Impact of Caesarean section on subsequent fertility: a systematic review and meta-analysis.

STUDY QUESTION: Is there an association between a Caesarean section and subsequent fertility? SUMMARY ANSWER: Most studies report that fertility is reduced after Caesarean section compared with vaginal delivery. However, studies with a more robust design show smaller effects and it is uncertain whether the association is causal. WHAT IS KNOWN ALREADY: A previous systematic review published in 1996 summarizing six studies including 85 728 women suggested that Caesarean section reduces subsequent fertility. The included studies suffer from severe methodological limitations. STUDY DESIGN, SIZE, DURATION: Systematic review and meta-analysis of cohort studies comparing subsequent reproductive outcomes of women who had a Caesarean section with those who delivered vaginally. PARTICIPANTS/MATERIALS, SETTING, METHODS: Searches of Cochrane Library, Medline, Embase, CINAHL Plus and Maternity and Infant Care databases were conducted in December 2011 to identify randomized and non-randomized studies that compared the subsequent fertility outcomes after a Caesarean section and after a vaginal delivery. Eighteen cohort studies including 591 850 women matched the inclusion criteria. Risk of bias was assessed by the Newcastle-Ottawa scale (NOS). Data extraction was done independently by two reviewers. The meta-analysis was based on a random-effects model. Subgroup analyses were performed to assess whether the estimated effect was influenced by parity, risk adjustment, maternal choice, cohort period, and study quality and size. MAIN RESULTS AND THE ROLE OF CHANCE: The impact of Caesarean section on subsequent pregnancies could be analysed in 10 studies and on subsequent births in 16 studies. A meta-analysis suggests that patients who had undergone a Caesarean section had a 9% lower subsequent pregnancy rate [risk ratio (RR) 0.91, 95% confidence interval (CI) (0.87, 0.95)] and 11% lower birth rate [RR 0.89, 95% CI (0.87, 0.92)], compared with patients who had delivered vaginally. Studies that controlled for maternal age or specifically analysed primary elective Caesarean section for breech delivery, and those that were least prone to bias according to the NOS reported smaller effects. LIMITATIONS, REASONS FOR CAUTION: There is significant variation in the design and methods of included studies. Residual bias in the adjusted results is likely as no study was able to control for a number of important maternal characteristics, such as a history of infertility or maternal obesity. WIDER IMPLICATIONS OF THE FINDINGS: Further research is needed to reduce the impact of selection bias by indication through creating more comparable patient groups and applying risk adjustment

Ethnicity and OPRM variant independently predict pain perception and patient-controlled analgesia usage for post-operative pain

<p>Abstract</p> <p>Background</p> <p>Morphine consumption can vary widely between individuals even for identical surgical procedures. As mu-opioid receptor (OPRM1) is known to modulate pain perception and mediate the analgesic effects of opioid compounds in the central nervous system, we examined the influence of two OPRM polymorphisms on acute post-operative pain and morphine usage in women undergoing elective caesarean delivery.</p> <p>Results</p> <p>Data on self-reported pain scores and amount of total morphine use according to patient-controlled analgesia were collected from 994 women from the three main ethnic groups in Singapore. We found statistically significant association of the OPRM 118A>G with self-administered morphine during the first 24-hour postoperative period both in terms of total morphine (p = 1.7 × 10^-5) and weight-adjusted morphine (p = 6.6 × 10^-5). There was also significant association of this OPRM variant and time-averaged self-rated pain scores (p = 0.024). OPRM 118G homozygotes used more morphine and reported higher pain scores than 118A carriers. Other factors which influenced pain score and morphine usage include ethnicity, age and paying class.</p> <p>Conclusion</p> <p>Our results suggest that ethnicity and OPRM 118A>G genotype are independent and significant contributors to variation in pain perception and postoperative morphine use in patients undergoing cesarean delivery.</p

A randomized neural network for data streams

© 2017 IEEE. Randomized neural network (RNN) is a highly feasible solution in the era of big data because it offers a simple and fast working principle in processing dynamic and evolving data streams. This paper proposes a novel RNN, namely recurrent type-2 random vector functional link network (RT2McRVFLN), which provides a highly scalable solution for data streams in a strictly online and integrated framework. It is built upon the psychologically inspired concept of metacognitive learning, which covers three basic components of human learning: what-to-learn, how-to-learn, and when-to-learn. The what-to-learn selects important samples on the fly with the use of online active learning scenario, which renders our algorithm an online semi-supervised algorithm. The how-to-learn process combines an open structure of evolving concept and a randomized learning algorithm of random vector functional link network (RVFLN). The efficacy of the RT2McRVFLN has been numerically validated through two real-world case studies and comparisons with its counterparts, which arrive at a conclusive finding that our algorithm delivers a tradeoff between accuracy and simplicity

Credit card fraud detection using a hierarchical behavior-knowledge space model

Data Availability: All relevant benchmark data are within the manuscript, given in references [24], [25], and [26]. Relevant real data records are available from a public repository: https://doi.org/10.6084/m9.figshare.17030138.Copyright: © 2022 Nandi et al. With the advancement in machine learning, researchers continue to devise and implement effective intelligent methods for fraud detection in the financial sector. Indeed, credit card fraud leads to billions of dollars in losses for merchants every year. In this paper, a multi-classifier framework is designed to address the challenges of credit card fraud detections. An ensemble model with multiple machine learning classification algorithms is designed, in which the Behavior-Knowledge Space (BKS) is leveraged to combine the predictions from multiple classifiers. To ascertain the effectiveness of the developed ensemble model, publicly available data sets as well as real financial records are employed for performance evaluations. Through statistical tests, the results positively indicate the effectiveness of the developed model as compared with the commonly used majority voting method for combination of predictions from multiple classifiers in tackling noisy data classification as well as credit card fraud detection problems.Funding: The author(s) received no specific funding for this work

An intelligent payment card fraud detection system

This is the author accepted manuscript. The final version is available from Springer via the DOI in this recordPayment cards offer a simple and convenient method for making purchases. Owing to the increase in the usage of payment cards, especially in online purchases, fraud cases are on the rise. The rise creates financial risk and uncertainty, as in the commercial sector, it incurs billions of losses each year. However, real transaction records that can facilitate the development of effective predictive models for fraud detection are difficult to obtain, mainly because of issues related to confidentially of customer information. In this paper, we apply a total of 13 statistical and machine learning models for payment card fraud detection using both publicly available and real transaction records. The results from both original features and aggregated features are analyzed and compared. A statistical hypothesis test is conducted to evaluate whether the aggregated features identified by a genetic algorithm can offer a better discriminative power, as compared with the original features, in fraud detection. The outcomes positively ascertain the effectiveness of using aggregated features for undertaking real-world payment card fraud detection problems

Differential Dynamics of Transposable Elements during Long-Term Diploidization of Nicotiana Section Repandae (Solanaceae) Allopolyploid Genomes

PubMed ID: 23185607This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited

Universal antenatal human immunodeficiency virus testing in Hong Kong: consensus statement.

Following the recommendations of the Advisory Council on AIDS, Hong Kong, the Hospital Authority announced plans to introduce universal antenatal screening for human immunodeficiency virus infection and hence, a consensus conference was held to discuss strategies for implementing such screening in Hong Kong. This paper reports the discussions of the consensus conference. The consensus meeting group consisted of 15 clinicians and scientists from Hong Kong, Macau, and Thailand. Seven commonly asked questions concerning mother-to-child transmission of human immunodeficiency virus were selected for discussion by the participating panellists. Information on the laboratory diagnosis of human immunodeficiency virus infection and the efficacy of preventive measures in reducing mother-to-child transmission of human immunodeficiency virus were reviewed. Data from local studies was also presented and discussed. The timing, potential problems, and cost issues involved in testing all pregnant women in Hong Kong for human immunodeficiency virus were then considered.published_or_final_versio

Decline in Clostridium difficile-associated disease rates in Singapore public hospitals, 2006 to 2008

<p>Abstract</p> <p>Background</p> <p><it>Clostridium difficile </it>is the major cause of pseudomembranous colitis associated with antibiotic use, and the spread of the hypervirulent epidemic ribotype 027/NAP-1 strain across hospitals worldwide has re-focused attention on this nosocomial pathogen. The overall incidence and trend of <it>C. difficile</it>-associated disease (CDAD) in Singapore is unknown, and a surveillance program to determine these via formal laboratory-based reporting was established.</p> <p>Findings</p> <p>Laboratory and pharmacy data were collated from one tertiary and two secondary hospitals on a quarterly basis between 2006 and 2008. All hospitals tested for <it>C. difficile </it>using Immunocard Toxins A&B (Meridian Bioscience Inc., Cincinnati, OH) during this period. Duplicate positive <it>C. difficile </it>results within a 14-day period were removed. The CDAD results were compared with trends in hospital-based prescription of major classes of antibiotics.</p> <p>Overall CDAD incidence-density decreased from 5.16 (95%CI: 4.73 - 5.62) cases per 10,000 inpatient-days in 2006 to 2.99 (95%CI: 2.67 to 3.33) cases per 10,000 inpatient-days in 2008 (<it>p </it>< 0.001), while overall rates for <it>C. difficile </it>testing increased significantly (<it>p </it>< 0.001) within the same period. These trends were mirrored at the individual hospital level. Evaluation of antibiotic prescription data at all hospitals showed increasing use of carbapenems and fluoroquinolones, while cephalosporin and clindamycin prescription remained stable.</p> <p>Conclusions</p> <p>Our results demonstrate a real decline of CDAD rates in three large local hospitals. The cause is unclear and is not associated with improved infection control measures or reduction in antibiotic prescription. Lack of <it>C. difficile </it>stool cultures as part of routine testing precluded determination of the decline of a major clone as a potential explanation. For more accurate epidemiological trending of CDAD and early detection of epidemic clones, data collection will have to be expanded and resources set in place for reference laboratory culture and typing.</p

A systematic review of the data, methods and environmental covariates used to map Aedes-borne arbovirus transmission risk

BACKGROUND: Aedes (Stegomyia)-borne diseases are an expanding global threat, but gaps in surveillance make comprehensive and comparable risk assessments challenging. Geostatistical models combine data from multiple locations and use links with environmental and socioeconomic factors to make predictive risk maps. Here we systematically review past approaches to map risk for different Aedes-borne arboviruses from local to global scales, identifying differences and similarities in the data types, covariates, and modelling approaches used. METHODS: We searched on-line databases for predictive risk mapping studies for dengue, Zika, chikungunya, and yellow fever with no geographical or date restrictions. We included studies that needed to parameterise or fit their model to real-world epidemiological data and make predictions to new spatial locations of some measure of population-level risk of viral transmission (e.g. incidence, occurrence, suitability, etc.). RESULTS: We found a growing number of arbovirus risk mapping studies across all endemic regions and arboviral diseases, with a total of 176 papers published 2002-2022 with the largest increases shortly following major epidemics. Three dominant use cases emerged: (i) global maps to identify limits of transmission, estimate burden and assess impacts of future global change, (ii) regional models used to predict the spread of major epidemics between countries and (iii) national and sub-national models that use local datasets to better understand transmission dynamics to improve outbreak detection and response. Temperature and rainfall were the most popular choice of covariates (included in 50% and 40% of studies respectively) but variables such as human mobility are increasingly being included. Surprisingly, few studies (22%, 31/144) robustly tested combinations of covariates from different domains (e.g. climatic, sociodemographic, ecological, etc.) and only 49% of studies assessed predictive performance via out-of-sample validation procedures. CONCLUSIONS: Here we show that approaches to map risk for different arboviruses have diversified in response to changing use cases, epidemiology and data availability. We identify key differences in mapping approaches between different arboviral diseases, discuss future research needs and outline specific recommendations for future arbovirus mapping