Publisher Correction: Nucleus segmentation across imaging experiments: the 2018 Data Science Bowl.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Deductive automated pollen classification in environmental samples via exploratory deep learning and imaging flow cytometry.

Pollen and tracheophyte spores are ubiquitous environmental indicators at local and global scales. Palynology is typically performed manually by microscopic analysis; a specialised and time-consuming task limited in taxonomical precision and sampling frequency, therefore restricting data quality used to inform climate change and pollen forecasting models. We build on the growing work using AI (artificial intelligence) for automated pollen classification to design a flexible network that can deal with the uncertainty of broad-scale environmental applications. We combined imaging flow cytometry with Guided Deep Learning to identify and accurately categorise pollen in environmental samples; here, pollen grains captured within c. 5500 Cal yr BP old lake sediments. Our network discriminates not only pollen included in training libraries to the species level but, depending on the sample, can classify previously unseen pollen to the likely phylogenetic order, family and even genus. Our approach offers valuable insights into the development of a widely transferable, rapid and accurate exploratory tool for pollen classification in 'real-world' environmental samples with improved accuracy over pure deep learning techniques. This work has the potential to revolutionise many aspects of palynology, allowing a more detailed spatial and temporal understanding of pollen in the environment with improved taxonomical resolution.

In vivo biodistribution and pharmacokinetics of sotrovimab, a SARS-CoV-2 monoclonal antibody, in healthy cynomolgus monkeys.

PURPOSE: Sotrovimab (VIR-7831), a human IgG1κ monoclonal antibody (mAb), binds to a conserved epitope on the SARS-CoV-2 spike protein receptor binding domain (RBD). The Fc region of VIR-7831 contains an LS modification to promote neonatal Fc receptor (FcRn)-mediated recycling and extend its serum half-life. Here, we aimed to evaluate the impact of the LS modification on tissue biodistribution, by comparing VIR-7831 to its non-LS-modified equivalent, VIR-7831-WT, in cynomolgus monkeys. METHODS: 89Zr-based PET/CT imaging of VIR-7831 and VIR-7831-WT was performed up to 14 days post injection. All major organs were analyzed for absolute concentration as well as tissue:blood ratios, with the focus on the respiratory tract, and a physiologically based pharmacokinetics (PBPK) model was used to evaluate the tissue biodistribution kinetics. Radiomics features were also extracted from the PET images and SUV values. RESULTS: SUVmean uptake in the pulmonary bronchi for 89Zr-VIR-7831 was statistically higher than for 89Zr-VIR-7831-WT at days 6 (3.43 ± 0.55 and 2.59 ± 0.38, respectively) and 10 (2.66 ± 0.32 and 2.15 ± 0.18, respectively), while the reverse was observed in the liver at days 6 (5.14 ± 0.80 and 8.63 ± 0.89, respectively), 10 (4.52 ± 0.59 and 7.73 ± 0.66, respectively), and 14 (4.95 ± 0.65 and 7.94 ± 0.54, respectively). Though the calculated terminal half-life was 21.3 ± 3.0 days for VIR-7831 and 16.5 ± 1.1 days for VIR-7831-WT, no consistent differences were observed in the tissue:blood ratios between the antibodies except in the liver. While the lung:blood SUVmean uptake ratio for both mAbs was 0.25 on day 3, the PBPK model predicted the total lung tissue and the interstitial space to serum ratio to be 0.31 and 0.55, respectively. Radiomics analysis showed VIR-7831 had mean-centralized PET SUV distribution in the lung and liver, indicating more uniform uptake than VIR-7831-WT. CONCLUSION: The half-life extended VIR-7831 remained in circulation longer than VIR-7831-WT, consistent with enhanced FcRn binding, while the tissue:blood concentration ratios in most tissues for both drugs remained statistically indistinguishable throughout the course of the experiment. In the bronchiolar region, a higher concentration of 89Zr-VIR-7831 was detected. The data also allow unparalleled insight into tissue distribution and elimination kinetics of mAbs that can guide future biologic drug discovery efforts, while the residualizing nature of the 89Zr label sheds light on the sites of antibody catabolism.

Objective assessment of stored blood quality by deep learning.

Stored red blood cells (RBCs) are needed for life-saving blood transfusions, but they undergo continuous degradation. RBC storage lesions are often assessed by microscopic examination or biochemical and biophysical assays, which are complex, time-consuming, and destructive to fragile cells. Here we demonstrate the use of label-free imaging flow cytometry and deep learning to characterize RBC lesions. Using brightfield images, a trained neural network achieved 76.7% agreement with experts in classifying seven clinically relevant RBC morphologies associated with storage lesions, comparable to 82.5% agreement between different experts. Given that human observation and classification may not optimally discern RBC quality, we went further and eliminated subjective human annotation in the training step by training a weakly supervised neural network using only storage duration times. The feature space extracted by this network revealed a chronological progression of morphological changes that better predicted blood quality, as measured by physiological hemolytic assay readouts, than the conventional expert-assessed morphology classification system. With further training and clinical testing across multiple sites, protocols, and instruments, deep learning and label-free imaging flow cytometry might be used to routinely and objectively assess RBC storage lesions. This would automate a complex protocol, minimize laboratory sample handling and preparation, and reduce the impact of procedural errors and discrepancies between facilities and blood donors. The chronology-based machine-learning approach may also improve upon humans' assessment of morphological changes in other biomedically important progressions, such as differentiation and metastasis.

Cholesterol-Depletion-Induced Membrane Repair Carries a Raft Conformer of P-Glycoprotein to the Cell Surface, Indicating Enhanced Cholesterol Trafficking in MDR Cells, Which Makes Them Resistant to Cholesterol Modifications.

The human P-glycoprotein (P-gp), a transporter responsible for multidrug resistance, is present in the plasma membrane's raft and non-raft domains. One specific conformation of P-gp that binds to the monoclonal antibody UIC2 is primarily associated with raft domains and displays heightened internalization in cells overexpressing P-gp, such as in NIH-3T3 MDR1 cells. Our primary objective was to investigate whether the trafficking of this particular P-gp conformer is dependent on cholesterol levels. Surprisingly, depleting cholesterol using cyclodextrin resulted in an unexpected increase in the proportion of raft-associated P-gp within the cell membrane, as determined by UIC2-reactive P-gp. This increase appears to be a compensatory response to cholesterol loss from the plasma membrane, whereby cholesterol-rich raft micro-domains are delivered to the cell surface through an augmented exocytosis process. Furthermore, this exocytotic event is found to be part of a complex trafficking mechanism involving lysosomal exocytosis, which contributes to membrane repair after cholesterol reduction induced by cyclodextrin treatment. Notably, cells overexpressing P-gp demonstrated higher total cellular cholesterol levels, an increased abundance of stable lysosomes, and more effective membrane repair following cholesterol modifications. These modifications encompassed exocytotic events that involved the transport of P-gp-carrying rafts. Importantly, the enhanced membrane repair capability resulted in a durable phenotype for MDR1 expressing cells, as evidenced by significantly improved viabilities of multidrug-resistant Pgp-overexpressing immortal NIH-3T3 MDR1 and MDCK-MDR1 cells compared to their parents when subjected to cholesterol alterations.

Preventing Risks of Infections and Medication Errors in IV therapy (PRIME): a patient safety initiative.

BACKGROUND: Two major avoidable reasons for adverse events in hospital are medication errors and intravenous therapy-induced infections or complications. Training for clinical staff and compliance to patient safety principles could address these. METHODS: Joint Commission International (JCI) consultants created a standardised, 6-month training programme for clinical staff in hospitals. Twenty-one tertiary care hospitals from across south-east Asia took part. JCI trained the clinical consultants, who trained hospital safety champions, who trained nursing staff. Compliance and knowledge were assessed, and monthly audits were conducted. RESULTS: There was an overall increase of 29% in compliance with parameters around medication preparation and vascular access device management. CONCLUSION: The programme improved safe practice around preparing medications management and managing vascular access devices. The approach could be employed as a continuous quality improvement initiative for the prevention of medication errors and infusion-associated complications.

Nucleus segmentation across imaging experiments: the 2018 Data Science Bowl.

Segmenting the nuclei of cells in microscopy images is often the first step in the quantitative analysis of imaging data for biological and biomedical applications. Many bioimage analysis tools can segment nuclei in images but need to be selected and configured for every experiment. The 2018 Data Science Bowl attracted 3,891 teams worldwide to make the first attempt to build a segmentation method that could be applied to any two-dimensional light microscopy image of stained nuclei across experiments, with no human interaction. Top participants in the challenge succeeded in this task, developing deep-learning-based models that identified cell nuclei across many image types and experimental conditions without the need to manually adjust segmentation parameters. This represents an important step toward configuration-free bioimage analysis software tools.

CellProfiler 3.0: Next-generation image processing for biology.

CellProfiler has enabled the scientific research community to create flexible, modular image analysis pipelines since its release in 2005. Here, we describe CellProfiler 3.0, a new version of the software supporting both whole-volume and plane-wise analysis of three-dimensional (3D) image stacks, increasingly common in biomedical research. CellProfiler's infrastructure is greatly improved, and we provide a protocol for cloud-based, large-scale image processing. New plugins enable running pretrained deep learning models on images. Designed by and for biologists, CellProfiler equips researchers with powerful computational tools via a well-documented user interface, empowering biologists in all fields to create quantitative, reproducible image analysis workflows.

Cytotoxic components from the leaves of Erythrophleum fordii induce human acute leukemia cell apoptosis through caspase 3 activation and PARP cleavage.

Cassaine diterpenoids as erythrofordins A-C (1-3), pseudo-erythrosuamin (4), and erythrofordin U (5) isolated from the leaves of Vietnamese Erythrophleum fordii Oliver were tested cytotoxic activity against human leukemia cancer cells. The results showed that these metabolites exhibited dose-dependent cytotoxicity against human leukemia HL-60 and KG cells with IC50 values ranging from 15.2 ± 1.5 to 42.2 ± 3.6 µM. Treatment with erythrofordin B led to the apoptosis of HL-60 and KG cells due to the activation of caspase 3, caspase 9, and poly (ADP-ribose) polymerase (PARP). Erythrofordin B significantly increased Bak protein expression, but downregulated the anti-apoptotic protein Bcl-2, in HL-60 cells. In silico results demonstrated that erythrofordin B can bind to both the procaspase-3 allosteric site and the PARP-1 active site, with binding energies of -7.36 and -10.76 kcal/mol, respectively. These results indicated that the leaves of Vietnamese E. fordii, which contain cassaine diterpenoids, can induce the apoptosis of human leukemia cancer cells.

Inter-laboratory automation of the in vitro micronucleus assay using imaging flow cytometry and deep learning.

The in vitro micronucleus assay is a globally significant method for DNA damage quantification used for regulatory compound safety testing in addition to inter-individual monitoring of environmental, lifestyle and occupational factors. However, it relies on time-consuming and user-subjective manual scoring. Here we show that imaging flow cytometry and deep learning image classification represents a capable platform for automated, inter-laboratory operation. Images were captured for the cytokinesis-block micronucleus (CBMN) assay across three laboratories using methyl methanesulphonate (1.25-5.0 µg/mL) and/or carbendazim (0.8-1.6 µg/mL) exposures to TK6 cells. Human-scored image sets were assembled and used to train and test the classification abilities of the "DeepFlow" neural network in both intra- and inter-laboratory contexts. Harnessing image diversity across laboratories yielded a network able to score unseen data from an entirely new laboratory without any user configuration. Image classification accuracies of 98%, 95%, 82% and 85% were achieved for 'mononucleates', 'binucleates', 'mononucleates with MN' and 'binucleates with MN', respectively. Successful classifications of 'trinucleates' (90%) and 'tetranucleates' (88%) in addition to 'other or unscorable' phenotypes (96%) were also achieved. Attempts to classify extremely rare, tri- and tetranucleated cells with micronuclei into their own categories were less successful (≤ 57%). Benchmark dose analyses of human or automatically scored micronucleus frequency data yielded quantitation of the same equipotent concentration regardless of scoring method. We conclude that this automated approach offers significant potential to broaden the practical utility of the CBMN method across industry, research and clinical domains. We share our strategy using openly-accessible frameworks.

Label-Free Leukemia Monitoring by Computer Vision.

Acute lymphoblastic leukemia (ALL) is the most common childhood cancer. While there are a number of well-recognized prognostic biomarkers at diagnosis, the most powerful independent prognostic factor is the response of the leukemia to induction chemotherapy (Campana and Pui: Blood 129 (2017) 1913-1918). Given the potential for machine learning to improve precision medicine, we tested its capacity to monitor disease in children undergoing ALL treatment. Diagnostic and on-treatment bone marrow samples were labeled with an ALL-discriminating antibody combination and analyzed by imaging flow cytometry. Ignoring the fluorescent markers and using only features extracted from bright-field and dark-field cell images, a deep learning model was able to identify ALL cells at an accuracy of >88%. This antibody-free, single cell method is cheap, quick, and could be adapted to a simple, laser-free cytometer to allow automated, point-of-care testing to detect slow early responders. Adaptation to other types of leukemia is feasible, which would revolutionize residual disease monitoring. © 2020 The Authors. Cytometry Part A published by Wiley Periodicals, Inc. on behalf of International Society for Advancement of Cytometry.

Label-Free Identification of White Blood Cells Using Machine Learning.

White blood cell (WBC) differential counting is an established clinical routine to assess patient immune system status. Fluorescent markers and a flow cytometer are required for the current state-of-the-art method for determining WBC differential counts. However, this process requires several sample preparation steps and may adversely disturb the cells. We present a novel label-free approach using an imaging flow cytometer and machine learning algorithms, where live, unstained WBCs were classified. It achieved an average F1-score of 97% and two subtypes of WBCs, B and T lymphocytes, were distinguished from each other with an average F1-score of 78%, a task previously considered impossible for unlabeled samples. We provide an open-source workflow to carry out the procedure. We validated the WBC analysis with unstained samples from 85 donors. The presented method enables robust and highly accurate identification of WBCs, minimizing the disturbance to the cells and leaving marker channels free to answer other biological questions. It also opens the door to employing machine learning for liquid biopsy, here, using the rich information in cell morphology for a wide range of diagnostics of primary blood. © 2019 The Authors. Cytometry Part A published by Wiley Periodicals, Inc. on behalf of International Society for Advancement of Cytometry.

Mother-Stepfather Coparenting in Stepfamilies as Predictor of Child Adjustment.

Coparenting between biological parents is a strong predictor of child adjustment. To date, however, little is known about the coparenting dynamics between parent and stepparent in stepfamilies. This study aimed at exploring the links between coparenting in the mother-stepfather dyad and child behavior in stepfamilies compared with the links between mother-father coparenting and child behavior in first-marriage families. Two modes of coparenting were assessed: overt coparenting, that is, coparental behaviors in the presence of the child, and covert coparenting, that is, the way each parent speaks of the other parent to the child. The sample (N = 80) comprised 48 stepfamilies and 32 first-marriage families with a child between 7 and 13 years old. Overt coparenting was assessed through direct observation in the standardized situation of the PicNic Game. Covert coparenting and child behavior were assessed through mother-reported questionnaires. Results showed (a) more covert coparenting behaviors in first-marriage families, (b) no differences in overt coparenting, (c) more child difficulties reported in stepfamilies, (d) less optimal overt coparenting being linked with more difficulties in children in both family structures, and (e) an interaction effect between family structure and coparenting, showing that overt coparenting is linked with child behavior mainly in stepfamilies.

La cocrianza entre padres biológicos es una buena predictora de la adaptación del niño. Sin embargo, hasta la fecha, se sabe muy poco acerca de la dinámica de cocrianza entre padre/madre y padrastro/madrastra en las familias ensambladas. La finalidad de este estudio fue analizar las relaciones entre la cocrianza en la díada madre-padrastro y comportamiento del niño en familias ensambladas comparadas con las relaciones entre la cocrianza de la madre y el padre y el comportamiento del niño en familias formadas por un matrimonio en primeras nupcias. Se evaluaron dos formas de cocrianza: la cocrianza abierta, es decir, los comportamientos coparentales en presencia del niño, y la cocrianza encubierta, es decir, la manera en la que cada padre habla del otro padre al niño. La muestra (N = 80) constituyó 48 familias ensambladas y 32 familias de matrimonios en primeras nupcias con un niño de entre 7 y 13 años. La cocrianza abierta se evaluó mediante la observación directa en la situación estandarizada del juego del picnic. La cocrianza encubierta y el comportamiento del niño se evaluaron mediante cuestionarios informados por la madre. Los resultados demostraron (a) más comportamientos de cocrianza encubierta en las familias formadas por matrimonios en primeras nupcias, (b) ninguna difrencia en la cocrianza abierta, (c) más dificultades de los niños informadas en las familias ensambladas, (d) menos cocrianza abierta óptima ligada a más dificultades en los niños en ambas estructuras familiares, y (e) un efecto de interacción entre la estructura familiar y la cocrianza, lo cual demuestra que la cocrianza abierta está asociada con el comportamiento del niño principalmente en las familias ensambladas.

Body Composition in Individuals with Asymptomatic Osteoarthritis of the Knee.

Greater body mass index (BMI) is associated with a greater risk of osteoarthritis (OA). This study sought to investigate whether the association is mediated by fat mass or lean mass. The study involved 170 men and 488 women aged between 20 and 90 (average age: 55) who were randomly recruited from Ho Chi Minh City, Vietnam. The presence of knee OA was radiographically diagnosed based on the Kellgren-Lawrence criteria. Lean mass (LM) and fat mass (FM) were obtained from the DXA whole body scan (Hologic QDR-4500). The relationship between OA, LM, and FM was analyzed by a series of multiple linear regression models which take into account the effects of gender and age. As expected, men and women with knee OA were older than those without OA (65 vs 51 year in men, and 64 vs 52 year in women). After adjusting for age, OA was associated with greater FM and percent body fat (PBF), but the association was only observed in women, not in men. There was no statistically significant difference in LM between OA and non-OA individuals. Moreover, after adjusting for age and BMI or PBF, bone density in OA patients was not significantly different from non-OA individuals. Women with OA of the knee have greater fat mass than non-OA individuals, and that there is no significant difference in bone density between OA and non-OA individuals. Thus, the association between body mass index and OA is mainly mediated by fat mass.

Prevalence and pattern of radiographic intervertebral disc degeneration in Vietnamese: a population-based study.

Intervertebral disc degeneration (IDD) is one of the most common skeletal disorders, yet few data are available in Asian populations. We sought to assess the prevalence and pattern of radiographic IDD in a Vietnamese population. This population-based cross-sectional investigation involved 170 men and 488 women aged ≥40 years, who were randomly sampled from the Ho Chi Minh City (Vietnam). Anthropometric data, clinical history and self-reported back and neck pain were ascertained by a questionnaire. Plain radiographs (from the cervical spine, thoracic spine to the lumbar spine) were examined for the presence of disc space narrowing and/or osteophytosis using the Kellgren-Lawrence (KL) grading system. The presence of radiographic IDD was defined if the KL grade was 2 or greater in at least one disc. The prevalence of radiographic IDD was 62.4% (n = 106) in men and 54.7% (n = 267) in women. The most frequently affected site was the lumbar spine with prevalence being 50.6 and 43.2% in men and women, respectively. The prevalence of IDD increased with advancing age: 18.8% among those aged 40-49 years, and increased to 83.4% in those aged ≥60 years. Self-reported neck pain and lower back pain were found in 30 and 44% of individuals, respectively. There was no statistically significant association between self-reported neck pain and cervical spine OA. These data suggest that radiographic IDD is highly prevalent in the Vietnamese population, and that self-reported back pain is not a sensitive indicator of IDD.

Prevalence of and risk factors for sarcopenia in community-dwelling people: The Vietnam Osteoporosis Study.

BACKGROUND: Sarcopenia is a geriatric disease characterized by the progressive and generalized loss of skeletal lean mass and strength with age. The prevalence of sarcopenia in the Vietnamese population is unknown. This study sought to estimate the prevalence of and risk factors for sarcopenia among community-dwelling individuals in Vietnam. METHODS: This cross-sectional study is part of the ongoing Vietnam Osteoporosis Study project. The study involved 1308 women and 591 men aged 50 years and older as at 2015 (study entry). Whole-body dual-energy X-ray absorptiometry was used to measure the appendicular skeletal lean mass. Anthropometric and clinical data were collected using a structured questionnaire. Sarcopenia was defined according to the criteria proposed by the Asian Working Group for Sarcopenia in 2019. Logistic regression analysis was used to determine the association between potential risk factors and sarcopenia. RESULTS: The prevalence of sarcopenia in women and men was 14% (n = 183) and 16% (n = 83), respectively. Age (odds ratio [OR] per 10 years = 1.37; 95% confidence interval [CI] 1.26-1.48) and being underweight (OR = 1.61; 95% CI 1.00-2.58) were independently associated with increased risk of sarcopenia. The combination of low physical activity, being underweight and advancing age accounted for ~27% of sarcopenic patients. However, most of the attributable fraction was due to ageing. CONCLUSIONS: Sarcopenia is common in community-dwelling Vietnamese adults, particularly those with advancing age, who are underweight and with low physical activity.

4CMenB Vaccination to Prevent Meningococcal B Disease in Vietnam: Expert Review and Opinion.

An advisory board meeting was held with experts in Vietnam (Hanoi, August 2022), to review the evidence on invasive meningococcal disease (IMD) epidemiology, clinical management, and meningococcal vaccines to reach a consensus on recommendations for meningococcal vaccination in Vietnam. IMD is a severe disease, with the highest burden in infants and children. IMD presents as meningitis and/or meningococcemia and can progress extremely rapidly. Almost 90% of deaths in children occur within the first 24 h, and disabling sequelae (e.g., limb amputations and neurological damage) occur in up to 20% of survivors. IMD patients are often hospitalized late, due to mild and nonspecific early symptoms and misdiagnosis. Difficulties related to diagnosis and antibiotic misuse mean that the number of reported IMD cases in Vietnam is likely to be underestimated. Serogroup B IMD is predominant in many regions of the world, including Vietnam, where 82% of IMD cases were due to serogroup B (surveillance data from 2012 to 2021). Four component meningococcal B vaccine (4CMenB) is used in many countries (and is part of the pediatric National Immunization Program in 13 countries), with infant vaccination starting from two months of age, and a 2 + 1 dosing schedule. Experts recommend 4CMenB vaccination as soon as possible in Vietnam, starting from two months of age, with a 2 + 1 dosing schedule, and at least completing one dose before 6 months of age.

Drowning prevention challenges and opportunities: An exploratory study of perspectives of delegates from ASEAN nations.

The South East Asian region has the world's second highest fatal drowning burden. This study reports analysis of survey data from representatives from nations within the Association of South East Asian Nations regarding current efforts, challenges and future opportunities for drowning prevention. Twenty-two responses were received from respondents from all ASEAN nations excepting Cambodia and Myanmar. Drowning prevention initiatives varied across ASEAN nations, with most efforts focused on public education and raising awareness, including the provision of drowning data to the media. The lack of comprehensive, national level data collection was identified as a challenge, necessitating strengthened data collection capacity. Governmental involvement spanned one to six different ministries, highlighting the multi-sectoral nature of drowning prevention. However, a lead ministry could be identified in only two countries. Despite the challenges identified, there remain many opportunities to strengthen drowning prevention across ASEAN nations, addressing a significant regional public health threat.

The impact of green intellectual capital on green innovation in Vietnamese textile and garment enterprises: mediate role of environmental knowledge and moderating impact of green social behavior and learning outcomes.

The knowledge economy system shifts focus on the significance of intellectual capital. Moreover, the concept itself has gained generous amount of recognition at global level due to the increasing pressure from competitors, stakeholders, and environmental forces. Indeed, its antecedents and consequences have been assessed by scholars. However, the assessment appears to be inexhaustive with respect to meaningful frameworks. With the help of preceding literature, the present paper designed a model which involves green intellectual capital, green innovation, environmental knowledge, green social behavior, and learning outcomes. The model stipulates that green intellectual capital makes green innovation possible which further results in competitive advantage in the presence of environmental knowledge as a mediator as green social behavior and learning outcomes as a moderator. Interestingly the model acknowledges the proposed relationship through the empirical evidence collected from 382 Vietnamese textile and garment enterprises. The findings provide deeper insights regarding the issue that how firms could extract maximum benefits from their green assets and capabilities in the form of intellectual capital and green innovation.