ELiAH: the atlas of E3 ligases in human tissues for targeted protein degradation with reduced off-target effect.

The development of therapeutic agents has mainly focused on designing small molecules to modulate target proteins or genes which are conventionally druggable. Therefore, targeted protein degradation (TPD) for undruggable cases has emerged as promising pharmaceutical approach. TPD, often referred PROTACs (PROteolysis TArgeting Chimeras), uses a linker to degrade target proteins by hijacking the ubiquitination system. Therefore, unravel the relationship including reversal and co-expression between E3 ligands and other possible target genes in various human tissues is essential to mitigate off-target effects of TPD. Here, we developed the atlas of E3 ligases in human tissues (ELiAH), to prioritize E3 ligase-target gene pairs for TPD. Leveraging over 2900 of RNA-seq profiles consisting of 11 human tissues from the GTEx (genotype-tissue expression) consortium, users of ELiAH can identify tissue-specific genes and E3 ligases (FDR P-value of Mann-Whitney test < .05). ELiAH unravels 933 830 relationships consisting of 614 E3 ligases and 20 924 of expressed genes considering degree of tissue specificity, which are indispensable for ubiquitination based TPD development. In addition, docking properties of those relationships are also modeled using RosettaDock. Therefore, ELiAH presents comprehensive repertoire of E3 ligases for ubiquitination-based TPD drug development avoiding off-target effects. Database URL: https://eliahdb.org.

Machine learning-based classification of valvular heart disease using cardiovascular risk factors.

Valvular Heart Disease (VHD) is a globally significant cause of mortality, particularly among aging populations. Despite advancements in percutaneous and surgical interventions, there are still uncertainties that remain regarding the risk factors that significantly contribute to this condition within the domain of cardiovascular disease. This study investigates these uncertainties and the role of machine learning in categorizing VHD based on cardiovascular risk factors. It follows a two-part investigation comprising feature extraction and classification phases. Feature extraction is initially performed using a wrapping approach and refined further with binary logistic regression. The second phase employs five classifiers: Artificial Neural Network (ANN), XGBoost, Random Forest (RF), Naïve Bayes, and Support Vector Machine (SVM), along with advanced methods such as SVM combined with Principal Component Analysis (PCA) and a majority-voting ensemble method (MV5). Data on VHD cases were collected from DHQ Hospital Faisalabad using simple random sampling. Various statistical measures, such as the ROC curve, F-measure, sensitivity, specificity, accuracy, MCC, and Kappa are applied to assess the results. The findings reveal that the combination of SVM with PCA achieves the highest overall performance while the MV5 ensemble method also demonstrates high accuracy and balance in sensitivity and specificity. The variation in VHD prevalence linked to specific risk factors highlights the importance of a comprehensive approach to reduce this disease's burden. The Exceptional performance of SVM + PCA and MV5 highlights their significance in diagnosing VHD and advancing knowledge in biomedicine.

Transforming healthcare performance monitoring - A cutting-edge approach with generalized additive profiles: GAMs for healthcare quality monitoring.

Recent findings indicate a growing trend in data analysis within healthcare using statistical process control. However, the diversity of variables involved necessitates the expansion of new process control methodologies. This study examined control chart applications in cardiology by using generalized additive models (GAMs) to construct profiles while involving multiple healthcare variables (08). Two distinct statistics: deviation (D), and Hotelling (T2) were employed for constructing control charts: a commonly used single-variable statistic for nonparametric profiles and an innovative multivariate statistic that assesses the contribution of each element to process changes. These statistics were tested for monitoring ischemic and hemorrhagic strokes in 1-year acute stroke (369) patients at the Faisalabad Institute of Cardiology. Demographic parameters (age, gender), vascular risk factors (diabetes, family history, sleep), socioeconomic variables (smoking, location), and blood pressure are included in the model. The research includes the computation of zero-state average run length (ARL) for assessing the performance of control charts. The characteristics of the proposed profile were analyzed, such as the T2 control chart, performing better than the D chart for medium-to-large shifts (δ ≥ 0.50). On the other hand, for small δ = 0.25, the D control chart produces smaller ARL values but more significant standard deviations. While both statistics contribute to profile monitoring, T2 is more effective at identifying and tracing medium and large shifts. In conclusion, such handy tools may aid healthcare performance monitoring, especially for complicated predictor-response relationships. Monitored profiles demonstrated that GAMs are useful for healthcare analysis and process monitoring.

Effects of SpGSH1 and SpPCS1 overexpression or co-overexpression on cadmium accumulation in yeast and Spirodela polyrhiza.

Cadmium (Cd) is one of the most toxic elements to all organisms. Glutathione (GSH)-dependent phytochelatin (PC) synthesis pathway is considered an extremely important mechanism in Cd detoxification in plants. However, few studies have focused on the roles of glutamate-cysteine ligase (GSH1) and phytochelatin synthase (PCS1) in Cd accumulation and detoxification in plants. In this study, SpGSH1 and SpPCS1 were identified and cloned from Spirodela polyrhiza and analyzed their functions in yeast and S. polyrhiza via single- or dual-gene (SpGP1) overexpression. The findings of this study showed that SpGSH1, SpPCS1, and SpGP1 could dramatically rescue the growth of the yeast mutant Δycf1. In S. polyrhiza, SpGSH1 was located in the cytoplasm and could promote Mn and Ca accumulation. SpPCS1 was located in the cytoplasm and nucleus, mainly expressed in meristem regions, and promoted Cd, Fe, Mn, and Ca accumulation. SpGSH1 and SpPCS1 co-overexpression increased the Cd, Mn, and Ca contents. Based on the growth data of S. polyrhiza, it was recommended that biomass as the preferable indicator for assessing plant tolerance to Cd stress compared to frond number in duckweeds. Collectively, this study for the first time systematically elaborated the function of SpGSH1 and SpPCS1 for Cd detoxification in S. polyrhiza.

Non-linear ground deformation detection and monitoring using time series InSAR along the coastal urban areas of Pakistan.

Conventional geodetic methods rely on point measurements, which have drawbacks for detecting and tracking geologic disasters at specific locations. In this study, the time series Interferometric Synthetic Aperture Radar (InSAR) approach was incorporated to estimate non-linear surface deformation caused by tectonic, shoreline reclamation, and other anthropogenic activities in economically important urban regions of Pakistan's southern coast, which possesses around 270 km. The shoreline is extended from the low-populated area on the premises of the Hub River in the west to the highly populated Karachi City and Eastern Industrial Zone, where we collected the Sentinel-1A C-band data from 2017 to 2023 to address urban security and threats to human life and property. The main advantage of opting for the non-linear persistent scatterer interferometric SAR (PSInSAR) approach for this study is that it exposes minute movements without any prior consideration of conventional monitoring techniques, making it valid in continuously varying regions. An average vertical displacement range of - 170 to + 82 mm per year was found, which was used to investigate the potential correlation with the most effective causative parameters of deformation. The densely populated areas of the study area experience an annual subsidence of 170 mm, and the less populated western region experiences an uplift of 82 mm annually. Land deformation varies along the coast of the study area, where the eastern region is highly reclaimed and is affected by erosion. Groundwater table-depleting regions experienced high levels of land subsidence, and tectonic activities controlled vertical displacement in the region. Major variation was detected after an earthquake occurred along fault lines. This study was designed because a non-linear approach is required to address ground movement activities acutely, and it will make it possible to plan surface infrastructure and handle issues brought on by subsidence more effectively.

The Use of Computer-Aided Design and Computer-Aided Manufacturing (CAD/CAM) Technology for Zirconia Crowns After Laser-Assisted Crown Lengthening and Retraction: A Case Report.

Maintenance of biological width serves as a primary factor in periodontal-restorative relationships. Crown lengthening (CL) is a technique to prevent violation of biological width, with the laser method offering the advantage of surgical and patient-related outcomes. Laser CL with retraction helps with the excision of tissues, increasing the CL, maintaining the gingival contour with adequate exposure to the finish line to record the tooth preparation features. This helps to achieve the functional and esthetic outcomes essential for restorative dentistry. The marginal fit, contour, and adaptation of the crown can be further enhanced by computer-aided design and computer-aided manufacturing (CAD/CAM) technology improving patient and clinical outcomes. Hence, this case report aims to indulge the laser-assisted procedures and CAD/CAM technology to fabricate and deliver a zirconia crown maintaining the periodontal-restorative factors.

Control charts in healthcare quality monitoring: a systematic review and bibliometric analysis.

Control charts, used in healthcare operations to monitor process stability and quality, are essential for ensuring patient safety and improving clinical outcomes. This comprehensive research study aims to provide a thorough understanding of the role of control charts in healthcare quality monitoring and future perspectives by utilizing a dual methodology approach involving a systematic review and a pioneering bibliometric analysis. A systematic review of 73 out of 223 articles was conducted, synthesizing existing literature (1995-2023) and revealing insights into key trends, methodological approaches, and emerging themes of control charts in healthcare. In parallel, a bibliometric analysis (1990-2023) on 184 articles gathered from Web of Science and Scopus was performed, quantitatively assessing the scholarly landscape encompassing control charts in healthcare. Among 25 countries, the USA is the foremost user of control charts, accounting for 33% of all applications, whereas among 14 health departments, epidemiology leads with 28% of applications. The practice of control charts in health monitoring has increased by more than one-third during the last 3 years. Globally, exponentially weighted moving average charts are the most popular, but interestingly the USA remained the top user of Shewhart charts. The study also uncovers a dynamic landscape in healthcare quality monitoring, with key contributors, research networks, research hotspot tendencies, and leading countries. Influential authors, such as J.C. Benneyan, W.H. Woodall, and M.A. Mohammed played a leading role in this field. In-countries networking, USA-UK leads the largest cluster, while other clusters include Denmark-Norway-Sweden, China-Singapore, and Canada-South Africa. From 1990 to 2023, healthcare monitoring evolved from studying efficiency to focusing on conditional monitoring and flowcharting, with human health, patient safety, and health surveys dominating 2011-2020, and recent years emphasizing epidemic control, COronaVIrus Disease of 2019 (COVID-19) statistical process control, hospitals, and human health monitoring using control charts. It identifies a transition from conventional to artificial intelligence approaches, with increasing contributions from machine learning and deep learning in the context of Industry 4.0. New researchers and journals are emerging, reshaping the academic context of control charts in healthcare. Our research reveals the evolving landscape of healthcare quality monitoring, surpassing traditional reviews. We uncover emerging trends, research gaps, and a transition in leadership from established contributors to newcomers amidst technological advancements. This study deepens the importance of control charts, offering insights for healthcare professionals, researchers, and policymakers to enhance healthcare quality. Future challenges and research directions are also provided.

Accelerating the Orthodontic Treatment Using Periodontally Accelerated Osteogenic Orthodontics (PAOO): A Periodontic-Orthodontic Interrelationship.

Periodontally accelerated osteogenic orthodontics (PAOO) is a periodontal-orthodontic interrelationship procedure that helps in accelerating orthodontic treatment by periodontal procedure reinforcement. This depends on the principle of the regional acceleratory phenomenon which involves inducing changes in the biology of periodontal tissues to fasten the orthodontic tooth movement by creating a surge in the osteopenic environment for tooth movement followed by bone deposition and mineralisation to stabilise the tooth in newly moved position. This PAOO involves the intentional creation of surgical corticotomy cuts followed by a grafting procedure to maintain bone resorption and thickness. Numerous modifications have been incorporated to reduce surgical complications and to improve treatment results by minimally invasive techniques. Hence, this case report incorporated piezosurgery-assisted corticotomy cuts involving the buccal side along with particulate bone grafting to fasten the orthodontic tooth movement, reducing the overall treatment time, root resorption and stabilising the orthodontic treatment results.

Global contribution of statistical control charts to epidemiology monitoring: A 23-year analysis with optimized EWMA real-life application on COVID-19.

Control charts help epidemiologists and healthcare professionals monitor disease incidence and prevalence in real time, preventing outbreaks and health emergencies. However, there remains a notable gap in the comprehensive exploration and application of these techniques, particularly in the context of monitoring and managing disease outbreaks. This study analyses and categorizes worldwide control chart applications from 2000 to 2023 in outbreak monitoring in over 20 countries, focusing on corona-virus (COVID-19), and chooses optimal control charts for monitoring US COVID-19 death waves from February 2020 to December 2023. The systematic literature review analyzes available 35 articles, categorizing data by year, variable, country, study type, and chart design. A selected optimal chart is applied to monitor COVID-19 death patterns and waves in the USA. Control chart adoption in epidemiology monitoring increased during the COVID-19 pandemic, with annual patterns showing a rise in 2021 to 2023 (18%, 36%, 41%). Important variables from 2000 to 2019 include influenza counts, Salmonella cases, and infection rates, while COVID-19 studies focus more on cases, infection rates, symptoms, and deaths. Among 22 countries, the USA (29%) is the top applier of control charts. The monitoring of USA COVID-19 deaths reveals 8 waves with varying severity  >  >  >  >  >  >  > . The associated with the JN.1 variant, highlights ongoing challenges. This study emphasizes the significance of control charts in outbreak monitoring for early disease diagnosis and intervention. Control charts help healthcare workers manage epidemics using data-driven methods, improving public health. COVID-19 mortality analysis emphasizes their importance, encouraging worldwide use.

Enhanced Heterogeneous Fenton Degradation of Organic Dyes by Bimetallic Zirconia-Based Catalysts.

The qualitative impact of pollutants on water quality is mainly related to their nature and their concentration, but in any case, they determine a strong impact on the involved ecosystems. In particular, refractory organic compounds represent a critical challenge, and several degradation processes have been studied and developed for their removal. Among them, heterogeneous Fenton treatment is a promising technology for wastewater and liquid waste remediation. Here, we have developed mono- and bimetallic formulations based on Co, Cu, Fe, and Mn, which were investigated for the degradation of three model organic dyes (methylene blue, rhodamine B, and malachite green). The treated samples were then analyzed by means of UV-vis spectrophotometry techniques. Bimetallic iron-based materials achieved almost complete degradation of all three model molecules in very short time. The Mn-Fe catalyst resulted in the best formulation with an almost complete degradation of methylene blue and malachite green at pH 5 in 5 min and of rhodamine B at pH 3 in 30 min. The results suggest that these formulations can be applied for the treatment of a broad range of liquid wastes comprising complex and variable organic pollutants. The investigated catalysts are extremely promising when compared to other systems reported in the literature.

Clinical predictions of COVID-19 patients using deep stacking neural networks.

The coronavirus disease 2019 (COVID-19) pandemic, which emerged in late 2019, has caused millions of infections and fatalities globally, disrupting various aspects of human society, including socioeconomic, political, and educational systems. One of the key challenges during the COVID-19 pandemic is accurately predicting the clinical development and outcome of the infected patients. In response, scientists and medical professionals globally have mobilized to develop prognostic strategies such as risk scores, biomarkers, and machine learning models to predict the clinical course and outcomes of COVID-19 patients. In this contribution, we deployed a mathematical approach called matrix factorization feature selection to select the most relevant features from the anonymized laboratory biomarkers and demographic data of COVID-19 patients. Based on these features, developed a model that leverages the deep stacking neural network (DSNN) to aid in clinical care by predicting patients' mortality risk. To gauge the performance of our suggested model, performed a comparative analysis with principal component analysis plus support vector machine, deep learning, and random forest, achieving outstanding performances. The DSNN model outperformed all the other models in terms of area under the curve (96.0%), F1-score (98.1%), recall (98.5%), accuracy (99.0%), precision (97.7%), specificity (97.0%), and maximum probability of correction decision (93.4%). Our model outperforms the clinical predictive models regarding patient mortality risk and classification in the literature. Therefore, we conclude that our robust model can help healthcare professionals to manage COVID-19 patients more effectively. We expect that early prediction of COVID-19 patients and preventive interventions can reduce the mortality risk of patients.

Colchicine prevents oxidative stress-induced endothelial cell senescence via blocking NF-κB and MAPKs: implications in vascular diseases.

BACKGROUND: Smoking, alcohol abuse, and hypertension are - among others, potential risk factors for cardiovascular diseases. These risk factors generate oxidative stress and cause oxidative stress-induced DNA damage, resulting in cellular senescence and senescence-associated secretory phenotype (SASP). The SASP factors in feed-forward response exacerbate inflammation and cause tissue remodeling, resulting in atherosclerotic plaque formation and rupture. RESULTS: Colchicine inhibited ROS generation and mitigated oxidative stress-induced DNA damage. It dampened oxidative stress-induced endothelial cell senescence and improved the expression of DNA repair protein KU80 and aging marker Lamin B1. The drug attenuated the expression of senescence marker P21 at mRNA and protein levels. The pathway analysis showed that colchicine inhibited NF-κB and MAPKs pathways and subdued mTOR activation. Colchicine also attenuated mRNA expression of interleukin (IL)-1ß, IL-6, IL-8, MCP-1, ICAM-1, and E-selectin. Furthermore, colchicine reduced the mRNA and protein expression of matrix metalloproteinase (MMP-2). CONCLUSION: In summary, colchicine blocked oxidative stress-induced senescence and SASP by inhibiting the activation of NF-κB and MAPKs pathways.

Research progress of neonatal hypoxic-ischemic encephalopathy in nonhuman primate models.

Neonatal hypoxic-ischemic encephalopathy (HIE) is one of the important complications of neonatal asphyxia, which not only leads to neurological disability but also seriously threatens the life of neonates. Over the years, animal models of HIE have been a research hotspot to find ways to cope with HIE and thereby reduce the risk of neonatal death or disability in moderate-to-severe HIE. By reviewing the literature related to HIE over the years, it was found that nonhuman primates share a high degree of homology with human gross neural anatomy. The basic data on nonhuman primates are not yet complete, so it is urgent to mine and develop new nonhuman primate model data. In recent years, the research on nonhuman primate HIE models has been gradually enriched and the content is more novel. Therefore, the purpose of this review is to further summarize the methods for establishing the nonhuman primate HIE model and to better elucidate the relevance of the nonhuman primate model to humans by observing the behavioral manifestations, neuropathology, and a series of biomarkers of HIE in primates HIE. Finally, the most popular and desirable treatments studied in nonhuman primate models in the past 5 years are summarized.

Bioinformatics, RNA sequencing, and targeted bisulfite sequencing analyses identify the role of PROM2 as a diagnostic and prognostic biomarker.

OBJECTIVES: Prominin 2 (PROM2) gene has been reported as a molecular biomarker of human cancers; however, its role is still controversial. This study was therefore arranged to seek the role of PROM2 in different cancers with Bioinformatics and in vitro analyses. METHODS: A combination of bioinformatics and molecular experiments. RESULTS: Through the utilization of Bioinformatics analysis, it was observed that in 19 out of the 24 human cancers studied, there was a significant increase in the expression of PROM2 compared to the respective control samples. Additionally, the overexpression of PROM2 was linked specifically to a decrease in overall survival (OS) among breast cancer (BRCA), lung adenocarcinoma (LUAD), and uterine corpus endometrial carcinoma (UCEC) patients. Furthermore, advanced molecular investigations were conducted, encompassing RNA sequencing (RNA-seq) as well as targeted bisulfite sequencing (bisulfite-seq) assessments of PROM2. These analyses were performed across an array of lung cancer cell lines (A549, ABC-1, EBC-1, and LK-2) and a normal control lung cell line (MRC-9). Results of these analysis revealed overexpression and reduced methylation of PROM2 within lung cancer cell lines, relative to the corresponding control cell line. This suggests that PROM2 assumes a substantial function in the advancement and course of BRCA, LUAD, and UCEC cancers. Subsequent pathway analysis revealed that genes enriched by PROM2 are actively engaged in four pivotal pathways. Additionally, intriguing associations were observed between PROM2 expression, tumor purity, infiltration of CD8+ T immune cells, and genetic modifications. Moreover, we also predicted a few MicroRNAs (miRNAs), transcription factors (TFs), and potential drugs that could help to understand and better manage these cancers via designing appropriate therapies targeting PROM2. CONCLUSION: Via this study, we effectively revealed PROM2 overexpression as a potential diagnostic and prognostic biomarker of survival in BRCA, LUAD, and UCEC.

Landslide detection and inventory updating using the time-series InSAR approach along the Karakoram Highway, Northern Pakistan.

Karakoram Highway (KKH) is frequently disrupted by geological hazards mainly landslides which pose a serious threat to its normal operation. Using documented inventory, optical imagery interpretation, and frequency-area statistics, the features of slope failure, the spatial distribution, and their link to numerous contributing factors have all been effectively explored along the KKH. An updated inventory for the area was recreated using the interferometric synthetic aperture radar (InSAR) persistent scatterer (PS) technology to further investigate millimetre-accurate measurements of slope deformation (Vslope). Utilizing the PS approach, Sentinel-1 data from Jan 2018 to Jan 2022 were processed by which we obtained a deformation rate (VSlope) that varies between 0 and 364 mm/year. A total number of 234 landslides were cited from the literature and classified while 29 new potential landslides were detected and several pre-existing landslides were redefined by the InSAR approach, which was incorporated to generate an updated landslide susceptibility model with 86.6% of prediction precision in the area under curve method. As previous studies done by applying the InSAR technique incorporated a short span temporally and they missed some highly deforming zones like Budalas and Khanabad landslides, contain mean velocities > 50 mm/yr, which we studied individually in this work. In this study, a comprehensive application of the InSAR technique to assessing its performance in detecting and analysing landslides has been applied. The deformation velocity (Vslope) model shows high displacement in some regions, which needed to be further investigated by geoscientists, and the updated developed landslide inventory and susceptibility map can be used for land use planning and landslide mitigation strategies.

EfectroH2O: Development and evaluation of a novel treatment technology for high-brine industrial wastewater.

Textile production is one of the main sources of freshwater consumption by industries worldwide. In addition, according to the world bank, 20 % of the wastewater generated globally is caused by textile wet-processing. Textile wet-processing includes the processes in textile production where garments are dyed or given the final functions like water-repellency. Several thousand chemicals were used in this process, some of which are highly toxic. Discharging untreated or insufficiently treated wastewater in water bodies results in high pollution levels, severely impacting the environment and human health. Especially in textile-producing countries like India, environmental pollution and water consumption from textile wet-processing have severe impacts. Next to the high volume of chemicals used in textile production, the high salt concentration in textile wastewater also poses a challenge and is critical for freshwater systems. Moreover, textile wastewater is one of the most difficult to treat wastewater. Currently, used treatment technologies do not meet the requirements to treat textile wastewater. Therefore, the further development of efficient treatment technologies for textile wastewater is critically important. Hence, in the interdisciplinary project, effect-based monitoring demonstrates the efficiency of electrically-driven water treatment processes to remove salts and micropollutants from process water (EfectroH2O), a low-energy Zero Liquid Discharge (ZLD) textile wastewater treatment technology is being developed consisting of a combination of capacitive deionization (CDI) and advanced oxidation processes (AOP). In addition to treatment technology development, methods for evaluating the efficiency of treatment technologies also need to be improved. Currently, mainly physicochemical parameters such as pH, biochemical oxygen demand (BOD) and chemical oxygen demand (COD) are tested worldwide to check water quality. However, these methods are insufficient to make a statement about the toxic potential of such complex mixtures as textile wastewater. Therefore, also next to chemical analyses, effect-based methods (EBM) are used to verify the treated wastewater.

Interplay Impact of Exogenous Application of Abscisic Acid (ABA) and Brassinosteroids (BRs) in Rice Growth, Physiology, and Resistance under Sodium Chloride Stress.

The hormonal imbalances, including abscisic acid (ABA) and brassinosteroid (BR) levels, caused by salinity constitute a key factor in hindering spikelet development in rice and in reducing rice yield. However, the effects of ABA and BRs on spikelet development in plants subjected to salinity stress have been explored to only a limited extent. In this research, the effect of ABA and BRs on rice growth characteristics and the development of spikelets under different salinity levels were investigated. The rice seedlings were subjected to three different salt stress levels: 0.0875 dS m-1 (Control, CK), low salt stress (1.878 dS m-1, LS), and heavy salt stress (4.09 dS m-1, HS). Additionally, independent (ABA or BR) and combined (ABA+BR) exogenous treatments of ABA (at 0 and 25 µM concentration) and BR (at 0 and 5 µM concentration) onto the rice seedlings were performed. The results showed that the exogenous application of ABA, BRs, and ABA+BRs triggered changes in physiological and agronomic characteristics, including photosynthesis rate (Pn), SPAD value, pollen viability, 1000-grain weight (g), and rice grain yield per plant. In addition, spikelet sterility under different salt stress levels (CK, LS, and HS) was decreased significantly through the use of both the single phytohormone and the cocktail, as compared to the controls. The outcome of this study reveals new insights about rice spikelet development in plants subjected to salt stress and the effects on this of ABA and BR. Additionally, it provides information on the use of plant hormones to improve rice yield under salt stress and on the enhancement of effective utilization of salt-affected soils.

Abatement of the ecotoxicological risk of landfill leachate by heterogeneous Fenton-like oxidation.

Landfill leachates are highly contaminated liquid waste, and their treatment and detoxification are a challenging task. The current system of ecotoxicological risk assessment is complex and time-consuming. It is of fundamental importance to develop simpler and faster tools for the evaluation of the treated liquid waste and for an easier preliminary screening of the most active catalytic formulation/reaction conditions of the Fenton-like process. Here, several analytical techniques have been used for the assessment of the reduction of toxicity of the landfill leachate after Fenton process over copper-zirconia catalyst (ZrCu). Ultraviolet-visible (UV-vis) spectroscopy and absorbable organic halogens (AOX) analysis have been coupled to achieve further insight into the degradation of contaminants. In addition, for the first time, the qualitative abatement of organic compounds is monitored through proton nuclear magnetic resonance (1H NMR) analysis, providing a new method for evaluating the effectiveness of the treatment. Spectroscopic techniques reveal that the Fenton process induces a significant abatement of the aromatic and halogen compounds (51%) in the landfill leachate with a reduction of the toxicity that has been confirmed by ecotoxicological test with algae. These results validate the investigated tool for a simple rapid preliminary evaluation of the detoxification efficacy.

Developing Ethics and Equity Principles, Terms, and Engagement Tools to Advance Health Equity and Researcher Diversity in AI and Machine Learning: Modified Delphi Approach.

BACKGROUND: Artificial intelligence (AI) and machine learning (ML) technology design and development continues to be rapid, despite major limitations in its current form as a practice and discipline to address all sociohumanitarian issues and complexities. From these limitations emerges an imperative to strengthen AI and ML literacy in underserved communities and build a more diverse AI and ML design and development workforce engaged in health research. OBJECTIVE: AI and ML has the potential to account for and assess a variety of factors that contribute to health and disease and to improve prevention, diagnosis, and therapy. Here, we describe recent activities within the Artificial Intelligence/Machine Learning Consortium to Advance Health Equity and Researcher Diversity (AIM-AHEAD) Ethics and Equity Workgroup (EEWG) that led to the development of deliverables that will help put ethics and fairness at the forefront of AI and ML applications to build equity in biomedical research, education, and health care. METHODS: The AIM-AHEAD EEWG was created in 2021 with 3 cochairs and 51 members in year 1 and 2 cochairs and ~40 members in year 2. Members in both years included AIM-AHEAD principal investigators, coinvestigators, leadership fellows, and research fellows. The EEWG used a modified Delphi approach using polling, ranking, and other exercises to facilitate discussions around tangible steps, key terms, and definitions needed to ensure that ethics and fairness are at the forefront of AI and ML applications to build equity in biomedical research, education, and health care. RESULTS: The EEWG developed a set of ethics and equity principles, a glossary, and an interview guide. The ethics and equity principles comprise 5 core principles, each with subparts, which articulate best practices for working with stakeholders from historically and presently underrepresented communities. The glossary contains 12 terms and definitions, with particular emphasis on optimal development, refinement, and implementation of AI and ML in health equity research. To accompany the glossary, the EEWG developed a concept relationship diagram that describes the logical flow of and relationship between the definitional concepts. Lastly, the interview guide provides questions that can be used or adapted to garner stakeholder and community perspectives on the principles and glossary. CONCLUSIONS: Ongoing engagement is needed around our principles and glossary to identify and predict potential limitations in their uses in AI and ML research settings, especially for institutions with limited resources. This requires time, careful consideration, and honest discussions around what classifies an engagement incentive as meaningful to support and sustain their full engagement. By slowing down to meet historically and presently underresourced institutions and communities where they are and where they are capable of engaging and competing, there is higher potential to achieve needed diversity, ethics, and equity in AI and ML implementation in health research.