Arsenic Incorporation in Pyrite at Ambient Temperature at Both Tetrahedral S-I and Octahedral FeII Sites: Evidence from EXAFS-DFT Analysis.

Pyrite is a ubiquitous mineral in reducing environments and is well-known to incorporate trace elements such as Co, Ni, Se, Au, and commonly As. Indeed, As-bearing pyrite is observed in a wide variety of sedimentary environments, making it a major sink for this toxic metalloid. Based on the observation of natural hydrothermal pyrites, As-I is usually assigned to the occupation of tetrahedral S-I sites, with the same oxidation state as in arsenopyrite (FeAsS), although rare occurrences of AsIII and AsII have been reported. However, the modes of As incorporation into pyrite during its crystallization under low-temperature diagenetic conditions have not yet been elucidated because arsenic acts as an inhibitor for pyrite nucleation at ambient temperature. Here, we provide evidence from X-ray absorption spectroscopy for AsII,III incorporation into pyrite at octahedral FeII sites and for As-I at tetrahedral S-I sites during crystallization at ambient temperature. Extended X-ray absorption fine structure (EXAFS) spectra of these As-bearing pyrites are explained by local structure models obtained using density functional theory (DFT), assuming incorporation of As at the Fe and S sites, as well as local clustering of arsenic. Such observations of As-I incorporation at ambient temperature can aid in the understanding of the early formation of authigenic arsenian pyrite in subsurface sediments. Moreover, evidence for substitution of AsII,III for Fe in our synthetic samples raises questions about both the possible occurrence and the geochemical reactivity of such As-bearing pyrites in low-temperature subsurface environments.

Strong electric fields at a prototypical oxide/water interface probed by ab initio molecular dynamics: MgO(001).

We report a density-functional theory (DFT)-based study of the interface of bulk water with a prototypical oxide surface, MgO(001), and focus our study on the often-overlooked surface electric field. In particular, we observe that the bare MgO(001) surface, although charge-neutral and defectless, has an intense electric field on the Å scale. The MgO(001) surface covered with 1 water monolayer (1 ML) is investigated via a supercell accounting for the experimentally-observed (2 × 3) reconstruction, stable at ambient temperature, and in which two out of six water molecules are dissociated. This 1 ML-hydrated surface is also found to have a high, albeit short-ranged, normal component of the field. Finally, the oxide/water interface is studied via room-temperature ab initio molecular dynamics (AIMD) using 34 H2O molecules between two MgO(001) surfaces. To our best knowledge this is the first AIMD study of the MgO(001)/liquid water interface in which all atoms are treated using DFT and including several layers above the first adsorbed layer. We observe that the surface electric field, averaged over the AIMD trajectories, is still very strong on the fully-wet surface, peaking at about 3 V Å(-1). Even in the presence of bulk-like water, the structure of the first layer in contact with the surface remains similar to the (2 × 3)-reconstructed ice ad-layer on MgO(001). Moreover, we observe proton exchange within the first layer, and between the first and second layers - indeed, the O-O distances close to the surface are found to be distributed towards shorter distances, a property which has been shown to directly promote proton transfer.

Generative AI-based knowledge graphs for the illustration and development of mHealth self-management content.

Background: Digital therapeutics (DTx) in the form of mobile health (mHealth) self-management programs have demonstrated effectiveness in reducing disease activity across various diseases, including fibromyalgia and arthritis. However, the content of online self-management programs varies widely, making them difficult to compare. Aim: This study aims to employ generative artificial intelligence (AI)-based knowledge graphs and network analysis to categorize and structure mHealth content at the example of a fibromyalgia self-management program. Methods: A multimodal mHealth online self-management program targeting fibromyalgia and post-viral fibromyalgia-like syndromes was developed. In addition to general content, the program was customized to address specific features and digital personas identified through hierarchical agglomerative clustering applied to a cohort of 202 patients with chronic musculoskeletal pain syndromes undergoing multimodal assessment. Text files consisting of 22,150 words divided into 24 modules were used as the input data. Two generative AI web applications, ChatGPT-4 (OpenAI) and Infranodus (Nodus Labs), were used to create knowledge graphs and perform text network analysis, including 3D visualization. A sentiment analysis of 129 patient feedback entries was performed. Results: The ChatGPT-generated knowledge graph model provided a simple visual overview with five primary edges: "Mental health challenges", "Stress and its impact", "Immune system function", "Long COVID and fibromyalgia" and "Pain management and therapeutic approaches". The 3D visualization provided a more complex knowledge graph, with the term "pain" appearing as the central edge, closely connecting with "sleep", "body", and "stress". Topical cluster analysis identified categories such as "chronic pain management", "sleep hygiene", "immune system function", "cognitive therapy", "healthy eating", "emotional development", "fibromyalgia causes", and "deep relaxation". Gap analysis highlighted missing links, such as between "negative behavior" and "systemic inflammation". Retro-engineering of the self-management program showed significant conceptual similarities between the knowledge graph and the original text analysis. Sentiment analysis of free text patient comments revealed that most relevant topics were addressed by the online program, with the exception of social contacts. Conclusion: Generative AI tools for text network analysis can effectively structure and illustrate DTx content. Knowledge graphs are valuable for increasing the transparency of self-management programs, developing new conceptual frameworks, and incorporating feedback loops.

Development of a Management App for Postviral Fibromyalgia-Like Symptoms: Patient Preference-Guided Approach.

BACKGROUND: Persistent fibromyalgia-like symptoms have been increasingly reported following viral infections, including SARS-CoV-2. About 30% of patients with post-COVID-19 syndrome fulfill the fibromyalgia criteria. This complex condition presents significant challenges in terms of self-management. Digital health interventions offer a viable means to assist patients in managing their health conditions. However, the challenge of ensuring their widespread adoption and adherence persists. This study responds to this need by developing a patient-centered digital health management app, incorporating patient preferences to enhance usability and effectiveness, ultimately aiming to improve patient outcomes and quality of life. OBJECTIVE: This research aims to develop a digital health self-management app specifically for patients experiencing postviral fibromyalgia-like symptoms. By prioritizing patient preferences and engagement through the app's design and functionality, the study intends to facilitate better self-management practices and improve adherence. METHODS: Using an exploratory study design, the research used patient preference surveys and usability testing as primary tools to inform the development process of the digital health solution. We gathered and analyzed patients' expectations regarding design features, content, and usability to steer the iterative app development. RESULTS: The study uncovered crucial insights from patient surveys and usability testing, which influenced the app's design and functionality. Key findings included a preference for a symptom list over an automated chatbot, a desire to report on a moderate range of symptoms and activities, and the importance of an intuitive onboarding process. While usability testing identified some challenges in the onboarding process, it also confirmed the importance of aligning the app with patient needs to enhance engagement and satisfaction. CONCLUSIONS: Incorporating patient feedback has been a significant factor in the development of the digital health app. Challenges encountered with user onboarding during usability testing have highlighted the importance of this process for user adoption. The study acknowledges the role of patient input in developing digital health technologies and suggests further research to improve onboarding procedures, aiming to enhance patient engagement and their ability to manage digital health resources effectively. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.2196/32193.

An mHealth App for Fibromyalgia-like Post-COVID-19 Syndrome: Protocol for the Analysis of User Experience and Clinical Data.

BACKGROUND: Post-COVID-19 syndrome, also referred as "long covid," describes persisting symptoms after SARS-CoV-2 infection, including myalgia, fatigue, respiratory, or neurological symptoms. Objective symptoms are often lacking, thus resembling a fibromyalgia-like syndrome. Digital therapeutics have shown efficiency in similar chronic disorders such as fibromyalgia, offering specific disease monitoring and interventions such as cognitive behavioral therapy or physical and respiratory exercise guidance. OBJECTIVE: This protocol aims to study the requirements and features of a new mobile health (mHealth) app among patients with fibromyalgia-like post-COVID-19 syndrome in a clinical trial. METHODS: We created a web application prototype for the post-COVID-19 syndrome called "POCOS," as a web-based rehabilitation tool aiming to improve clinical outcomes. Patients without organ damage or ongoing inflammation will be included in the study. App use will be assessed through user experience questionnaires, focus groups, and clinical data analysis. Subsequently, we will analyze cross-sectional and longitudinal clinical data. RESULTS: The developed mHealth app consists of a clinically adapted app interface with a simplified patient-reported outcome assessment, monitoring of medical interventions, and disease activity as well as web-based instructions for specific physical and respiratory exercises, stress reduction, and lifestyle instructions. The enrollment of participants is expected to be carried out in November 2021. CONCLUSIONS: User experience plays an important role in digital therapeutics and needs to be clinically tested to allow further improvement. We here describe this process for a new app for the treatment of the fibromyalgia-like post-COVID-19 syndrome and discuss the relevance of the potential outcomes such as natural disease course and disease phenotypes. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/32193.

Dorsal Finger Fold Recognition by Convolutional Neural Networks for the Detection and Monitoring of Joint Swelling in Patients with Rheumatoid Arthritis.

Digital biomarkers such as wearables are of increasing interest in monitoring rheumatic diseases, but they usually lack disease specificity. In this study, we apply convolutional neural networks (CNN) to real-world hand photographs in order to automatically detect, extract, and analyse dorsal finger fold lines as a correlate of proximal interphalangeal (PIP) joint swelling in patients with rheumatoid arthritis (RA). Hand photographs of RA patients were taken by a smartphone camera in a standardized manner. Overall, 190 PIP joints were categorized as either swollen or not swollen based on clinical judgement and ultrasound. Images were automatically preprocessed by cropping PIP joints and extracting dorsal finger folds. Subsequently, metrical analysis of dorsal finger folds was performed, and a CNN was trained to classify the dorsal finger lines into swollen versus non-swollen joints. Representative horizontal finger folds were also quantified in a subset of patients before and after resolution of PIP swelling and in patients with disease flares. In swollen joints, the number of automatically extracted deep skinfold imprints was significantly reduced compared to non-swollen joints (1.3, SD 0.8 vs. 3.3, SD 0.49, p < 0.01). The joint diameter/deep skinfold length ratio was significantly higher in swollen (4.1, SD 1.4) versus non-swollen joints (2.1, SD 0.6, p < 0.01). The CNN model successfully differentiated swollen from non-swollen joints based on finger fold patterns with a validation accuracy of 0.84, a sensitivity of 88%, and a specificity of 75%. A heatmap of the original images obtained by an extraction algorithm confirmed finger folds as the region of interest for correct classification. After significant response to disease-modifying antirheumatic drug ± corticosteroid therapy, longitudinal metrical analysis of eight representative deep finger folds showed a decrease in the mean diameter/finger fold length (finger fold index, FFI) from 3.03 (SD 0.68) to 2.08 (SD 0.57). Conversely, the FFI increased in patients with disease flares. In conclusion, automated preprocessing and the application of CNN algorithms in combination with longitudinal metrical analysis of dorsal finger fold patterns extracted from real-world hand photos might serve as a digital biomarker in RA.

Concentrations of Seven Phthalate Monoesters in Infants and Toddlers Quantified in Urine Extracted from Diapers.

Carrying out exposure studies on children who are not toilet trained is challenging because of the difficulty of urine sampling. In this study, we optimized a protocol for urine collection from disposable diapers for the analysis of phthalate metabolites. The exposure of Swiss children (n = 113) between 6 months and 3 years of life to seven phthalates was assessed by gas chromatography-mass spectrometry measurements. The study showed limited exposures to phthalates, with only 22% of the samples containing some of the metabolites investigated. The three most frequently detected metabolites were monoethyl phthalate, mono-cyclohexyl phthalate, and mono-benzyl phthalate. We also detected mono-n-octyl phthalate and mono(3,5,5-trimethylhexyl) phthalate, which have rarely been observed in urine from infants and toddlers; therefore, di-n-octyl phthalate and bis(3,5,5-trimethylhexyl) phthalate can be considered as potentially new emerging phthalates. This study presents an initial snapshot of the Swiss children's exposure to phthalates and provides a promising approach for further phthalate biomonitoring studies on young children using disposable diapers as urine sampling technique.

van der Waals Contribution to the Relative Stability of Aqueous Zn(2+) Coordination States.

Many properties of aqueous cations depend on their coordination state. However, the lack of long-range order and the dynamic character of aqueous solutions make it difficult to obtain information beyond average coordination parameters. A thorough understanding of the molecular-scale environment of aqueous cations usually requires a combination of experimental and theoretical approaches. In the case of Zn2+, significant discrepancies occur among theoretical investigations based on first-principles molecular dynamics (FPMD) or free-energy calculations, although experimental data consistently point to a dominant hexaaquo-zinc complex (Zn[H2O]6)2+ in pure water. In the present study, the aqueous speciation of zinc is theoretically investigated by combining FPMD simulations and free-energy calculations based on metadynamics and umbrella-sampling strategies. The simulations are carried out within the density functional theory (DFT) framework using for the exchange-correlation functional either a standard generalized gradient approximation (GGA) or a nonlocal functional (vdw-DF2) which includes van der Waals interactions. The theoretical environment of Zn is confronted to experiment by comparing calculated and measured X-ray absorption spectra. It is shown that the inclusion of van der Waals interactions is crucial for the correct modeling of zinc aqueous speciation, whereas GGA incorrectly favors tetraaquo- (Zn[H2O]4)2+ and pentaaquo-zinc (Zn[H2O]5)2+ complexes, results obtained with the vdW-DF2 functional show that the hexaaquo-zinc complex is more stable than the tetraaquo and pentaaquo-zinc complexes by 13 and by 4 kJ mol-1, respectively. These results highlight the critical importance of even subtle interactions for the correct balance of different coordination states in aqueous solutions. However, for a given coordination state, GGA leads to a reasonable description of the geometry of the aqueous complex.

Electronic structure study of the high-pressure vibrational spectrum of FeS2 pyrite.

Plane-wave density functional calculations are used to investigate the pressure dependence of the geometry and Gamma-point phonons of FeS(2) pyrite up to 150 GPa. The linear response method is employed to calculate the vibrational properties. Raman-active modes are in excellent agreement with the experimental data available up to 50 GPa,(1) and we predict the evolution with pressure of the IR-active modes for which no high-pressure spectroscopic data have been reported so far. Over the wide pressure range investigated here, all vibrational frequencies depend nonlinearly on pressure; their pressure dependence is quantified by determining the full set of mode Grüneisen parameters and their pressure derivatives.