Abstract

Maternal mortality data and review are important indicators of the effectiveness of maternity healthcare systems and an impetus for action. Recently, a rising incidence of maternal mortality in high income countries has been reported. Various publications have raised concern about data collection methods at country level, as this usually relies mainly on national vital statistics. It is therefore essential that the collected data are complete and accurate and conform to international definitions and disease classification. Accurate data and review can only be truly available when an Enhanced Obstetric Surveillance System is in place. EBCOG calls for action by national societies to work closely with their respective ministries of health to ensure that high quality surveillance systems are in place.

Introduction

The death of a woman during pregnancy, childbirth or postpartum is a devastating outcome for any pregnancy and the maternal mortality rate serves as an important reflection of the maternity healthcare standards provided. In most high-income countries, the maternal mortality ratio (MMR) has become very low and appears to have remained stable for many years. The MMR in low-income regions has also gradually come down over the last two decades but remains persistently high [Fig. 1] [1]. On a global level, the highest burden of maternal mortality lies in lower and lower-middle income countries, where almost 95 % of all maternal deaths occur [2]. Even in some high-income countries, an apparent rise has become a matter of concern. In the United States, maternal mortality rates have been reported to have nearly doubled from 17.4 per 100,000 live births [LB] in 2018 to 32.9 in 2020 [3]. Europe has contrastingly seen a gradual fall in MMR from 26 per 100,000 LB in 2000 to 13 in 2020 [Fig. 1] [1]. Continuing MMR surveillance remains an valuable obstetric performance parameter. The importance of maternal healthcare and the efforts to reduce maternal mortality rates worldwide are reflected in the multiple statements and goals set by UN network agencies: “Millenium Development Goals”, “Sustainable Development Goals”, or the “Strategies toward ending preventable maternal mortality” [4, 5].

Conclusion

Enhanced obstetric surveillance systems are an indispensable component of quality assessment of maternal health care including in high income countries. High standards of case ascertainment and the reviewing and formulation of lessons learned lead to a better understanding of the events behind every maternal death and thus providing tools to improvements for future healthcare (Table 1). Involvement in the obstetric surveillance process should not only involve healthcare providers, but also the political/governmental authorities who should embrace and support the surveillance system by providing financial support and introducing legislation to protect anonymity and data. Systems must also be in place whereby fear of disciplinary or medico-legal procedures is reduced thus encouraging the healthcare providers to voluntarily come forward to report such cases. Only by ensuring that an effective Enhanced Obstetric Surveillance System is in place, can we ensure an improvement of maternal mortality death mortality numbers.

Abstract

Genetic defects in sperm are responsible for a great percentage of male infertility. Dysregulation of these genes directly influences sperm morphology, motility, and viability. Therefore, analyzing gene expression aberrancies is a must in male infertility. Microarray analysis is practically used for several aspects of male infertility, including the detection of differentially expressed genes and the identification of potential infertility biomarkers. We conducted a meta-analysis using microarray datasets, including the datasets containing sperm tissues from both healthy and infertile males. Seven datasets qualified for inclusion in this study and were then transformed into a single set of meta-data. For these genes, expression and diagnostic analyses were conducted. Additionally, enrichment analysis revealed the role and function of these genes in cellular processes. Six genes—S100Z, SLC2A2, IMPG1, HOXD12, RAPGEFL1, and DMBX1—were found to be significantly down-regulated in the sperm of infertile men. Notably, the expression of these genes was highly correlated in the sperm of these men. In addition, receiver operating curve analysis indicated that these genes may serve as useful biomarkers for infertility diagnosis. The role of these genes in transporting glucose, vitamins, and fructose as the sperm’s primary fuel source was suggested by pathway analysis.

Introduction

Infertility is a developing concern, affecting between 10 and 15% of couples worldwide (Wasilewski et al., 2020). Male factor infertility accounts for approximately 50 % of all sterility cases, with sperm abnormalities being the most common cause (Kumar and Singh, 2015). The traditional analysis of sperm provides fundamental information regarding sperm count, motility, and morphology but fails to identify the underlying molecular abnormalities (Boitrelle et al., 2021; Vasan, 2011; Auger, 2010).

Microarray analysis is a high-throughput technique that permits the simultaneous identification of numerous gene expression alterations (Mutch et al., 2001). Recently, it has been utilized to investigate the genetic basis of male infertility and sperm dysfunction (Garrido et al., 2013; Garrido et al., 2009). Sperm microarray analysis can provide a comprehensive picture of the sperm’s molecular landscape, including gene expression patterns and DNA copy number variations (CNVs) that may be responsible for infertility (Waclawska and Kurpisz, 2012). The identification of specific genes and pathways involved in sperm function and fertilization can lead to the development of novel therapeutic targets for male infertility (You et al., 2019). So far, over 2000 genes and pathways are recognized to be involved in spermatogenesis; hence, spotting differentially expressed genes (DEGs) may disclose infertility etiology (Babakhanzadeh et al., 2020; Lee and Ramasamy, 2018). Among the genes associated with male infertility and the disorders caused by their defect are CFTR (congenital unilateral/bilateral absence of vas deferens), AR (non-obstructive azoospermia) (Bieniek et al., 2021), LRRC6 (primary ciliary dyskinesia) (Li et al., 2023), APOA1 (testicular amyloidosis) (Houston et al., 2022), and SRY (sexual development disorders) (Jiang et al., 2013). Signaling pathways such as JAKs, MAPKs, etc. are also involved in this disease (Fig. 1). Genetic factors account for approximately 15% of the causes of male infertility, which may manifest as deficiencies in sperm count or sperm quality (Salas-Huetos and Aston, 2021). However, the genetic cause of male infertility remains unknown in about 40% of cases (Krausz and Riera-Escamilla, 2018). Several DEGs and CNVs have been confirmed to play a role in spermatogenesis and fertilization as a result of microarray analysis of sperm from infertile men. For instance, Hashemi et al. used microarray analysis to reveal multiple gene dysregulations such as RAD23B, OBFC2A, CHEK2, TRIP13, and POLD4, which primarily mediate DNA damage detection and repair and regulate cell proliferation (Hashemi Karoii et al., 2022).

Conclusion

Downregulation of six novel genes (S100Z, SLC2A2, IMPG1, HOXD12, RAPGEFL1, and DMBX1) in the sperm of infertile males was identified by this study via meta-analysis of microarray data. Furthermore, the potential use of these dysregulations as diagnostic biomarkers was demonstrated through ROC curve analysis.

5.1. Future prospective
Advantages of using microarrays for infertile sperm include the discovery of underlying genetic pathways and the suggestion of novel diagnostic biomarkers. Our findings may prove useful in the treatment and accurate diagnosis of male infertility.

5.2. Limitations
Although the study of microarrays is a high-throughput method, we recommend verifying the data with a gold-standard method, such as real-time polymerase chain reaction (RT-PCR). Given the scarcity of samples, we would be overjoyed if subsequent research confirmed the molecular level findings reported here. As for the diagnostics, commenting on ideal biomarkers require several studies with a wide sample size, therefore, testing our findings on a cohort will disclose the accurate and real ability of these DEGs in identifying infertility.

Abstract
Objectives
This study aims to determine the rates of clinically-significant anxiety and depressive symptoms during the immediate postpartum in a sample of women referred to a university maternity department, as well as the associated risk factors and the relations with the level of maternal bonding.

Patients and methods
During the third national lockdown for the COVID-19 pandemic (February-April 2021), on days 2–3 after delivery 127 mothers were administrated the Edinburgh postnatal depression scale (EPDS), the state-trait anxiety inventory (STAI-YA), the mother-to-infant bonding scale (MIBS) and questions issued from the coronavirus health impact survey questionnaire (CRISIS).

Results
The rate of perinatal clinically-significant symptoms were 17% for depression (EPDS cut-off ≥ 12) and 15% for anxiety (STAI-YA cut-off ≥ 40). In the multivariate analysis, being a single mother, risk of being infected by the SARS-CoV2, risk that a close relative might be infected by the SARS-CoV2 and the length of stay in maternity were associated with an increased EPDS total score, while breastfeeding was associated with a lower EPDS total score. Six variables remained positively associated with the STAI-YA total score in the multivariate model: the maternal level of academic achievement, a hospitalization during the pregnancy, peripartum medical complications, risk of being infected by the SARS-CoV2, risk of a close relative being infected by the SARS-CoV2 and physical fatigue. Low but statistically significant correlations were found between the MIBS total score and the EPDS total score (rs = 0.26) and with the STAI-YA total score (rs = 0.26).

Discussion
The observed rates of anxiety and depressive symptoms were in the same range as those reported in observational studies conducted in high-resource countries during the COVID-19 pandemic. Risk of being infected by the SARS-CoV2 was both an independent risk factor for anxiety and depressive symptoms. The relations between the measure of maternal bonding and the severity of maternal emotional symptoms call for a better consideration of the long-term consequences of the pandemic on children’s socio-emotional development.

Introduction
     Cumulative evidence supports an increased risk of maternal peripartum psychological difficulties since the beginning of the COVID-19 pandemic [1], [2], [3], [4], [5], [6]. The meta-analysis conducted by Zhang and Yu [3] found an average prevalence of 43% for anxiety and of 32% for depression among pregnant women exposed to the context of the pandemic. These figures conceal although significant disparity across countries, with in particular a more substantial increase of postpartum depressive symptoms in low-resource compared to high-resource countries [7]. The capacity of the healthcare professionals providing adapted mental health interventions and care continuity during the pandemic appears a key moderator factor of maternal psychopathology risk [8], [9]. In line with this, Chmielewska and Barratt [7] found that the estimated efficiency of the national health care system was a stronger predictor of the observed between-study heterogeneity for maternal health outcomes than factors-related to the stringency of mitigation measures for the COVID-19 epidemic (e.g. the duration of lockdown).

     Alongside, several concerns have been raised that the application of social distancing measures in maternity wards led to lower social and professional support, ultimately contributing to increase the risk of maternal psychological difficulties [9], [10], [11]. The application of social distancing measures in maternity led to a strict restriction of partner and family visits while pregnant women faced additional sources of stress related to COVID-19 pandemic [11]. It has also been raised that a reduction of the length of stay in maternity and a constriction of paramedical team linked to the reorganization of workforce during the pandemic complicate the recognition of psychological needs of women during the perinatal period. An underestimation of maternal psychological difficulties could also be reinforced by a lower access to face-to-face supportive care (including social worker, psychologist and psychiatrist) and subsequently decreased opportunity to receive professional help [12].

Conclusion
     The rates of clinically-significant depressive and anxiety symptoms in the immediate postpartum among women referred to a university hospital center was roughly comparable to the range observed in previous observational studies prior the context of the COVID-19 pandemic. This report stresses the possible contribution of maternal worries about SARS-CoV2 infection in maternal anxiety and depressive symptoms, as long as other well-recognized risk factors. In a modern hospital where digital technologies and telehealth interventions were available the effect of social distancing measures in the maternity care was not clear. The relationships reported between the severity of maternal emotional symptoms and maternal bonding in the days following childbirth are worth considering to discuss the possible long-term impact of the pandemic on children socioemotional development.

Abstract

     5-10% of all breast cancers are associated with mutations in hereditary breast cancer genes several of which are involved in the DNA damage response.

     BRCA1 and BRCA2 mutation is not associated with poor prognosis in breast cancers, but infact better prognosis when treated with chemotherapy in comparison to similar non BRCA mutated breast cancers.

    Treatment with the small molecule PARP inhibitor olaparib after chemotherapy improves overall survival in patients with early breast cancer and germline BRCA1 or BRCA2 mutations.

     Platinum based chemotherapy is highly active in those with germline BRCA1 or BRCA2 mutations but cross resistance between platinum agents and PARP inhibitors can be mediated by somatic “reversion mutations” in BRCA1 or BRCA2 and is a clinical challenge.

     Understanding the underlying mechanisms of overlapping or distinctive resistance is vital to therapy development in this evolving area of translational medicine.

Introduction

     Approximately 5% to 10% of the 2.3 million breast cancer cases diagnosed annually are associated with a mutation in a known hereditary breast cancer predisposition gene such as BRCA1 or BRCA2.1,2 The integration of genomics into the standard diagnostic pathways for breast cancer patients and the availability of targeted treatment approaches for those with hereditary breast cancer predisposition gene mutations means that the management of these patients is now distinct to that a decade ago. In this review, we discuss the recent advances made in systemic treatments for hereditary breast cancer and highlight future challenges that must be addressed for improvements in clinical outcomes to be achieved in this distinct subgroup.

Hereditary Breast Cancer Genes

     Many of the germline mutations associated with hereditary breast cancer occur in “caretaker” tumor suppressor genes (genes whose normal function is to maintain the integrity of the genome and whose dysfunction leads to genome instability) and include BRCA1, BRCA2, PALB2, ATM, CHEK2, and p53.3 For example, germline deleterious mutations in BRCA1 (gBRCA1m) or BRCA2 (gBRCA2m), which play key roles in DNA repair by homologous recombination (HR), are associated with an increased lifetime risk of developing breast, ovarian, prostate and pancreatic cancer.2,4,5 gBRCA1m carriers have a cumulative lifetime breast cancer risk of 46% to 60% and when diagnosed with breast cancer typically have the basal-like, triple negative (B-L, TNBC), subtype of the disease. gBRCA2m carriers have a cumulative lifetime breast cancer risk of 43% to 55% and tend to develop estrogen receptor-positive (ER+), luminal B subtype, breast cancers (significantly more so than for gBRCA1m carriers).6, 7, 8, 9 gBRCA1/2m breast cancers in those who have a strong family history are usually detected at a younger age than sporadic breast cancers.

Disclosure

     E.H.-J. is a clinical PhD fellow funded by Cancer Research UK and AstraZeneca. A.T. is a consultant for AstraZeneca, Merck KGaA, Artios, Pfizer, Vertex, GE Healthcare, Inbiomotion, MD Anderson Cancer Centre; has received grant/research support from AstraZeneca, Myriad, Medivation, and Merck KGaA; and is a stockholder in Inbiomotion. Stands to gain from the use of PARP inhibitors as part of the ICR’s “rewards to inventors” scheme. C.J.L. makes the following disclosures: receives and/or has received research funding from: AstraZeneca, Merck KGaA, Artios. Received consultancy, SAB membership or honoraria payments from: Syncona, Sun Pharma, Gerson Lehrman Group, Merck KGaA, Vertex, AstraZeneca, Tango, 3rd Rock, Ono Pharma, Artios, Abingworth, Tesselate, Dark Blue Therapeutics. Has stock in: Tango, Ovibio, Enedra Tx., Hysplex, Tesselate. C.J.L. is also a named inventor on patents describing the use of DNA repair inhibitors and stands to gain from their development and use as part of the ICR “Rewards to Inventors” scheme.

Abstract

     Several lines of research suggest that reproductive-related hormonal events may affect the course of bipolar disorder in some women. However, data on associations between bipolar disorder and menarche, menstrual cycle, and menopause are mixed. This article reviews the literature on the potential effects of menarche, menstrual cycle, and menopause on bipolar disorder.

     A narrative review of published articles on bipolar disorder and menstrual cycle events was conducted. The primary outcome assessed was the impact of menarche, menstrual cycle and menopause on the course of bipolar illness. Databases searched were PubMed, Ovid, Scopus, PsycINFO, Medline, and Cochrane Libraries from inception to August 2021.

     Twenty-two studies were identified and included in the narrative synthesis. Research suggested that a subset of women with bipolar disorder are vulnerable to the impact of menstrual cycle events. Menarche seems to be associated with age at onset of bipolar illness especially in case of bipolar disorder type I and the specific age at menarche may predict some clinical features of the disorder. Menstrual cycle likely affects the course of bipolar disorder but the pattern of mood variability is not clear. Menopause appears to be not only a period of vulnerability to mood alteration, especially depressive episodes, and impairment of quality of life, but also a potential trigger of bipolar illness onset.

     The impact of menarche, menstrual cycle, and menopause on bipolar disorder is largely understudied. Preliminary evidence suggests that a subset of women with bipolar disorder may have their mood shifts affected by menstrual cycle events, with different patterns depending on the type of bipolar disorder also. Further researches are needed to deep the impact of menarche, menstrual cycle, and menopause on bipolar illness.

Introduction

     Bipolar disorder is a lifelong episodic illness characterized by fuctuations in mood state and energy (Grande et al. 2016). The course of bipolar disorder is associated with both inter-individual variation and heterogeneity between patients and can lead to functional, occupational, and cognitive impairments (Miller and Black 2020). Thus, over a lifetime any one patient may experience many forms of the disorder including depressive and manic or hypomanic episodes, mixed emotional states, and psychosis (Vieta et al. 2011). Bipolar disorders are classifed according to the longitudinal course: bipolar I disorder requires the occurrence of at least one fully syndromal lifetime manic episode, although depressive episodes are common; bipolar II disorder requires the occurrence of at least one hypomanic episode and one major depressive episode (American Psychiatric Association, 2013). The prevalence of bipolar illness is consistent across diverse cultures and ethnic groups, with an aggregate lifetime prevalence of 2.4% (Carvalho et al. 2020). Although bipolar disorder has an equal rate among men and women, some gender-related diferences have been found in the clinical features of the illness(Diforio and Jones 2010). Overall, it is reported that depressive episodes, rapid cycling, and mixed mania are more frequent in women than men (Altshuler et al. 2010; Marsh et al. 2012). Furthermore, there is accumulating evidence that bipolar disorder course is affected by reproductive-related hormonal events in women (Diforio and Jones 2010). Sex diferences have been hypothesized to be related to organizational and activational efects of sex hormones on the nervous system (Steiner et al. 2003). Gonadal steroids play a role in all stages of neurodevelopment, including neurogenesis, synaptogenesis, neural migration, growth, diferentiation, and cell survival (Payne 2003).

Results

     A fowchart of studies selected and included in the narrative review is provided in Fig. 1. From all databases combined, the initial search yielded 1229 results. After duplicates had been removed, 1197 articles remained. At title and abstract screening, 1028 articles were removed as they were not clinically relevant. A total of 169 articles were found to be suitable for inclusion in the full text screening. Full text screening of these articles eliminated 147 articles that did not meet the inclusion criteria. Twenty-two articles in total were included. Table 1, Table 2, and Table 3 list the studies that were included in the narrative synthesis.

Abstract

     This paper presents an Artificial Nerual Network (ANN) for identification of postmenopausal women who are at high risk for developing osteopathy. While 800 patients took part in the study, 180 were used for network training. The following parameters were used: T-score (from −2,5 to −4), Age, Blood calcium level (<1,9 mmol/L), Blood vitamin D level (<20 ng/ml), Hip fracture, Spine fracture, Joint fracture, Glucocorticoids use, Smoking status, and BMI. The network has 10 input parameters and 1 output parameter. For the final architecture of expert system, a neural network with 20 neurons in hidden layer was chosen based on the training results. The signal from each neuron from hidden layer is directed to neuron in output layer, where this neuron processes the signal and gives desired output of the network. The sensitivity was 97,5%, specificity 70%, and accuracy 94,44%.

Introduction

     Osteoporosis is a skeletal illness that causes weakening of the bones, which can lead to increased fractures. People with osteoporosis have decreased bone mass and microarchitectural degeneration of bone tissue, in addition to lower bone strength [1-3].

     Osteoporosis is a skeletal illness that causes weakening of the bones, which can lead to increased fractures. People with osteoporosis have decreased bone mass and microarchitectural degeneration of bone tissue, in addition to lower bone strength [1-3].

     According to data from 2010, 6,6% of men and 22,1% of women over 50 in the EU have osteoporosis. Because of the expanding number of patients worldwide, it is now referred to as a “silent epidemic.” Variable and non-variable factors that raise the chance of developing osteoporosis and bone fractures can be separated. One of the most important variables is smoking, which is linked to decreased bone resistance to mechanical stresses and friction. The effects of excessive alcohol use on bone homeostasis are significant. Caffeine, glucocorticoid therapy, insufficient calcium and vitamin D intake, insufficient physical activity, low BMI, past bone fractures, and a family history of osteoporosis are all risk factors [4-6].

Conclusion

     As osteoporosis in postmenopausal women is a rising problem in the modern world, predictive modelling of the risk of developing osteoprosis is highly desirable. This paper presents the development and validation of an ANN based model for prediction and automated diagnosis of osteoporosis in post-menopausal women based on risk-contributing factors. Giving the high accuracy and sensitivity of proposed ANN for identification of high risk of osteoporosis development in postmenopausal women, it can be concluded that AI has a high potential for decission making for this specific purpose. Prediction of high risk for osteoprosis development can contribute to adjustments in lifestyle and possible prevention of osteoporosis. In addition to the benefit this would have to each individual, the cost reduction in terms of preventing costly interventions necessary in case of osteoporosis development is a significant contribution.

Abstract

     Ultrasound is an imaging modality that is highly operator dependent. This article reviews the challenges in learning how to perform obstetric sonography, as well as the processes necessary to acquire expert performance skills in sonography. Simulation-based education and learning, and the value of medical simulation are also discussed. Ultrasound simulators are an effective means of teaching obstetric sonography, because it provides training, deliberate practice, and performance evaluation/feedback which allows continuous and critical self-evaluation. We review evidence that simulation can improve performance in obstetric ultrasound examination, review current simulators, and discuss the current problems/gaps in ultrasound simulation. Optical positioning ultrasound simulation is a novel high-fidelity simulation learning system, which addresses many of these problems/gaps and is introduced for the first time here.

Introduction

     Ultrasound imaging is integral to the practice of obstetrics, as it allows comprehensive anatomic and physiologic evaluation of the fetus and maternal pelvic organs [1]. Therefore, ultrasound examinations are recommended for all pregnant women [2–4]. Over the years, there has been an overall increase in the utilization of obstetric ultrasound in the U.S., with women receiving on average 4–5 sonographic examinations per pregnancy [5]. Yet, there is substantial variability in the sensitivity of routine sonography to detect fetal anomalies [6–8]. In a review of 36 studies that included more than 900,000 fetuses, an overall sensitivity of only approximately 40% for detecting fetal anomalies was noted [9]. Although the sensitivity of obstetric sonography depends upon gestational age at screening, type of congenital anomaly, selection of pregnant women, etc. the level of training and expertise of the sonologist is crucial to examination performance [10].

Conclusions

     Ultrasound examinations are recommended for all pregnant women. Yet, sonography is a highly operator-dependent modality that is challenging to master. Simulator training is a reliable tool to assess learners, and improves healthcare education and practice, as well as patient safety. There is broad consensus on the value of integrating simulation into ultrasound education and training programs. Ultrasound simulators are an innovative and effective means of teaching obstetric sonography, because it provides training, deliberate practice, and performance evaluation/feedback which allows continuous and critical self-evaluation. Moreover, teaching occurs in a controlled and safe environment, where skills can be practiced repetitively, and without patient anxiety and medicolegal concerns. Simulation-based training also allows trainees to practice a systematic approach to ultrasonography before the start of clinical training. Optical positioning ultrasound simulation is a high-fidelity simulation learning system which can address many of the current problems/gaps in ultrasound simulation, due to its unique characteristics.

Abstract

     Osteoporosis is a disease characterized by impaired bone microarchitecture and reduced bone mineral density (BMD) resulting in bone fragility and increased risk of fracture. In western societies, one in three women and one in five men will sustain an osteoporotic fracture in their remaining lifetime from the age of 50 years. Fragility fractures, especially of the spine and hip, commonly give rise to increased morbidity and mortality. In the five largest European countries and Sweden, fragility fractures were the cause of 2.6 million disability-adjusted life years in 2016 and the fracture-related costs increased from €29.6 billion in 2010 to €37.5 billion in 2017. In the European Union and the USA, only a small proportion of women eligible for pharmacological treatment are being prescribed osteoporosis medication. Secondary fracture prevention, using Fracture Liaison Services, can be used to increase the rates of fracture risk assessment, BMD testing and use of osteoporosis medication in order to reduce fracture numbers. Additionally, established primary prevention strategies, based on case-finding methods utilizing fracture prediction tools, such as FRAX, to identify women without fracture but with elevated risk, are recommended in order to further reduce fracture numbers.

Postmenopausal osteoporosis

     Osteoporosis is a disease characterized by impaired bone microarchitecture and reduced bone mineral density (BMD) resulting in bone fragility and increased risk of fracture [1,2]. BMD measured using dual-energy X-ray absorptiometry to a large extent reflects bone strength [3], and for each standard deviation (SD) decrease in femoral neck BMD, the fracture risk is increased two-fold to three-fold [4] in postmenopausal women. In 1994, the World Health Organization (WHO) defined osteoporosis using a BMD threshold of −2.5 SDs or lower than the mean value for young adult women, referred to as a T-score of −2.5 SD or less. A measurement of BMD at the femoral neck, and derivation of the T-score using the National Health and Nutrition Examination Survey (NHANES) III reference database with women aged 20–29 years, has been proposed as the reference standard for describing osteoporosis. However, other sites such as the total hip, lumbar spine and radius are frequently used in clinical practice [5,6]. Aging leads to bone loss, and, particularly, the first few years after menopause represent a period of accelerated bone loss [7]. Thus, the prevalence of osteoporosis increases with age [2].

Concluding remarks

     Bone fragility and the resulting fractures are very common in postmenopausal women and projections indicate that, due to the aging population, an increase in the number of osteoporotic fractures, accompanied by substantially increased DALYs and financial costs, is to be expected globally. Secondary fracture prevention, orchestrated via implementation of FLS, so that a growing proportion of women at risk are evaluated and treated with osteoporosis medications is a crucial step in reducing fracture numbers. In addition, established primary prevention strategies, based on case-finding methods utilizing fracture prediction tools, such as FRAX, to identify women without fracture but with elevated risk, could be increasingly used in order to further reduce fracture numbers.

Nowadays, it is important for the detection of ultrasound images of breast tumors. In this paper, a new ultrasonic image feature extraction algorithm combining edge-based features and morphologic feature information is proposed, which has good effect on benign and malignant identification of breast tumors. This paper mainly studies three features (Sum of maximum curvature, Sum of maximum curvature and peak, Sum of maximum curvature and standard deviation) according to the shape histogram of ultrasound breast tumors from a local perspective. Based on the results of SVM classifier, it was found that the edgebased features have higher classification accuracy. The recognition system would perform better when morphologic features (Roughness, Regularity, Aspect ratio, Ellipticity, Roundness) were incorporated, compared with the control group whose input only with morphologic features. The results show that edge-based features can well describe breast tumors in ultrasound images, and have the potential to be used in breast ultrasound computer-aided design.

Introduction

The statistic report in 2017 shows that the average age of breast cancer patients in China is 48.7 [1]. Breast cancer has become a common disease among women in the current society [2]. Both the breast cancer’s morbidity and mortality are higher than that of other female malignant tumors. Clinical studies have shown that early detection and effective treatments can greatly improve the survival rate of female patients. However, there was no obvious symptoms in the initial when the patient got the cancer, which makes the detection difficult. Therefore, how to discover the lesion area of breast as early as possible so as to improve the cure rate of breast cancer has become a very important topic in the medical field. Ultrasound imaging is a convenient, low-cost, effective, realtime, non-radiation imaging tool, which has been widely used in clinical breast cancer detection [3,4]. In breast tumor diagnosis, the breast ultrasound computer-aided diagnosis (CAD) has been becoming more and more important. It performs better in image preprocessing, segmentation, feature extraction and selection, and tumor classification, including the objective evaluation ∗ Corresponding author. E-mail address: tongying@njit.edu.cn (Y. Tong). results, the classification accuracy, and the diagnostic sensitivity [5,6]. The extraction of different features is crucial in breast ultrasound CAD. Over the past years, research prevailingly concerned the morphologic feature extraction and texture feature extraction [7–9]. Texture features mainly reflect the surface properties of objects through pixels’ gray distribution and their surrounding spatial neighborhoods’ properties like the clarity, thickness and depth of the image texture.

Purpose: In this study, we used time-driven activity-based costing to increase efficiency in our ultrasound-guided breast biopsy practice by understanding costs associated with this procedure. Methods: We assembled a multidisciplinary team of all relevant stakeholders involved in ultrasound-guided breast biopsies, including a radiologist, a lead technologist, a clinical assistant, a licensed practical nurse, and a procedural support assistant. The team mapped each step in an ultrasound-guided breast biopsy from the time of scheduling a biopsy to patient checkout. We completed on average 20 time observations of each step involved in these biopsies from a provider’s perspective. Using capacity cost rate, we calculated the cost of all resources including personnel, supply, room, and equipment costs. Several costly steps were identified in the process, which led to the intervention of changing our overlapping biopsy times to staggered biopsy times. Time observations for each step and cost calculations were repeated postintervention. Results: Our postintervention data showed that the total time spent by the radiologist in an ultrasound breast biopsy decreased by 28%, accounting for 56% of the total cost in comparison with 63% pre-intervention. The radiologist’s wait time decreased by 38%, accounting for 28% of the total cost in comparison with 35% pre-intervention. Our total cost of the procedure decreased by 20%, and the personnel cost decreased by 25%. Conclusions: Time-driven activity-based costing is a practical way to calculate costs and identify non-value-added steps, which can foster strategies to improve efficiency and minimize waste.

Introduction

With increasing health care costs and evolving payment models, it is more critical than ever that medical providers maximize value by increasing efficiency and decreasing costs. Although value is difficult to define in health care, Porter’s widely cited definition of value is outcomes achieved relative to costs (outcomes/cost) [1]. To enhance value, one must improve outcomes or decrease cost. Although there has been a significant focus on improving patient outcomes in the recent past through regulatory and financial incentive models, there has been scant emphasis on decreasing cost. To decrease cost, one must be able to evaluate the true cost of a medical process or procedure. Calculating the cost of a medical process or procedure is challenging because various shareholders in the health care arena often define costs differently. Health care policy makers may refer to cost as the money paid to providers for medical services, and providers may define their costs by their reimbursement, but these are not necessarily the costs incurred by the health care system for a medical process or procedure [2,3]. Not knowing true costs prevents one from truly decreasing costs, which can have substantial negative downstream consequences such as the inability to adequately negotiate reimbursement in a bundled payment model.

The aim of this systematic review was to report pregnancy and perinatal outcomes of coronavirus spectrum infections, and particularly coronavirus 2019 (COVID-19) disease because of severe acute respiratory syndromeecoronavirus-2 infection during pregnancy. DATA SOURCES: Medline, Embase, Cinahl, and Clinicaltrials.gov databases were searched electronically utilizing combinations of word variants for coronavirus or severe acute respiratory syndrome or SARS or Middle East respiratory syndrome or MERS or COVID-19 and pregnancy. The search and selection criteria were restricted to English language. STUDY ELIGIBILITY CRITERIA: Inclusion criteria were hospitalized pregnant women with a confirmed coronavirus relatedeillness, defined as severe acute respiratory syndrome, Middle East respiratory syndrome, or COVID-19. STUDY APPRAISAL AND SYNTHESIS METHODS: We used meta-analyses of proportions to combine data and reported pooled proportions. The pregnancy outcomes observed included miscarriage, preterm birth, preeclampsia, preterm prelabor rupture of membranes, fetal growth restriction, and mode of delivery. The perinatal outcomes observed were fetal distress, Apgar score <7 at 5 minutes, neonatal asphyxia, admission to a neonatal intensive care unit, perinatal death, and evidence of vertical transmission. RESULTS: Nineteen studies including 79 hospitalized women were eligible for this systematic review: 41 pregnancies (51.9%) affected by COVID-19, 12 (15.2%) by Middle East respiratory syndrome, and 26 (32.9%) by severe acute respiratory syndrome. An overt diagnosis of pneumonia was made in 91.8%, and the most common symptoms were fever (82.6%), cough (57.1%), and dyspnea (27.0%). For all coronavirus infections, the rate of miscarriage was 39.1% (95% confidence interval, 20.2e59.8); the rate of preterm birth <37 weeks was 24.3% (95% confidence interval, 12.5e38.6); premature prelabor rupture of membranes occurred in 20.7% (95% confidence interval, 9.5e34.9), preeclampsia in 16.2% (95% confidence interval, 4.2e34.1), and fetal growth restriction in 11.7% (95% confidence interval, 3.2e24.4); 84% were delivered by cesarean; the rate of perinatal death was 11.1% (95% confidence interval, 84.8e19.6), and 57.2% of newborns (95% confidence interval, 3.6e99.8) were admitted to the neonatal intensive care unit. When focusing on COVID-19, the most common adverse pregnancy outcome was preterm birth <37 weeks, occurring in 41.1% of cases (95% confidence interval, 25.6e57.6), while the rate of perinatal death was 7.0% (95% confidence interval, 1.4e16.3). None of the 41 newborns assessed showed clinical signs of vertical transmission. CONCLUSION: In hospitalized mothers infected with coronavirus infections, including COVID-19, >90% of whom also had pneumonia, preterm birth is the most common adverse pregnancy outcome. COVID-19 infection was associated with a relatively higher rate of preterm birth, preeclampsia, cesarean, and perinatal death.

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Abstract

Background
Acute stroke during pregnancy or within 6 weeks of childbirth is devastating for the mother and her family, yet data regarding incidence and contemporary trends are very limited.
Objectives
This study sought to investigate the incidence and outcomes of acute stroke and transient ischemic attack during pregnancy or within 6 weeks of childbirth in a large database.
Methods
The National Inpatient Sample was queried to identify women age ≥18 years in the United States with pregnancy-related hospitalizations from January 1, 2007, to September 30, 2015. Temporal trends in acute stroke (ischemic and hemorrhagic)/transient ischemic attack incidence and in-hospital mortality were extracted.
Results
Among 37,360,772 pregnancy-related hospitalizations, 16,694 (0.045%) women had an acute stroke. The rates of acute stroke did not change (42.8 per 100,000 hospitalizations in 2007 vs. 42.2 per 100,000 hospitalizations in 2015; ptrends = 0.10). Among those with acute stroke, there were increases in prevalence of obesity, smoking, hyperlipidemia, migraine, and gestational hypertension. Importantly, in-hospital mortality rates were almost 385-fold higher among those who had a stroke (42.1 per 1,000 pregnancy-related hospitalizations vs. 0.11 per 1,000 pregnancy-related hospitalizations; p < 0.0001). The rates of in-hospital mortality among pregnant women with acute stroke decreased (5.5% in 2007 vs. 2.7% in 2015; ptrends < 0.001).
Conclusions
In this contemporary analysis of pregnancy-related hospitalizations, acute stroke occurred in 1 of every 2,222 hospitalizations, and these rates did not decrease over approximately 9 years. The prevalence of most stroke risk factors has increased. Acute stroke during pregnancy and puerperium was associated with high maternal mortality, although it appears to be trending downward. Future studies to better identify mechanisms and approaches to prevention and management of acute stroke during pregnancy and puerperium are warranted.