Breakthrough Series Research Articles

The application advances of dendrimers in biomedical field

AbstractDendrimers are a family of nano‐sized three‐dimensional polymers with unique dendritic branching structures and compact spherical geometries. In recent years, dendrimers have made a series of breakthroughs in the biomedical field. In this review, we introduce the synthesis principles, modification methods, and new materials designed based on dendrimers; discuss the importance of cytotoxicity of dendrimers for applications; and elaborate on their applications in the field of molecular assembly and cancer diagnosis and treatment. We speculate that in the near future, more new materials based on dendrimers will be applied in the biomedical field.

Understanding How Pretraining Regularizes Deep Learning Algorithms.

Deep learning algorithms have led to a series of breakthroughs in computer vision, acoustical signal processing, and others. However, they have only been popularized recently due to the groundbreaking techniques developed for training deep architectures. Understanding the training techniques is important if we want to further improve them. Through extensive experimentation, Erhan et al. (2010) empirically illustrated that unsupervised pretraining has an effect of regularization for deep learning algorithms. However, theoretical justifications for the observation remain elusive. In this article, we provide theoretical supports by analyzing how unsupervised pretraining regularizes deep learning algorithms. Specifically, we interpret deep learning algorithms as the traditional Tikhonov-regularized batch learning algorithms that simultaneously learn predictors in the input feature spaces and the parameters of the neural networks to produce the Tikhonov matrices. We prove that unsupervised pretraining helps in learning meaningful Tikhonov matrices, which will make the deep learning algorithms uniformly stable and the learned predictor will generalize fast w.r.t. the sample size. Unsupervised pretraining, therefore, can be interpreted as to have the function of regularization.

Impact of 1-hour and 3-hour sepsis time bundles on antibiotic use in emergency departments in Queensland, Australia: a before-and-after cohort study

ObjectiveTo determine if the introduction of an emergency department (ED) sepsis screening tool and management bundle affects antibiotic prescribing and use.DesignMulticentre, cohort, before-and-after study design.SettingThree tertiary hospitals in Queensland, Australia...

Real life test of a novel super performance dew point cooling system in operational live data centre

This paper presents the development and application of a super performance dew point cooling technology for data centres. The novel super performance dew point cooler showed considerably improved energy saving and carbon reduction for data centre cooling. The innovations of this technology are built upon a series of technological breakthroughs including, a novel hybrid flat/corrugated heat and mass exchanging sheets, an innovative highly water absorptive and diffusive wet-material for the sheets which enable an intermittent water supply with well-tuned water pressure and flow rate, and the optimised fan configurations. Following a list of fundamental research including theoretical, numerical and lab experimental testing of a small scale prototype system, a specialist 100 kW rated data centre dew point cooling system was dedicated designed, constructed, installed and real life tested in an operational live data centre environment, i.e., Maritime Data Centre at Hull (UK) to investigate its dynamic performance, suitability and stability for application in operational data centre environment conditions.During the testing period, the system showed its reliability and capability to remove a tremendous amount of heat dissipated from the IT equipment and maintain an adequate space temperature in the operational live data centre. The dynamic data collection and analysis during the continuous testing and monitoring period showed the average COP of 29.7 with the maximum COP of 48.3. Compared to the existing traditional vapor compression air conditioning system in the data centre, the energy saving using the super performance dew point cooling system is around 90 %. The work presented in this paper include detailed innovation aspects of the technology and the system operation, as well as the established bridging knowledges, methodology and technical procedure for bringing this new technology into real life operation which involve in data centre survey, optimum design and modularization of the specialist cooling system for data centre application, proven system installation, operating method and cooling air management for data centre as well as the assurance of the continuous sufficient cooling supply to the data centre.

Mechanisms of immune modulation in the tumor microenvironment and implications for targeted therapy.

The efficacy of cancer therapies is limited to a great extent by immunosuppressive mechanisms within the tumor microenvironment (TME). Numerous immune escape mechanisms have been identified. These include not only processes associated with tumor, immune or stromal cells, but also humoral, metabolic, genetic and epigenetic factors within the TME. The identification of immune escape mechanisms has enabled the development of small molecules, nanomedicines, immune checkpoint inhibitors, adoptive cell and epigenetic therapies that can reprogram the TME and shift the host immune response towards promoting an antitumor effect. These approaches have translated into series of breakthroughs in cancer therapies, some of which have already been implemented in clinical practice. In the present article the authors provide an overview of some of the most important mechanisms of immunosuppression within the TME and the implications for targeted therapies against different cancers.

A virtual breakthrough series collaborative to support deprescribing interventions across Veterans Affairs healthcare settings.

Virtual collaborative models are a practical way to implement a supportive environment for multi-team learning. In this project, we aimed to describe the processes and outcomes of a virtual deprescribing collaborative that facilitated implementation of deprescribing interventions around the country. Two successive cohorts comprised of multidisciplinary teams from geographically diverse veterans affairs (VA) sites were selected via an application process to participate in a virtual deprescribing collaborative. Each site developed its own deprescribing protocol and took part in regular meetings, mentoring groups, monthly data reporting, and other learning activities over an approximate 9 month period, per cohort. Standard measures were number of veterans served and medications deprescribed. Descriptive and qualitative analyses were utilized. Twenty-one total VA sites were selected to participate in the deprescribing collaborative in two cohorts (Cohort 1, n = 12 sites; Cohort 2, n = 9 sites). The majority of sites' practice areas directly served the older adult population, and the majority of site leads were pharmacists. The most utilized tool used by the collaborative sites was the VA VIONE decision support tool (n = 14) and the most common strategy was individualized medication review. Combining outcomes from both Cohorts 1 and 2, a total of n = 4770 veterans were served, with 8332 medications deprescribed. Eighty-two percent of Cohort 1 sites surveyed reported their deprescribing program was still being utilized after 1 year follow up. This virtual deprescribing collaborative aided in the successful implementation of both established and novel deprescribing practices across a variety of VA practice sites that care for older adults. The shared learning experience enhanced problem solving and allowed for interdisciplinary teamwork. Overall the collaborative was successful in improving polypharmacy for several thousand older adults.

LVSegNet: A novel deep learning-based framework for left ventricle automatic segmentation using magnetic resonance imaging

Automatic segmentation of the left ventricle(LV) using magnetic resonance image (MRI) is essential for assessing cardiac functionality such as area, volume, and ejection fraction in clinical applications. With the development of deep learning(DL), LV segmentation has achieved a series of huge breakthroughs and promising performance in recent years. However, it also encounters some dilemmas in which ill-defined borders and operator dependence problems. U-net is a well-known architecture in medical image segmentation based on an encoder–decoder method, which model achieves outstanding performance. However, the most current methods that follow the existing U-net-based paradigm ignore the contribution of context semantic strengths during the segmentation, which cannot explicitly and thoroughly exploit the intrinsic similarity of medical images. To tackle these problems, we propose LVSegNet, a comprehensive model, which explicitly fulfills the segmentation of LV using MRI via elaborately combining various semantic learning modules. This model includes three parts: encoder, center, and decoder. Specifically, the ResLink block of the encoder part designs pipeline attention, which integrates the context feature across channels. The dense atrous spatial pyramid pooling (DenseASPP) module of the center part is leveraged to magnify the receptive field. The dense up-sampling convolution (DUC) module of the decoder is utilized to replace common up-sampling, which recovers the feature information of images to the greatest extent. Moreover, we also conduct extensive experiments on public LV segmentation benchmarks of Sunnybrook Cardiac Data and our clinical dataset. We demonstrate and validate the performance superiority of LVSegNet, outperforming the previous outstanding counterpart methods by 15.4%, 6.6%, and 5.3% in F1-score compared to U-net, deeplabV3, and deeplab V3+ respectively. Experimental results have verified that the proposed LV automatic segmentation model is more robust than various encoder–decoder counterparts.

IMplementing best practice post-partum contraceptive services through a quality imPROVEment initiative for and with immigrant women in Sweden (IMPROVE it): a protocol for a cluster randomised control trial with a process evaluation

BackgroundImmigrant women’s challenges in realizing sexual and reproductive health and rights (SRHR) are exacerbated by the lack of knowledge regarding how to tailor post-partum contraceptive services to their needs. Therefore, the overall aim of the IMPROVE-it project is to promote equity in SRHR through improvement of contraceptive services with and for immigrant women, and, thus, to strengthen women’s possibility to choose and initiate effective contraceptive methods post-partum.MethodsThis Quality Improvement Collaborative (QIC) on contraceptive services and use will combine a cluster randomized controlled trial (cRCT) with a process evaluation. The cRCT will be conducted at 28 maternal health clinics (MHCs) in Sweden, that are the clusters and unit of randomization, and include women attending regular post-partum visits within 16 weeks post birth. Utilizing the Breakthrough Series Collaborative model, the study’s intervention strategies include learning sessions, action periods, and workshops informed by joint learning, co-design, and evidence-based practices. The primary outcome, women’s choice of an effective contraceptive method within 16 weeks after giving birth, will be measured using the Swedish Pregnancy Register (SPR). Secondary outcomes regarding women’s experiences of contraceptive counselling, use and satisfaction of chosen contraceptive method will be evaluated using questionnaires completed by participating women at enrolment, 6 and 12 months post enrolment. The outcomes including readiness, motivation, competence and confidence will be measured through project documentation and questionnaires. The project’s primary outcome involving women’s choice of contraceptive method will be estimated by using a logistic regression analysis. A multivariate analysis will be performed to control for age, sociodemographic characteristics, and reproductive history. The process evaluation will be conducted using recordings from learning sessions, questionnaires aimed at participating midwives, intervention checklists and project documents.DiscussionThe intervention’s co-design activities will meaningfully include immigrants in implementation research and allow midwives to have a direct, immediate impact on improving patient care. This study will also provide evidence as to what extent, how and why the QIC was effective in post-partum contraceptive services.Trial registrationNCT05521646, August 30, 2022.

Remote Sensing Image Compression Based on the Multiple Prior Information

Learned image compression has achieved a series of breakthroughs for nature images, but there is little literature focusing on high-resolution remote sensing image (HRRSI) datasets. This paper focuses on designing a learned lossy image compression framework for compressing HRRSIs. Considering the local and non-local redundancy contained in HRRSI, a mixed hyperprior network is designed to explore both the local and non-local redundancy in order to improve the accuracy of entropy estimation. In detail, a transformer-based hyperprior and a CNN-based hyperprior are fused for entropy estimation. Furthermore, to reduce the mismatch between training and testing, a three-stage training strategy is introduced to refine the network. In this training strategy, the entire network is first trained, and then some sub-networks are fixed while the others are trained. To evaluate the effectiveness of the proposed compression algorithm, the experiments are conducted on an HRRSI dataset. The results show that the proposed algorithm achieves comparable or better compression performance than some traditional and learned image compression algorithms, such as Joint Photographic Experts Group (JPEG) and JPEG2000. At a similar or lower bitrate, the proposed algorithm is about 2 dB higher than the PSNR value of JPEG2000.

Enhancing strain performance in KNN-based ceramics profiting from synergistic effect at ferroelectric-to-relaxor cross region

Owing to the demand for sustainable development, a lot of attention has been received by eco-friendly lead-free KNN based ceramics. Although a series of breakthroughs have been achieved in their electric properties, their poor temperature stability still hinders their practical application. A KNN-based ceramic of (1-x)K0·54Na0·46NbO3-xBi0.5Na0·5ZrO3 (KNN-xBNZ) was designed to solve the issue of temperature-dependent degradation while taking advantage of the synergistic effect at the ferroelectric-to-relaxor cross region. A mixed-phase boundary, including tetragonal and pseudocubic phases, is observed in all samples, and the dominant phase structure gradually transforms from a tetragonal phase to a pseudocubic phase as BNZ increases. A favorable strain performance (strain ≥0.08%, and temperature stability ≥95.35%) is observed at the temperature range of 20–180 °C, profiting from the synergistic effect at the ferroelectric-to-relaxor cross region at x = 0.08. It is superior or comparable to some typical NN-based, KNN-based, and PZT-based materials regardless of the driving electric field. These results would shed light on the future work of achieving excellent comprehensive performance in KNN-based materials.

Discovery of the Hrp Type III Secretion System in Phytopathogenic Bacteria: How Investigation of Hypersensitive Cell Death in Plants Led to a Novel Protein Injector System and a World of Inter-Organismal Molecular Interactions Within Plant Cells.

In the early 1960s, Pseudomonas syringae and other host-specific phytopathogenic proteobacteria were discovered to elicit a rapid, resistance-associated death when infiltrated at high inoculum levels into nonhost tobacco leaves. This hypersensitive reaction (or response; HR) was a useful indicator of basic pathogenic ability. Research over the next 20 years failed to identify an elicitor of the HR but revealed that its elicitation required contact between metabolically active bacterial and plant cells. Beginning in the early 1980s, molecular genetic tools were applied to the HR puzzle, revealing the presence in P. syringae of clusters of hrp genes, so named because they are required for the HR and pathogenicity, and of avr genes, so named because their presence confers HR-associated avirulence in resistant cultivars of a host plant species. A series of breakthroughs over the next two decades revealed that (i) hrp gene clusters encode a type III secretion system (T3SS), which injects Avr (now "effector") proteins into plant cells, where their recognition triggers the HR; (ii) T3SSs, which are typically present in pathogenicity islands acquired by horizontal gene transfers, are found in many bacterial pathogens of plants and animals and inject many effector proteins, which are collectively essential for pathogenicity; and (iii) a primary function of phytopathogen effectors is to subvert non-HR defenses resulting from recognition of conserved microbial features presented outside of plant cells. In the 2000s, Hrp system research shifted to extracellular components enabling effector delivery across plant cell walls and plasma membranes, regulation, and tools for studying effectors. [Formula: see text] Copyright © 2023 The Author(s). This is an open access article distributed under the CC BY-NC-ND 4.0 International license.

Graph Flow: Cross-layer Graph Flow Distillation for Dual Efficient Medical Image Segmentation.

With the development of deep convolutional neural networks, medical image segmentation has achieved a series of breakthroughs in recent years. However, high-performance convolutional neural networks always mean numerous parameters and high computation costs, which will hinder the applications in resource-limited medical scenarios. Meanwhile, the scarceness of large-scale annotated medical image datasets further impedes the application of high-performance networks. To tackle these problems, we propose Graph Flow, a comprehensive knowledge distillation framework, for both network efficiency and annotation-efficiency medical image segmentation. Specifically, the Graph Flow Distillation transfers the essence of cross-layer variations from a well trained cumbersome teacher network to a non-trained compact student network. In addition, an unsupervised Paraphraser Module is integrated to purify the knowledge of the teacher, which is also beneficial for the training stabilization. Furthermore, we build a unified distillation framework by integrating the adversarial distillation and the vanilla logits distillation, which can further refine the final predictions of the compact network. With different teacher networks (traditional convolutional architecture or prevalent transformer architecture) and student networks, we conduct extensive experiments on four medical image datasets with different modalities (Gastric Cancer, Synapse, BUSI, and CVC-ClinicDB).We demonstrate the prominent ability of our method on these datasets, which achieves competitive performances. Moreover, we demonstrate the effectiveness of our Graph Flow through a novel semi-supervised paradigm for dual efficient medical image segmentation. Our code will be available at Graph Flow.

A National Implementation Project to Prevent Healthcare-Associated Infections in Intensive Care Units: A Collaborative Initiative Using the Breakthrough Series Model.

Although there are simple and low-cost measures to prevent healthcare-associated infections (HAIs), they remain a major public health problem. Quality issues and a lack of knowledge about HAI control among healthcare professionals may contribute to this scenario. In this study, our aim is to present the implementation of a project to prevent HAIs in intensive care units (ICUs) using the quality improvement (QI) collaborative model Breakthrough Series (BTS). A QI report was conducted to assess the results of a national project in Brazil between January 2018 and February 2020. A 1-year preintervention analysis was conducted to determine the incidence density baseline of the 3 main HAIs: central line-associated bloodstream infections (CLABSIs), ventilation-associated pneumonia (VAP), and catheter-associated urinary tract infections (CA-UTIs). The BTS methodology was applied during the intervention period to coach and empower healthcare professionals providing evidence-based, structured, systematic, and auditable methodologies and QI tools to improve patients' care outcomes. A total of 116 ICUs were included in this study. The 3 HAIs showed a significant decrease of 43.5%, 52.1%, and 65.8% for CLABSI, VAP, and CA-UTI, respectively. A total of 5140 infections were prevented. Adherence to bundles inversely correlated with the HAI incidence densities: CLABSI insertion and maintenance bundle (R = -0.50, P = .010 and R = -0.85, P < .001, respectively), VAP prevention bundle (R = -0.69, P < .001), and CA-UTI insertion and maintenance bundle (R = -0.82, P < .001 and R = -0.54, P = .004, respectively). Descriptive data from the evaluation of this project show that the BTS methodology is a feasible and promising approach to preventing HAIs in critical care settings.

Timely Recognition of Abusive Injuries (TRAIN): Results from a Statewide Quality Improvement Collaborative.

The TRAIN Collaborative adopted the Institute for Healthcare Improvement's Breakthrough Series Collaborative Model, where partnerships between organizations facilitate learning from each other and experts. Collaborative members identified opportunities to improve injury recognition, implemented changes, responded to data, and reconvened to share successes and obstacles. As a result, institutions implemented different interventions, including education for clinical staff, increased social work involvement, and scripting for providers. Data collected over 3 years were compared to a 12-month baseline. The number of injuries increased from 51 children with concerning injuries identified monthly to 76 children sustained throughout the collaborative. However, within 2 years, the 3- and 12-month reinjury rates ultimately significantly decreased from 5.7% to 2.1% and 6.5% to 3.7%, respectively. Our data suggest the Institute for Healthcare Improvement's Breakthrough Series model can be applied across large populations to improve secondary injury prevention in infants.

Improving Stroke Measure Compliance and Outcomes Through Hospital Collaboration.

Globally, national stroke registries have been shown to improve the quality of patient care and outcomes. However, registry utilization and implementation vary by country. In the United States, stroke-specific performance measures must be met to achieve and maintain stroke center certification awarded by the state or nationally accredited certifying bodies. The 2 stroke registries available in the United States are the American Heart Association Get With The Guidelines-Stroke registry, which is voluntary, and the Paul Coverdell National Acute Stroke Registry, funded competitively to states by the Centers for Disease Control and Prevention. Compliance with stroke processes of care is variable, and quality improvement initiatives among organizations have been shown to have an impact on improving stroke care delivery. However, the effectiveness of interorganizational continuous quality improvement approaches, especially among competing institutions, to improving stroke care is ambiguous, and no uniform governance for successful interhospital collaboration has been identified. The purpose of this article is to review national initiatives focused on interorganizational collaboration to improve stroke care delivery with a focus on interhospital collaboration in the United States to improve stroke performance measures specific to stroke center certification. The state of Kentucky's experience and utilization of the Institute for Healthcare Improvement Breakthrough Series model with key strategies for success will be discussed to serve as a foundation and empower novice stroke leaders in learning health systems. The models may be adapted internationally for application to stroke-specific care process improvement locally, regionally, and nationally; among organizations within the same health system or competing systems; and among organizations with funding or without funding to improve stroke performance measures.

REAL WORLD IMPLEMENTATION OF TREAT-TO-TARGET IN PATIENTS WITH IBD IN A LEARNING HEALTH SYSTEM: AN IBD QORUS COLLABORATIVE STUDY

Abstract BACKGROUND AND AIMS A treat-to-target (TTT) strategy of aiming for symptomatic and endoscopic remission is recommended for the management of inflammatory bowel diseases (IBD). Yet, real-world uptake of TTT strategies has been modest. We sought to assess the feasibility of implementing TTT and improve its uptake in the Crohn’s and Colitis Foundation’s IBD Qorus learning health system (LHS). METHODS We examined an ‘intention to TTT’ for each eligible clinic visit (patients not in endoscopic remission on most recent evaluation) across a multi-center LHS over a 12-month period (November 2020-November 2021). ‘Intention to TTT’ (or TTT score) was defined as an affirmative response to all three of the following: (1) Was treat-to target discussed with patient?, (2) Was objective assessment for inflammation (endoscopy, radiology and/or fecal calprotectin) performed within the preceding 12m?, (3) Is there an intention to change treatment since target has not been reached? Over a 12-month breakthrough series (BTS) collaborative, we sought to improve implementation of TTT with monthly report cards, webinars, an active listserv and two all-Qorus learning sessions. Changes in rates of ‘intention to TTT’ were examined over 12 months, overall and at each site, with an earliest clinically meaningful signal of improvement defined a priori as 2% increase per month. In secondary analyses, we compared overall rates of achieving endoscopic remission and corticosteroid-free clinical remission in sites with improvement in rates of ‘intention to TTT’ relative to sites without improvement (only sites with valid data for 8/12 months). RESULTS Over 12 months, there were 7934 patient visits across 38 sites in the collaborative; patients in 37% visits (n=2913) were not in endoscopic remission at time of visit. Overall, rates of ‘intention to TTT’ increased from 23% at the beginning of the BTS, to 49% at the end of the BTS, corresponding to a clinically significant 2.4% per month increase in rates of implementing TTT (Figure 1). While rates of discussion of TTT (88% of eligible visits) and intending to adjust therapy (70% of eligible visits) were consistently high, there was a significant increase in objective evaluation for inflammation during the course of the BTS. There was considerable variability across sites in rates of ‘intention to TTT’. Comparing 9/14 sites with improvement in rates of implementing TTT vs. 5/14 sites without improvement, there were trends towards increasing rates of site-level endoscopic remission and steroid-free clinical remission (Figure 2). CONCLUSIONS Through a quality improvement initiative in a LHS, rates of ‘intention to TTT’ increased significantly over 12-months. This improvement in implementation of TTT was accompanied by increase in rates of achieving endoscopic remission and steroid-free clinical remission.

Combination prevention package of interventions for reducing vulnerability to HIV among adolescent girls and young women in Nigeria: An action research.

Adolescent girls and young women (AGYW) in Nigeria are especially at risk of HIV in Nigeria. Their vulnerability to HIV is linked to multiple concurrent sexual relationships, transgenerational sex, and transactional sex, amongst other factors. These factors have sociocultural contexts that vary across a multi-cultural country like Nigeria. The aim of this study was to use an innovative collaborative approach to develop a minimum HIV prevention package for AGYW which is responsive to sociocultural settings and based on combination HIV prevention. We conducted action research to develop and implement actionable HIV prevention intervention models that address AGYW's vulnerabilities to HIV in three Nigerian States and the Federal Capital Territory (FCT) Abuja. The action research adopted the breakthrough series (BTS) collaborative, which accelerates improvement through mutual learning. The BTS implementation involved rapid Plan-Do-Study-Act (PDSA) cycles: an iterative process to plan and implement a basket of interventions. Problems or problematic situations, termed change topics, for which interventions could be carried out were identified in each study location. Using participatory approaches during a series of meetings called learning sessions, specific and innovative interventions, termed change ideas, were developed. These learning sessions were conducted with young women groups and other stakeholders. The change ideas were tested, studied, adapted, adopted, or discarded at each participating site. Exposure to and uptake of the implemented interventions was assessed in the study areas using a household survey with 4308 respondents, 53 focus group discussions, and 40 one-on-one interviews in intervention and control study sites. Five categories of interventions were collaboratively developed, namely: Parental communication; Peer to peer interventions; Facilitator-led interventions; Non-traditional outlets for condoms, and Social media-based interventions. A good reach of the interventions was demonstrated as 77.5% of respondents reported exposure to at least one type of intervention. Nearly half of the respondents reported being exposed to the parental communication interventions, while 45.1% reported being exposed to the youth facilitator-driven interventions. Social media interventions had the lowest penetration. Also, there was between 15 to 20 positive percentage point difference between intervention and control for the uptake of HIV testing, and between 5 to 9 positive percentage point difference for uptake of male condoms. These differences were statistically significant at p<0.001. Interventions developed through participatory approaches with young people and well-tailored to local realities can improve the acceptability and accessibility of programs that are able to reduce the risk of HIV infection among AGYW.

Integration of high-throughput omics technologies in medicinal plant research: The new era of natural drug discovery.

Medicinal plants are natural sources to unravel novel bioactive compounds to satisfy human pharmacological potentials. The world's demand for herbal medicines is increasing year by year; however, large-scale production of medicinal plants and their derivatives is still limited. The rapid development of modern technology has stimulated multi-omics research in medicinal plants, leading to a series of breakthroughs on key genes, metabolites, enzymes involved in biosynthesis and regulation of active compounds. Here, we summarize the latest research progress on the molecular intricacy of medicinal plants, including the comparison of genomics to demonstrate variation and evolution among species, the application of transcriptomics, proteomics and metabolomics to explore dynamic changes of molecular compounds, and the utilization of potential resources for natural drug discovery. These multi-omics research provide the theoretical basis for environmental adaptation of medicinal plants and allow us to understand the chemical diversity and composition of bioactive compounds. Many medicinal herbs' phytochemical constituents and their potential health benefits are not fully explored. Given their large diversity and global distribution as well as the impacts of growth duration and environmental factors on bioactive phytochemicals in medicinal plants, it is crucial to emphasize the research needs of using multi-omics technologies to address basic and applied problems in medicinal plants to aid in developing new and improved medicinal plant resources and discovering novel medicinal ingredients.

Absenteeism prevention in preschools in Chile: Impact from a quasi-experimental evaluation of 2011–2017 Ministry of Education data

IntroductionIn 2007, to promote social equity, Chile expanded coverage and subsidies for early childhood education (ECE). Fundación Educacional Oportunidad (OFE) aimed to improve ECE quality through its professional development program for teachers and school leaders, Un Buen Comienzo (UBC). An experimental evaluation showed that high levels of absenteeism moderated UBC’s impact: despite moderate to large positive impacts on preschool quality, only children who attended most frequently experienced positive impacts on targeted language and literacy skills. In 2012, OFE began prioritizing attendance promotion and chronic absenteeism prevention.MethodsUsing a Breakthrough Series Collaborative (BTS) that combines continuous quality improvement methods and networked peer learning, OFE tested a theory of change and several innovations, including Absenteeism Committees comprised of school teams and families; a new real-time data platform; and a set of universal and targeted strategies to apply with families at risk for chronic absenteeism. In 2014-2015, OFE expanded the UBC program to nineteen schools in Chile’s VI Region, five of which prioritized attendance promotion. This study describes the intervention strategies and BTS implementation approach, and we use publicly available Ministry of Education databases to analyze rates of absences and chronic absenteeism in public preschools in Chile’s VI region from 2011 to 2017 (n = 1,218 children per year; 63,689 child-months of data), comparing rates between UBC schools that prioritized attendance (n = 5), UBC schools that did not prioritize attendance (n = 14), and non-UBC schools (n = 27).ResultsChildren missed, on average, 14.0%–14.4% of schooldays. Rates of chronic absenteeism were 50.9%–54.2%. Statistical Process Control charts show an initial increase in the percentage of days absent per child each month (13.4% to 16.3%) in UBC Schools prioritizing attendance, followed by a decrease to 12.9%. The percentage of children with chronic absenteeism decreased from 54.2% to 35%. Interrupted time series analyses suggest that these reductions can be attributed to UBC participation.DiscussionThis study (1) replicates findings from prior research indicating that absenteeism rates are sufficiently high among Chilean preschoolers to diminish ECE’s potential benefits, and (2) demonstrates the effectiveness of multi-level strategies implemented using continuous improvement methods and networked peer learning to promote attendance and reduce chronic absenteeism.

Social risks of the genetic revolution: from the standpoint of the legal approach

Technological progress and law development until recently were different facets of one common process of mankind progressive advancement towards asserting greater independence from the natural elements and expanding the scope of human freedom. At the same time, law, on the one hand, was the most important incentive for the development of the creative potential of mankind as a source of scientific and technical innovations, and on the other hand, it was an effective way to correct deformations within the system of techno-humanitarian balance, guaranteeing against the most dangerous manifestations of technological power. However, with the advent of the 21st century convergent NBIC-technologies, the law increasingly demonstrates its inability to reduce the technological development risks to an acceptable level. The novelty of the situation is that social risks come to the fore. This trend is most clearly manifested in the field of legal regulation of the processes of creating and applying technologies for editing the human genome. A whole series of breakthroughs in the field of studying the human genome, carried out in recent decades, has opened up huge prospects not only for the development of medicine, but also for changing the natural qualities of a person, up to the possibility (so far theoretical) to control the mankind biological evolution. These new opportunities lead to entail proportionate social risks, connected primarily with the danger of an irreconcilable split of humanity into different socio-biological groups. In the current conditions law is not able to cope with the threat of humanity losing its biosocial unity. The hopes expressed in the public space for moral and religious factors to counteract the dangers of technological dehumanization, fraught with a surge of social injustice, seem to be greatly overestimated. There is even less reason to count on the so-called “moral bioimprovement” of mankind. In the current situation, apparently, there is no other way than a difficult creative search, focused on improving social, economic and political relations in line with an equitable legal approach. Successes along this path would provide the conditions under which the human community would be able to preserve the law vector of biotechnological development and, at the same time, avoid its catastrophic consequences.