant nutrient assessment in other crops, further research is warranted to investigate the right sampling time and plant organs under various N, K, and Ca sufficiency levels to study if this method can be used to predict plant nutrient status and application needs in red raspberry.3–, K+, Ca2+ and leaf tissue TN, K, and Ca concentrations did not differ by N fertilizer rate treatments but differed by year and sampling time depending on the nutrient. Due to the lack of plant response to N fertilizer rate and non-strong and inconsistent correlations between petiole sap NO3– and leaf tissue TN concentration, petiole sap K+ and leaf tissue K concentration, and petiole sap Ca2+ and leaf tissue Ca concentration, results from this study were inconclusive. Our results were suggestive that developing robust nutrient management plans on the basis of petiole sap for N, K, and Ca status in floricane raspberry would be challenging. Based on the cost savings and the shorter time for attaining results with the sap analysis compared to conventional tissue nutrient analysis, growers’ interest in rapid sap analysis might increase. Thus, further field research to investigate the right sampling time and plant organs under various N, K, and Ca sufficiency levels to study the feasibility of using plant sap analysis to guide real-time decision-making may be valuable.Raspberry petiole sap NOThe raw data supporting the conclusions of this article will be made available by the authors, without undue reservation. Environmental data is available for free via WSU AgWeatherNet. All other data are available upon request to the corresponding authors.QL: conceptualization, methodology, software, validation, formal analysis, investigation, data curation, writing – original draft and review and editing, and visualization. CM: conceptualization, methodology, resources, and writing – review and editing. HT: resources, writing – review and editing, and funding acquisition. LD: conceptualization, methodology, resources, writing – review and editing, project administration, and funding acquisition. All authors contributed to the article and approved the submitted version.


Dovyalis caffra (kei apple). The physicochemical properties of these nanomaterials were established using some characterization techniques including X-ray diffraction analysis (XRD), ultraviolet-visible spectroscopy (UV-vis), scanning electron microscopy (SEM), transmission electron microscopy (TEM), and energy-dispersive X-ray spectroscopy (EDX). The XRD result confirmed the presence of a monoclinic CuO (Tenorite), and a hexagonal ZnO (Zincite) nanoparticles phase, which were both confirmed in the CuO–ZnO composite. The electron microscopy of the CuO–ZnO, CuO, and ZnO NPs showed a mixture of nano-scale sizes and spherical/short-rod morphologies, with some agglomeration. In the constituent’s analysis (EDX), no unwanted peak was found, which showed the absence of impurities. Antioxidant properties of the nanoparticles was studied, which confirmed that CuO–ZnO nanocomposite exhibited better scavenging potential than the individual metal oxide nanoparticles , and ascorbic acid with respect to their minimum inhibitory concentration (IC50) values. Similarly, the in vitro anticancer studies using MCF7 breast cancer cell lines indicated a concentration-dependent profile with the CuO–ZnO nanocomposite having the best activity over the respective metal oxides, but slightly lower than the standard 5-Fluorouracil drug. Biogenic metal oxide nanoparticles (NPs) have emerged as a useful tool in biology due to their biocompatibility properties with most biological systems. In this study, we report the synthesis of copper oxide (CuO), zinc oxide (ZnO) nanoparticles (NPs), and their nanocomposite (CuO–ZnO) prepared using the phytochemical extracts from the leaves of  Catharanthus roseus), which has given a new insight into the role of nature, especially medicinal plants in cancer therapy. The extracts of these plant materials were found to possess cytotoxic properties which are cancer cell-specific with little or no side effects unlike their synthetic counterparts [Cancer has continued to be one of the leading causes of death all over the world and in 2020, was reported to account for about 10 million deaths, worldwide . Therefoterparts ,8.It is well documented that over 60% of most currently used medicines are derived either directly or indirectly from a natural source such as animals, plants, and minerals . This haMetal oxide nanoparticles have received considerable attention in the past few decades owing to their useful properties and applications in many areas of life. Amongst these are copper oxide (CuO)  and zincGundelia tournefortii [Juglans regia [Abutilon indicum [barbadensis Miller [Dovyalis caffra) leaves. The synthesis of multimetallic and bimetallic NPs has continued to gain attention in recent times. However, their synthesis is time-consuming, costly, and not void of hazardous toxic substances. Hence, they were generated using different eco-friendly methods such as those prepared using different plant materials. A prominent example is the CuO–ZnO nanoparticles due to their ability to individually confer different biological actions synergistically and their biocompatibility with human cells [Cardiospermum halicacabum and Mangifera indica leaves extracts were used to prepare ZnO nanoparticles that have shown useful antitumor properties against A375 and A549 cell lines. Similarly, CuO nanoparticles from a green synthetic route were also reported to inhibit the continuous proliferation of A549 cancer cells by stimulating apoptosis [Generally, the use of nanomaterials is dependent on properties such as morphology, size, and homogeneity. These properties are dependent on several other factors including the method of synthesis and precursor type. Several methods were used for the preparation of metal-based nanoparticles, such as sol–gel, thermal decomposition of a precursor compound, microwave, and chemical precipitation ,29,30. Hnefortii , Juglansns regia , Abutilo indicum , Aloe, as Miller . Despitean cells . Severalpoptosis . Furtherpoptosis . It was poptosis . Additiopoptosis .Dovyalis caffra) for the synthesis of nanoparticles. To the best of our knowledge, the synthesis of CuO, ZnO NPs, and CuO–ZnO nanocomposites are yet to be mediated using extracts form this plant’s leaves. The antioxidant and cytotoxicity potentials of these nanomaterials were then examined using the 1,1-diphenyl-2-picryhydrazyl (DPPH) assay and MTT assay method, respectively.In this study, therefore, we report the use of Kei apple leaves  the formation of respective hydroxides [Plants containing high content of polyphenolic compounds have already shown the capacity to adequately reduce different metal salts such as silver into their metallic nanomaterials due to the reducing potentials of these phenolic compounds ,36. As axide NPs . Hence,  climate . Thus, b climate . In thisldehydes ,39. The ldehydes . The fordroxides . The proxZn1−xO Form [FWHM) of the peak with the highest intensity (2θ = 35.6°) and the angle parameter (θ) was estimated using the OriginPro 8. The obtained particle sizes found for the CuO–ZnO, CuO, and ZnO nanoparticles were 23.21, 29.19, and 25.29 nm, respectively. The sizes and the pattern of trend  observed for these three materials seemed to be in good agreement with those found in earlier study reported by Mohammadi et al. using Mentha longifolia leaf extract [The phase identification and the crystallinity of the obtained CuO, ZnO, and CuO–ZnO nanoparticles were confirmed using the XRD analysis, and the obtained diffractograms are presented in −xO Form . Compari−xO Form . However−xO Form . The CuO−xO Form ,45. Henc extract .The morphological properties  of the nanoparticles were reported to influence their applications, which makes the microscopic investigation a useful technique in establishing the nature of a nanomaterial . The morTo further understand the morphological composition of these materials and to ascertain their purity, the EDX analysis of CuO, ZnO, and CuO–ZnO NPs was carried out and the obtained spectra are presented in 50) values of 3.92 mg/mL) than the individual metal oxide nanoparticles of CuO NPs (6.76 mg/mL), ZnO NPs (8.99 mg/mL) and ascorbic acid (4.96 mg/mL). This showed that the as-prepared CuO–ZnO nanocomposite has better antioxidant properties than the individual metal oxide and the standard ascorbic acid. The enhanced scavenging potential of the as-prepared CuO–ZnO nanocomposite may have emerged due to the increased surface-to-volume ratio and small particle size estimated for the CuO–ZnO nanocomposite in the TEM and XRD analysis [50 of the respective metal oxide could be another reason for the enhanced activity in the nanocomposite. Although, several studies have reported the synthesis and the usefulness of CuO–ZnO nanocomposite, only a few have attempted to study their antioxidant potentials. An example is the reported nanoparticles from Tulsi extract (Ocimum tenuiflorum), Neem extract (Azadirachta indica), and Aloe Vera extract (Aloe barbadansis), that showed that the activity profile was concentration-dependent, which was similar to the results observed in our current study [Free radicals are formed in many biological systems, and one of the fundamental studies and applications of nanoparticles is their usage in the removal of these radicals, which emanates from the interaction of biomolecules with molecular oxygen . Differeanalysis . Furthernt study . This rent study . Table 350 values obtained. The obtained IC50 for the CuO and ZnO NPs are 4.04 and 3.90 µg/mL, respectively. The observed result showed that the CuO–ZnO nanocomposite showed the best potential compared to both individual metal oxide components which may be due to the synergy created from the different modes of action exhibited by both CuO and ZnO nanoparticles. ZnO NPs were reported to possess the ability to inhibit cancerous and bacteria cells, by inducing intracellular ROS generation and activating apoptotic signaling pathways, which in turn makes ZnO NPs a good candidate as an anticancer agent [The cytotoxic efficacy of trace elements against cancerous cells has made metals and metal oxides NPs potential anticancer agents in the fight against this menace . Exampleer agent . Furtherer agent . Similarer agent . As alreer agent .Dovyalis caffra leaves. They were washed thoroughly with tap water, followed by double distilled water to remove all unwanted substances. This was followed by drying in the laboratory for three weeks and was grounded into a fine powder. The obtained powder (100 g) was then mixed with deionized water (1 L) in a water bath while maintaining a heating temperature of 80 °C for 2 h. The resulting extract was filtered using a Whatman filter paper with a retention size pore of 11 μm.Kei apple leaves were collected fresh from the thorny stalks at the North-West University Garden, Mafikeng, and were correctly identified to be 4‧5H2O and the pH was raised to an alkaline condition (pH 10). Afterward, the solution was heated to 85 °C and maintained for 1 h while stirring steadily [The synthesis of the CuO nanoparticles followed a reported procedure with slight modifications . About 5steadily . A color3CO2)2·2H₂O and the pH was raised to an alkaline condition (pH 10). Afterward, the solution was heated to 85 °C and maintained for 1 h while stirring steadily [The synthesis of ZnO nanoparticles followed a similar procedure as in the case of CuO. About 50 mL of the extract was mixed with 150 mL of 1 mM of Zn2·2H₂O . This was followed by the adjustment of the pH of the solution to 10 by dropwise addition of an aqueous solution of NaOH. The resulting mixture was vigorously mixed and heated for 30 min until a white solution was observed. Thereafter, the 100 mL solution of CuSO4‧5H2O  was added dropwise to the stirring mixture at 85 °C which then changes to a dark green color. This mixture was allowed to stir for 2 h. This mixture was collected after washing and centrifuging at 10,000 rpm for 15 min, followed by drying in the oven at 50 °C and then calcined in a furnace for 2 h at 400 °C.In the preparation of CuO–ZnO nanocomposite with the mole ratio 1:5, about 50 mL of the −1. The morphological properties were characterized using a TECNAI G2 (ACI) in both the scanning and transmission electron microscopy with an accelerating voltage of 200 kV. The available functional group in the plant extract, which acted as stabilizing and reducing agents, was studied using Bruker Alpha (FTIR) spectrophotometer. The elemental composition and the morphology of the as-prepared nanomaterials were studied using FEI Quanta FEG 250 field emission gun microscope operating at 15 kV with the Energy dispersive X-ray (EDX) spectra obtained using Oxford Inca software.The crystalline phase of the prepared nanoparticles was characterized using a Rőntgen PW3040/60 X’Pert Pro XRD diffractometer equipped with nickel filtered Cu Kα radiation (k = 1.5418 Å) at room temperature and a scanning rate of 0.0018° minAcontrol is the absorbance of the control reactions , while Asample is the absorbance of the test compound.The 2,2-diphenyl-1-picrylhydrazyl hydrate (DPPH) free radical scavenging capacity of the CuO nanoparticles followed a reported standard procedure . This me2 tissue culture flasks in EMEM which already contained 10% fetal bovine serum 100 µg mL−1 penicillin, and 100 μg mL−1 streptomycin. The MTT assay in a 96-well plate containing 2.5 × 102 cells/well in 100 μL EMEM cell were used to investigate the viability of the MCF7 cells. Afterward, the prepared cells were incubated overnight at 37 °C. This was followed by the replacement of the medium and subsequently, the addition of samples at various concentrations . These were then incubated for 48 h at 37 °C, followed by the MTT assay. Untreated cells were used as positive control 1 and the untreated cell with DMSO were used as positive control 2, while 5-Fluorouracil was used as standard. A fresh medium containing 10% MTT reagent was used to replace the medium in the assay followed by incubation at 37 °C for 4 h. This was removed, and the insoluble formazan crystals were dissolved in 100 μL of DMSO followed by the absorbance readings taken at 570 nm using DMSO as a blank. The experiment was carried out in triplicate. The in vitro anticancer studies using MCF7 breast cancer cell lines obtained from the ATCC, Manassas, USA, were performed according to a similar reported procedure . The celDovyalis caffra, popularly known as Kei apple, have successfully mediated the synthesis of both CuO and ZnO nanoparticles, and their composite (CuO–ZnO) by acting as a stabilizing agent due to the presence of useful phytochemicals present in their extracts. These crystalline structures and purity of these nanomaterials was confirmed using XRD analysis, which revealed that the obtained diffraction patterns for the three prepared materials were reflection of a monoclinic CuO and hexagonal ZnO phase. The particle sizes of the prepared materials, using an electron microscope, were in agreement with those estimated from the XRD peaks which are found in the nano-scale range. The composite revealed better crystallinity and morphological properties than the respective CuO and ZnO NPs, as shown by the intensity of peaks in the diffractogram, in addition to the observed shape and sizes in the micrograms. The antioxidant and cytotoxicity property studies of these materials were carried out, and both reflected a concentration-dependent pattern in the respective study, with the nanocomposite having a better activity than the individual metal oxides. Although, the CuO–ZnO nanocomposite showed slightly better antioxidant activity than the corresponding metal oxides and the standard ascorbic acid, its cytotoxic properties were slightly less than 5-Fluorouracil. Therefore, CuO–ZnO nanocomposite could be considered as a medicinal agent upon further study owing to their good antioxidant and cytotoxic properties, which may have stemmed from the activity synergy of both CuO and ZnO nanoparticles.The desire to synthesize more biocompatible and environmentally friendly nanomaterials has brought about the use of different plant materials for nanoparticle synthesis. Leaf extracts of