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Intrauterine adhesion (IUA) formation and the resulting Asherman’s syndrome (AS) is an unfortunate clinical condition that occurs when the endometrium is damaged as a consequence of trauma, such as vigorous curettage, infection, or some Müllerian anomaly. The most frequent symptoms include hypo/amenorrhea, infertility, and adverse reproductive outcomes. Prevention of IUA formation is essential; however, when present, accurate diagnosis and surgical intervention (hysteroscopic adhesiolysis) are required. The outcome of this treatment is based on the technique and the extent of surgery performed which depends on the severity and complexity of the disease. Hence its classification becomes particularly important to determine a standardized therapy for each case and patient counseling regarding the prognosis. In this article, we aim to describe the IUAs classification systems that have been proposed comparing the merits and demerits of each one.

To examine the uterine cavity and/or to perform hysteroscopic surgery, one has to access the uterine cavity through the cervix, distend the cavity with a fluid (liquid or gas) to visualize it with a telescope and/or a camera system and use energy (mechanical or thermal) to affect and/or remove tissue. Distension of the uterine cavity then is an important component of hysteroscopy, and during the last century, numerous attempts have been made to achieve an undistorted and unobstructed panoramic view of the uterine cavity. In order to achieve this goal, the uterine cavity has been distended with fluids using a variety of techniques, including gravity-assisted systems, pressure cuffed systems, and electronic pumps. Excessive fluid intravasation during hysteroscopy can lead to significant complications, and hence, automated fluid delivery systems have been developed recently to provide a safe and more efficient method of fluid delivery. This review aims to describe the evolution of distension media delivery systems chronologically from the 1900s to the present day.

During the last decades, the number of couples with reproductive issues has substantially increased. Many different factors are implicated in reproductive failure, including uterine factors. Endometrial pathologies, such as endometrial polyps, hyperplasia, endometritis, and Mullerian anomalies, can also hinder embryo implantation. Hysteroscopy remains the gold standard for the evaluation and treatment of intrauterine pathology. Over the last few years, advances in hysteroscopic instrumentations and surgical techniques have significantly evolved, the refinement in technology, miniaturization of instruments, and improved image quality have rendered hysteroscopy a more patient and user-friendly procedure that has enhanced its use in reproductive medicine. Nowadays, hysteroscopy is essential in the evaluation and treatment of women with infertility. This article underscores the major technological breakthroughs achieved over the last few years with emphasis on the role of artificial intelligence, augmented reality, and 3D hysteroscopy, which can set new benchmarks in hysteroscopy applied to reproductive medicine.

Sepsis is a severe infectious disease with high mortality, and it occurs when chemicals released in the bloodstream to fight an infection trigger inflammation throughout the body and it can cause a cascade of changes that damage multiple organ systems, leading them to fail, even resulting in death. In order to reduce the possibility of sepsis or infection antiseptics are used and process is known as antisepsis. Antiseptic peptides (ASPs) show properties similar to antigram-negative peptides, antigram-positive peptides and many more. Machine learning algorithms are useful in screening and identification of therapeutic peptides and thus provide initial filters or built confidence before using time consuming and laborious experimental approaches. In this study, various machine learning algorithms like Support Vector Machine (SVM), Random Forest (RF), K-Nearest Neighbour (KNN) and Logistic Regression (LR) were evaluated for prediction of ASPs. Moreover, the characteristics physicochemical features of ASPs were also explored to use them in machine learning. Both manual and automatic feature selection methodology was employed to achieve best performance of machine learning algorithms. A 5-fold cross validation and independent data set validation proved RF as the best model for prediction of ASPs. Our RF model showed an accuracy of 97%, Matthew’s Correlation Coefficient (MCC) of 0.93, which are indication of a robust and good model. To our knowledge this is the first attempt to build a machine learning classifier for prediction of ASPs.

Intrauterine adhesions (IUA) were first reported by Heinrich Fritsch in the late nineteenth century. However, its etiology, symptoms, and diagnosis were later described in detail by Joseph Asherman in 1948 [1].

PURPOSE OF THE STUDY: To estimate and to compare the levels of cervical phIGFBP-1 among primigravida with prolonged pregnancy, with and without successful induction of labor (IOL). METHODS: A diagnostic study (cross-sectional study design) was conducted in our institution from November 2016 to April 2018 on 84 primigravida at ≥ 41 weeks with uncomplicated singleton pregnancy. The results were analyzed using SPSS software and receiver operating characteristics curves to determine the best cutoff using Youden Index. Sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), positive (+ LR) and negative likelihood ratio (− LR) were calculated. P value 7.8 µg/l) were 0.87, 0.87, 0.89, 0.85, 6.76 and 0.15, respectively. Using logistic regression analysis, phIGFBP-1 was found to be the best predictor of successful IOL (OR 44.200; 95% CI 12.378–157.831, p