Gynecology and Obstetrics Clinical Medicine最新文献

Objective

Pregnancy has been identified as a risk factor for pelvic floor dysfunction (PFD). The aim of this study was to establish primary prevention measures for PFD during pregnancy and reduce the overall incidence of PFD.

Methods

We assembled a panel of 36 experts, including gynecologists, obstetricians, and physiotherapists. Through surveys and expert meetings, the panel reviewed and assessed the safety and effectiveness of various clinical interventions. Based on expert comments from Round 1, a revised list of 8 clinical interventions was developed and submitted for a second round of expert review.

Results

A consensus was reached on the importance of implementing prevention measures to protect pelvic floor function during pregnancy. Experts particularly emphasized the significance of health education, weight management, pelvic floor muscle training, respiratory training, overall exercise, physical activity, and perineal massage.

Conclusion

The expert consensus provides comprehensive clinical measures to safeguard pelvic floor function during pregnancy. This paper represents the initial step toward developing scientific recommendations for pregnant women regarding the primary prevention of PFD. Future research should focus on the implementation of these recommendations in clinical practice.

Objectives

This research aimed to investigate changes in defecation sensory threshold and related factors in patients with posterior vaginal wall prolapse.

Methods

A total of 214 patients with pelvic organ prolapse were recruited between October 2019 to January 2021. All patients underwent a defecation sensory threshold examination, physical examination, and pelvic floor ultrasound examination. Factors related to the defecation sensory threshold were analyzed.

Results

(1) Among the participants, 57 patients (26.6%) had a defecation sensory threshold of more than 90 ​ml. Patients with a threshold > 90 ​ml showed higher scores of defecation dysfunction in the Constipation Scoring System (CSS) score (p=0.003) and higher scores of constipation in the Colorectal-anal Distress Inventory 8 (CRADI-8) score (p=0.002). (2) The defecation sensation threshold positively correlated with the Ap point (r=0.448, p＜0.001), the Bp point (r=0.345, p=0.009), the area of the levator-ani hiatus measured by transvaginal ultrasound (r=0.403, p=0.002), and parity (r=0.355, p=0.007).

Conclusions

Patients diagnosed with pelvic organ prolapse commonly experience an increased threshold of defecation sensation. Elevated thresholds were associated with more frequent constipation symptoms. Additionally, the severity of posterior pelvic prolapse positively correlated with the defecation sensory threshold.

Background

The use of uterine balloon tamponade (UBT) devices for intrauterine packing and management of vaginal bleeding by uterine atony has shown promising results in improving the quality of care and reducing maternal mortality.

Objective

This report aims to provide an overview of progress made in implementing UBT devices in northern Cote d'Ivoire.

Material and methods

A four-year retrospective study was conducted in the North-East (163,645), North-Center (351,909), and North-West (57,983). In 2017, UBT was adopted by members of the healthcare system. Subsequently, 5 national and 32 regional trainers have been trained. The training session was a theoretical and practical program with a low simulator. UBT is a male condom tied to a urinary catheter, filled with liquid. Positive outcomes included stopping bleeding, avoiding the need for surgery, and preventing maternal deaths (MD). In 2018, 3,515 UBT devices were distributed. In 2019, monitoring tools and transmission circuits of the data were validated. In 2020, the collection of data and local manufacturing was launched.

Results

During the process, 978 health workers, mainly midwife (52.0%) and nurses (32.2%) out of the 1,295 assigned were trained. The number of trained individuals decreased from 209 in 2019 to 160 in 2020. A total of 1,715 UBT devices were locally manufactured, adding to the existing gift of 5,080 devices, with total availability of 6,795. The distribution of devices increased from 2017 to 2019 but decreased in 2020. Success rates increased from 87.3% in 2017 (365/418) to 95.0% in 2019 (556/585) and slightly decreased in 2020 to 98.0% (681/695). Adverse outcomes (144/2,193), included MD (35/2,193) and medical evacuation to the surgical center (109/2,193).

Conclusion

The implementation of UBT in northern Cote d'Ivoire successfully reduced maternal death rates caused by immediate post-partum hemorrhage (IPPH). However, to ensure sustainability, further improvements are needed, including increased monitoring, ongoing training, and device availability.

Background

Postpartum hemorrhage (PPH) could be avoided by identifying high-risk women. The objective of this systematic review is to determine PPH predictors using machine learning (ML) approaches.

Method

This strategy included searching for studies from inception through November 2022 through the database included: Cochrane Central Register, PubMed, MEDLINE, EMBASE, ProQuest, Scopus, WOS, IEEE Xplore, and the Google Scholar database. The search methodology employed the PICO framework (population, intervention, control, and outcomes). In this study, “P” represents PPH populations, “I” represents the ML approach as intervention, “C” represents the traditional statistical analysis approach as control, and “O” represents prediction and diagnosis outcomes. The quality assessment of each included study was performed using the PROBAST methodology.

Results

The initial search strategy resulted in 2048 citations, which were subsequently refined by removing duplicates and irrelevant studies. Ultimately, four studies were deemed eligible for inclusion in the review. Among these studies, three were classified as having a low risk of bias, while one was considered to have a low to moderate risk of bias. A total of 549 unique variables were identified as candidate predictors from the included studies. Nine distinct models were chosen as ML algorithms from the four studies. Each of the four studies employed different metrics, such as the area under the curve, false positive rate, false negative rate, and sensitivity, to report the accuracy of their models. The ML models exhibited varying accuracies, with the area under the curve (AUC) ranging from 0.706 to 0.979. Several weighted predictors were identified as significant factors in PPH risk prediction. These included pre-pregnancy maternal weight, maternal weight at the time of admission, fetal macrosomia, gestational age, level of hematocrit at the time of admission, shock index, frequency of contractions during labor, white blood cell count, pregnancy-induced hypertension, the weight of the newborn, duration of the second stage of labor, amniotic fluid index, body mass index, and cesarean delivery before labor. These factors were determined to have a notable influence on the prediction of PPH risk.

Conclusion

The findings from ML models used to predict PPH are highly encouraging.

Since 1998, Preventive Oncology International, Inc. (POI) has been at the forefront of studying human papillomavirus (HPV) self-collection for cervical cancer screening, with a significant focus in China. Through multiple clinical trials over the past 25 years, POI has explored various aspects related to self-collection methodologies. In 2004–2006, POI established that self-collection could be equivalent to direct endocervical samples. Subsequently, a large randomized trial involving 10,000 patients in 2010 further confirmed that self-collected vaginal specimens, tested for high-risk HPV (hrHPV) using a PCR-based assay with high analytic sensitivity, could effectively replace endocervical specimens with minimal loss of sensitivity and a slight decrease in specificity. Throughout the years, POI's research has encompassed several crucial topics, including patient acceptance, the development of new cost-effective, simpler, and faster assays, exploring different collection devices, devising efficient methods of specimen transport, and implementing population-based screening systems. The findings strongly support the integration of self-collection methodologies into cervical cancer control programs worldwide, particularly in medically underserved regions. As HPV self-collection continues to evolve, ongoing research and innovations are expected to play a pivotal role in achieving the global mission of combating cervical cancer.

Objective

The objective of this study is to differentiate between uterine leiomyomas (ULM) and uterine leiomyosarcomas (ULMS) by conducting molecular differential analysis and identifying potential prognostic biomarkers for diagnosis.

Methods

The microarray datasets (GSEID: GSE64763 and GSE185543) were retrieved from the Gene Expression Omnibus database. Data preprocessing and differential gene expressions (DEGs) analysis were performed. The DEGs were further intersected to find the common DEGs in ULM and ULMS and further validation of selected DEGs were performed. Further, a machine learning classifier was also applied in the selection of biomarkers. Protein-protein interaction network based upon STRING v 10.5, was constructed. Additionally, Gene Ontology (GO) and KEGG (Kyoto Encyclopedia of Genes and Genomes) pathway enrichment analyses were also performed to dissect possible functions and pathways.

Results

A total of 50 significant DEGs for ULM while 321 DEGs for ULMS have been identified with their official gene symbol. Between ULM and ULMS, a total of 14 common DEGs were identified of which 8 were up-regulated while 6 were down-regulated. The DEGs of (GSE185543) were also analyzed and the significant genes were retrieved common in both datasets for further analysis. Using a machine learning approach, 10 feature genes were identified. Using the expression profiles of these genes, a sequential minimal optimization (SMO) prediction model was built on the training set, and it accurately and reliably classified features expression in ULM and ULMS in the independent test set. Furthermore, Co- Enrichment analysis was also performed.

Conclusion

The study identified several DEGs, including ZNF365, EPYC, COL11A1, SHOX2, MMP13, TNN, GPM6A, and GATA2, through cross-validation, machine learning classifier, and Co- Enrichment analysis. These candidate disease genes may provide valuable insight into the underlying mechanisms and could be used as potential diagnostic biomarkers for ULM and ULMS. However, further validation of these genes is necessary to better understand their roles in the pathogenesis of ULM and ULMS.