To examine whether incorrect AI results impact radiologist performance, and if so, whether human factors can be optimized to reduce error.Multi-reader design, 6 radiologists interpreted 90 identical chest radiographs (follow-up CT needed: yes/no) on four occasions (09/20–01/22). No AI result was provided for session 1. Sham AI results were provided for sessions 2–4, and AI for 12 cases were manipulated to be incorrect , 4 false negatives (FN)) (0.87 ROC-AUC). In the Delete AI (No Box) condition, radiologists were told AI results would not be saved for the evaluation. In Keep AI (No Box) and Keep AI (Box), radiologists were told results would be saved. In Keep AI (Box), the ostensible AI program visually outlined the region of suspicion. AI results were constant between conditions.ps < 0.05). FNs were higher in the Keep AI (No Box) condition (33.0%) than in the Keep AI (Box) condition (20.7%) (p = 0.04). FPs were higher in the Keep AI (No Box) (86.0%) condition than in the Delete AI (No Box) condition (80.5%) (p = 0.03).Relative to the No AI condition , FN and FPs were higher in the Keep AI (No Box) , Delete AI (No Box) , and Keep AI (Box)  conditions (all Incorrect AI causes radiologists to make incorrect follow-up decisions when they were correct without AI. This effect is mitigated when radiologists believe AI will be deleted from the patient’s file or a box is provided around the region of interest.When AI is wrong, radiologists make more errors than they would have without AI. Based on human factors psychology, our manuscript provides evidence for two AI implementation strategies that reduce the deleterious effects of incorrect AI.When AI provided incorrect results, false negative and false positive rates among the radiologists increased.• False positives decreased when AI results were deleted, versus kept, in the patient’s record.• False negatives and false positives decreased when AI visually outlined the region of suspicion.• The online version contains supplementary material available at 10.1007/s00330-023-09747-1. Artificial intelligence (AI) in radiology has increased dramatically over the past decade. While the first AI system was approved in 2008, only 6 received approval through 2014, and 141 were approved from 1/1/2020 to 10/11/2022 [AI will return a false positive diagnosis for 11 out of 100 true negative cases. More concerning, a sensitivity of 0.87 means that AI will return a false negative diagnosis for 13 out of 100 true positive cases. While radiologist performance improves when they are using versus not using AI [In a review of 503 studies of AI algorithms, receiver operating characteristic area under the curve (ROC-AUC) for diagnostic imaging ranged from 0.864 to 0.937 for lung nodules or lung cancer on chest CT and chest X-ray examinations [What are the consequences of incorrect AI? Can it persuade a radiologist to make a wrong call when they would have otherwise made the right call? One study with CAD suggests this may be the case. Alberdi et al.  had two implementation (emphasis added) of AI applications that will help radiology professionals provide improved medical care” [, research examining how AI could adversely impact radiologist decision making is severely lacking (notwithstanding the aforementioned CAD study). To improve patient outcomes, the AI algorithm, as well as the implementation of the AI, should both be optimized.Another question remains: How can the deleterious effect of inaccurate feedback be prevented or mitigated by simply modifying how the AI feedback is presented? Answering this is consistent with the stated goals of the ACR’s Data Science Institute (DSI) to “facilitate the development and al care” . Indeed,in the context of AI implementation, although it has been broached in several theoretical papers [One field of study that can address these questions is human factors psychology—the study of interactions between humans and systems—here, AI . A few sl papers –17.The present study, to our knowledge, is the first attempt to examine two related questions: 1) Do incorrect AI results deleteriously impact radiologist performance, and if so, (2) can human factors be optimized to reduce this impact? We focus on two key human factors with real-world implications: (1) whether AI results are kept or deleted in the patient’s file, and (2) whether AI does or does not visually outline the area of suspicion. Hypotheses are presented in Table  Do incorA fully crossed, complete, and balanced block design was used where all radiologists read the same 90 chest x-rays from 90 patients across four different conditions , AI results Delete (No Box), AI results Keep (Box)) Fig. . The ordRadiologists were told that the goal of the study was to look at several different AI programs that have been developed for chest radiography. As such, radiologists were told they would interpret radiographs on several different trials. Radiologists were told they should indicate whether follow-up imaging was needed for a possible pulmonary lesion. Unbeknownst to the radiologists, the only pathology present was lung cancer. For one trial, radiologists were told they would not have AI (No AI) so they could serve as the control group for that AI system. For each of the other sessions, radiologists were told they would be interpreting cases with a unique AI program.AI Keep (Box) condition, a box was placed when the AI result was “abnormal.” No such box was used for the other conditions. Because the effect of AI feedback implementation was of interest, AI feedback needed to be controlled. Thus, no real AI systems were actually used. The AI feedback for each image was chosen by MKA. This enabled us to control the number of true positives (TPs), true negatives (TNs), FPs, and FNs, and which cases fell into each of these categories. The AI result was identical between experimental conditions; thus, only the instructions regarding keep vs. delete vs. box differed, (Supplement 1).The AI results for each image were provided in two ways. First, either “Abnormal” or “Normal” was shown visually in the top right-hand corner of the image. Second, the experimenter read the AI results aloud to reinforce the Keep vs. Delete manipulation (Supplement 1). For the Radiologists were told the ROC-AUC value of each system was higher than 0.85, which approximates clinical practice  and was A total of 90 frontal chest radiographs (CXR) (50% positive for lung cancer) taken from patients imaged between 2013 and 2016 were used. Positive CXRs were obtained within 2 weeks of a CT demonstrating a single dominant lung nodule/mass confirmed at biopsy to be lung cancer and NO other airspace opacities, consolidations, or macronodules detectable at CXR; negative CXRs were obtained within 2 weeks of a CT demonstrating NO nodule/mass. More information is provided in supplement 2.Due to COVID-19, the experimenter and radiologist communicated over videoconferencing. A convenience sample comprising six radiologists consented and participated  with 3, 1, 14, 6, 5, and 12 years of experience, respectively. At the appointed time, the radiologist and experimenter (M.H.B.) joined a videoconference. Radiologists were reminded about the ostensible goal of the experiment . Radiologists were then told that the ROC-AUC for the company’s AI program in the present session was higher than 0.85 (sessions 2–4). To encourage effortful participation, radiologists were told that their performance relative to the other radiologists would be evaluated by M.H.B. and M.K.A. (Vice Chair of Research and thoracic radiologist) upon conclusion of the study . Radiologists viewed a sample evaluation sheet. Radiologists were told that in order to fully evaluate each AI system, all images were taken from 55–75-year-old smokers who were outpatients, so the dataset was therefore enriched. In actuality, as noted above, the ratio of positive and negative cases was equal though radiologists were not told prevalence rate to avoid confounding their performance . No histRadiologists responded to two questions for each image: (1) whether they recommend follow-up CT imaging (yes/no) and (2) their level of confidence . All responses were read aloud and transcribed by the experimenter. Follow-up imaging, as a clinical decision, was used as the primary outcome. Thus, a failure to follow up a positive is a FN, not following up a negative is a TN, following up a positive is a TP, and following up a negative is a FP.Following the conclusion of the study, radiologists were debriefed, and all deception was explained. One radiologist was excluded from analyses, so analyses were performed on the remaining 5 radiologists. The excluded radiologist did not fully believe the cover story, and correctly thought the experimenter might have been interested in his/her behavior. He/she also reported being unaware of the keep versus delete manipulation. All procedures were approved by the Rhode Island Hospital IRB.All analyses were conducted using SAS Software 9.4 (SAS Inc.). FPs and FNs were examined between conditions to test the hypotheses using generalized linear mixed modeling (GLMM) assuming a binary distribution. GLMM was used to examine diagnostic confidence between conditions. Alpha was established a priori at the 0.05 level and all interval estimates are calculated for 95% confidence. Analyses were conducted by the statistical author (G.L.B.).AI Delete (No Box), radiologists were told AI results would not be saved. In AI Keep (No Box) and AI Keep (Box), radiologists were told the results of AI would be saved. AI Keep (Box), unlike both other AI conditions, provided a box around suspicious regions when AI indicated it was abnormal.The AI result manipulations  were introduced at three points during the experiment: (1) at the beginning of the session during preliminary instructions, (2) when the experimenter provided oral AI results, and (3) during the sample evaluation sheet. These are detailed in Supplement 1. Briefly, in Patients were a median of 68 years old (IQR = 62–77). In total, 53.3% identified as female and 46.7% identified as male. Regarding race, 81.1% were White/Caucasian, 7.8% were Black/African American, 1.1% were Asian, 1.1% were Native Hawaiian/other Pacific Islander, and 8.9% reported their race as “other.” Regarding ethnicity, 8.9% were Hispanic and 91.1% were non-Hispanic.p = 0.006), increased to 26.7% ) in the AI Delete (No Box) condition (p = 0.009), and increased to 20.7% ) in the AI Keep (Box) condition (p = 0.02).As anticipated in hypothesis 1, incorrect AI results that a true pathology positive case was “normal” increased false negatives Fig. . We focup = 0.13). As anticipated in hypothesis 5, FNs were higher in AI Keep (No Box) than in AI Keep (Box) .As anticipated in hypothesis 2, this effect was higher in the AI Keep (No Box) versus AI Delete (No Box) condition (33.0% vs. 26.7%), though this difference failed to achieve significance (p = 0.01), increased to 80.5% ) in the AI Delete (No Box) condition (p = 0.008), and increased to 80.5% ) in the AI Keep (Box) condition, though this did not quite achieve significance (p = 0.052).As anticipated in hypothesis 3, incorrect AI results that a true pathology negative case was “abnormal” increased false positives Fig. . We focup = 0.03). As anticipated in hypothesis 5, false positives were higher in the AI Keep (No Box) condition versus the AI Keep (Box) condition (86.0% vs. 80.5%), though this did not achieve significance (p = 0.19), and it had an identical value to the Delete (No Box) condition .As anticipated in hypothesis 4, false positives were higher in the AI Keep (No Box) condition versus the AI Keep (Delete) condition . Full results are described in Supplement 4.As anticipated, when AI provided correct positive and correct negative results, radiologists were more accurate in all AI conditions compared to the no AI condition. Furthermore, TPs were higher in the AI Keep (Box) condition compared to the AI Keep (No Box) condition  and a non-significant trend towards fewer false positives (86.0% vs. 80.5%) than did the absence of a box. The fact that a box reduced false negatives from 33.0 to 20.7% may be driven by differences in cognitive load. When radiologists interpret positive cases with a box, they might have more cognitive resources to carefully search ostensibly negative cases and contradict AI when AI is wrong. Said differently, providing a box around suspicious regions may mitigate fatigue during a reading session by reducing the region needed to visually search an image, thereby enhancing performance even on cases where no box is provided . Also, tMost importantly, we could not realistically emulate the real-world consequences from AI results being kept or deleted in a patient’s file thereby limiting ecological validity. Unlike clinical practice, radiologists in this experiment knew they were participating in research without the possibility of any real legal (discoverability), financial, ethical, and psychological repercussions to making a mistake—a lack of ecological validity.Cases were read in an artificial setting with only frontal views . Participants comprised a small number of radiologists at one site. There was no AI Delete (Box) condition. Radiologists only indicated whether or not follow-up was needed. Thus, when a case was positive, and a radiologist said it was positive, we technically cannot be certain they focused on the correct region. However, all positive cases contained only one lesion. Although lateral views are valuable  and may more likely to be correct, and thus were presumably less effected by incorrect AI. This limitation, along some others discussed above related to the artificiality of the setting, probably blunted the true effect of our manipulations and thus shows the robustness of our results. If true, this would mean that the effects observed in this study are actually larger in clinical practice.Condition order was not fully counterbalanced. We used an enriched dataset consisting of more pathology than typically found in clinical practice, although this was practically needed to have enough positive cases and is consistent with other reader studies , 25. Radwhen AI was both correct and incorrect.AI is often right but sometimes is wrong. Since we do not know when it is accurate, we must consider how to minimize the extent to which radiologists are influenced by incorrect results. In this study, we show that incorrect AI results can influence a radiologist to make a wrong decision. However, this effect is mitigated when radiologists are told the AI results are deleted, versus kept, in the patient’s file, and when AI provides a box that visually outlines suspicious regions. In fact, AI that included a box improved radiologists’ performance This study offers compelling initial evidence that human factors of AI can impact radiologists. To enhance patient care, radiology practices should consider how AI is implemented. Radiological societies should formulate guidelines for radiologists regarding the integration of AI results into the reporting of examinations. Moreover, radiologists should be trained in best practices for using AI tools clinically.Supplementary file1 (PDF 306 kb)Below is the link to the electronic supplementary material.


The expansion of bean genome technologies has prompted new perspectives on generating resources and knowledge essential to research and implementing biotechnological tools for the practical operations of plant breeding programs. This study aimed to resequence the entire genome (whole genome sequencing—WGS) of 40 bean genotypes selected based on their significance in breeding programs worldwide, with the objective of generating an extensive database for the identification of single nucleotide polymorphisms (SNPs). Over 6 million SNPs were identified, distributed across the 11 bean chromosomes. After quality variant filtering, 420,509 high-quality SNPs were established, with an average of 38,228 SNPs per chromosome. These variants were categorized based on their predicted effects, revealing that the majority exerted a modifier impact on non-coding genome regions (94.68%). Notably, a significant proportion of SNPs occurred in intergenic regions (62.89%) and at least one SNP was identified in 58.63% of the genes annotated in the bean genome. Of particular interest, 7841 SNPs were identified in 85% of the putative plant disease defense-related genes, presenting a valuable resource for crop breeding efforts. These findings provide a foundation for the development of innovative and broadly applicable technologies for the routine selection of superior genotypes in global bean improvement and germplasm characterization programs. Its grains are gluten-free and serve as a valuable nutritional source, abundant in proteins, minerals, vitamins, and bioactive compounds3. Additionally, they are high in fiber, low in fat, and rich in slow-digesting carbohydrates, which are associated with a considerably reduced risk of heart disease, obesity, and diabetes5. Despite its numerous health benefits, bean cultivation presents several challenges, including enhancing yield in the presence of biotic and abiotic stresses, as well as improving grain quality and nutritional value7. To overcome these challenges, plant breeders have been empowered by the knowledge and understanding of the common bean's genomic resources to conduct more targeted and specific studies that align with global requirements8.Among pulses, the common bean stands out as one of the most significant food crops globally, with a profound socioeconomic impact10. For common bean, WGS projects aim to uncover novel insights into evolution and diversity, contributing to the generation of valuable genomic resources that accelerate common bean improvement and enhance its efficiency12.The generation of a substantial amount of genomic information has led to a significant transformation in the overall field of plant breeding. The availability of the sequence of the entire genome allows researchers to understand and exploit the genetic variations and their control over complex traits, enabling plant breeders to conduct more targeted and specific studies aligned with the global demands. Among the approaches available for generating a vast amount of genomic information, whole-genome sequencing (WGS) provides an opportunity to analyze and identify genetic variations at the base level throughout the entire genome, such as single nucleotide polymorphisms (SNPs), insertions, deletions, and structural variations15. In addition, this integration has provided valuable insights into the evolutionary and domestication history of plants, as well as the genetic mechanisms of adaptations and selective breeding that have contributed to the environment’s adaptation16. This has empowered plant breeders to propose and implement new breeding approaches, such as genome-wide selection (GS) and marker-assisted selection (MAS) specifically aimed at selecting traits of high economic importance19. The availability of the new genomic tools has increasingly provided improvements in terms of speed, efficiency, accuracy, cost-effectiveness of plant breeding processes opening new ways to improve agronomic traits20, addressing the demands of the productive sector26.By integrating genomic knowledge, such as genomic structure, function, diversity, with quantitative genetics, a solid foundation has been established for detailed dissection of the genomic architecture of genetic variationIn this study, we performed a comprehensive analysis of genomic variations using WGS in 40 important breeding lines/cultivars from the Middle American and Andean gene pools, developed by Brazilian and international breeding programs. The objective was to assist in the genetic studies of important agronomic traits, addressing the challenges faced in common bean cultivation.27 (Supplementary Table The WGS of the 40 common bean lines (Supplementary Table S1) generated 7.4 billion paired end read fragments, the equivalent to a raw data set of 1.18 Terabases (Tb), ranging from 5.14 billion (Rosinha G2) to 210.47 billion base pairs (BRS Pérola) (Supplementary Table –9). The estimated transition/transversion ratio (Ts/Tv) was 1.29.More than 6.5 million SNPs were identified in the dataset. After filtering out SNPs in repetitive regions and applying quality metrics, 420,509 (6.37%) with a minor allele frequency (MAF) of ≥ 0.5 were maintained Table . The avenon-reference SNPs was 14.21%, while it was 49.24% for Middle American accessions . Among the SNPs identified in genes, the majority were in introns (58.11%), followed by exons (30.99%), 3′ Untranslated region (UTR) (6.17%), and 5′ UTR (4.73%) regions  were detected in intergenic regions, while 23.44%  were found in gene regions , accounting for a total of 14.05% of the effects. Furthermore, 2.48% of the effects were in the 5′ and 3′ UTR regions of genes, indicating potential in regulatory regions (Table The predicted effects of SNP were categorized based on the variants as follows: modifier (94.68%), moderate (2.69%), low (2.59%) and high (0.05%) Table . Most SNst) based on common SNPs  was 0.27. For the SNPs exclusive to the Middle American germplasm  and Andean germplasms  the Fst values were 0.31 and 0.85, respectively. The average Fst for the total set of SNPs  was 0.45, with 63,366 differentiating the two groups and resulting in an average Fst of 0.97. Chromosome 2 exhibited the highest number of differentiating SNPs (8520), followed by chromosomes 9 (8274) and 8 (7548)  value for the entire sample set was estimated to be 0.36, with 0.29 for the Andean and 0.28 for the Middle American gene pools. The Middle American gene pool exhibited higher nucleotide diversity  compared to its Andean counterpart . Tajima’s D values were estimated as D = 2.46 for the Middle American and D = 0.93 for the Andean germplasms. Phylogenetic analysis based on a set of 30,503 SNPs with a resolution of one bin per 10 kb, revealed a clear division between the Andean and Middle American gene pools, consistent with the well-established classification in the common bean , with a predominance of NB-ARC, TIR, CC, kinase, LRR, Serine/threonine-LRR and Kinase-LRR domains. Additionally, 45 genes were classified as putative disease resistance functions, including zinc finger transcription factors and dirigent-like proteins , while chromosome 9 had only two annotated genes, as shown in Fig. The SNPs identified in disease resistance genes were classified according to their predicted effects. The majority, 75.35%, were categorized as modifier type (impacting non-coding regions), followed by 15.71% classified as moderate (involving non-synonymous substitutions), 8.60% as low (involving synonymous coding/start/stop effects) and a small percentage, 0.34%, fell into the high category  (Supplementary Table The Gene Ontology (GO) enrichment analysis was performed for the 27 SNPs predicted to have a high impact, considering both biological process (BP), and molecular function (MF) as shown in Supplementary Fig. The analysis of annotated terms showed that the 27 R-genes were overrepresented and played important roles in “protein biding”, ADP biding” and “signal transduction”  associated to disease resistance and providing valuable markers for use in marker-assisted breeding programs.E of 0.334 (± 0.008) for the entire collection of SNPs. The genotypes from the Middle American gene pool exhibited higher heterozygosity (0.27 ± 0.009) compared to the Andean genotypes (0.19 ± 0.010). The coefficient of gene differentiation between the gene pools (Fst) was 0.386 (± 0.001).Out of the 438 SNPs associated with R-genes that were evaluated, a total of 356 non-redundant R-genes were sampled , Fusarium Wilt (FW), and Cowpea mild mottle virus (CPMMV). QTL positions were identified on chromosomes Pv04, Pv07, and Pv08 at specific positions as reported in the respective references. A total of 110 SNPs were identified in silico within the genomic window containing the QTLs, and 8% of them were annotated to disease genes, with the aim of approaching the disease-causal variants  and P. coccineus L. leading to the identification of over 40 million genomic variants providing comprehensive resources for molecular breeding and further understanding of the genetic diversity within common bean populations. In a more extensive study, Wu et al.12 conducted resequencing of 683 accessions from the National Crop Genebank of China and successfully identified variants associated with yield components, of high value for breeding purposes. These collaborative initiatives in the scientific community are significantly contributing to the establishment of a comprehensive knowledge base, paving the way for advancements, facilitating gene discovery, and enhancing our understanding of key agronomic traits.In the present study, we conducted resequencing of 40 agronomically important common bean breeding lines of Middle American and Andean origin, resulting in the identification of nearly 500,000 SNPs. These germplasms have been extensively studied worldwide for their resistance/susceptibility to various diseases, such as anthracnose, angular leaf spot, fusarium wilt, common bacterial blight, bean golden mosaic virus, and cowpea mild mottle virus. In a related study, Lobaton et al.32. Moreover, a remarkable number of SNPs with MAF ≥ 0.05  and MAF ≥ 0.1  were identified, of which 63,366 SNPs effectively contributed to differentiate the Andean and Middle American groups (Fst ≥ 0.97), confirmed by the phylogenetic analysis. These extensively characterized set of SNPs covering the common bean genome holds great potential of applications, mainly if combined with datasets generated by other research groups33, as they enable the accurate selection of SNPs for candidate genes, as well as those distributed across the entire genome.The analysis of the common bean accessions' raw data in the present study revealed an extensive collection of SNPs, exceeding 6.5 million within the dataset. Among these SNPs, 4.07 million were identified in non-repetitive regions, representing a significant increase of approximately 68% compared to the 1.3 million SNPs reported in non-repetitive regions by Lobaton et al.33 . The displayed higher genetic diversity in this study correlates with the wide range of genetic backgrounds of the sampled accessions, which was crucial for capturing a broader spectrum of genetic variations across the entire genome. The Ts/Tv ratio (1.29) observed exhibited minimal variation compared to other relevant bean-related studies35, aligning with our expectations36 and affirming the high quality of the data. Despite the substantial presence of introgressions in our study, only 99 of them exceeded 1 Mb in length, reflecting the findings of Lobaton et al.32, who reported 100 introgressions longer than 1 Mb in the evaluated germplasm. In general, a greater enrichment of the Middle American germplasm was observed, with 92.65% of the introgressions consisting of Andean germplasm segments. These findings are consistent with the history of bean domestication in Brazil, where Middle American germplasm prevails, but there has been a notable focus on hybridization with Andean germplasm to introduce target genes and enhance genetic variability in the development of breeding lines and cultivars38. Interestingly, the TO cultivar, which has a Middle American background exhibited a larger proportion of Andean introgression (21.63%), as shown on the phylogenetic tree , we observed higher estimates of nucleotide diversity  compared to those reported by Delfini et al.33. Variation in non-coding regions have an established role in plant diseases and regulatory mechanisms, with growing evidence from many areas, including yield, abiotic stresses, and adaptation to environment41. Genes conferring resistance to bean diseases have been extensively documented in the literature43. In this study, a total of 501 putative disease resistance genes were selected from the common bean genome obtained from phytozome, surpassing the previously reported numbers for the Andean (376 NB-LRR) and Middle American (234 NBS-LRR) germplasms44. Moreover, the discovery of SNPs in R-genes (7841 SNPs in 85.03% of the R-genes) uncovers significant polymorphisms associated with the defense response in beans enabling the development of genotyping arrays that target and explore multiple genomic regions45. These SNPs can have a functional impact in terms of phenotypic resistance or susceptibility to certain diseases.Among the 98,584 SNPs identified, 58% fell in introns, as also observed by Delfini et al.Colletotrichum lindemuthianum, pathogen causing anthracnose46, and Phaeoisariopsis griseola which causes angular leaf spot11, have demonstrated their importance as valuable sources of resistance genes for breeders in the bean community. The validation and testing of this set of specific SNPs, associated with disease genes, using SNP arrays serves as the foundation for genome association studies and whole-genome-based selection in common beans. There is a growing demand for breeding programs to incorporate these SNPs into genotyping panels for diversity analysis, associative mapping, QTL mapping, and genomic selection47. The inclusion of SNPs associated with resistance genes in these genotyping panels enhances the value of molecular tools, enabling the detection of genetic variation in target genes. Our work provides a set of operationally evaluated SNPs that are readily suitable for incorporation into any genotyping chip.The genotyping of 438 SNPs we conducted aimed to operationally assess a set of SNPs associated with resistance genes annotated in common bean. This research contribution is significant and unprecedented as it provides allelic variants located within resistance genes derived from 38 genotypes of great scientific value  tolerant cultivars led to the identification of SNPs associated with target QTLs49, aiming to increase the likelihood and identifying more accurate molecular tools for MAS. Recently, Yan et al.50 refined four major QTLs for oil content in Brassica napus by integrating resequencing data and transcriptomics. In soybean, whole-genome sequencing allowed the identification of genomic variations and candidate resistance genes for soybean mosaic virus (SMV), opening possibilities for the development of resistant varieties51. Additionally, genotyping these target SNPs can optimize genomic selection approaches, enhancing the accuracy of predictions, as demonstrated for fusarium head blight resistance and yield-related traits in wheat52.Continuing within the scope of this study, the resequencing of inbred lines that incorporate crosses in which important QTLs were mappedP. vulgaris publicly available, we can achieve a more comprehensive understanding of the genetic variation within this species. This expanded dataset allows for the identification of both common and rare genetic variants, as well as a deeper understanding of their frequencies, enabling more robust analyses and accurate predictions of agronomic trait mechanisms. Ultimately, the availability of this valuable genetic information contributes to the development of personalized breeding approaches that can lead to improved crop varieties with desired traits. The integration of this dataset with other valuable omics data available for common bean can also drives advancements in various research areas beyond genomics. Additionally, it creates new opportunities for scientific advancement and interdisciplinary collaborations, accelerating progress in common bean genetics, genomics, and related fields.In conclusion, by re-sequencing and making more variants of A germplasm panel comprising 40 common bean lines/cultivars was carefully selected for this study. The genotypes were chosen based on their significance in breeding programs worldwide. Some of these have served as essential background accessions for modern lines  and have been cultivated in Brazil for many years . To represent the historical breeding genetic diversity of common bean, we sequenced a combination of modern elite cultivars  and older varieties . We also included lines associated with important disease resistance  as well as members of differential series used to differentiate races of pathogens causing anthracnose and angular leaf spot diseases . Additionally, we carefully selected lines with other desirable agronomic traits, such as specific plant architecture and darkening of the tegument ; adapted to different regions and exhibiting variations in grain types ; from diverse institutions of origins and representing both the Andean and Mesoamerican gene pools . The seeds utilized in this study were obtained from the germplasm bank of Embrapa Rice and Beans (Supplementary Table ® DNA flex kit was employed. DNA sequencing was carried out on the Illumina HiSeqX platform using a PE150 (paired-end 150) strategy.Total genomic DNA was extracted from leaves at the V4 growth stage (4th trifoliolate unfolded at node 6 and branching), using the Invisorb Spin Plant Mini Kit  following the manufactures’ instructions. For genomic DNA library preparation, the Nextera53 for quality trimming (SLIDINGWINDOW:10:30) and adaptor sequences removal (TruSeq3-PE-2), keeping reads with a length of at least 50 base pairs (bp). The filtered reads were subsequently aligned to the reference bean genome27 (https://phytozome-next.jgi.doe.gov/info/Pvulgaris_v2_1) using BWA mem software54 using default parameters.The raw sequence data obtained were processed using Trimmomatic software v. 0.3955 and VCFTools software v.0.1.1656 was utilized for variant filtering, applying the following parameters: Min_Alleles = 2, Max_Alleles = 2; Min_Mean_Depth = 10; Max_Mean_Depth = 750; Mindp = 3; Maf = 0,01, Mac = 1; Perc_Missing = 0.4; Min_Qual = 10.0.For variant calling we used FreeBayes v.1.3.127. SNPs located within these repetitive regions were filtered out using VCFTools software56 with the “–exclude-bed” command. SNPs with a Minor allele frequency (MAF) ≥ 0.05 were retained. Ultimately, a hard filtering approach was applied to ensure quality control using the vcfR package57 in the R environment58, following the parameters: call_rate ≥ 0.8; Allele frequency for each alternative allele (AF) >  = 0.1 and <  = 0.9; QUAL ≥ 30; Strand balance probability for the alternate allele (SAP) > 20; Strand balance probability for the reference allele (SRP) > 20; End placement probability (EPP) > 20 and Depth(DP) ≤ 1500.To identify the chromosomal coordinates of repetitive DNA we accessed Phytozome platform using the Andean common bean reference genome59, genotypes for each accession were determined using the VCF Summary Stats command. The genotypes were classified into three categories for a biallelic site in a diploid individual: (i) Homozygous and identical to the reference allele, (ii) heterozygous, and (iii) homozygous but different from the reference . SNP predictions of functional impact were performed using the command VCF Summary Annotate at NGSEP software59.Using the NGSEP software v.4.0.160 and Quick GO web-based tool61. A significance level of p-value < 0.05 was applied, and the results from GO and KEGG were visualized using R with the ggplot2 package62. The enrichment in the protein domain was established using the protein family (PFAM) analysis tool available at InterPro website63. Additionally, gene–gene interaction prediction was conducted based on the protein sequences alignment (genes and domains) using the resources available at Phytozome.For SNPs with putative effect predicted to be high, a functional enrichment Tusing the MeSH over-representation analysis (ORA), including gene ontology (GO) and KEGG (Kyoto Encyclopedia of Genes and Genomes) pathway enrichment analysis, were performed using the function g:PROFILER in PythonE) was calculated using the complete set of SNPs, filtered at every 50 kb interval using the hierfstat v.0.5-1064 and pegas v.1.1 packages65 in the R environment58. A Venn diagram depicting the distribution of SNPs within and between gene pools was generated using the JVENN tool66. Polymorphic SNPs that were found exclusively or common among the gene pools were used to estimate fixation indices (Fst)67, nucleotide diversity (π) and Tajima’s D68 within a 100 kb sliding window using the VCFtools software56.Genetic diversity . The General Time Reversible (GTR) model coupled with a gamma-distribution (G) rate, was applied. Branch consistency was accessed through 10,000 bootstraps. The resulting tree was visualized using FigTree program v.1.4.4 accessible at http://tree.bio.ed.ac.uk/software/figtree/.Phylogenetic analysis was conducted using the SNPs grouped into bins of 10 kb employing the RAxML v.8.259 was utilized for this purpose considering non-overlapping windows of 50 SNPs.Introgressed analysis was performed to identify genomic regions that have undergone introgression between Andean and Middle American. The VCF Introgression Analysis module in the NGSEP software70 was conducted. SNPs located within 5 kb upstream/downstream regions of the UTR 5' and 3' of the R-genes were filtered using VCFtools56. Pearson's correlation coefficients were calculated in R software58 to assess linear relationships between key variables56.Genes associated with disease resistance, specifically R-genes, were obtained from the Phytozome v.2.1 platform (Phytozome (doe.gov). Furthermore, a search for conserved putative domains for R-genes, well documented in the literatureGenexa—Adn evolutivo) facility for genotyping. Overall genetic parameters were calculated using GenAlex v. 6.50271.A set of 438 SNPs was selected from a total of 7841 SNPs  previously identified in the R-gene sequences Fig. . Genomic28, BRS Notável X BRS Supremo29, BRS Cometa X Sel 130830, and BRS Sublime X CNFCT 1620731(Table P. vulgaris reference genome (https://phytozome-next.jgi.doe.gov/). Additionally, functional annotations were gathered for each SNP, describing the type of genetic variant found, such as intergenic variants, variants located in gene promoter regions, synonymous variants, and intronic variants.The identification of SNPs flanking the disease QTL in four bi-parental crosses Rosinha G2 X BRS Cometa731Table  was perfSupplementary Information.


This study was to explore the relationship between chronic constipation, chronic diarrhea, and frailty in older Americans.This cross-sectional study selected a total of 4241 community-dwelling individuals aged 60 years and older from the 2005–2010 National Health and Nutrition Examination Survey. Frailty was measured using a 49-item frailty index, and a frailty index > 0.21 was defined as a frail status. Chronic constipation and chronic diarrhea were defined as the “usual or most common type of stool” by the Bristol Stool Form Scale (BSFS) Types 1 and 2 and BSFS Types 6 and 7, respectively. Weighted logistic regression analysis was used to examine the relationship between gut health and frailty status. Restricted cubic spline (RCS) curves were built to assess the association between frailty index and stool frequency.Frailty status was associated with higher odds of constipation in an unadjusted model; however, after further adjusting for confounding variables, the relationship between frailty status and constipation was not statistically significant. We discovered a positive correlation between the frailty status and diarrhea after adjustment for all variables. The frailty index showed a U-shaped relationship with stool frequency, and the frailty index was the smallest at a frequency of 10 stools/week.Negative associations were observed between frailty status and chronic constipation and diarrhea among older adults. Older adults who have a bowel movement frequency of about 10 times per week are the least frail. Future studies are warranted to confirm the causal relationship in this association.The online version contains supplementary material available at 10.1186/s12877-023-04438-4. Frailty is defined as a decreased function of multiple physiological systems and increased vulnerability to stressors, resulting in elevated mortality, as well as a heightened probability of hospitalization, falls, and receipt of long-term care . The rapBowel symptoms, including constipation and diarrhea, are common in the elderly population. The prevalence of chronic constipation and diarrhea is estimated to be as high as 17% and 20% in the general population, with a higher prevalence in the elderly , 7. The Bowel symptoms may exacerbate frailty through different pathways such as the absorption of nutrients, intestinal motility, and gut microbiota , 15. Althttps://www.cdc.gov/nchs/data_access/restrictions.htm). The study population for this analysis included subjects from the 2005–2010 National Health and Nutrition Examination Survey (NHANES) cycle. We chose these three cycles because data on bowel health questionnaires are not available in the recent cycles. A total of 31,034 participants were extracted from the NHANES database. After excluding participants aged < 60 years , missing data on the bowel health questionnaire (n = 956), not completing at least one valid 24-h dietary recall (n = 83), and insufficient baseline information (property (n = 393), education (n = 5), smoking (n = 2), alcohol use (n = 10)), 4348 participants were included. Further, participants with less than 80% (< 40 items) of the characteristics of the included frailty index were rejected (n = 9). Finally, participants with colorectal cancer (n = 81), and Crohn’s disease/ulcerative colitis (n = 17) were eliminated, and 4241 participants were eligible for analysis  that includes a nationally representative sample of noninstitutionalized subjects. Participants were chosen using a hierarchical, multistage, probabilistic clustering design and then analyzed using complex sampling weighting methods . WrittenThe frailty index devised by Hakeem and his coworkers was used to evaluate frailty in our study . The FraThe NHANES 2005–2010 bowel health questionnaire was applied to analyze bowel symptoms in participants who self-reported their bowel habits. We evaluated stool consistency using Bristol Stool Form Scale (BSFS) cards (color picture cards with pictures and written descriptors of the seven stool types) and the following written questions: “Please look at this card and tell me the number that corresponds with your usual or most common stool type.” Individuals with chronic constipation are considered to have the usual or most common type of stool identified as BSFS type 1 (separate hard lumps like nuts) or BSFS type 2  . IndividCertain covariates formerly shown or hypothesized to be related to chronic diarrhea and/or constipation and frailty were evaluated as covariates in our study, including sociodemographic variables, behavioral risk factors, and dietary intake. The sociodemographic factors were age, measured in years; sex; race, categorized into five groups ; education, divided into three levels , and greater than high school); marital status, classified into three groups ; and poverty to income ratio, used as a continuous variable to represent income. Behavioral risk factors comprised smoking and alcohol consumption. Smoking status was divided into three groups . Alcohol users were divided into “yes/no” groups. Overall dietary quality was assessed using the 2015 version of the Healthy Eating Index (HEI). HEI—2015, scored according to the Dietary Guidelines for Americans (DGA), consists of 13 food blocks or nutrients that correspond to their recommended intakes. There are nine adequacy components in the HEI  and four moderation components . For each component, there is a maximum score of 5 or 10, with a total score of 100, with higher scores indicating better overall diet quality , and statistical significance was determined by a two-sided P value < 0.05.For the study, appropriate weights were used  to account for the complex sampling design of NHANES. Descriptive statistics were performed stratified by frailty or not. A t-test was used to compare differences between groups for continuous data with a mean (standard error) (S.E). A chi-square test was used to determine differences between groups based on categorical data expressed as number (n) and percentage (%). The correlation between independent variables was analyzed using Spearman correlation analysis. It is generally accepted that correlations are classified as strong, moderate, weak, and no correlation by the correlation coefficient (ρ) with three cutoff values of 0.8, 0.5, and 0.3. Weighted logistic regression analysis was conducted to investigate the association between frailty index and chronic constipation and diarrhea. The crude model was unadjusted. In Model 1, age, sex, race education, marital status, and poverty-income ratio factors were adjusted. In Model 2, smoking and alcohol use were additionally adjusted. In Model 3, HEI and total energy intake were further adjusted. Restricted cubic spline (RCS) regression adjusted for all confounding variables was used to examine the linear/nonlinear relationship between frailty index and stool frequency. If the association was nonlinear, the inflection point of stool frequency on the frailty index was sought, i.e., the value at which change was observed in the curve. All statistical analyses were performed using R version 4.2.1 , as was chronic diarrhea (9.11% (95% CI 7.53–10.69) vs. 6.70% (95% CI 5.26–8.13), p = 0.02).The study included 4241 individuals representing 402.5 million non-institutionalized residents in the United States. The prevalence of frailty among U.S. seniors over 60 was 24.49% [95% confidence interval (CI) 21.65–27.33], chronic constipation was 6.66% (95% CI 5.69–7.62), and chronic diarrhea was 8.01% (95% CI 6.44–9.58). Among them, the prevalence of chronic constipation was lower in older men than in older women (9.74% (95% CI 8.55–10.93) vs. 2.97% (95% CI 2.30–3.64), p < 0.0001) and female  and had a lower income-to-poverty ratio , total energy intake  and HEI score than non-frail participants . Significant differences in race, education, marital status, and alcohol exposure (p < 0.05) were also observed. Smokers were more likely to be frail than never-smokers , but the difference was not statistically significant. Frailty was more prevalent in subjects with constipation  and diarrhea . In the correlation analysis in Fig. p < 0.05) and total energy intake and sex .The baseline characteristics of the research participants are presented in Table p < 0.001). In patients with diarrhea, the frailty index was also higher .The frailty index was higher in constipated subjects than in non-constipated subjects . On the other hand, we observed a positive correlation between the frailty status and diarrhea . After adjustment for all covariates, a statistically significant positive relationship between frailty status and diarrhea remained .As shown in Table P for nonlinearity < 0.0001, Fig. In a restricted cubic spline plot (RCS), stool frequency was related to the frailty index as shown by a U-shaped curve.  and greater age of frail patients, with a significantly higher prevalence in women (27.48%) than in men in the sex distribution, has been widely reported . The preThere are some limitations in our study. Primarily, since this study is a cross-sectional study, we cannot conclude a causal relationship between frailty and chronic constipation and diarrhea. In addition, the bowel condition in this study was collected through a questionnaire, which may be affected by recall bias. Moreover, although we adjusted for potential confounders affecting the experimental results, there were still some confounders such as drug administration that were not considered. Despite these limitations, to our knowledge, this is the first study to assess the relationship between chronic diarrhea and frailty index, providing important insights into the connection between constipation, diarrhea, and frailty, and ultimately providing a framework for future prevention and interventions to reduce the risk of frailty by improving gut health.In conclusion, we found that frail older adults were more likely to suffer from chronic constipation and diarrhea. A bowel movement frequency of about 10 times per week had the lowest frailty index. It is important to maintain good gut health in older adults, and it is necessary to include gut health in routine health assessments and surveys of older adults.Additional file 1: Supplementary Table 1. The variables in the 49-item frailty index and their respective scoring criteria. Supplementary Table 2. Healthy Eating Index -2015 components and scoring standards.


There remains uncertainty as to which risk factors are important for the development of defaecatory problems as a result of heterogeneity of published evidence. Understanding the impact of risk factors may be important in selecting targets for disease prevention or reversal. The aim of this study was to identify and evaluate risk factors for faecal incontinence and chronic constipation.Risk factors for chronic constipation and faecal incontinence were long-listed from scientific literature, then anonymously evaluated  using a Delphi technique. Each risk factor was rated as independent, a co-factor, or not a risk factor. Independent risk factors were rated between 1 (not important) and 10  with mean (± standard deviation) calculated.Thirty-eight risk factors for chronic constipation were evaluated. Eighteen were classed as independent and 16 as co-factors. Opioid analgesia (7.87 ± 2.05), eating disorders (7.80 ± 1.72), and history of abuse (7.70 ± 1.89) were scored as most important independent risk factors. Female sex (6.60 ± 2.02) was considered an independent risk factor but increasing age was rated a co-factor. Thirty-three risk factors for faecal incontinence were evaluated. Twenty were classed as independent and eight as co-factors. Third- or fourth-degree tear (8.88 ± 1.57), instrumental delivery (8.47 ± 1.58), and grand multiparity (8.00 ± 1.63) were rated most important. Increasing age (7.41 ± 2.14) and female sex (7.58 ± 2.05) were both considered independent risk factors.Several risk factors for chronic constipation and faecal incontinence were selected by Delphi approach. These factors will feed forward into Bayesian models of disease prediction that combine data and expert knowledge.The online version contains supplementary material available at 10.1007/s10151-023-02843-w. Faecal incontinence and chronic constipation are common disorders of defaecation, with faecal incontinence affecting around 6% , and chrSeveral risk factors for developing faecal incontinence and chronic constipation have been identified, predominantly through cross-sectional studies which vary in the definitions used, methodology, study population, and potential risk factors assessed. These limitations prevent aggregation of results by meta-analysis .The Delphi technique is an effective method for arriving at consensus on broad and complex problems , 8. UsinThe Delphi technique is characterised by four methodological features: expert participants, iterative rounds of enquiry, a dependency of the design of subsequent rounds on the basis of the response of the previous round, and anonymous participation , 10.Participants were recruited voluntarily from members of The Pelvic Floor Society (TPFS), a UK subspecialty society affiliated to the Association of Coloproctology of Great Britain & Ireland. Email invitations to take part in the survey in May 2021 were sent to the whole membership (399 members) in April 2021. TPFS membership criteria ensured the selection of current specialists in pelvic floor disorders.In reviewing recent Delphi studies conducted in the field of coloproctology , most conducted between two and three rounds of voting , with modification of survey items between rounds . This current study included two rounds of anonymous voting by online surveys, followed by a final round of anonymous voting within a consensus meeting  as illustrated in Fig. The first-round questionnaire (Supplement B) was developed by PC, MH, and CK on the basis of a pilot survey carried out at TPFS annual meeting in November 2019 and a review of current literature. Potential risk factors were long-listed from textbooks –14, the For potential risk factors, each item could be rated by the participant as an independent risk factor, a co-factor, or ‘not a risk factor’. An independent risk factor was defined as a risk factor which can increase the risk of faecal incontinence or chronic constipation in an individual even if there are no other risk factors present. For a co-factor to increase the risk, an individual must also have at least one other risk factor. Classification to ‘not a risk factor’ was based on the belief that it neither increased the risk of faecal incontinence or chronic constipation alone or in combination with other factors. All risk factors classified as independent were then rated by importance using a score between 1 (not important) to 10  on a Likert scale.The second and third rounds were dependent on the results of the first and second rounds, respectively. Any risk factor which reached consensus was not carried forward into the subsequent round; any rating option with 10% of the votes or fewer were removed in the subsequent round; new items from participants’ suggestions were reviewed and added to the subsequent round as appropriate . At the third-round consensus meeting, the results from the previous rounds were presented along with a summary of evidence. Participants voted on the basis of real-time presentation of the results. If consensus was not reached, the participants were invited to have a discussion followed by a re-vote, which was then accepted as final.www.onlinesurveys.ac.uk) and the live voting (www.mentimeter.com). The study was co-ordinated by non-voting members of the research team for impartiality.Anonymous online tools were used to conduct the surveys .The study was approved by the Queen Mary Ethics of Research Committee (QMERC20.228).n = 36; 72%). Median experience was 15 (8–20) years and the number of patients with faecal incontinence and chronic constipation seen in an average month by participants was 10 (5–20) and 15 (10–20), respectively. Thirty-one members participated in the second round of the study and nine members participated in all three rounds.Fifty members of TPFS participated in the first round of the study (response rate of 14.7%). Participant clinical roles are summarised in Fig. In the first round, 28 potential risk factors for faecal incontinence based on long-listing were evaluated , female sex (71% agreement), prolonged second stage of labour (87% agreement), surgical trauma such as haemorrhoidectomy or lateral sphincterotomy (94% agreement), anal trauma or rape (87% agreement), atraumatic conditions such as scleroderma or idiopathic internal sphincter atrophy (81% agreement), chronic diarrhoea (81% agreement), and central nervous system conditions such as stroke or multiple sclerosis (74% agreement). Four were classified as co-factors, and three were considered ‘not risk factors’ for faecal incontinence, including ethnicity (97% agreement), unemployment (90% agreement), and low socioeconomic status (71% agreement).In the third round, 11 potential risk factors were evaluated (including 3 new items generated in the consensus meeting). Five were considered independent risk factors including extreme exercise (78% agreement), pelvic radiotherapy (100%), inflammatory bowel disease (100% agreement), previous rectal resection (100% agreement), and evacuation disorders (89% agreement). Four were considered co-factors, and episiotomy, first- or second-degree tear (100% agreement), and depressive disorders (100% agreement) were considered not risk factors for faecal incontinence.In the first round, 35 long-listed risk factors for chronic constipation were evaluated , Ehlers-Danlos syndrome or joint hypermobility syndrome (84% agreement), hypothyroidism (71% agreement), history of childhood constipation (77% agreement), multiple sclerosis (94% agreement), eating disorders (94% agreement), and history of abuse (90% agreement). Twelve were considered co-factors, and ethnicity (87% agreement) was considered not a risk factor for chronic constipation.In the third round, 13 potential risk factors remained without consensus (including 1 new item generated in the consensus meeting). Six were considered independent risk factors, including female sex (88% agreement), metabolic conditions such as hypercalcaemia (100% agreement), connective tissue diseases such as scleroderma (89% agreement), neurodiverse conditions such as autism (78% agreement), previous rectal resection (100% agreement), and anticholinergic agents (100% agreement). Four were considered co-factors and three were considered not risk factors for chronic constipation, including unemployment (71% agreement), family history of constipation (100% agreement), and previous abdominal surgery such as appendicectomy or cholecystectomy (89% agreement).The independent risk factors considered most important for faecal incontinence were third- and fourth-degree tear (mean importance score of 8.88 ± 1.57), instrumental delivery (mean importance score of 8.47 ± 15.8), and grand multiparity (mean importance score of 8.00 ± 1.63) , eating disorders (mean importance score of 7.80 ± 1.72), and history of abuse (mean importance score of 7.70 ± 1.89) Fig. . The indTo our knowledge, this is the first study to comprehensively evaluate the importance of potential risk factors for benign disorders of defaecation using Delphi methodology. Consensus was achieved for classification  of all potential risk factors evaluated . Mean importance scores were also produced for 19 of the 20 independent risk factors for faecal incontinence and all the 18 independent risk factors for chronic constipation.Age and sex were the most evaluated risk factors for faecal incontinence and chronic constipation in the literature. While female sex was considered an independent risk factor for faecal incontinence and chronic constipation, increasing age was classified as an independent risk factor for faecal incontinence but a co-factor for chronic constipation. Meta-analyses of studies reporting the prevalence of chronic constipation in the general population have reported a significant association with female sex , 16, 17.The independent risk factors considered most important through Delphi approach for faecal incontinence were obstetric factors, including third- or fourth-degree tears , instrumental delivery, and grand multiparity. Several systematic reviews have concluded that obstetric anal sphincter injury is significantly associated with an increased risk of anal –22 and fp = 0.002) but not faecal  incontinence when an episiotomy is performed. Interpretation of these findings is challenging because of heterogeneity in episiotomy practice (routine vs. selective) and type of episiotomy performed .The current study did not consider episiotomy and first- or second-degree tears to be a risk factor for faecal incontinence. Several systematic reviews and meta-analyses have been performed to assess the risk of anal or faecal incontinence associated with episiotomy. LaCross et al.  suggesteOne of the most important independent risk factors for chronic constipation was history of abuse, but no large population study has examined this risk factor, most likely because of its sensitive nature. Several small observational studies have consistently reported a significant association between history of abuse and constipation , functioPotentially modifiable risk factors for faecal incontinence and chronic constipation included dietary factors and obesity. Dietary factors were considered a co-factor for faecal incontinence and an independent risk factor for chronic constipation. A systematic review by Colavita and Andy  found veDiarrhoea (or loose stools) was considered one of the most important risk factors for faecal incontinence by several observational studies , 19, 26 This study has highlighted some risk factors that may be modifiable in terms of prevention or treatment. The results will be used to inform a Bayesian risk prediction tool to assist clinical assessment of a patient’s risk of developing disorders of defaecation.Supplementary file1 (XLSX 111 KB)Supplementary file2 (PDF 122 KB)Below is the link to the electronic supplementary material.


To create and authenticate MRI-based radiomic signatures to identify dorsal root ganglia (DRG) lesions in post-herpetic neuralgia (PHN) patients generalizable and interpretable.This prospective diagnostic study was conducted between January 2021 and February 2022. Lesioned DRG in patients with PHN and normal DRG in age-, sex-, height-, and weight-matched healthy controls were selected for assessment and divided into two groups (8:2) randomly: training and testing sets. The least absolute shrinkage and selection operator algorithm was employed to generate feature signatures and construct a model, followed by the assessment of model efficacy using the area under the curve (AUC) of the receiver operating characteristic (ROC), as well as sensitivity and specificity metrics.n = 78) and a testing set (n = 20). Five radiomic features were chosen to construct the models. In the training dataset, the area under the curve (AUC) was 0.847, while the sensitivity and specificity were 71.79 and 97.44%, respectively. In the test dataset, the AUC was 0.87, and the sensitivity and specificity were 80.00 and 100.00%, respectively.The present investigation involved 30 patients diagnosed with postherpetic neuralgia (PHN), consisting of 18 males and 12 females (mean age 60.70 ± 10.18 years), as well as 30 healthy controls, comprising 18 males and 12 females (mean age 58.13 ± 10.54 years). A total of 98 DRG were randomly divided into two groups (8:2), namely a training set (An MRI-based radiomic signatures model has the capacity to uncover the micro-change of damaged DRG in individuals afflicted with postherpetic neuralgia. Recentlllenging . Howeverllenging . Furtherllenging . So, ideOur previous study found DRG on the lesioned side to be swollen during PHN, but there was a tendency for a reduction in the volume of DRG with the prolongation of the disease course (unpublished). This implies that the identification of DRGs through macroscopic changes poses a challenge. Radiomics is a nascent discipline in quantitative imaging that employs sophisticated mathematical algorithms to extract high-throughput quantitative features from medical images, thereby providing an objective and quantitative depiction of tissue characteristics that are imperceptible to the human eye , 10. By Over the last 10 years, radiomics has gained significant traction in cancer research. Notably, radiomic features have demonstrated potential in prognosticating treatment response, distinguishing between benign and malignant tumors, and evaluating cancer genetics across various cancer types , 13. Forin vivo and (2) postulate an anatomical basis for individualized treatment of PHN.Here, based on the region of interest (ROI) for lesioned and control DRG, we aimed to extract high-throughput radiomics features and construct a predictive model to distinguish lesioned DRG from normal DRG. We aimed to (1) provide a group of radiomics features for the identification of lesioned DRG 2ClinicalTrials.gov. Prior to their involvement in the study, all participants provided written informed consent.The present investigation was executed in compliance with the Declaration of Helsinki and the research plan was sanctioned by the institutional Ethics Committee (2021S084). The study is duly registered at 2.1This was a prospective, diagnostic study. Thirty patients with PHN in the thoracic segments admitted to our hospital between January 2021 and February 2022 were recruited. The patient inclusion criteria were age > 18 years and pain lasting for at least 1 month after lesion crusting. The study employed exclusion criteria that encompassed severe systemic, metabolic, or neurological diseases that may be associated with peripheral polyneuropathies, multiple myeloma, diabetes mellitus, or thyroid disease, as well as a history of psychiatric diseases, chronic pain, or substance abuse, inability to complete magnetic resonance neurography (MRN) scans, and a history of thoracic surgery and pain. Furthermore, the study included 30 healthy controls who were matched in terms of age, sex, height, and weight. A flowchart of participant enrolment is shown in 2.2The dermatome lesions of all patients were identified based on the cutaneous nerve distribution schematic in the trunk  by two pain specialists with over a decade of clinical experience (Authors X and Y).2.32; matrix size, 320 × 320; slice thickness, 1.0 mm; slice number, 60; no gap; voxel size, 1.0 × 1.0 × 1.0 mm3; 50% phase over-sampling; and acquisition time, 8 min 35 s; imaging all bilateral DRG in the thoracic spine. All the bilateral DRG in the thoracic spine were also scanned in the healthy controls. Subsequently, 49 lesioned DRG from 30 patients were chosen for evaluation based on dermatome lesions, while an equivalent number of 49 normal DRG from the corresponding segments of 30 healthy controls were also chosen for comparison. A 3.0 Tesla MRI clinical scanner  was used. All the patients underwent magnetic resonance imaging (MRI) before treatment initiation. All the participants laid in supine position on the scanner bed with their thorax immobilized in a tight-fitting thoracic coil, and underwent MRN as follows: Three-dimensional T2-weighted sampling perfection with application-optimized contrasts using different flip-angle evolution short-tau inversion-recovery (3-D SPACE STIR) sequence of the spine was used for DRG and spinal nerve imaging: coronal plane; repetition time/echo time, 3,000/178 ms; inversion time, 220 ms; field of view, 305 × 240 mm2.4A radiologist (Author Z) with 4 years of experience performed manual segmentation of two-dimensional ROI on the 3-D SPACE STIR coronal MR image of the DRG utilizing the 3D-slicer software (version 5.0.3).2.53 through resampling.Texture recognition was improved by applying several preprocessing methods. All the MR images in this study were scanned by the same radiologist using the same MRI scanner with the same parameters to ensure homogeneity of the MR Images. The voxel sizes of all images were standardized to 1 × 1 × 1 mmThe radiomics workflow is shown in 2.6The current investigation employed the LASSO regression analysis technique to ascertain the most relevant characteristics in the training dataset. LASSO has been shown to be a proficient technique for generating sparse solutions in the context of high-dimensional data. Through the integration of a penalty function and linear regression, the coefficients of candidate features with negligible impact are reduced to zero, while those with non-zero coefficients are retained. The LASSO algorithm was employed for the purpose of feature selection and signature establishment in this investigation, encompassing both training and validation phases.The LASSO algorithm  was employed for the purpose of feature selection and signature establishment in this investigation. The training and validation cohorts were partitioned into two datasets at an 8:2 ratio, with DRGs being randomly allocated to either the training or validation cohorts. The training and testing cohorts were comprised of 78 and 20 DRGs, respectively.mRMR algorithm (Max-Relevance and Min-Redundancy)  was used to eliminate redundant and irrelevant features. Finally, the top 15 features were selected. We then employed LASSO to reduce some feature coefficients to zero, ultimately retaining only the five most significant coefficient features to construct the LASSO regression model. By combining these features, a radiomics signature was established for each DRG.2.7In order to assess the efficacy of the models, a variety of performance metrics were employed, including the area under the receiver operating characteristic (ROC) curve (AUC), sensitivity, and specificity. The ROC curve indicated that the optimal cutoff risk score was situated closest to the upper left corner, resulting in heightened sensitivity and specificity, as well as a reduction in false positives and negatives. The maximum Youden index (YI = sensitivity + specificity-1) was utilized to determine the optimal cutoff risk score.2.8t-tests, paired t-tests, or Wilcoxon tests, while categorical variables were analyzed using the chi-squared test or Fisher’s exact test. LASSO regression analysis was performed using Python software (version 3.11.4). All statistical tests were two-sided, and statistical significance was determined.The study collected demographic information of participants in the form of numerical data, mean ± standard deviation, or median (interquartile range) for continuous and categorical variables. Statistical analyses were conducted using Prism software (version 9.0), and AUC curves were plotted. Continuous variables were assessed using independent 33.1The research cohort consisted of 49 lesioned DRGs obtained from 30 individuals diagnosed with PHN. Of these individuals, 18 were male and 12 were female, with ages ranging from 36 to 82 years and a mean age of 60.7 years. The average duration of the disease course for these patients was 2.65 (1.5–6.0) months. The control group comprised 49 DRGs obtained from 30 healthy individuals, matched for age, sex, weight, and height. The control group consisted of 18 male and 12 female individuals, with ages ranging from 36 to 78 years and a mean age of 58.13 years. 3.2Intra-and inter observer reliabilities were evaluated using correlation coefficients, and the ICCs were 0.92 and 0.85, respectively, indicating good stability. A total of 103 features were retained for the analysis. To construct the model, three plots were generated: log lambda and coefficient plots, log lambda and mean squared error (MSE) plots, and the impact of each feature on the target variable was assessed . From th3.33.4p ≤ 0.0001). The Short Run Low Gray Level Emphasis of lesioned DRG were significantly less than the DRG in healthy controls .As showed in 4The objective of this investigation was to examine the radiomic characteristics that differentiate lesioned DRG in individuals with PHN from healthy DRGs. A total of forty-five radiomics features were observed to exhibit differences between lesioned DRG and normal DRG. Among these features, five were utilized to develop diagnostic models for the identification of lesioned DRG. To our knowledge, this is the initial study to construct a radiomics-based diagnostic model for a lesioned DRG in PHN. The findings indicate that the model exhibited a higher AUC than the five individual radiomics features, suggesting its superiority over the latter.Medical imaging is widely employed in clinical practice worldwide for the purposes of diagnosing, guiding treatment, and monitoring diseases, due to its noninvasive and timely assessment of human tissue characteristics . The morATRX mutations and was a potential in vivo biomarker for the diagnosis of glioma   and reflThe dorsal root ganglia (DRG) comprise diverse populations of sensory and satellite glial cells with varying neurochemical profiles. DRG stress can induce notable changes in cell size (swelling/atrophy) and in distinct subcellular structures  of neurons and/or satellite glial cells . ConsequBased on the aforementioned analysis, the model may be deemed a valuable biomarker for detecting lesioned DRG in PHN. Furthermore, in contrast to a solitary radiomics feature, the radiomics signature comprising the aforementioned five features presents a superior advantage in identifying the lesioned DRG, thereby exhibiting significant potential for personalized clinical applications.Nonetheless, our study has certain limitations: (1) the selection of lesion DRG was based on dermatome lesions, Errors are inevitable; (2) a relatively limited sample size due to the singular-center nature of the study; (3) the absence of external validation as DRG imaging detection has yet to be implemented in clinical practice. To substantiate our results, a multicenter study with a more expansive sample size is recommended for future research.In summary, a model based on radiomic signatures for identifying lesioned DRGs in patients with PHN was constructed for the first time, which was superiority over five individual radiomics features. The findings of this study could offer direction for the non-intrusive identification of damaged dorsal root ganglia and establish a visual foundation for personalized interventional management of postherpetic neuralgia.The original contributions presented in the study are included in the article/The studies involving humans were approved by the Ethics Committee of Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China. The studies were conducted in accordance with the local legislation and institutional requirements. The participants provided their written informed consent to participate in this study.XC: Conceptualization, Writing – original draft, Writing – review & editing, Data curation, Formal analysis, Investigation, Project administration, Software, Visualization. DW: Data curation, Formal analysis, Investigation, Software, Visualization, Writing – original draft, Writing – review & editing. SY: Data curation, Writing – original draft, Writing – review & editing, Validation. HZ: Validation, Writing – original draft, Writing – review & editing, Methodology. GW: Methodology, Writing – original draft, Writing – review & editing, Formal analysis, Resources, Visualization. XZ: Writing – original draft, Writing – review & editing, Conceptualization, Supervision.


Introduction: Leaves of the Australian tea tree plant Melaleuca alternifolia were used traditionally by First Nations Australians for treating wounds, burns, and insect bites. Tea tree oil, the essential oil steam-distilled from M. alternifolia, is well-known for its medicinal properties, the evidence for most applications however is limited. This review aimed to critically appraise evidence from clinical trials examining the therapeutic efficacy and safety of tea tree oil on outcomes.Methods: Randomized controlled trials with participants of any age, gender, or health status, comparing tea tree oil to any control were included, without limit on publication date. Electronic databases were searched on 12 August 2022 with additional records sourced from article reference sections, reviews, and industry white papers. Risk of bias was assessed by two authors independently using the Cochrane risk-of-bias 1.0 tool. Results were summarized and synthesized thematically.Results: Forty-six articles were eligible from the following medical fields . Results indicate that oral mouthwashes with 0.2%–0.5% tea tree oil may limit accumulation of dental plaque. Gels containing 5% tea tree oil applied directly to the periodontium may aid treatment of periodontitis as an adjunctive therapy to scaling and root planing. More evidence is needed to confirm the benefits of tea tree oil for reducing acne lesions and severity. Local anti-inflammatory effects on skin, if any, also require further elucidation. Topical tea tree oil regimens show similar efficacy to standard treatments for decolonizing the body from methicillin-resistant Staphylococcus aureus, although intra-nasal use of tea tree oil may cause irritation to mucous membranes. Tea tree oil with added iodine may provide an effective treatment for molluscum contagiosum lesions in young children. More evidence on efficacy of tea tree oil-based eyelid wipes for Demodex mite control are needed. Side effects were reported in 60% of included studies and were minor, except where tea tree oil was applied topically in concentrations ≥ 25%.Discussion: Overall, the quality of research was poor to modest and higher quality trials with larger samples and better reporting are required to substantiate potential therapeutic applications of tea tree oil.Systematic Review Registration: PROSPERO, identifier [CRD42021285168]. M. alternifolia (Maiden & Betche) Cheel plant, an Australian native plant endemic to north-eastern New South Wales and Southern Queensland  Cheel contains more than 100 components. For the purpose of assessing the quality of manufactured tea tree oil, the International Organization for Standardization has defined minimum and maximum concentrations for 15 of these components, with the primary active constituent being terpinen-4-ol, comprising 35%–48% of tea tree oil  Cheel accounts for nearly 100% of tea tree oil manufactured worldwide and is therefore the most common form of tea tree oil available for purchase. Tea tree oil may be purchased over the counter or online as pure essential oil (100%), retailing for around $2-5 USD per 10 ml, or diluted to 5%–15% in a carrier oil. The majority of tea tree oil production occurs in Australia, although China, South Africa, Zimbabwe and Kenya also produce tea tree oil for commercial sale  CRD42021285168. The research was funded by AgriFutures Australia (Grant No. PRJ-012616). Reporting of this systematic review is in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement .M. alternifolia present in any dilution and within any type of carrier . We excluded interventions testing tea tree oil within a product containing other active ingredients known to have the same anticipated effect(s) as tea tree oil, e.g., an herbal shampoo with other essential oils, as the effect of tea tree oil was not studied in isolation. We also excluded interventions using tea tree oil in combination with other therapies unless these therapies were present in both intervention and comparison groups . Comparison groups eligible for inclusion were inactive controls  or an active control testing the same product without tea tree oil added or testing some variation of the tea tree oil intervention . Eligible studies measured the effect of tea tree oil on outcomes related to human health or disease including mortality, physiological  measures, quality of life and functional capacity, and safety outcomes such as toxicity or adverse reactions. Both self-reported and objectively measured outcomes were included. We included all types of RCTs, i.e., parallel, cluster, factorial, cross-over, as well as “split-body” and “split-mouth” designs where different treatments were applied to separate parts of the body or mouth, respectively, provided allocation was randomized, e.g., treatments allocated at random to right and left hands. Furthermore, we limited publication type to peer reviewed journal articles and included articles published in languages in which the review authors were fluent  which enabled an accurate translation of the text to be obtained within available resources.We included randomized controlled trials (RCTs) testing the effect of tea tree oil on any health-related outcome in human participants. Participants of any age, gender and health status were included. Interventions included tea tree oil from the species Electronic databases PubMed, Scopus, and the Cochrane Central Register of Controlled Trials (CENTRAL) were searched on 12 August 2022 using pre-defined search strategies see . No limiOne author (LK) extracted the following data from all included articles first author, year of publication, trial registration (if applicable), study design, country, study setting, participant characteristics, eligibility criteria, details of the intervention including tea tree oil source and dose(s), details of comparison group(s) including product name(s), brand(s) and manufacturer(s), outcome measures including their methods and timepoints of assessment, main findings (efficacy) and safety, funding source(s) and any potential conflicts of interest. Where one or more data items required for extraction were missing from the article text, an attempt was made to contact the corresponding author(s) for these data. Data were verified by another author (RL).Risk of bias within included studies was assessed using the Cochrane risk-of-bias 1.0 tool for assessing risk of bias in randomized trials (RoB 1.0) . The RoBn = 12;  count  count  or, in ous count . Only onus count , while tus count , and anous count . Ten trius count  and/or sus count . Of thescount . No differences were observed in these parameters between the tea tree oil, aloe vera and chlorhexidine mouthwashes . These results however, were not compared to the placebo group, for which, the authors reported no data (not defined) on plaque and gingival indices in children  in 60 adult subjects. Participants using Periogard® had less dental plaque after 4 days than those using the tea tree oil mouthwash on both biofilm free and biofilm covered surfaces (both p < 0.05). However, as this parameter was not measured at baseline, it is not possible to know whether those in the Periogard® group had less dental plaque at baseline. No significant difference was observed in gingival crevicular volume between the two groups. S. mutans of a 0.2% tea tree oil mouthwash, compared with a 2.5% garlic mouthwash or 0.12% chlorhexidine mouthwash in 30 adults aged 18–35 years  with a 0.05% cetylpyridinium chloride mouthwash (Aquafresh®), a 0.12% chlorhexidine mouthwash (Oro-Clense®), or a placebo (colored water) . Gingival bleeding was found to decrease to a similar extent with all mouthwashes (including placebo). ®, comparing this to a 0.12% chlorhexidine mouthwash, an essential oil mouthwash (without tea tree oil) and a placebo mouthwash  in 16 participants aged 21–37 years . However, both mouthwashes reduced dental plaque and gingival bleeding to a similar extent  . After 537 years . Partici37 years . Saxer e65 years . Partici65 years . Particir extent .Adverse effects of tea tree oil mouthwashes in adult participants were assessed in all but one study , while aS. mutans after 2 weeks, including the distilled water. However, any between group comparisons were compromised by the significant difference in S. mutans counts observed between groups at baseline (p < 0.001) , for 8 weeks  . Finally< 0.001) . Twice d 8 weeks . No diff 8 weeks .Three studies tested the application of a tea tree oil-based gel to the periodontium as a treatment for periodontitis . Two triCandida albicans on the palate mucosa  and placebo mouthwash, for treatment for oral halitosis (Solobacterium moorei (S. moorei) bacteria on the tongue has been positively correlated with oral halitosis. In this study, researchers found 100% of participants diagnosed with oral halitosis (n = 120) had S. moorei bacteria in their saliva and on the posterior tongue surface, compared with 17% and 24%, respectively, of participants who did not have oral halitosis (n = 40). Participants with halitosis (n = 120) rinsed daily with 30 ml of their allocated mouthwash for 1 week. A significantly greater reduction in S. moorei count in saliva and on tongue was found for the tea tree oil and chlorhexidine mouthwashes, compared with the placebo group—which showed no change in S. moorei counts. Both organoleptic score and VSCs also significantly decreased with the tea tree oil and chlorhexidine mouthwashes relative to placebo—which showed no change in these parameters after 1 week. Researchers noted no adverse effects of the mouthwashes  to a chlorhexidine mouthwash (Rexidine®) or distilled water, for reducing the bacterial count of dental aerosol  released during ultrasonic tooth scaling  in dental aerosol compared with distilled water (p < 0.001). However, Rexidine® was found to be more effective than Emoform® for this parameter (p < 0.001). Safety of the mouthwashes was not assessed. scaling . The purAll domains were assessed as low risk or unclear risk , except Trials conducted in the field of dermatology were published 1990 to 2022, and conducted in Australia , Germanyp < 0.001). Finally, p < 0.001).Three trials tested the effect of tea tree oil-based gels containing 5%–6% tea tree oil on acne lesion counts and acne severity in subjects with mild to moderate acne vulgaris . Bassettp < 0.001). p = 0.005), although, after 12 weeks, there was no difference between groups in irritation.Safety was measured in all three studies. Application of a 5% tea tree oil gel resulted in minimal pruritis (3/30), burning sensation (1/30), and minimal scaling (1/30), although similar numbers of subjects applying a placebo gel reported these events . However® foam wound dressings containing tea tree oil with Burn Cool Spray® or running tap water, in 94 adult subjects  . Cho andsubjects . SubjectUnguentum leniens) to which 5% tea tree oil was added, in 40 subjects aged 19–58 years  for preventing colonization with MRSA in 391 patients across two hospital intensive care units (p = 0.50). A limitation to this study was that subjects using the Novobac® 5% tea tree oil had a longer admission, longer duration of ventilation, greater number of days with devices in place, and a higher percentage of these patients were nursed adjacent to a patient infected with MRSA. However, in a multiple regression analysis including the strongest independent predictors of MRSA colonization, there remained no statistically significant difference between treatment group (tea tree oil or standard care) and incident MRSA colonization (p = 0.0286). The 10% tea tree oil cream which was applied to the nostrils three times daily, was however, found to be less effective at clearing MRSA from nasal passages when compared with Bactroban® nasal ointment. Lastly, re units . Subjectnization . Caelli nization . MRSA denization . Fourtee10 microbial count decreased in all groups, except in the no treatment control group. The greatest reduction was with use of the tea tree oil-based hand disinfectant, which achieved a significantly greater reduction compared with the alcohol-based hand sanitizer, the benzalkonium chloride-based hand sanitizer and no treatment . In two similar studies, Escherichia coli K12 on the hand surface   (® liquid soap), used to wash hands after E. coli K12 contamination, reduced the CFU/mL count E. coli K12 to a similar extent as Soft Soap and was inferior to Soft Soap with 60% propan-2-ol (p = 0.001). In the 2021 trial, the tea tree oil soap (2% tea tree oil) used significantly reduced E. coli K12 compared with chlorhexidine (p = 0.006), and with Soft Soap (p < 0.001), although no difference was found between tea tree oil soap and Rioderm®. Adverse events were not assessed in these three trials  , or to R1 study) . In the e trials .p < 0.01). Adverse events included redness at lesion site reported by one subject each in the tea tree oil + iodine and tea tree oil alone groups and by two subjects in the iodine alone group. A warm sensation on application was also reported in groups receiving tea tree oil i.e., tea tree oil + iodine (3/19) and tea tree oil (4/18).highly diagnostic of infection with Candida), decreased in all groups, although larger reductions were seen in the tea tree oil mouthwash and clotrimazole ointment groups, compared with standard care . Adverse effects were not reported by any subjects using the tea tree oil mouthwash.All domains were mostly assessed as low or unclear risk of bias . Two triSix trials were published in the field of ophthalmology from 2012 to 2021, and were conducted in Australia , Canada Demodex mites in subjects with diagnosed demodectic blepharitis  with Blepharitis Shampoo (without tea tree oil) in 135 adult subjects  to a saline solution , in terms of tear film break up time, as well as ocular discomfort and redness , and 30 s for EyeCleanse™ (p < 0.001)  . Only Ey®, with 5% tea tree oil), with and without an additional 5% tea tree oil added, for treating dry eye post phacoemulsification cataract surgery   . Furthershampoo) .All domains were mostly assessed as low or unclear risk of bias . Two triThree trials were published in the field of podiatry from 1992 to 2002, and conducted in Australia  or the U® (p < 0.01) (p = 0.393) . In both trials, clinical signs, and symptoms of tinea pedis  improved in a greater proportion of subjects using tea tree oil, compared with placebo , compared with a placebo  , or with® . Satchel® . After 4 < 0.01) . In a se= 0.393) . When co placebo . Adverse placebo . Three o placebo . One suberythema .All domains were mostly assessed as low or unclear risk of bias . UnclearThis review aimed to critically appraise evidence from randomized control trials examining the therapeutic efficacy and safety of tea tree oil on outcomes related to human health. The search yielded 46 eligible studies spanning the fields of dentistry, dermatology, infectious disease, ophthalmology, podiatry, as well as anxiety and sleep quality—a substantial expansion in research since the 2000 review by Ernst & Huntley which identified only four trials .S. mutans , and acne products may require an alternative carrier. In the last decade, many novel formulations have been developed for drug delivery, and it is essential to understand the limitations and benefits of delivery systems to design appropriate tea tree oil products with improved effectiveness and safety, adequate stability, and shelf-life. As such, it is recommended that all new tea tree oil formulations are rigorously tested https://www.cpsc.gov/Research--Statistics/NEISS-Injury-Data), most injuries associated with tea tree oil were due to ingestion in toddlers and children, with an overall low hospitalization rate, and no deaths. The large number of incidents (nearly 350 in 2017), however, warrants better consumer protection, such as mandatory child-proof safety caps on tea tree oil bottles.The question of the safety of tea tree oil has been a controversial topic for decades. Issues have been raised around its ability to cause dermal irritation and its potential as an allergic sensitizer. Several studies have been conducted, and while tea tree oil was ranked twenty-third on the list of most frequent allergens reported in Australia , studiesThis systematic review provides the first comprehensive appraisal of human trials testing the therapeutic efficacy and safety of tea tree oil. Therapeutic indications for which tea tree oil has been tested span multiple fields of medicine including dentistry, dermatology, infectious disease, ophthalmology, and podiatry.In contrast to the well-established traditional uses of tea tree plant, no trials were identified for the treatment of minor wounds, furuncles, or insect bites of tea tree oil. Such analgesic and antipruritic properties of tea tree oil are not well documented, and further research into the modes of action are warranted. One recent trial was located on the treatment of vaginitis, suggesting tea tree oil pessaries were an effective treatment for vaginitis, although this trial was excluded on the basis of study design . While tDemodex mite infestation. In future trials, emphasis should focus on comparing tea tree oil to current standard care as well as an appropriate placebo. Novel formulations should also be considered to enhance localized delivery of active components of tea tree oil. Further, it is critical to assess safety of tea tree oil treatments to better understand the risks to patients and consumers of these products.Reliability of the evidence from many of these trials suffered from inadequate reporting, particularly reporting of the source and quality of tea tree oils used on patients. Future investment in rigorous human clinical trials is warranted to clarify potential benefits of tea tree oil for treatment of acne, molluscum contagiosum lesions, microbial causes of tooth decay, periodontal disease, and oral fungal conditions, infections caused by antibiotic-resistant bacteria, and ocular conditions caused by


Observational epidemiological studies suggested an association between the gut microbiota and breast cancer, but it remains unclear whether the gut microbiota causally influences the risk of breast cancer. We employed two-sample Mendelian randomization (MR) analysis to investigate this association.We used summary statistics of the gut microbiome from a genome-wide association study (GWAS) of 18,340 individuals in the MiBioGen study. GWAS summary statistics for overall breast cancer risk and hormone receptor subtype-specific analyses were obtained from the UK Biobank and FinnGen databases, totaling 400,000 individuals. The inverse variance-weighted (IVW) MR method was used to examine the causal relationship between the gut microbiome and breast cancer and its subtypes. Sensitivity analyses were conducted using maximum likelihood, MR-Egger, and MR pleiotropic residual sums and outliers methods.p = 1.72E−04, false discovery rate (FDR) = 0.02], whereas an increased abundance of Genus_Adlercreutzia was protective against ER+ breast cancer . For Her2+ breast cancer, an increased abundance of Genus_Ruminococcus2 was associated with a decreased risk , whereas an increased abundance of Genus_Erysipelatoclostridium was associated with an increased risk . No evidence of heterogeneity or horizontal pleiotropy was found.The IVW estimates indicated that an increased abundance of Genus_Sellimonas is causally associated with an increased risk of ER+ breast cancer [odds ratio (OR) = 1.09, Our study revealed a gut microbiota–mammary axis, providing important data supporting the potential use of the gut microbiome as a candidate target for breast cancer prevention, diagnosis, and treatment. IncidenAlthough the exact mechanisms of breast cancer development are not fully understood, studies have suggested that the gut microbiota plays a role . In partAlthough the causal relationship between the gut microbiota and breast cancer is not fully understood, observational epidemiological studies have revealed an association . However2.2.1.In this study, we conducted a rigorous MR analysis that strictly adhered to the three major assumptions of MR analysis . First, 2.2.p < 1 × 10−5 and performed clump on all IVs of each gut flora  to reduce the gap between SNPs (2 (n − k − 1)/ k (1 − R2), where R2 represents the variance explained by each IV of the gut microbiota, R2 = 2 MAF (1 − MAF) β2, n represents the sample size of the exposure data, k represents the number of IVs, and MAF represents the minor allele frequency.We obtained summary statistics of gut bacteria from the genome-wide association meta-analysis of the MiBioGen study, which is currently the largest study of transgenic genetics in the human microbiome . The stueen SNPs . Linkageeen SNPs . Subsequeen SNPs . The cal2.3.https://finngen.gitbook.io/documentation/v/r8/).We obtained breast cancer genome-wide association study (GWAS) data as outcomes from two databases. The genetic influence of cancer risk for overall breast cancer and the estrogen receptor status in the UK Biobank database was obtained from a large GWAS of the Breast Cancer Association Consortium involving 228,951 participants of European ancestry, including 122,977 patients with breast cancer  and 105,974 controls . The Fin2.4.p of <0.05 was considered statistically significant. All MR analyses were conducted using the TwoSampleMR package  method as the primary analysis tool to assess the impact of the gut microbiota on breast cancer risk . We also package  in R sof package .3.3.1.IVW = 1.09, PIVW = 1.72E−04, FDRIVW = 0.02; ORWM = 1.08, PWM = 1.01E−02, FDRWM = 0.82; ORMR-Egger = 0.97, PMR-Egger = 0.84, FDRMR-Egger = 1.00). Conversely, an increase in the abundance of Genus_Adlercreutzia was associated with a reduced risk of ER+ breast cancer  database . Conversely, an increased abundance of Genus_Erysipelatoclostridium was associated with an increased risk of Her2+ breast cancer  of the MR-PRESSO global test indicated the absence of horizontal pleiotropy, with no IVs identified as potential outliers. The intercepts of the MR-Egger regression did not deviate significantly from 0, and all P-values were greater than 0.05, indicating the absence of pleiotropy ; FinnGen database (https://finngen.gitbook.io/documentation/v/r8/).Publicly available datasets were analyzed in this study. This data can be found at: 211 gut bacteria from MiBioGen , the Technology Research from the Department of Education of Liaoning Province (No. JCZR2020013) and 345 Talent Project of Shengjing hospital of China Medical University (No. M0367).The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.