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Biomedicines
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Advanced Research in Endometriosis 4.0
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Advanced Research in Endometriosis 4.0

A special issue of Biomedicines (ISSN 2227-9059). This special issue belongs to the section "Molecular and Translational Medicine".

Deadline for manuscript submissions: 30 September 2024 | Viewed by 3934

Special Issue Editor

Dr. Nikolaos Machairiotis

E-Mail Website
Guest Editor
Department Obstetrics/Gynaecology, Northwick Park Hospital, London North West University Healthcare NHS Trust, Watford Rd, Harrow HA1 3UJ, London, UK
Interests: endometriosis; pain; inflammation; inhibitors
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Endometriosis is a chronic, inflammatory, benign disease commonly found in women of reproductive age. However, it can affect people of all ages, and it has also been found in men and animals as well as fetuses. Unfortunately, 7–10 years tend to pass until the diagnosis is made. This Special Issue provides an excellent opportunity for a thorough analysis of the pathophysiology of the disease, the signs and symptoms, and the type of pain associated with endometriosis. Categories of endometriosis can be identified, and conservative and surgical techniques can be provided. An important research area is the pain associated with endometriosis. One of the causative factors for the pain in endometriosis is inflammation. The suppression of inflammatory mediators by inhibiting their synthesis might offer novel and effective treatments for inflammatory pain in endometriosis. This Special Issue welcomes new and innovative original studies and detailed reviews in this field.

Dr. Nikolaos Machairiotis
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Biomedicines is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • mutations
  • biomarkers
  • diagnosis
  • treatment
  • pelvic pain
  • endometriosis
  • patient care
  • imaging
  • infertility
  • inhibitors
  • comorbidity
  • recurrence

Related Special Issue

Published Papers (3 papers)

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Research

10 pages, 1659 KiB  
Article
Innovative Ultrasound Criteria for the Diagnosis of Adenomyosis and Correlation with Symptoms: A Retrospective Re-Evaluation
by Anna Biasioli, Matilde Degano, Stefano Restaino, Margherita Bagolin, Francesca Moro, Francesca Ciccarone, Antonia Carla Testa, Pantaleo Greco, Giovanni Scambia, Giuseppe Vizzielli, Lorenza Driul and The Udine Hospital Endometriosis Group
Biomedicines 2024, 12(2), 463; https://0-doi-org.brum.beds.ac.uk/10.3390/biomedicines12020463 - 19 Feb 2024
Viewed by 940
Abstract
The 2022 Delphi revision of the MUSA (Morphological Uterus Sonographic Assessment) criteria for the ultrasound diagnosis of adenomyosis divides the ultrasound signs for diagnosis into direct and indirect ones, considering the presence of at least one direct sign as a mandatory criterion. This [...] Read more.
The 2022 Delphi revision of the MUSA (Morphological Uterus Sonographic Assessment) criteria for the ultrasound diagnosis of adenomyosis divides the ultrasound signs for diagnosis into direct and indirect ones, considering the presence of at least one direct sign as a mandatory criterion. This study aimed to reclassify the patients referred to the Pelvic Pain specialist outpatient clinic of the Gynecological Clinic of Udine according to the new criteria, evaluating the number of overdiagnoses and the possible correlation between the direct and indirect signs and the patients’ symptoms. 62 patients affected by adenomyosis were retrospectively recruited. The patients were then re-evaluated by ultrasound and clinically. At least one direct sign of adenomyosis was found in 52 patients, while 16% of the population examined did not present any. There was no statistically significant difference between patients presenting direct signs and those presenting none for the symptoms considered. According to the new criteria, 16% of the patients examined were not affected by adenomyosis; applying the new consensus to symptomatic patients could increase false negatives. In a population of symptomatic patients, the diagnosis of adenomyosis is still highly probable even without direct ultrasound signs, given the clinical symptoms and having ruled out other causes of such symptoms. Full article
(This article belongs to the Special Issue Advanced Research in Endometriosis 4.0)
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11 pages, 1495 KiB  
Article
Diagnosis of Endometriosis Based on Comorbidities: A Machine Learning Approach
by Ulan Tore, Aibek Abilgazym, Angel Asunsolo-del-Barco, Milan Terzic, Yerden Yemenkhan, Amin Zollanvari and Antonio Sarria-Santamera
Biomedicines 2023, 11(11), 3015; https://0-doi-org.brum.beds.ac.uk/10.3390/biomedicines11113015 - 10 Nov 2023
Cited by 1 | Viewed by 1183
Abstract
Endometriosis is defined as the presence of estrogen-dependent endometrial-like tissue outside the uterine cavity. Despite extensive research, endometriosis is still an enigmatic disease and is challenging to diagnose and treat. A common clinical finding is the association of endometriosis with multiple diseases. We [...] Read more.
Endometriosis is defined as the presence of estrogen-dependent endometrial-like tissue outside the uterine cavity. Despite extensive research, endometriosis is still an enigmatic disease and is challenging to diagnose and treat. A common clinical finding is the association of endometriosis with multiple diseases. We use a total of 627,566 clinically collected data from cases of endometriosis (0.82%) and controls (99.18%) to construct and evaluate predictive models. We develop a machine learning platform to construct diagnostic tools for endometriosis. The platform consists of logistic regression, decision tree, random forest, AdaBoost, and XGBoost for prediction, and uses Shapley Additive Explanation (SHAP) values to quantify the importance of features. In the model selection phase, the constructed XGBoost model performs better than other algorithms while achieving an area under the curve (AUC) of 0.725 on the test set during the evaluation phase, resulting in a specificity of 62.9% and a sensitivity of 68.6%. The model leads to a quite low positive predictive value of 1.5%, but a quite satisfactory negative predictive value of 99.58%. Moreover, the feature importance analysis points to age, infertility, uterine fibroids, anxiety, and allergic rhinitis as the top five most important features for predicting endometriosis. Although these results show the feasibility of using machine learning to improve the diagnosis of endometriosis, more research is required to improve the performance of predictive models for the diagnosis of endometriosis. This state of affairs is in part attributed to the complex nature of the condition and, at the same time, the administrative nature of our features. Should more informative features be used, we could possibly achieve a higher AUC for predicting endometriosis. As a result, we merely perceive the constructed predictive model as a tool to provide auxiliary information in clinical practice. Full article
(This article belongs to the Special Issue Advanced Research in Endometriosis 4.0)
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21 pages, 1469 KiB  
Article
Puzzling Out the Genetic Architecture of Endometriosis: Whole-Exome Sequencing and Novel Candidate Gene Identification in a Deeply Clinically Characterised Cohort
by Aurora Santin, Beatrice Spedicati, Anna Morgan, Stefania Lenarduzzi, Paola Tesolin, Giuseppe Giovanni Nardone, Daniela Mazzà, Giovanni Di Lorenzo, Federico Romano, Francesca Buonomo, Alessandro Mangogna, Maria Pina Concas, Gabriella Zito, Giuseppe Ricci and Giorgia Girotto
Biomedicines 2023, 11(8), 2122; https://0-doi-org.brum.beds.ac.uk/10.3390/biomedicines11082122 - 27 Jul 2023
Viewed by 1132
Abstract
Endometriosis (EM) is a common multifactorial gynaecological disorder. Although Genome-Wide Association Studies have largely been employed, the current knowledge of the genetic mechanisms underlying EM is far from complete, and other approaches are needed. To this purpose, whole-exome sequencing (WES) was performed on [...] Read more.
Endometriosis (EM) is a common multifactorial gynaecological disorder. Although Genome-Wide Association Studies have largely been employed, the current knowledge of the genetic mechanisms underlying EM is far from complete, and other approaches are needed. To this purpose, whole-exome sequencing (WES) was performed on a deeply characterised cohort of 80 EM patients aimed at the identification of rare and damaging variants within 46 EM-associated genes and novel candidates. WES analysis detected 63 rare, predicted, and damaging heterozygous variants within 24 genes in 63% of the EM patients. In particular, (1) a total of 43% of patients carried variants within 13 recurrent genes (FCRL3, LAMA5, SYNE1, SYNE2, GREB1, MAP3K4, C3, MMP3, MMP9, TYK2, VEGFA, VEZT, RHOJ); (2) a total of 8.8% carried private variants within eight genes (KAZN, IL18, WT1, CYP19A1, IL1A, IL2RB, LILRB2, ZNF366); (3) a total of 24% carried variants within three novel candidates (ABCA13, NEB, CSMD1). Finally, to deepen the polygenic architecture of EM, a comprehensive evaluation of the analysed genes was performed, revealing a higher burden (p < 0.05) of genes harbouring rare and damaging variants in the EM patients than in the controls. These results highlight new insights into EM genetics, allowing for the definition of novel genotype–phenotype correlations, thereby contributing, in a long-term perspective, to the development of personalised care for EM patients. Full article
(This article belongs to the Special Issue Advanced Research in Endometriosis 4.0)
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