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Objective: To compare oocyte maturation rates and pregnancy outcomes in women with polycystic ovary syndrome (PCOS) undergoing biphasic in vitro maturation (capacitation in vitro maturation [CAPA-IVM]) with vs. without follicle-stimulating hormone (FSH) priming., Design: Randomized, controlled, assessor-blinded trial., Setting: Private hospital., Patient(s): Women aged 18-37 years with PCOS and an indication for CAPA-IVM., Intervention(s): Participants were randomized (1:1) to undergo CAPA-IVM with or without FSH priming. The FSH priming group had 2 days of FSH injections before oocyte pickup; no FSH was given in the non-FSH group. After CAPA-IVM, day-5 embryos were vitrified for transfer in a subsequent cycle., Main Outcome Measure(s): The primary endpoint was number of matured oocytes. Secondary outcomes included rates of live birth, implantation, clinical pregnancy, ongoing pregnancy, pregnancy complications, obstetric and perinatal complications, and neonatal complications., Result(s): The number (interquartile range) of matured oocytes did not differ significantly in the non-FSH vs. FSH group (13 [9-18] vs. 14 [7-18]; absolute difference -1 [95% confidence interval -5 to 4]); other oocyte and embryology outcomes did not differ between groups. Rates of ongoing pregnancy and live birth were 38.3% in the non-FSH group and 31.7% in the FSH group (risk ratio for both outcomes: 1.21, 95% confidence interval 0.74-1.98). Maternal complications were infrequent and occurred at a similar rate in the two groups; there were no preterm deliveries before 32 weeks gestation., Conclusion(s): These findings open the possibility of a new, hormone-free approach to infertility treatment of women with PCOS., Competing Interests: Declaration of Interests L.N.V. has received speaker and conference fees from Merck; grant, speaker, and conference fees from Merck Sharpe and Dohme; and speaker, conference, and scientific board fees from Ferring. V.N.A.H. has nothing to disclose. A.H.L. has nothing to disclose. N.T.N. has nothing to disclose. T.D.P. has nothing to disclose. M.H.N.N. has nothing to disclose. H.L.L. has nothing to disclose. T.K.L. has nothing to disclose. A.N.H. has nothing to disclose. X.T.H.L. has nothing to disclose. H.H.P. has nothing to disclose. C.T.T. has nothing to disclose. B.G.H. has nothing to disclose. J.E.J.S. is a cofounder of Lavima Fertility Inc., a start-up company from the Vrije Universiteit Brussel that aims to develop new in vitro maturation systems. R.B.G. is a Scientific Advisory Board member for CooperSurgical. T.M.H. has received speaker fees from Merck, Merck Sharp and Dohme, and Ferring., (Copyright © 2024 American Society for Reproductive Medicine. Published by Elsevier Inc. All rights reserved.)

Importance: At least 10 million people in the United States have an intellectual and/or developmental disability (IDD). People with IDD experience considerably higher rates of poor overall health, chronic conditions including diabetes, mental health challenges, maternal mortality, and preventable deaths. This Special Communication proposes national goals based on a community-led consensus model that advances priority health outcomes for people with IDD and their caregivers/partners and identifies critical policy opportunities and challenges in achieving these goals. A community-led consensus agenda offers a foundation for focusing research, improving data collection and quality measurement, enhancing coverage and payment for services, and investing in a prepared clinical workforce and infrastructure in ways that align with lived experiences and perspectives of community members., Observations: People with IDD prioritize holistic health outcomes and tailored supports and services, driven by personalized health goals, which shift over their life course. Caregivers/partners need support for their own well-being, and easy access to resources to optimize how they support loved ones with IDD. Development of an adequately prepared clinical workforce to serve people with IDD requires national and regional policy changes that incentivize and structure training and continuing education. Ensuring effective and high-value coverage, payment, and clinical decisions requires investments in new data repositories and data-sharing infrastructure, shared learning across public and private payers, and development of new technologies and tools to empower people with IDD to actively participate in their own health care., Conclusions and Relevance: Consensus health priorities identified in this project and centered on IDD community members' perspectives are generalizable to many other patient populations. Public and private payers and regulators setting standards for health information technology have an opportunity to promote clinical data collection that focuses on individuals' needs, quality measurement that emphasizes person-centered goals rather than primarily clinical guidelines, and direct involvement of community members in the design of payment policies. Clinical education leaders, accrediting bodies, and investors/entrepreneurs have an opportunity to innovate a better prepared health care workforce and shared data infrastructure to support value-based care programs.

Sleep apnea (SA) is a significant respiratory condition that poses a major global health challenge. Deep Learning (DL) has emerged as an efficient tool for the classification problem in electrocardiogram (ECG)-based SA diagnoses. Despite these advancements, most common conventional feature extractions derived from ECG signals in DL, such as R-peaks and RR intervals, may fail to capture crucial information encompassed within the complete ECG segments. In this study, we propose an innovative approach to address this diagnostic gap by delving deeper into the comprehensive segments of the ECG signal. The proposed methodology draws inspiration from Matrix Profile algorithms, which generate an Euclidean distance profile from fixed-length signal subsequences. From this, we derived the Min Distance Profile (MinDP), Max Distance Profile (MaxDP), and Mean Distance Profile (MeanDP) based on the minimum, maximum, and mean of the profile distances, respectively. To validate the effectiveness of our approach, we use the modified LeNet-5 architecture as the primary CNN model, along with two existing lightweight models, BAFNet and SE-MSCNN. Our experiment results on the PhysioNet Apnea-ECG dataset (70 overnight recordings), and the UCDDB dataset (25 overnight recordings) revealed that our new feature extraction method achieved per-segment accuracies of up to 92.11% and 81.25%, respectively. Moreover, using the PhysioNet data, we achieved a per-recording accuracy of 100% and yielded the highest correlation of 0.989 compared to state-of-the-art methods. By introducing a new feature extraction method based on distance relationships, we enhanced the performance of certain lightweight models in DL, showing potential for home sleep apnea test (HSAT) and SA detection in IoT devices. The source code for this work is made publicly available in GitHub: https://github.com/vinuni-vishc/MPCNN-Sleep-Apnea.

Purpose: This study investigated the differences in the maturation rate of single versus grouped cumulus-oocyte complexes (COCs) culture methods for capacitation in vitro maturation (CAPA-IVM) in women with polycystic ovary syndrome (PCOS)., Methods: This study was performed at My Duc Phu Nhuan Hospital, Vietnam from October 1, 2020 to October 24, 2021. Women aged 18-37 years with a diagnosis of PCOS were recruited. COCs from each woman were randomly divided into two groups: single or grouped culture during CAPA-IVM culture. The primary outcome was the maturation rate., Results: A total of 322 COCs from 15 eligible women included were randomly assigned to the two study groups. The maturation rate was comparable between the single and grouped culture groups (61.3% vs. 64.8%; p  = 0.56). There were no significant differences in the number of 2-pronuclei fertilized oocytes, number of day-3 embryos, and number of good-quality embryos in the two culture method groups. In the single culture group, COCs morphology was associated with the day-3 embryo formation rate but not the maturation rate., Conclusions: Comparable oocyte maturation and embryology outcomes between single and grouped COCs culture utilizing sibling COCs derived from women with PCOS suggest the feasibility of both methods for CAPA-IVM culture., Competing Interests: LNV has received speaker and conference fees from Merck; and grant, speaker, and conference fees from Merck Sharpe & Dohme and Ferring. TMH has received speaker fees from Merck, Merck Sharp & Dohme, and Ferring. JS reports lecture fees from Ferring Pharmaceuticals, BioMerieux, Besins Female Healthcare and Merck, grants from Fund for Research Flanders (FWO), and is a co‐inventor on granted patents on CAPA‐IVM methodology in the USA (US10392601B2) and Europe (EP3234112B1). JS is currently Corporate Science Officer (CSO) of Lavima Inc, a spinoff company of the Free University Brussels (VUB) that aims to commercialize in vitro maturation media for oocytes. Other authors have no financial relationships with any organizations that might have an interest in the submitted work in the previous 3 years and no other relationships or activities that could appear to have influenced the submitted work., (© 2024 The Author(s). Reproductive Medicine and Biology published by John Wiley & Sons Australia, Ltd on behalf of Japan Society for Reproductive Medicine.)

Purpose: The purpose of this cohort study was to evaluate differences in rate of co-occurring mental health (MH) conditions among transition-age autistic youth (TAYA) who are Black, indigenous, and other people of color, and to identify enabling variables associated with any community MH visit in this population., Methods: Medicare-Medicaid Linked Enrollees Analytic Data Source 2012 data were used for this study. TAYA 14-29 years old who received fee-for-service Medicare, Medicaid, or both were included. Predisposing, enabling, and need variables associated with both presence of MH conditions and any community MH visit were examined with general linear modeling., Results: N = 122,250 TAYA were included. Black, Asian/Pacific Islander, and Hispanic TAYA were significantly less likely than White TAYA to have a diagnosis of substance-use, depressive, anxiety, attention-deficit hyperactivity disorder, or post-traumatic stress disorders. These groups were also significantly less likely to have had a community MH visit in the past year after controlling for predisposing, enabling, and need variables. Enabling variables associated with greater use of at least one community MH visit included dual enrollment in both Medicare and Medicaid and 12+ months of enrollment in 1115 or 1915(C) Medicaid waivers., Discussion: Service delivery factors are an important area of future research, particularly dual enrollment and coverage disparities for Black, indigenous, and other people of color TAYA. Examining coverage of managed care enrollees, including differences by state, may offer additional insights on how these factors impact care., (Copyright © 2024 Society for Adolescent Health and Medicine. Published by Elsevier Inc. All rights reserved.)

For the construction of hierarchical superstructures with biaxial anisotropic absorption, a newly synthesized diacetylene-functionalized bipyridinium is self-assembled to use an electron-accepting host for capturing and arranging guests. The formation of the donor-acceptor complex triggers an intermolecular charge transfer, leading to chromophore activation. Polarization-dependent multichroic thin films are prepared through a sequential process of single-coating, self-assembly, and topochemical polymerization of host-guest chromophores. Molecular packing structures constructed in the single-layer optical thin film possess orthogonal absorption axes for two different wavelengths. By tuning the linear polarization angle, the color of the optical thin film can be intentionally controlled. This single-layered multichroic film provides a new pathway for the development of anticounterfeiting and multiplexing encryptions.

Rotavin-M1 (POLYVAC) was licensed in Vietnam in 2012. The association of Rotavin-M1 with intussusception, a rare adverse event associated with rotavirus vaccines, and with adverse events following immunization (AEFI) have not been evaluated and monitored under conditions of routine use. From February 2017 to May 2021, we conducted a pilot introduction of Rotavin-M1 into the routine vaccination program in two provinces. Surveillance for intussusception was conducted at six sentinel hospitals. AEFI reports at 30 min and 7 days after vaccination were recorded. Among 443 children <12 months of age admitted for intussusception, most (92.3%) were children ≥ 6 months. Of the 388 children who were age-eligible to receive Rotavin-M1, 116 (29.9%) had received ≥1 dose. No intussusception cases occurred in the 1-21 days after dose 1 and one case occurred on day 21 after dose 2. Among the 45,367 children who received ≥1 dose of Rotavin-M1, 9.5% of children reported at least one AEFI after dose 1 and 7.3% after dose 2. Significantly higher AEFI rates occurred among children given Rotavin-M1 with pentavalent vaccines (Quinvaxem ® , ComBE Five ® ) compared to Rotavin-M1 without pentavalent vaccines. There was no association between intussusception and Rotavin-M1. The vaccine was generally safe when administered alone and when co-administered with other vaccines.

Objective: The aim was to identify healthcare payment and financing reforms to promote health equity and ways that the Agency for Healthcare Research and Quality (AHRQ) may promote those reforms., Data Sources and Study Setting: AHRQ convened a payment and financing workgroup-the authors of this paper-as part of its Health Equity Summit held in July 2022. This workgroup drew from its collective experience with healthcare payment and financing reform, as well as feedback from participants in a session at the Health Equity Summit, to identify the evidence base and promising paths for reforms to promote health equity., Study Design: The payment and financing workgroup developed an outline of reforms to promote health equity, presented the outline to participants in the payment and financing session of the July 2022 AHRQ Health Equity Summit, and integrated feedback from the participants., Data Collection/extraction Methods: This paper did not require novel data collection; the authors collected the data from the existing evidence base., Principal Findings: The paper outlines root causes of health inequity and corresponding potential reforms in five domains: (1) the differential distribution of resources between healthcare providers serving different communities, (2) scarcity of financing for populations most in need, (3) lack of integration/accountability, (4) patient cost barriers to care, and (5) bias in provider behavior and diagnostic tools., Conclusions: Additional research is necessary to determine whether the proposed reforms are effective in promoting health equity., (© 2023 The Authors. Health Services Research published by Wiley Periodicals LLC on behalf of Health Research and Educational Trust.)

Major advances in the treatment of multiple myeloma (MM) have been achieved by effective new agents such as proteasome inhibitors, immunomodulatory drugs, or monoclonal antibodies. Despite significant progress, MM remains still incurable and, recently, cellular immunotherapy has emerged as a promising treatment for relapsed/refractory MM. The emergence of chimeric antigen receptor (CAR) technology has transformed immunotherapy by enhancing the antitumor functions of T cells and natural killer (NK) cells, leading to effective control of hematologic malignancies. Recent advancements in gene delivery to NK cells have paved the way for the clinical application of CAR-NK cell therapy. CAR-NK cell therapy strategies have demonstrated safety, tolerability, and substantial efficacy in treating B cell malignancies in various clinical settings. However, their effectiveness in eliminating MM remains to be established. This review explores multiple approaches to enhance NK cell cytotoxicity, persistence, expansion, and manufacturing processes, and highlights the challenges and opportunities associated with CAR-NK cell therapy against MM. By shedding light on these aspects, this review aims to provide valuable insights into the potential of CAR-NK cell therapy as a promising approach for improving the treatment outcomes of MM patients., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. The author(s) declared that they were an editorial board member of Frontiers, at the time of submission. This had no impact on the peer review process and the final decision., (Copyright © 2023 Vu, Pham, Pham, Le, Vo, Jung, Lee and Nguyen.)

Due to the significant resemblance in visual appearance, pill misuse is prevalent and has become a critical issue, responsible for one-third of all deaths worldwide. Pill identification, thus, is a crucial concern that needs to be investigated thoroughly. Recently, several attempts have been made to exploit deep learning to tackle the pill identification problem. However, most published works consider only single-pill identification and fail to distinguish hard samples with identical appearances. Also, most existing pill image datasets only feature single pill images captured in carefully controlled environments under ideal lighting conditions and clean backgrounds. In this work, we are the first to tackle the multi-pill detection problem in real-world settings, aiming at localizing and identifying pills captured by users during pill intake. Moreover, we also introduce a multi-pill image dataset taken in unconstrained conditions. To handle hard samples, we propose a novel method for constructing heterogeneous a priori graphs incorporating three forms of inter-pill relationships, including co-occurrence likelihood, relative size, and visual semantic correlation. We then offer a framework for integrating a priori with pills' visual features to enhance detection accuracy. Our experimental results have proved the robustness, reliability, and explainability of the proposed framework. Experimentally, it outperforms all detection benchmarks in terms of all evaluation metrics. Specifically, our proposed framework improves COCO mAP metrics by 9.4% over Faster R-CNN and 12.0% compared to vanilla YOLOv5. Our study opens up new opportunities for protecting patients from medication errors using an AI-based pill identification solution., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2023 Nguyen et al. This 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.)

Developing effective electrocatalysts for the oxygen evolution reaction (OER) that are highly efficient, abundantly available, inexpensive, and environmentally friendly is critical to improving the overall efficiency of water splitting and the large-scale development of water splitting technologies. We, herein, introduce a facile synthetic strategy for depositing the self-supported arrays of 1D-porous nanoneedles of a manganese cobalt oxide (Mn 0.21 Co 2.79 O 4 : MCO) thin film demonstrating an enhanced electrocatalytic activity for OER in an alkaline electrolyte. For this, an MCO film was synthesized via thermal treatment of a hydroxycarbonate film obtained from a hydrothermal route. The deposited films were characterized through scanning electron microscopy (SEM), X-ray diffractometry (XRD), energy dispersive X-ray analysis (EDX), transmission electron microscopy (TEM), and X-ray photoelectron spectroscopy (XPS). In contrast to a similar 1D-array of a pristine Co 3 O 4 (CO) nanoneedle film, the MCO film exhibits a remarkably enhanced electrocatalytic performance in the OER with an 85 mV lower overpotential for the benchmark current density of 10 mA cm -2 . In addition, the MCO film also demonstrates long-term electrochemical stability for the OER in 1.0 M KOH aqueous electrolyte.

The National Academies of Sciences, Engineering, and Medicine's (NASEM's) 2021 report on primary care called for a hybrid payment approach-a mix of fee-for-service and population-based payment-with performance accountability to strike the proper balance for desired practice transformation and to support primary care's important and expanding role. The NASEM report also proposed substantial increases to primary care payment and reforms to the Medicare Physician Fee Schedule. This paper addresses pragmatic ways to implement these recommendations, describing and proposing solutions to the main implementation challenges. The urgent need for primary care payment reform calls for adopting a hybrid model within the Medicare fee schedule rather than engaging in another round of demonstrations, despite legal and practical obstacles to adoption. The paper explores reasons for adopting a roughly 50:50 blend of fee-for-service and population-based payment and addresses other design features, presenting reasons why spending accountability should rely on utilization measures under primary care control rather than performance on total cost of care, and proposes a fresh approach to quality, emphasizing that quality measures should be parsimonious, focused on important outcomes with demonstrated quality improvement., Competing Interests: Please see ICMJE form(s) for author conflicts of interest. These have been provided as supplementary materials., (© The Author(s) 2023. Published by Oxford University Press on behalf of Project HOPE - The People-To-People Health Foundation, Inc.)

Urbanization, storms, and floods have compromised the benefits derived from various types of sand dune landscapes, particularly in developing countries located in humid monsoon tropical regions. One pertinent question is which driving forces have had a dominant impact on the contributions of sand dune ecosystems to human well-being. Has the decline in sand dune ecosystem services (ES) been primarily due to urbanization or flooding hazards? This study aims to address these issues by developing a Bayesian Belief Network (BBN) to analyze six different sand dune landscapes worldwide. The study uses various data types, including multi-temporal and -sensor remote sensing (SAR and optical data), expert knowledge, statistics, and GIS to analyze the trends in sand dune ecosystems. A support tool based on probabilistic approaches was developed to assess changes in ES over time due to the effects of urbanization and flooding. The developed BBN has the potential to assess the ES values of sand dunes during both rainy and dry seasons. The study calculated and tested the ES values in detail over six years (from 2016 to 2021) in Quang Nam province, Vietnam. The results showed that urbanization has led to an increase in the total ES values since 2016, while floods only had a minimal impact on dune ES values during the rainy season. The fluctuations of ES values were found to be more significant due to urbanization than floods. The study's approach can be useful in future research on coastal ecosystems., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier B.V. All rights reserved.)

Mammography, or breast X-ray imaging, is the most widely used imaging modality to detect cancer and other breast diseases. Recent studies have shown that deep learning-based computer-assisted detection and diagnosis (CADe/x) tools have been developed to support physicians and improve the accuracy of interpreting mammography. A number of large-scale mammography datasets from different populations with various associated annotations and clinical data have been introduced to study the potential of learning-based methods in the field of breast radiology. With the aim to develop more robust and more interpretable support systems in breast imaging, we introduce VinDr-Mammo, a Vietnamese dataset of digital mammography with breast-level assessment and extensive lesion-level annotations, enhancing the diversity of the publicly available mammography data. The dataset consists of 5,000 mammography exams, each of which has four standard views and is double read with disagreement (if any) being resolved by arbitration. The purpose of this dataset is to assess Breast Imaging Reporting and Data System (BI-RADS) and breast density at the individual breast level. In addition, the dataset also provides the category, location, and BI-RADS assessment of non-benign findings. We make VinDr-Mammo publicly available as a new imaging resource to promote advances in developing CADe/x tools for mammography interpretation., (© 2023. The Author(s).)

Computer-aided diagnosis systems in adult chest radiography (CXR) have recently achieved great success thanks to the availability of large-scale, annotated datasets and the advent of high-performance supervised learning algorithms. However, the development of diagnostic models for detecting and diagnosing pediatric diseases in CXR scans is undertaken due to the lack of high-quality physician-annotated datasets. To overcome this challenge, we introduce and release PediCXR, a new pediatric CXR dataset of 9,125 studies retrospectively collected from a major pediatric hospital in Vietnam between 2020 and 2021. Each scan was manually annotated by a pediatric radiologist with more than ten years of experience. The dataset was labeled for the presence of 36 critical findings and 15 diseases. In particular, each abnormal finding was identified via a rectangle bounding box on the image. To the best of our knowledge, this is the first and largest pediatric CXR dataset containing lesion-level annotations and image-level labels for the detection of multiple findings and diseases. For algorithm development, the dataset was divided into a training set of 7,728 and a test set of 1,397. To encourage new advances in pediatric CXR interpretation using data-driven approaches, we provide a detailed description of the PediCXR data sample and make the dataset publicly available on https://physionet.org/content/vindr-pcxr/1.0.0/ ., (© 2023. The Author(s).)

Anti-nutritional factors (ANFs) substances in plant products, such as indigestible non-starchy polysaccharides (α-galactooligosaccharides, α-GOS), phytate, tannins, and alkaloids can impede the absorption of many critical nutrients and cause major physiological disorders. To enhance silage quality and its tolerance threshold for humans as well as other animals, ANFs must be reduced. This study aims to identify and compare the bacterial species/strains that are potential use for industrial fermentation and ANFs reduction. A pan-genome study of 351 bacterial genomes was performed, and binary data was processed to quantify the number of genes involved in the removal of ANFs. Among four pan-genomes analysis, all 37 tested Bacillus subtilis genomes had one phytate degradation gene, while 91 out of 150 Enterobacteriacae genomes harbor at least one genes (maximum three). Although, no gene encoding phytase detected in genomes of Lactobacillus and Pediococcus species, they have genes involving indirectly in metabolism of phytate-derivatives to produce Myo-inositol, an important compound in animal cells physiology. In contrast, genes related to production of lectin, tannase and saponin degrading enzyme did not include in genomes of B. subtilis and Pediococcus species. Our findings suggest a combination of bacterial species and/or unique strains in fermentation, for examples, two Lactobacillus strains (DSM 21115 and ATCC 14869) with B. subtilis SRCM103689, would maximize the efficiency in reducing the ANFs concentration. In conclusion, this study provides insights into bacterial genomes analysis for maximizing nutritional value in plant-based food. Further investigations of gene numbers and repertories correlated to metabolism of different ANFs will help clarifying the efficiency of time consuming and food qualities., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Pham, Kim and Nguyen.)

Purpose: This study evaluated the 24-month cumulative live birth rate (CLBR) for women with polycystic ovary syndrome (PCOS) or high antral follicle count (AFC) who underwent oocyte in vitro maturation (IVM) with pre-maturation step (CAPA-IVM)., Methods: This multicenter, retrospective study was performed at IVFMD, My Duc Hospital, and IVFMD Phu Nhuan, My Duc Phu Nhuan Hospital from 1 January 2017 to 31 December 2019. All women with PCOS or high AFC treated with a CAPA-IVM cycle were included. Cumulative live birth was defined as at least one live birth resulting from the initiated CAPA-IVM cycle. Where a woman did not return for embryo transfer, outcomes were followed up until 24 months from the day of oocyte aspiration. Logistic regression was performed to identify factors predicting the CLBR., Results: Data from 374 women were analyzed, 368 of whom had embryos for transfer (98.4%), and six had no embryos for transfer (1.6%). The oocyte maturation rate was 63.2%. The median number of frozen embryos was 4 [quartile 1, 2; quartile 3, 6]. Cumulative clinical pregnancy and ongoing pregnancy rates were 60.4% and 43.6%, respectively. At 24 months after starting CAPA-IVM treatment, the CLBR was 38.5%. Multivariate analysis showed that patient age and number of frozen embryos were significant predictors of cumulative live birth after CAPA-IVM., Conclusions: CAPA-IVM could be considered as an alternative to in vitro fertilization for the management of infertility in women with PCOS or a high AFC who require assisted reproductive technology., (© 2023. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)

The COVID-19 pandemic has exposed the vulnerability of healthcare services worldwide, raising the need to develop novel tools to provide rapid and cost-effective screening and diagnosis. Clinical reports indicated that COVID-19 infection may cause cardiac injury, and electrocardiograms (ECG) may serve as a diagnostic biomarker for COVID-19. This study aims to utilize ECG signals to detect COVID-19 automatically. We propose a novel method to extract ECG signals from ECG paper records, which are then fed into one-dimensional convolution neural network (1D-CNN) to learn and diagnose the disease. To evaluate the quality of digitized signals, R peaks in the paper-based ECG images are labeled. Afterward, RR intervals calculated from each image are compared to RR intervals of the corresponding digitized signal. Experiments on the COVID-19 ECG images dataset demonstrate that the proposed digitization method is able to capture correctly the original signals, with a mean absolute error of 28.11 ms. The 1D-CNN model (SEResNet18), which is trained on the digitized ECG signals, allows to identify between individuals with COVID-19 and other subjects accurately, with classification accuracies of 98.42% and 98.50% for classifying COVID-19 vs. Normal and COVID-19 vs. other classes, respectively. Furthermore, the proposed method also achieves a high-level of performance for the multi-classification task. Our findings indicate that a deep learning system trained on digitized ECG signals can serve as a potential tool for diagnosing COVID-19., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2022 Nguyen et al. This 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.)

Deep learning, in recent times, has made remarkable strides when it comes to impressive performance for many tasks, including medical image processing. One of the contributing factors to these advancements is the emergence of large medical image datasets. However, it is exceedingly expensive and time-consuming to construct a large and trustworthy medical dataset; hence, there has been multiple research leveraging medical reports to automatically extract labels for data. The majority of this labor, however, is performed in English. In this work, we propose a data collecting and annotation pipeline that extracts information from Vietnamese radiology reports to provide accurate labels for chest X-ray (CXR) images. This can benefit Vietnamese radiologists and clinicians by annotating data that closely match their endemic diagnosis categories which may vary from country to country. To assess the efficacy of the proposed labeling technique, we built a CXR dataset containing 9,752 studies and evaluated our pipeline using a subset of this dataset. With an F1-score of at least 0.9923, the evaluation demonstrates that our labeling tool performs precisely and consistently across all classes. After building the dataset, we train deep learning models that leverage knowledge transferred from large public CXR datasets. We employ a variety of loss functions to overcome the curse of imbalanced multi-label datasets and conduct experiments with various model architectures to select the one that delivers the best performance. Our best model (CheXpert-pretrained EfficientNet-B2) yields an F1-score of 0.6989 (95% CI 0.6740, 0.7240), AUC of 0.7912, sensitivity of 0.7064 and specificity of 0.8760 for the abnormal diagnosis in general. Finally, we demonstrate that our coarse classification (based on five specific locations of abnormalities) yields comparable results to fine classification (twelve pathologies) on the benchmark CheXpert dataset for general anomaly detection while delivering better performance in terms of the average performance of all classes., Competing Interests: The authors have declared that no competing interests exist.

Objective: This study evaluated the effectiveness of Paraffin oil versus Mineral oil for day-5 embryo culture in couples undergoing assisted reproductive technology (ART)., Methods: We performed a multi-centre, retrospective cohort study at IVFMD (My Duc Hospital) and IVFMD Phu Nhuan (My Duc Phu Nhuan Hospital) from January 2019 to September 2019. We studied couples treated by intracytoplasmic sperm injection (ICSI), using fresh, ejaculated semen and undergoing day-5 embryo transfer. Couples who underwent in vitro maturation (IVM) or oocyte donation cycles or couples where the woman had uterine abnormalities were excluded. From January 2019 to May 2019, we used Mineral oil (LiteOil, LifeGlobal) while Paraffin oil (Liquid Paraffin, Origio) was used from June 2019 to September 2019. The primary outcome was live birth rate after the first transfer, either from a fresh transfer or frozen embryo transfer., Results: Between 1st January 2019 to 30th September 2019, there were 2,312 couples undergoing ART in both centres, of which 762 (377 in the Paraffin group and 385 in the Mineral group) eligible couples were included in the study. Baseline characteristics of couples were comparable between the two groups, with mean female age 31.5 ± 4.3 versus 31.9 ± 4.7 in the Paraffin and Mineral group. Live birth after the first transfer occurred in 153 (40.6%) couples in the Paraffin group, compared to 152 (39.5%) couples in the Mineral group (risk ratio 1.02, 95% confidence interval 0.91 - 1.14). Other secondary outcomes were comparable between the two groups., Conclusion: In day-5 embryo culture, Paraffin and Mineral oil resulted in a comparable live birth rate., (Copyright © 2022 Elsevier B.V. All rights reserved.)

Background: The purpose of this paper is to demonstrate a mechanism for deploying and validating an AI-based system for detecting abnormalities on chest X-ray scans at the Phu Tho General Hospital, Vietnam. We aim to investigate the performance of the system in real-world clinical settings and compare its effectiveness to the in-lab performance., Method: The AI system was directly integrated into the Hospital's Picture Archiving and Communication System (PACS) after being trained on a fixed annotated dataset from other sources. The system's performance was prospectively measured by matching and comparing the AI results with the radiology reports of 6,285 chest X-ray examinations extracted from the Hospital Information System (HIS) over the last 2 months of 2020. The normal/abnormal status of a radiology report was determined by a set of rules and served as the ground truth., Results: Our system achieves an F1 score-the harmonic average of the recall and the precision-of 0.653 (95% CI 0.635, 0.671) for detecting any abnormalities on chest X-rays. This corresponds to an accuracy of 79.6%, a sensitivity of 68.6%, and a specificity of 83.9%., Conclusions: Computer-Aided Diagnosis (CAD) systems for chest radiographs using artificial intelligence (AI) have recently shown great potential as a second opinion for radiologists. However, the performances of such systems were mostly evaluated on a fixed dataset in a retrospective manner and, thus, far from the real performances in clinical practice. Despite a significant drop from the in-lab performance, our result establishes a reasonable level of confidence in applying such a system in real-life situations., Competing Interests: Authors HN, NTN, and TN were employed by the company VinBigData JSC. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Nguyen, Nguyen, Nguyen, Nguyen, Pham and Nguyen.)

Most of the existing chest X-ray datasets include labels from a list of findings without specifying their locations on the radiographs. This limits the development of machine learning algorithms for the detection and localization of chest abnormalities. In this work, we describe a dataset of more than 100,000 chest X-ray scans that were retrospectively collected from two major hospitals in Vietnam. Out of this raw data, we release 18,000 images that were manually annotated by a total of 17 experienced radiologists with 22 local labels of rectangles surrounding abnormalities and 6 global labels of suspected diseases. The released dataset is divided into a training set of 15,000 and a test set of 3,000. Each scan in the training set was independently labeled by 3 radiologists, while each scan in the test set was labeled by the consensus of 5 radiologists. We designed and built a labeling platform for DICOM images to facilitate these annotation procedures. All images are made publicly available in DICOM format along with the labels of both the training set and the test set., (© 2022. The Author(s).)

Advanced deep learning (DL) algorithms may predict the patient's risk of developing breast cancer based on the Breast Imaging Reporting and Data System (BI-RADS) and density standards. Recent studies have suggested that the combination of multi-view analysis improved the overall breast exam classification. In this paper, we propose a novel multi-view DL approach for BI-RADS and density assessment of mammograms. The proposed approach first deploys deep convolutional networks for feature extraction on each view separately. The extracted features are then stacked and fed into a Light Gradient Boosting Machine (LightGBM) classifier to predict BI-RADS and density scores. We conduct extensive experiments on both the internal mammography dataset and the public dataset Digital Database for Screening Mammogra-phy (DDSM). The experimental results demonstrate that the proposed approach outperforms the single-view classification approach on two benchmark datasets by huge F1-score margins (+5% on the internal dataset and +10% on the DDSM dataset). These results highlight the vital role of combining multi-view information to improve the performance of breast cancer risk prediction.

Purpose: A fully automated system for interpreting abdominal computed tomography (CT) scans with multiple phases of contrast enhancement requires an accurate classification of the phases. Current approaches to classify the CT phases are commonly based on three-dimensional (3D) convolutional neural network (CNN) approaches with high computational complexity and high latency. This work aims at developing and validating a precise, fast multiphase classifier to recognize three main types of contrast phases in abdominal CT scans., Methods: We propose in this study a novel method that uses a random sampling mechanism on top of deep CNNs for the phase recognition of abdominal CT scans of four different phases: noncontrast, arterial, venous, and others. The CNNs work as a slicewise phase prediction, while random sampling selects input slices for the CNN models. Afterward, majority voting synthesizes the slicewise results of the CNNs to provide the final prediction at the scan level., Results: Our classifier was trained on 271 426 slices from 830 phase-annotated CT scans, and when combined with majority voting on 30% of slices randomly chosen from each scan, achieved a mean F1 score of 92.09% on our internal test set of 358 scans. The proposed method was also evaluated on two external test sets: CTPAC-CCRCC (N = 242) and LiTS (N = 131), which were annotated by our experts. Although a drop in performance was observed, the model performance remained at a high level of accuracy with a mean F1 scores of 76.79% and 86.94% on CTPAC-CCRCC and LiTS datasets, respectively. Our experimental results also showed that the proposed method significantly outperformed the state-of-the-art 3D approaches while requiring less computation time for inference., Conclusions: In comparison to state-of-the-art classification methods, the proposed approach shows better accuracy with significantly reduced latency. Our study demonstrates the potential of a precise, fast multiphase classifier based on a two-dimensional deep learning approach combined with a random sampling method for contrast phase recognition, providing a valuable tool for extracting multiphase abdomen studies from low veracity, real-world data., (© 2022 American Association of Physicists in Medicine.)

Purpose: This study evaluated the influence of post-warming culture time on the live birth rate in day-3 and day-5 frozen embryo transfer (FET) cycles., Methods: This multicenter, retrospective cohort study was performed at IVFMD, My Duc Hospital and IVFMD Phu Nhuan, My Duc Phu Nhuan Hospital in Vietnam between October 2019 and October 2020. Women who underwent FET cycles with the transfer of ≤2 day-3 or day-5 embryos were included in the study. FET cycles were divided into four groups based on the quartiles for the time between embryo warming and embryo transfer. The primary outcome was live birth after FET., Results: Of 2548 FET cycles, 885 and 1663 cycles, respectively, had transfer of day-3 or day-5 embryos. Post-warming culture time ranged from 0.07 to 6.1 h. There were no significant differences between the post-warming culture time quartiles with respect to the number of embryos thawed, the number of embryos transferred, and the number of top-quality embryos transferred. Post-warming culture time was not significantly associated with the live birth rate in FET cycles using either day-3 or day-5 embryos., Conclusions: Post-warming culture time did not affect live birth rate in FET cycles. Therefore, IVF centers should consider scheduling workflows to best suit the patient., Competing Interests: LNV has received speaker and conference fees from Merck; and grant, speaker, and conference fees from Merck Sharpe & Dohme and Ferring. TMH has received speaker fees from Merck, Merck Sharp & Dohme, and Ferring. HHP, TMV, CHN, DPN, AHL, and TDP have no financial relationships with any organizations that might have an interest in the submitted work in the previous 3 years and no other relationships or activities that could appear to have influenced the submitted work., (© 2022 The Authors. Reproductive Medicine and Biology published by John Wiley & Sons Australia, Ltd on behalf of Japan Society for Reproductive Medicine.)

Despite improvements in medical care, the burden of sepsis remains high. In this study, we evaluated the incremental cost associated with postoperative sepsis and the impact of postoperative sepsis on clinical outcomes among surgical patients in Vietnam. We used the national database that contained 1,241,893 surgical patients undergoing seven types of surgery. We controlled the balance between the groups of patients using propensity score matching method. Generalized gamma regression and logistic regression were utilized to estimate incremental cost, readmission, and reexamination associated with postoperative sepsis. The average incremental cost associated with postoperative sepsis was 724.1 USD (95% CI 553.7-891.7) for the 30 days after surgery, which is equivalent to 28.2% of the per capita GDP in Vietnam in 2018. The highest incremental cost was found in patients undergoing cardiothoracic surgery, at 2,897 USD (95% CI 530.7-5263.2). Postoperative sepsis increased patient odds of readmission (OR = 6.40; 95% CI 6.06-6.76), reexamination (OR = 1.67; 95% CI 1.58-1.76), and also associated with 4.9 days longer of hospital length of stay among surgical patients. Creating appropriate prevention strategies for postoperative sepsis is extremely important, not only to improve the quality of health care but also to save health financial resources each year., (© 2022. The Author(s).)

&lt;b&gt;Background and Objective:&lt;/b&gt; &lt;i&gt;Pieris rapae&lt;/i&gt; L., is one of the most widespread and destructive pests of cruciferous plants. At present, synthetic chemical insecticide is still the main approach to control this pest despite several disadvantages to human health and the wildlife environment as well as biological resistance. To search for plants having insecticidal activity, the biological effects of two medicinal plants &lt;i&gt;Kaempferia galanga&lt;/i&gt; L. and &lt;i&gt;Amomum subulatum &lt;/i&gt;on &lt;i&gt;Pieris rapae&lt;/i&gt; L., were investigated. &lt;b&gt;Materials and Methods:&lt;/b&gt; The methanol extracts of dry rhizomes and fruits of &lt;i&gt;Kaempferia galanga&lt;/i&gt; L. and &lt;i&gt;Amomum subulatum &lt;/i&gt;were used to determine the mortality, feeding and oviposition deterrence of larvae and adult of &lt;i&gt;Pieris rapae&lt;/i&gt; L. &lt;b&gt;Results:&lt;/b&gt; &lt;i&gt;Kaempferia galanga&lt;/i&gt; L. and &lt;i&gt;Amomum subulatum&lt;/i&gt; exhibited insecticidal activity against &lt;i&gt;Pieris rapae&lt;/i&gt; L., with LC&lt;sub&gt;50&lt;/sub&gt; values of 2.11 and 11.80% (w/v), respectively. In the antifeedant test, &lt;i&gt;Kaempferia galanga&lt;/i&gt; L., extract showed no significant difference with the control at the low concentration (0.5 and 1%). Whereas, with a concentration of 0.5%, &lt;i&gt;Amomum subulatum&lt;/i&gt; extract demonstrated a high antifeedant effect on &lt;i&gt;Pieris rapae&lt;/i&gt; L., larvae. In addition, plants treated with these two extracts reduced eggs laid by &lt;i&gt;Pieris rapae&lt;/i&gt; L., in field conditions showing the oviposition deterrent properties. &lt;b&gt;Conclusion:&lt;/b&gt; These results indicated that &lt;i&gt;Kaempferia galanga&lt;/i&gt; L. and &lt;i&gt;Amomum subulatum &lt;/i&gt;extracts have insecticidal substances against &lt;i&gt;Pieris rapae &lt;/i&gt;L., which can be used for developing effective pesticides or/and oviposition deterrents for integrated pest management.

Myocardial infarction is a considerable burden on public health. However, there is a lack of information about its economic impact on both the individual and national levels. This study aims to estimate the incremental cost, readmission risk, and length of hospital stay due to myocardial infarction as a post-operative complication. We used data from a standardized national system managed by the Vietnam Social Insurance database. The original sample size was 1,241,893 surgical patients who had undergone one of seven types of surgery. A propensity score matching method was applied to create a matched sample for cost analysis. A generalized linear model was used to estimate direct treatment costs, the length of stay, and the effect of the complication on the readmission of surgical patients. Myocardial infarction occurs most frequently after vascular surgery. Patients with a myocardial infarction complication were more likely to experience readmission within 30 and 90 days, with an OR of 3.45 (95%CI: 2.92-4.08) and 4.39 (95%CI: 3.78-5.10), respectively. The increments of total costs at 30 and 90 days due to post-operative myocardial infarction were 4,490.9 USD (95%CI: 3882.3-5099.5) and 4,724.6 USD (95%CI: 4111.5-5337.8) per case, while the increases in length of stay were 4.9 (95%CI: 3.6-6.2) and 5.7 (95%CI: 4.2-7.2) per case, respectively. Perioperative myocardial infarction contributes significantly to medical costs for the individual and the national economy. Patients with perioperative myocardial infarction are more likely to be readmitted and face a longer treatment duration., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2021 Bui, Khuong, Dao, Le, Nguyen, Tran, Duong, Duong, Tran, Pham, Dao and Le.)