Analysis of survey on menstrual disorder among teenagers using Gaussian copula model with graphical lasso prior

Reliability analysis of the solidification cooling of solid rocket motor grain material

The reliability of solid rocket motor grain structure during solidification cooling is analyzed. First, a three-dimensional parametric modeling of the grain is carried out by ANSYS finite element software. The dangerous point and dangerous moment can be obtained based on the transient and dynamic thermo-structure coupling under the cooling condition. Moreover, the maximum equivalent strain and temperature values are extracted. Second, a dual neural network model is established based on the probability distribution of the copula function and specific parameters. Finally, the instantaneous reliability during the solidification cooling process of the grain is calculated. Then, the dynamic reliability analysis is realized. The proposed method reduces the computational cost of dynamic reliability of grain structure, demonstrating its applicability in practical engineering problems. Furthermore, comparing the results of the proposed method with the MCS method demonstrates that the proposed method has high computational accuracy.

Instruments to Identify Menstrual Complaints and Their Impact on Adolescents: A Systematic Review

STUDY OBJECTIVE

The aim of this review was to create an overview of available instruments used to evaluate the menstrual complaints of dysmenorrhea and heavy menstrual bleeding (HMB) in adolescents.

METHODS

The search was conducted in PubMed, Web of Science, Embase, and PsycINFO on December 1, 2022. Studies published in English reporting on menstrual complaints among girls aged 10-19 were included. The quality and bias risk was assessed using the Quality Assessment of Diagnostic Accuracy Studies-2.

RESULTS

Of 8994 identified studies, 57 were included. In these studies, 19 instruments were described, of which 8 were validated in adolescents. One was difficult to use in daily practice, 1 had low sensitivity and specificity in the detection of HMB, 2 were validated by face validity, and 2 were not specifically developed to evaluate (the impact of) menstrual complaints. Therefore, only 2 were validated and feasible to measure adolescent menstrual complaints: the Period ImPact and Pain Assessment (PIPPA) and the Adolescent Menstrual Bleeding Questionnaire (aMBQ). Remarkably, the Pictorial Blood Loss Assessment Chart (PBAC) was the most frequently used instrument in adolescents. There was one modified version of the PBAC for adolescents, which had a lower cutoff value for HMB and low sensitivity and specificity.

CONCLUSION

We found that the PIPPA and aMBQ are suitable for assessing menstrual complaints and their impact on daily activities and health-related quality of life in adolescents. The PBAC, which is used frequently in adults, needs to be further assessed specifically for adolescents. In particular, the clinical relevance of the PBAC cutoff value for HMB should be determined for this age group. Considering the different interpretations, experiences, and limited knowledge of menstrual complaints, instruments suitable for adults may not be suitable for adolescents.

An artificial intelligence approach for investigating multifactorial pain-related features of endometriosis

Endometriosis is a debilitating, chronic disease that is estimated to affect 11% of reproductive-age women. Diagnosis of endometriosis is difficult with diagnostic delays of up to 12 years reported. These delays can negatively impact health and quality of life. Vague, nonspecific symptoms, like pain, with multiple differential diagnoses contribute to the difficulty of diagnosis. By investigating previously imprecise symptoms of pain, we sought to clarify distinct pain symptoms indicative of endometriosis, using an artificial intelligence-based approach. We used data from 473 women undergoing laparoscopy or laparotomy for a variety of surgical indications. Multiple anatomical pain locations were clustered based on the associations across samples to increase the power in the probability calculations. A Bayesian network was developed using pain-related features, subfertility, and diagnoses. Univariable and multivariable analyses were performed by querying the network for the relative risk of a postoperative diagnosis, given the presence of different symptoms. Performance and sensitivity analyses demonstrated the advantages of Bayesian network analysis over traditional statistical techniques. Clustering grouped the 155 anatomical sites of pain into 15 pain locations. After pruning, the final Bayesian network included 18 nodes. The presence of any pain-related feature increased the relative risk of endometriosis (p-value < 0.001). The constellation of chronic pelvic pain, subfertility, and dyspareunia resulted in the greatest increase in the relative risk of endometriosis. The performance and sensitivity analyses demonstrated that the Bayesian network could identify and analyze more significant associations with endometriosis than traditional statistical techniques. Pelvic pain, frequently associated with endometriosis, is a common and vague symptom. Our Bayesian network for the study of pain-related features of endometriosis revealed specific pain locations and pain types that potentially forecast the diagnosis of endometriosis.

Inferred networks, machine learning, and health data

This paper presents a network science approach to investigate a health information dataset, the Sexual Acquisition and Transmission of HIV Cooperative Agreement Program (SATHCAP), to uncover hidden relationships that can be used to suggest targeted health interventions. From the data, four key target variables are chosen: HIV status, injecting drug use, homelessness, and insurance status. These target variables are converted to a graph format using four separate graph inference techniques: graphical lasso, Meinshausen Bühlmann (MB), k-Nearest Neighbors (kNN), and correlation thresholding (CT). The graphs are then clustered using four clustering methods: Louvain, Leiden, and NBR-Clust with VAT and integrity. Promising clusters are chosen using internal evaluation measures and are visualized and analyzed to identify marker attributes and key relationships. The kNN and CT inference methods are shown to give useful results when combined with NBR-Clust clustering. Examples of cluster analysis indicate that the methodology produces results that will be relevant to the public health community.

Diagnostic delay of endometriosis in adults and adolescence-current stage of knowledge

PURPOSE

The purpose of this article is to provide a modern perspective on the diagnosis of endometriosis with particular attention to the role of ultrasound examination. In the present study, we highlight the problem of endometriosis in teenage girls and discuss the patients' perspective on the diagnostic process.

METHODS

In order to present the most recent reports on the diagnosis of endometriosis, the PubMed database was searched. Articles published within the last 3 years (2019-2021) and those considered relevant during the bibliographic review were analyzed.

RESULTS

The role of ultrasound examination and assessment of patients' perspective related to delayed and incorrect diagnosis were considered to be the most important in the recent reports. Attention was also paid to the problem of endometriosis diagnosis in adolescent girls.

CONCLUSIONS

Appropriately constructed and used questionnaires help to determine the risk of endometriosis in a particular patient. The primary method for diagnosis is extended ultrasound examination, which should be performed especially in patients with a high risk of developing the disease. This procedure is applicable to both adult and adolescent women. Awareness of the possibility of developing the disease in a particular patient, combined with appropriate use of ultrasound examination, can contribute to the decrease in diagnostic delay.