Investigating the spatiotemporal associations between meteorological conditions and air pollution in the federal state Baden-Württemberg (Germany).

When analyzing health data in relation to environmental stressors, it is crucial to identify which variables to include in the statistical model to exclude dependencies among the variables. Four meteorological parameters: temperature, ultraviolet radiation, precipitation, and vapor pressure and four outdoor air pollution parameters: ozone ( O 3 ), nitrogen dioxide ( NO 2 ), particulate matter ( P M 2.5 , P M 10 ) were studied on a daily basis for Baden-Württemberg (Germany). This federal state covers urban and rural compartments including mountainous and river areas. A temporal and spatial analysis of the internal relationships was performed among the variables using (a) cross-correlations, both on the grand ensemble of data as well as within subsets, and (b) the Local Indications of Spatial Association (LISA) method. Meteorological and air pollution variables were strongly correlated within and among themselves in time and space. We found a strong interaction between nitrogen dioxide and ozone, with correlation coefficients varying over time. The coefficients ranged from negative correlations in January (-0.84), April (-0.47), and October (-0.54) to a positive correlation in July (0.45). The cross-correlation plot showed a noticeable change in the correlation direction for O 3 and NO 2 . Spatially, NO 2 , P M 2.5 , and P M 10 concentrations were significantly higher in urban than rural regions. For O 3 , this effect was reversed. A LISA analysis confirmed distinct hot and cold spots of environmental stressors. This work examined and quantified the spatio-temporal relationship between air pollution and meteorological conditions and recommended which variables to prioritize for future health impact analyses. The results found are in line with the underlying physico-chemical atmospheric processes. It also identified postal code areas with dominant environmental stressors for further studies.

Comparison of Hemodynamics in Jet Ventilation vs. Intermittent Apnea for Airway Stenosis Surgery.

OBJECTIVE: The objective of this study is to assess the impact of two different ventilation techniques, jet ventilation and apneic anesthesia with intermittent ventilation (AAIV), on patient hemodynamics and operative time during endoscopic laryngotracheal stenosis surgery. METHODS: Retrospective chart review of patients who underwent airway dilation for laryngotracheal stenosis by a single surgeon at a single institution from October 1, 2000 through January 2, 2020. Logistic regression, Mann-Whitney U tests and chi square analysis were used to determine statistical significance. RESULTS: A total of 157 patients, 43 (27.4%) male and 114 (72.6%) female, and 605 total encounters were included for analysis. There were no significant differences in hemodynamic outcomes when comparing the AAIV and jet ventilation groups. Specifically, there was no significant difference in either peak end-tidal CO2 or nadir O2 saturation between the AAIV and jet ventilation groups (p = 0.4016) and (p = 0.1357), respectively. The patients in the AAIV group had a significantly higher median BMI 32.93 (27.40-39.40) compared with 28.80 (24.1-32.65) (p = 0.0001). Although not necessarily clinically significant, patients with higher BMI had lower median O2 nadirs (97.8%) than non-obese patients (99.2%) (p < 0.0001). The median total procedure time was equivalent when comparing the two ventilation techniques. CONCLUSION: AAIV is a safe method of ventilation for patients undergoing endoscopic laryngotracheal stenosis surgery with no significant differences in patient hemodynamics or procedure time when compared with jet ventilation. AAIV was the preferred method of ventilation for obese patients undergoing endoscopic laryngotracheal stenosis surgery. LEVEL OF EVIDENCE: 3 Laryngoscope, 134:1343-1348, 2024.

Temperature and particulate matter as environmental factors associated with seasonality of influenza incidence - an approach using Earth observation-based modeling in a health insurance cohort study from Baden-Württemberg (Germany).

BACKGROUND: Influenza seasonality has been frequently studied, but its mechanisms are not clear. Urban in-situ studies have linked influenza to meteorological or pollutant stressors. Few studies have investigated rural and less polluted areas in temperate climate zones. OBJECTIVES: We examined influences of medium-term residential exposure to fine particulate matter (PM2.5), NO2, SO2, air temperature and precipitation on influenza incidence. METHODS: To obtain complete spatial coverage of Baden-Württemberg, we modeled environmental exposure from data of the Copernicus Atmosphere Monitoring Service and of the Copernicus Climate Change Service. We computed spatiotemporal aggregates to reflect quarterly mean values at post-code level. Moreover, we prepared health insurance data to yield influenza incidence between January 2010 and December 2018. We used generalized additive models, with Gaussian Markov random field smoothers for spatial input, whilst using or not using quarter as temporal input. RESULTS: In the 3.85 million cohort, 513,404 influenza cases occurred over the 9-year period, with 53.6% occurring in quarter 1 (January to March), and 10.2%, 9.4% and 26.8% in quarters 2, 3 and 4, respectively. Statistical modeling yielded highly significant effects of air temperature, precipitation, PM2.5 and NO2. Computation of stressor-specific gains revealed up to 3499 infections per 100,000 AOK clients per year that are attributable to lowering ambient mean air temperature from 18.71 °C to 2.01 °C. Stressor specific gains were also substantial for fine particulate matter, yielding up to 502 attributable infections per 100,000 clients per year for an increase from 7.49 µg/m3 to 15.98 µg/m3. CONCLUSIONS: Whilst strong statistical association of temperature with other stressors makes it difficult to distinguish between direct and mediated temperature effects, results confirm genuine effects by fine particulate matter on influenza infections for both rural and urban areas in a temperate climate. Future studies should attempt to further establish the mediating mechanisms to inform public health policies.

Anti-tumor effects of cryptotanshinone (C19H20O3) in human osteosarcoma cell lines.

Osteosarcoma is the most prevalent malignant bone tumor and occurs most commonly in the adolescent and young adult population. Despite the recent advances in surgeries and chemotherapy, the overall survival in patients with resectable metastases is around 20%. This challenge in osteosarcoma is often attributed to the drastic differences in the tumorigenic profiles and mutations among patients. With diverse mutations and multiple oncogenes, it is necessary to identify the therapies that can attack various mutations and simultaneously have minor side-effects. In this paper, we constructed the osteosarcoma pathway from literature and modeled it using ordinary differential equations. We then simulated this network for every possible gene mutation and their combinations and ranked different drug combinations based on their efficacy to drive a mutated osteosarcoma network towards cell death. Our theoretical results predict that drug combinations with Cryptotanshinone (C19H20O3), a traditional Chinese herb derivative, have the best overall performance. Specifically, Cryptotanshinone in combination with Temsirolimus inhibit the JAK/STAT, MAPK/ERK, and PI3K/Akt/mTOR pathways and induce cell death in tumor cells. We corroborated our theoretical predictions using wet-lab experiments on SaOS2, 143B, G292, and HU03N1 human osteosarcoma cell lines, thereby demonstrating the potency of Cryptotanshinone in fighting osteosarcoma.

In Silico Modeling of the Induction of Apoptosis by Cryptotanshinone in Osteosarcoma Cell Lines.

Osteosarcoma (OS) is the most common primary malignant bone tumor of both children and pet canines. Its characteristic genomic instability and complexity coupled with the dearth of knowledge about its etiology has made improvement in the current treatment difficult. We use the existing literature about the biological pathways active in OS and combine it with the current research involving natural compounds to identify new targets and design more effective drug therapies. The key components of these pathways are modeled as a Boolean network with multiple inputs and multiple outputs. The combinatorial circuit is employed to theoretically predict the efficacies of various drugs in combination with Cryptotanshinone. We show that the action of the herbal drug, Cryptotanshinone on OS cell lines induces apoptosis by increasing sensitivity to TNF-related apoptosis-inducing ligand (TRAIL) through its multi-pronged action on STAT3, DRP1 and DR5. The Boolean framework is used to detect additional drug intervention points in the pathway that could amplify the action of Cryptotanshinone.

Targeting oncogenic mutations in colorectal cancer using cryptotanshinone.

Colorectal cancer (CRC) is one of the most prevalent types of cancer in the world and ranks second in cancer deaths in the US. Despite the recent improvements in screening and treatment, the number of deaths associated with CRC is still very significant. The complexities involved in CRC therapy stem from multiple oncogenic mutations and crosstalk between abnormal pathways. This calls for using advanced molecular genetics to understand the underlying pathway interactions responsible for this cancer. In this paper, we construct the CRC pathway from the literature and using an existing public dataset on healthy vs tumor colon cells, we identify the genes and pathways that are mutated and are possibly responsible for the disease progression. We then introduce drugs in the CRC pathway, and using a boolean modeling technique, we deduce the drug combinations that produce maximum cell death. Our theoretical simulations demonstrate the effectiveness of Cryptotanshinone, a traditional Chinese herb derivative, achieved by targeting critical oncogenic mutations and enhancing cell death. Finally, we validate our theoretical results using wet lab experiments on HT29 and HCT116 human colorectal carcinoma cell lines.

Comprehensive live-cell imaging analysis of cryptotanshinone and synergistic drug-screening effects in various human and canine cancer cell lines.

BACKGROUND: Several studies have highlighted both the extreme anticancer effects of Cryptotanshinone (CT), a Stat3 crippling component from Salvia miltiorrhiza, as well as other STAT3 inhibitors to fight cancer. METHODS: Data presented in this experiment incorporates 2 years of in vitro studies applying a comprehensive live-cell drug-screening analysis of human and canine cancer cells exposed to CT at 20 µM concentration, as well as to other drug combinations. As previously observed in other studies, dogs are natural cancer models, given to their similarity in cancer genetics, epidemiology and disease progression compared to humans. RESULTS: Results obtained from several types of human and canine cancer cells exposed to CT and varied drug combinations, verified CT efficacy at combating cancer by achieving an extremely high percentage of apoptosis within 24 hours of drug exposure. CONCLUSIONS: CT anticancer efficacy in various human and canine cancer cell lines denotes its ability to interact across different biological processes and cancer regulatory cell networks, driving inhibition of cancer cell survival.

Cryptotanshinone Induces Cell Death in Lung Cancer by Targeting Aberrant Feedback Loops.

Signaling pathways oversee highly efficient cellular mechanisms such as growth, division, and death. These processes are controlled by robust negative feedback loops that inhibit receptor-mediated growth factor pathways. Specifically, the ERK, the AKT, and the S6K feedback loops attenuate signaling via growth factor receptors and other kinase receptors to regulate cell growth. Irregularity in any of these supervised processes can lead to uncontrolled cell proliferation and possibly Cancer. These irregularities primarily occur as mutated genes, and an exhaustive search of the perfect drug combination by performing experiments can be both costly and complex. Hence, in this paper, we model the Lung Cancer pathway as a Modified Boolean Network that incorporates feedback. By simulating this network, we theoretically predict the drug combinations that achieve the desired goal for the majority of mutations. Our theoretical analysis identifies Cryptotanshinone, a traditional Chinese herb derivative, as a potent drug component in the fight against cancer. We validated these theoretical results using multiple wet lab experiments carried out on H2073 and SW900 lung cancer cell lines.

A Gaussian Mixture-Model Exploiting Pathway Knowledge for Dissecting Cancer Heterogeneity.

In this work, we develop a systematic approach for applying pathway knowledge to a multivariate Gaussian mixture model for dissecting a heterogeneous cancer tissue. The downstream transcription factors are selected as observables from available partial pathway knowledge in such a way that the subpopulations produce some differential behavior in response to the drugs selected in the upstream. For each subpopulation, each unique (drug, observable) pair is considered as a unique dimension of a multivariate Gaussian distribution. Expectation-maximization (EM) algorithm with hill-climbing is then used to rank the most probable estimates of the mixture composition based on the log-likelihood value. A major contribution of this work is to examine the efficacy of the EM based approach in estimating the composition of experimental mixture sets from cell-by-cell measurements collected on a dynamic cell imaging platform. Towards this end, we apply the algorithm on hourly data collected for two different mixture compositions of A2058, HCT116, and SW480 cell lines for three scenarios: untreated, Lapatinib-treated, and Temsirolimus-treated. Additionally, we show how this methodology can provide a basis for comparing the killing rate of different drugs for a heterogeneous cancer tissue. This obviously has important implications for designing efficient drugs for treating heterogeneous malignant tumors.

In Silico Design and Experimental Validation of Combination Therapy for Pancreatic Cancer.

The number of deaths associated with Pancreatic Cancer has been on the rise in the United States making it an especially dreaded disease. The overall prognosis for pancreatic cancer patients continues to be grim because of the complexity of the disease at the molecular level involving the potential activation/inactivation of several diverse signaling pathways. In this paper, we first model the aberrant signaling in pancreatic cancer using a multi-fault Boolean Network. Thereafter, we theoretically evaluate the efficacy of different drug combinations by simulating this boolean network with drugs at the relevant intervention points and arrive at the most effective drug(s) to achieve cell death. The simulation results indicate that drug combinations containing Cryptotanshinone, a traditional Chinese herb derivative, result in considerably enhanced cell death. These in silico results are validated using wet lab experiments we carried out on Human Pancreatic Cancer (HPAC) cell lines.

Bayesian Inference Identifies Combination Therapeutic Targets in Breast Cancer.

OBJECTIVE: Breast cancer is the second leading cause of cancer death among US women; hence, identifying potential drug targets is an ever increasing need. In this paper, we integrate existing biological information with graphical models to deduce the significant nodes in the breast cancer signaling pathway. METHODS: We make use of biological information from the literature to develop a Bayesian network. Using the relevant gene expression data we estimate the parameters of this network. Then, using a message passing algorithm, we infer the network. The inferred network is used to quantitatively rank different interventions for achieving a desired phenotypic outcome. The particular phenotype considered here is the induction of apoptosis. RESULTS: Theoretical analysis pinpoints to the role of Cryptotanshinone, a compound found in traditional Chinese herbs, as a potent modulator for bringing about cell death in the treatment of cancer. CONCLUSION: Using a mathematical framework, we showed that the combination therapy of mTOR and STAT3 genes yields the best apoptosis in breast cancer. SIGNIFICANCE: The computational results we arrived at are consistent with the experimental results that we obtained using Cryptotanshinone on MCF-7 breast cancer cell lines and also by the past results of others from the literature, thereby demonstrating the effectiveness of our model.

An in-silico study examining the induction of apoptosis by Cryptotanshinone in metastatic melanoma cell lines.

BACKGROUND: Metastatic melanoma is an aggressive form of skin cancer that evades various anti-cancer treatments including surgery, radio-,immuno- and chemo-therapy. TRAIL-induced apoptosis is a desirable method to treat melanoma since, unlike other treatments, it does not harm non-cancerous cells. The pro-inflammatory response to melanoma by nF κB and STAT3 pathways makes the cancer cells resist TRAIL-induced apoptosis. We show that due to to its dual action on DR5, a death receptor for TRAIL and on STAT3, Cryptotanshinone can be used to increase sensitivity to TRAIL. METHODS: The development of chemoresistance and invasive properties in melanoma cells involves several biological pathways. The key components of these pathways are represented as a Boolean network with multiple inputs and multiple outputs. RESULTS: The possible mutations in genes that can lead to cancer are captured by faults in the combinatorial circuit and the model is used to theoretically predict the effectiveness of Cryptotanshinone for inducing apoptosis in melanoma cell lines. This prediction is experimentally validated by showing that Cryptotanshinone can cause enhanced cell death in A375 melanoma cells. CONCLUSION: The results presented in this paper facilitate a better understanding of melanoma drug resistance. Furthermore, this framework can be used to detect additional drug intervention points in the pathway that could amplify the action of Cryptotanshinone.

Phenotype Classification Using Moment Features of Single-Cell Data.

Features for standard expression microarray and RNA-Seq classification are expression averages over collections of cells. Single cell provides expression measurements for individual cells in a collection of cells from a particular tissue sample. Hence, it can yield feature vectors consisting of higher order and mixed moments. This article demonstrates the advantage of using these expression moments in cancer-related classification. We use synthetic data generated from 2 real networks, the mammalian cell cycle network and a melanoma-related pathway network, and real single-cell data generated via fluorescent protein reporters from 2 cell lines, HT-29 and HCT-116. The networks consist of hidden binary regulatory networks with Gaussian observations. The steady-state distributions of both the original and mutated networks are found, and data are drawn from these for moment-based classification using the mean, variance, skewness, and mixed moments. For the real data, we only observe 1 gene at a time, so that only the mean, variance, and skewness are considered, the analysis being done for 2 genes, EGFR and ERRB2. For the synthetic data, classification improves as we move from just the mean to mean, variance, and skewness and then to these plus the mixed moments. Comparisons are done with 3, 4, or 5 features, using feature selection. Sample size effects are considered. For the real data, we only consider mean, variance, and skewness, with results improving when the higher order moments are used as features.

A Bayesian approach to determine the composition of heterogeneous cancer tissue.

BACKGROUND: Cancer Tissue Heterogeneity is an important consideration in cancer research as it can give insights into the causes and progression of cancer. It is known to play a significant role in cancer cell survival, growth and metastasis. Determining the compositional breakup of a heterogeneous cancer tissue can also help address the therapeutic challenges posed by heterogeneity. This necessitates a low cost, scalable algorithm to address the challenge of accurate estimation of the composition of a heterogeneous cancer tissue. METHODS: In this paper, we propose an algorithm to tackle this problem by utilizing the data of accurate, but high cost, single cell line cell-by-cell observation methods in low cost aggregate observation method for heterogeneous cancer cell mixtures to obtain their composition in a Bayesian framework. RESULTS: The algorithm is analyzed and validated using synthetic data and experimental data. The experimental data is obtained from mixtures of three separate human cancer cell lines, HCT116 (Colorectal carcinoma), A2058 (Melanoma) and SW480 (Colorectal carcinoma). CONCLUSION: The algorithm provides a low cost framework to determine the composition of heterogeneous cancer tissue which is a crucial aspect in cancer research.

All-Oxide Thermoelectric Module with in Situ Formed Non-Rectifying Complex p-p-n Junction and Transverse Thermoelectric Effect.

All-oxide thermoelectric modules for energy harvesting are attractive because of high-temperature stability, low cost, and the potential to use nonscarce and nontoxic elements. Thermoelectric modules are mostly fabricated in the conventional π-design, associated with the challenge of unstable metallic interconnects at high temperature. Here, we report on a novel approach for fabrication of a thermoelectric module with an in situ formed p-p-n junction made of state-of-the-art oxides Ca3Co4-x O9+δ (p-type) and CaMnO3-CaMn2O4 composite (n-type). The module was fabricated by spark plasma co-sintering of p- and n-type powders partly separated by insulating LaAlO3. Where the n- and p-type materials originally were in contact, a layer of p-type Ca3CoMnO6 was formed in situ. The hence formed p-p-n junction exhibited Ohmic behavior and a transverse thermoelectric effect, boosting the open-circuit voltage of the module. The performance of the module was characterized at 700-900 °C, with the highest power output of 5.7 mW (around 23 mW/cm2) at 900 °C and a temperature difference of 160 K. The thermoelectric properties of the p- and n-type materials were measured in the temperature range 100-900 °C, where the highest zT of 0.39 and 0.05 were obtained at 700 and 800 °C, respectively, for Ca3Co4-x O9+δ and the CaMnO3-CaMn2O4 composite.

Systems biology for organotypic cell cultures.

Translating in vitro biological data into actionable information related to human health holds the potential to improve disease treatment and risk assessment of chemical exposures. While genomics has identified regulatory pathways at the cellular level, translation to the organism level requires a multiscale approach accounting for intra-cellular regulation, inter-cellular interaction, and tissue/organ-level effects. Tissue-level effects can now be probed in vitro thanks to recently developed systems of three-dimensional (3D), multicellular, "organotypic" cell cultures, which mimic functional responses of living tissue. However, there remains a knowledge gap regarding interactions across different biological scales, complicating accurate prediction of health outcomes from molecular/genomic data and tissue responses. Systems biology aims at mathematical modeling of complex, non-linear biological systems. We propose to apply a systems biology approach to achieve a computational representation of tissue-level physiological responses by integrating empirical data derived from organotypic culture systems with computational models of intracellular pathways to better predict human responses. Successful implementation of this integrated approach will provide a powerful tool for faster, more accurate and cost-effective screening of potential toxicants and therapeutics. On September 11, 2015, an interdisciplinary group of scientists, engineers, and clinicians gathered for a workshop in Research Triangle Park, North Carolina, to discuss this ambitious goal. Participants represented laboratory-based and computational modeling approaches to pharmacology and toxicology, as well as the pharmaceutical industry, government, non-profits, and academia. Discussions focused on identifying critical system perturbations to model, the computational tools required, and the experimental approaches best suited to generating key data.

High-Content Assay Multiplexing for Toxicity Screening in Induced Pluripotent Stem Cell-Derived Cardiomyocytes and Hepatocytes.

Cell-based high-content screening (HCS) assays have become an increasingly attractive alternative to traditional in vitro and in vivo testing in pharmaceutical drug development and toxicological safety assessment. The time- and cost-effectiveness of HCS assays, combined with the organotypic nature of human induced pluripotent stem cell (iPSC)-derived cells, open new opportunities to employ physiologically relevant in vitro model systems to improve screening for potential chemical hazards. In this study, we used two human iPSC types, cardiomyocytes and hepatocytes, to test various high-content and molecular assay combinations for their applicability in a multiparametric screening format. Effects on cardiomyocyte beat frequency were characterized by calcium flux measurements for up to 90 min. Subsequent correlation with intracellular cAMP levels was used to determine if the effects on cardiac physiology were G-protein-coupled receptor dependent. In addition, we utilized high-content cell imaging to simultaneously determine cell viability, mitochondrial integrity, and reactive oxygen species (ROS) formation in both cell types. Kinetic analysis indicated that ROS formation is best detectable 30 min following initial treatment, whereas cytotoxic effects were most stable after 24 h. For hepatocytes, high-content imaging was also used to evaluate cytotoxicity and cytoskeletal integrity, as well as mitochondrial integrity and the potential for lipid accumulation. Lipid accumulation, a marker for hepatic steatosis, was most reliably detected 48 h following treatment with test compounds. Overall, our results demonstrate how a compendium of assays can be utilized for quantitative screening of chemical effects in iPSC cardiomyocytes and hepatocytes and enable rapid and cost-efficient multidimensional biological profiling of toxicity.

Dynamical modeling of uncertain interaction-based genomic networks.

BACKGROUND: Most dynamical models for genomic networks are built upon two current methodologies, one process-based and the other based on Boolean-type networks. Both are problematic when it comes to experimental design purposes in the laboratory. The first approach requires a comprehensive knowledge of the parameters involved in all biological processes a priori, whereas the results from the second method may not have a biological correspondence and thus cannot be tested in the laboratory. Moreover, the current methods cannot readily utilize existing curated knowledge databases and do not consider uncertainty in the knowledge. Therefore, a new methodology is needed that can generate a dynamical model based on available biological data, assuming uncertainty, while the results from experimental design can be examined in the laboratory. RESULTS: We propose a new methodology for dynamical modeling of genomic networks that can utilize the interaction knowledge provided in public databases. The model assigns discrete states for physical entities, sets priorities among interactions based on information provided in the database, and updates each interaction based on associated node states. Whenever uncertainty in dynamics arises, it explores all possible outcomes. By using the proposed model, biologists can study regulation networks that are too complex for manual analysis. CONCLUSIONS: The proposed approach can be effectively used for constructing dynamical models of interaction-based genomic networks without requiring a complete knowledge of all parameters affecting the network dynamics, and thus based on a small set of available data.

Assessing Combinational Drug Efficacy in Cancer Cells by Using Image-based Dynamic Response Analysis.

The landscape of translational research has been shifting toward drug combination therapies. Pairing of drugs allows for more types of drug interaction with cells. In order to accurately and comprehensively assess combinational drug efficacy, analytical methods capable of recognizing these alternative reactions will be required to prioritize those drug candidates having better chances of delivering appreciable therapeutic benefits. Traditional efficacy measures are primarily based on the "extent" of drug inhibition, which is the percentage of cells being killed after drug exposure. Here, we introduce a second dimension of evaluation criterion, speed of killing, based on a live cell imaging assay. This dynamic response trajectory approach takes advantage of both "extent" and "speed" information and uncovers synergisms that would otherwise be missed, while also generating hypotheses regarding important mechanistic modes of drug action.

Dioxygen reactivity of biomimetic Fe(II) complexes with noninnocent catecholate, o-aminophenolate, and o-phenylenediamine ligands.

This study describes the O2 reactivity of a series of high-spin mononuclear Fe(II) complexes each containing the facially coordinating tris(4,5-diphenyl-1-methylimidazol-2-yl)phosphine ((Ph2)TIP) ligand and one of the following bidentate, redox-active ligands: 4-tert-butylcatecholate ((tBu)CatH(-)), 4,6-di-tert-butyl-2-aminophenolate ((tBu2)APH(-)), or 4-tert-butyl-1,2-phenylenediamine ((tBu)PDA). The preparation and X-ray structural characterization of [Fe(2+)((Ph2)TIP)((tBu)CatH)]OTf, [3]OTf and [Fe(2+)((Ph2)TIP)((tBu)PDA)](OTf)2, [4](OTf)2 are described here, whereas [Fe(2+)((Ph2)TIP)((tBu2)APH)]OTf, [2]OTf was reported in our previous paper [Bittner et al., Chem.-Eur. J. 2013, 19, 9686-9698]. These complexes mimic the substrate-bound active sites of nonheme iron dioxygenases, which catalyze the oxidative ring-cleavage of aromatic substrates like catechols and aminophenols. Each complex is oxidized in the presence of O2, and the geometric and electronic structures of the resulting complexes were examined with spectroscopic (absorption, EPR, Mössbauer, resonance Raman) and density functional theory (DFT) methods. Complex [3]OTf reacts rapidly with O2 to yield the ferric-catecholate species [Fe(3+)((Ph2)TIP)((tBu)Cat)](+) (3(ox)), which undergoes further oxidation to generate an extradiol cleavage product. In contrast, complex [4](2+) experiences a two-electron (2e(-)), ligand-based oxidation to give [Fe(2+)((Ph2)TIP)((tBu)DIBQ)](2+) (4(ox)), where DIBQ is o-diiminobenzoquinone. The reaction of [2](+) with O2 is also a 2e(-) process, yet in this case both the Fe center and (tBu2)AP ligand are oxidized; the resulting complex (2(ox)) is best described as [Fe(3+)((Ph2)TIP)((tBu2)ISQ)](+), where ISQ is o-iminobenzosemiquinone. Thus, the oxidized complexes display a remarkable continuum of electronic structures ranging from [Fe(3+)(L(2-))](+) (3(ox)) to [Fe(3+)(L(•-))](2+) (2(ox)) to [Fe(2+)(L(0))](2+) (4(ox)). Notably, the O2 reaction rates vary by a factor of 10(5) across the series, following the order [3](+) > [2](+) > [4](2+), even though the complexes have similar structures and Fe(3+/2+) redox potentials. To account for the kinetic data, we examined the relative abilities of the title complexes to bind O2 and participate in H-atom transfer reactions. We conclude that the trend in O2 reactivity can be rationalized by accounting for the role of proton transfer(s) in the overall reaction.