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At least 253 records · Page 14
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Using case-level context to classify cancer pathology reports

Individual electronic health records (EHRs) and clinical reports are often part of a larger sequence—for example, a single patient may generate multiple reports over the trajectory of a disease. In applications such as cancer pathology reports, it is necessary not only to extract information from individual reports, but also to capture aggregate information regarding the entire cancer case based off case-level context from all reports in the sequence. In this paper, we introduce a simple modular add-on for capturing case-level context that is designed to be compatible with most existing deep learning architectures for text classification on individual reports. We test our approach on a corpus of 431,433 cancer pathology reports, and we show that incorporating case-level context significantly boosts classification accuracy across six classification tasks—site, subsite, laterality, histology, behavior, and grade. We expect that with minimal modifications, our add-on can be applied towards a wide range of other clinical text-based tasks.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2020

Metabolic Signaling Cascades Prompted by Glutaminolysis in Cancer

Aberrant glutamatergic signaling has been implicated in altered metabolic activity and the demand to synthesize biomass in several types of cancer including melanoma. In the last decade, there has been a significant contribution to our understanding of metabolic pathways. An increasing number of studies are now emphasizing the importance of glutamate functioning as a signaling molecule and a building block for cancer progression. To that end, our group has previously illustrated the role of glutamatergic signaling mediated by metabotropic glutamate receptor 1 (GRM1) in neoplastic transformation of melanocytes in vitro and spontaneous development of metastatic melanoma in vivo. Glutamate, the natural ligand of GRM1, is one of the most abundant amino acids in humans and the predominant excitatory neurotransmitter in the central nervous system. Elevated levels of glutaminolytic mitochondrial tricarboxylic acid (TCA) cycle intermediates, especially glutamate, have been reported in numerous cancer cells. Herein, we highlight and critically review metabolic bottlenecks that are prevalent during tumor evolution along with therapeutic implications of limiting glutamate bioavailability in tumors.

Shah, Raj (ORCID:0000000204400189)↗
DOE OSTI2022

Pathophysiological Integration of Metabolic Reprogramming in Breast Cancer

Metabolic changes that facilitate tumor growth are one of the hallmarks of cancer. The triggers of these metabolic changes are located in the tumor parenchymal cells, where oncogenic mutations induce an imperative need to proliferate and cause tumor initiation and progression. Cancer cells undergo significant metabolic reorganization during disease progression that is tailored to their energy demands and fluctuating environmental conditions. Oxidative stress plays an essential role as a trigger under such conditions. These metabolic changes are the consequence of the interaction between tumor cells and stromal myofibroblasts. The metabolic changes in tumor cells include protein anabolism and the synthesis of cell membranes and nucleic acids, which all facilitate cell proliferation. They are linked to catabolism and autophagy in stromal myofibroblasts, causing the release of nutrients for the cells of the tumor parenchyma. Metabolic changes lead to an interstitium deficient in nutrients, such as glucose and amino acids, and acidification by lactic acid. Together with hypoxia, they produce functional changes in other cells of the tumor stroma, such as many immune subpopulations and endothelial cells, which lead to tumor growth. Thus, immune cells favor tissue growth through changes in immunosuppression. This review considers some of the metabolic changes described in breast cancer.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2024

Mechanistic Insights into the Successful Development of Combination Therapy of Enfortumab Vedotin and Pembrolizumab for the Treatment of Locally Advanced or Metastatic Urothelial Cancer

Antibody–drug conjugates (ADCs) consist of an antibody backbone that recognizes and binds to a target antigen expressed on tumor cells and a small molecule chemotherapy payload that is conjugated to the antibody via a linker. ADCs are one of the most promising therapeutic modalities for the treatment of various cancers. However, many patients have developed resistance to this form of therapy. Extensive efforts have been dedicated to identifying an effective combination of ADCs with other types of anticancer therapies to potentially overcome this resistance. A recent clinical study demonstrated that a combination of the ADC enfortumab vedotin (EV) with the immune checkpoint inhibitor (ICI) pembrolizumab can achieve remarkable clinical efficacy as the first-line therapy for the treatment of locally advanced or metastatic urothelial carcinoma (la/mUC)—leading to the first approval of a combination therapy of an ADC with an ICI for the treatment of cancer patients. In this review, we highlight knowledge and understanding gained from the successful development of EV and the combination therapy of EV with ICI for the treatment of la/mUC. Using urothelial carcinoma as an example, we will focus on dissecting the underlying mechanisms necessary for the development of this type of combination therapy for a variety of cancers.

Oncology↗
DOE OSTI2022

Tracing and Forecasting Metabolic Indices of Cancer Patients Using Patient-Specific Deep Learning Models

We develop a patient-specific dynamical system model from the time series data of the cancer patient’s metabolic panel taken during the period of cancer treatment and recovery. The model consists of a pair of stacked long short-term memory (LSTM) recurrent neural networks and a fully connected neural network in each unit. It is intended to be used by physicians to trace back and look forward at the patient’s metabolic indices, to identify potential adverse events, and to make short-term predictions. When the model is used in making short-term predictions, the relative error in every index is less than 10% in the L ∞ norm and less than 6.3% in the L 1 norm in the validation process. Once a master model is built, the patient-specific model can be calibrated through transfer learning. As an example, we obtain patient-specific models for four more cancer patients through transfer learning, which all exhibit reduced training time and a comparable level of accuracy. This study demonstrates that this modeling approach is reliable and can deliver clinically acceptable physiological models for tracking and forecasting patients’ metabolic indices.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2020

Nanoparticles for targeted cancer radiotherapy

Radiotherapy, where ionizing radiation is locally delivered either through an external beam or by surgically implanting radionuclide-based seeds in the tumor, is one of the gold standard treatments for cancer. Due to the non-selective nature of radiation, healthy tissue surrounding the cancerous region is usually affected by the treatment. Hence, new strategies, including targeted alpha therapy, are being studied to improve the selectivity of the treatment and minimize side effects. Several challenges, however, limit the current development of targeted radiotherapy, such as the functionalization of the therapeutic agent with targeting vectors and controlling the release of recoiling daughters. Nanoparticles offer unique opportunities as drug delivery vehicles, since they are biocompatible, enhance the cellular uptake of drugs, and are easily functionalized with targeting molecules. In this review, we examine how nanoparticles can be used for targeted radiotherapy, either as sensitizers of external beams or as delivery vehicles for therapeutic radionuclides. Further, we describe the clinical relevance of different types of nanoparticles, followed by an analysis of how these nanoconstructs can solve some of the main limitations of conventional radiotherapy. Finally, we critically discuss the current situation of nanoparticle-based radiotherapy in clinical settings and challenges that need to be overcome in the future for further development of the field.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2024

Comparative Uptake Patterns of Radioactive Iodine and [18F]-Fluorodeoxyglucose (FDG) in Metastatic Differentiated Thyroid Cancers

Background: Metastatic differentiated thyroid cancer (DTC) represents a molecularly heterogeneous group of cancers with varying radioactive iodine (RAI) and [ 18 F]-fluorodeoxyglucose (FDG) uptake patterns potentially correlated with the degree of de-differentiation through the so-called “flip-flop” phenomenon. However, it is unknown if RAI and FDG uptake patterns correlate with molecular status or metastatic site. Materials and Methods: A retrospective analysis of metastatic DTC patients (n = 46) with radioactive 131-iodine whole body scan (WBS) and FDG-PET imaging between 2008 and 2022 was performed. The inclusion criteria included accessible FDG-PET and WBS studies within 1 year of each other. Studies were interpreted by two blinded radiologists for iodine or FDG uptake in extrathyroidal sites including lungs, lymph nodes, and bone. Cases were stratified by BRAF V600E mutation status, histology, and a combination of tumor genotype and histology. The data were analyzed by McNemar’s Chi-square test. Results: Lung metastasis FDG uptake was significantly more common than iodine uptake (WBS: 52%, FDG: 84%, p = 0.04), but no significant differences were found for lymph or bone metastases. Lung metastasis FDG uptake was significantly more prevalent in the papillary pattern sub-cohort (WBS: 37%, FDG: 89%, p = 0.02) than the follicular pattern sub-cohort (WBS: 75%, FDG: 75%, p = 1.00). Similarly, BRAF V600E+ tumors with lung metastases also demonstrated a preponderance of FDG uptake (WBS: 29%, FDG: 93%, p = 0.02) than BRAF V600E- tumors (WBS: 83%, FDG: 83%, p = 1.00) with lung metastases. Papillary histology featured higher FDG uptake in lung metastasis (WBS: 39%, FDG: 89%, p = 0.03) compared with follicular histology (WBS: 69%, FDG: 77%, p = 1.00). Patients with papillary pattern disease, BRAF V600E+ mutation, or papillary histology had reduced agreement between both modalities in uptake at all metastatic sites compared with those with follicular pattern disease, BRAF V600E- mutation, or follicular histology. Low agreement in lymph node uptake was observed in all patients irrespective of molecular status or histology. Conclusions: The pattern of FDG-PET and radioiodine uptake is dependent on molecular status and metastatic site, with those with papillary histology or BRAF V600E+ mutation featuring increased FDG uptake in distant metastasis. Further study with an expanded cohort may identify which patients may benefit from specific imaging modalities to recognize and surveil metastases.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2022

Dietary B group vitamin intake and the bladder cancer risk: a pooled analysis of prospective cohort studies

Abstract Purpose Diet may play an essential role in the aetiology of bladder cancer (BC). The B group complex vitamins involve diverse biological functions that could be influential in cancer prevention. The aim of the present study was to investigate the association between various components of the B group vitamin complex and BC risk. Methods Dietary data were pooled from four cohort studies. Food item intake was converted to daily intakes of B group vitamins and pooled multivariate hazard ratios (HRs), with corresponding 95% confidence intervals (CIs), were obtained using Cox-regression models. Dose–response relationships were examined using a nonparametric test for trend. Results In total, 2915 BC cases and 530,012 non-cases were included in the analyses. The present study showed an increased BC risk for moderate intake of vitamin B1 (HR B1 : 1.13, 95% CI: 1.00–1.20). In men, moderate intake of the vitamins B1, B2, energy-related vitamins and high intake of vitamin B1 were associated with an increased BC risk (HR (95% CI): 1.13 (1.02–1.26), 1.14 (1.02–1.26), 1.13 (1.02–1.26; 1.13 (1.02–1.26), respectively). In women, high intake of all vitamins and vitamin combinations, except for the entire complex, showed an inverse association (HR (95% CI): 0.80 (0.67–0.97), 0.83 (0.70–1.00); 0.77 (0.63–0.93), 0.73 (0.61–0.88), 0.82 (0.68–0.99), 0.79 (0.66–0.95), 0.80 (0.66–0.96), 0.74 (0.62–0.89), 0.76 (0.63–0.92), respectively). Dose–response analyses showed an increased BC risk for higher intake of vitamin B1 and B12. Conclusion Our findings highlight the importance of future research on the food sources of B group vitamins in the context of the overall and sex-stratified diet.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2023

Comparison and multi-model inference of excess risks models for radiation-related solid cancer

In assessments of detrimental health risks from exposures to ionising radiation, many forms of risk to dose–response models are available in the literature. The usual practice is to base risk assessment on one specific model and ignore model uncertainty. The analysis illustrated here considers model uncertainty for the outcome all solid cancer incidence, when modelled as a function of colon organ dose, using the most recent publicly available data from the Life Span Study on atomic bomb survivors of Japan. Seven recent publications reporting all solid cancer risk models currently deemed plausible by the scientific community have been included in a model averaging procedure so that the main conclusions do not depend on just one type of model. The models have been estimated with different baselines and presented for males and females at various attained ages and ages at exposure, to obtain specially computed model-averaged Excess Relative Risks (ERR) and Excess Absolute Risks (EAR). Monte Carlo simulated estimation of uncertainty on excess risks was accounted for by applying realisations including correlations in the risk model parameters. Three models were found to weight the model-averaged risks most strongly depending on the baseline and information criteria used for the weighting. Fitting all excess risk models with the same baseline, one model dominates for both information criteria considered in this study. Based on the analysis presented here, it is generally recommended to take model uncertainty into account in future risk analyses.

63 RADIATION, THERMAL, AND OTHER ENVIRON. POLLUTAN↗
DOE OSTI2024

Multiplexed Quantitative Proteomics in Prostate Cancer Biomarker Development

Prostate cancer (PCa) is the most common non-skin cancer among men in the United States. However, the widely used protein biomarker in PCa, prostate-specific antigen (PSA), while useful for initial detection, its use alone cannot detect aggressive PCa and can lead to overtreatment. This chapter provides an overview of PCa protein biomarker development. It reviews the state-of-the-art liquid chromatography-mass spectrometry-based proteomics technologies for PCa biomarker development, such as enhancing the detection sensitivity of low-abundance proteins through antibody-based or antibody-independent protein/peptide enrichment, enriching post-translational modifications such as glycosylation as well as information-rich extracellular vesicles, and increasing accuracy and throughput using advanced data acquisition methodologies. This chapter also summarizes recent PCa biomarker validation studies that applied those techniques in diverse specimen types, including cell lines, tissues, proximal fluids, urine, and blood, developing novel protein biomarkers for various clinical applications, including early detection and diagnosis, prognosis, and therapeutic intervention of PCa.

Prostate cancer, SRM, PRM, DIA, protein biomarker↗
DOE OSTI2023

Succinate based polymers drive immunometabolism in dendritic cells to generate cancer immunotherapy

Boosting the metabolism of immune cells while restricting cancer cell metabolism is challenging. Herein, we report that using biomaterials for the controlled delivery of succinate metabolite to phagocytic immune cells activates them and modulates their metabolism in the presence of metabolic inhibitors. In young immunocompetent mice, polymeric microparticles, with succinate incorporated in the backbone, induced strong pro-inflammatory anti-melanoma responses. Administration of poly(ethylene succinate) (PES MP)-based vaccines and glutaminase inhibitor to young immunocompetent mice with aggressive and large, established B16F10 melanoma tumors increased their survival three-fold, a result of increased cytotoxic T cells expressing RORγT (Tc17). Mechanistically, PES MPs directly modulate glutamine and glutamate metabolism, upregulate succinate receptor SUCNR1, activate antigen presenting cells through and HIF-1alpha, TNFa and TSLP-signaling pathways, and are dependent on alpha-ketoglutarate dehydrogenase for their activity, which demonstrates correlation of succinate delivery and these pathways. Altogether, our findings suggest that immunometabolism-modifying PES MP strategies provide an approach for developing robust cancer immunotherapies.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Deep learning integrates histopathology and proteogenomics at a pan-cancer level

We introduce a pioneering approach that integrates pathology imaging with transcriptomics and proteomics to identify predictive histology features associated with critical clinical outcomes in cancer. We utilize 2,755 H&E-stained histopathological slides from 657 patients across 6 cancer types from CPTAC. Our models effectively recapitulate distinctions readily made by human pathologists: tumor vs. normal (AUROC = 0.995) and tissue-of-origin (AUROC = 0.979). We further investigate predictive power on tasks not normally performed from H&E alone, including TP53 prediction and pathologic stage. Importantly, we describe predictive morphologies not previously utilized in a clinical setting. The incorporation of transcriptomics and proteomics identifies pathway-level signatures and cellular processes driving predictive histology features. Model generalizability and interpretability is confirmed using TCGA. We propose a classification system for these tasks, and suggest potential clinical applications for this integrated human and machine learning approach. A publicly available web-based platform implements these models.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2020

Enzyme-mediated depletion of serum L -Met abrogates prostate cancer growth via multiple mechanisms without evidence of systemic toxicity

Extensive studies in prostate cancer and other malignancies have revealed that L -methionine ( L -Met) and its metabolites play a critical role in tumorigenesis. Preclinical and clinical studies have demonstrated that systemic restriction of serum L -Met, either via partial dietary restriction or with bacterial L -Met–degrading enzymes exerts potent antitumor effects. However, administration of bacterial L -Met–degrading enzymes has not proven practical for human therapy because of problems with immunogenicity. As the human genome does not encode L -Met–degrading enzymes, we engineered the human cystathionine-γ-lyase (hMGL-4.0) to catalyze the selective degradation of L -Met. At therapeutically relevant dosing, hMGL-4.0 reduces serum L -Met levels to >75% for >72 h and significantly inhibits the growth of multiple prostate cancer allografts/xenografts without weight loss or toxicity. We demonstrate that in vitro, hMGL-4.0 causes tumor cell death, associated with increased reactive oxygen species, S-adenosyl-methionine depletion, global hypomethylation, induction of autophagy, and robust poly(ADP-ribose) polymerase (PARP) cleavage indicative of DNA damage and apoptosis.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Targeting a proteolytic neoepitope on CUB domain containing protein 1 ( CDCP1 ) for RAS -driven cancers

Extracellular proteolysis is frequently dysregulated in disease and can generate proteoforms with unique neoepitopes not found in healthy tissue. Here, we demonstrate that Abs that selectively recognize a proteolytic neoepitope on CUB domain containing protein 1 (CDCP1) could enable more effective and safer treatments for solid tumors. CDCP1 is highly overexpressed in RAS-driven cancers, and its ectodomain is cleaved by extracellular proteases. Biochemical, biophysical, and structural characterization revealed that the 2 cleaved fragments of CDCP1 remain tightly associated with minimal proteolysis-induced conformational change. Using differential phage display, we generated recombinant Abs that are exquisitely selective to cleaved CDCP1 with no detectable binding to the uncleaved form. These Abs potently targeted cleaved CDCP1-expressing cancer cells as an Ab-drug conjugate, an Ab-radionuclide conjugate, and a bispecific T cell engager. In a syngeneic pancreatic tumor model, these cleaved-specific Abs showed tumor-specific localization and antitumor activity with superior safety profiles compared with a pan-CDCP1 approach. Targeting proteolytic neoepitopes could provide an orthogonal "AND" gate for improving the therapeutic index.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Emulator-Based Bayesian Calibration of the CISNET Colorectal Cancer Models

Purpose To calibrate Cancer Intervention and Surveillance Modeling Network (CISNET)'s SimCRC, MISCAN-Colon, and CRC-SPIN simulation models of the natural history colorectal cancer (CRC) with an emulator-based Bayesian algorithm and internally validate the model-predicted outcomes to calibration targets.Methods We used Latin hypercube sampling to sample up to 50,000 parameter sets for each CISNET-CRC model and generated the corresponding outputs. We trained multilayer perceptron artificial neural networks (ANNs) as emulators using the input and output samples for each CISNET-CRC model. We selected ANN structures with corresponding hyperparameters (i.e., number of hidden layers, nodes, activation functions, epochs, and optimizer) that minimize the predicted mean square error on the validation sample. We implemented the ANN emulators in a probabilistic programming language and calibrated the input parameters with Hamiltonian Monte Carlo-based algorithms to obtain the joint posterior distributions of the CISNET-CRC models' parameters. We internally validated each calibrated emulator by comparing the model-predicted posterior outputs against the calibration targets.Results The optimal ANN for SimCRC had 4 hidden layers and 360 hidden nodes, MISCAN-Colon had 4 hidden layers and 114 hidden nodes, and CRC-SPIN had 1 hidden layer and 140 hidden nodes. The total time for training and calibrating the emulators was 7.3, 4.0, and 0.66 h for SimCRC, MISCAN-Colon, and CRC-SPIN, respectively. The mean of the model-predicted outputs fell within the 95% confidence intervals of the calibration targets in 98 of 110 for SimCRC, 65 of 93 for MISCAN, and 31 of 41 targets for CRC-SPIN.Conclusions Using ANN emulators is a practical solution to reduce the computational burden and complexity for Bayesian calibration of individual-level simulation models used for policy analysis, such as the CISNET CRC models. In this work, we present a step-by-step guide to constructing emulators for calibrating 3 realistic CRC individual-level models using a Bayesian approach.

artificial neural networks↗
DOE OSTI2024

Development of message passing-based graph convolutional networks for classifying cancer pathology reports

Abstract Background Applying graph convolutional networks (GCN) to the classification of free-form natural language texts leveraged by graph-of-words features (TextGCN) was studied and confirmed to be an effective means of describing complex natural language texts. However, the text classification models based on the TextGCN possess weaknesses in terms of memory consumption and model dissemination and distribution. In this paper, we present a fast message passing network (FastMPN), implementing a GCN with message passing architecture that provides versatility and flexibility by allowing trainable node embedding and edge weights, helping the GCN model find the better solution. We applied the FastMPN model to the task of clinical information extraction from cancer pathology reports, extracting the following six properties: main site, subsite, laterality, histology, behavior, and grade. Results We evaluated the clinical task performance of the FastMPN models in terms of micro- and macro-averaged F1 scores. A comparison was performed with the multi-task convolutional neural network (MT-CNN) model. Results show that the FastMPN model is equivalent to or better than the MT-CNN. Conclusions Our implementation revealed that our FastMPN model, which is based on the PyTorch platform, can train a large corpus (667,290 training samples) with 202,373 unique words in less than 3 minutes per epoch using one NVIDIA V100 hardware accelerator. Our experiments demonstrated that using this implementation, the clinical task performance scores of information extraction related to tumors from cancer pathology reports were highly competitive.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

The FBXW7-binding sites on FAM83D are potential targets for cancer therapy

Increasing evidence shows the oncogenic function of FAM83D in human cancer, but how FAM83D exerts its oncogenic function remains largely unclear. Here, we investigated the importance of FAM83D/FBXW7 interaction in breast cancer (BC). We systematically mapped the FBXW7-binding sites on FAM83D through a comprehensive mutational analysis together with co-immunoprecipitation assay. Mutations at the FBXW7-binding sites on FAM83D led to that FAM83D lost its capability to promote the ubiquitination and proteasomal degradation of FBXW7; cell proliferation, migration, and invasion in vitro; and tumor growth and metastasis in vivo, indicating that the FBXW7-binding sites on FAM83D are essential for its oncogenic functions. A meta-evaluation of FAM83D revealed that the prognostic impact of FAM83D was independent on molecular subtypes. The higher expression of FAM83D has poorer prognosis. Moreover, high expression of FAM83D confers resistance to chemotherapy in BCs, which is experimentally validated in vitro. We conclude that identification of FBXW7-binding sites on FAM83D not only reveals the importance for FAM83D oncogenic function, but also provides valuable insights for drug target.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2020

Effects on Human Non-Small Lung Cancer Cell growth of Cu(II), Au(III) and Re(I) Complexes Containing N-donor Pyridyl Ligands: A Senior Undergraduate Research Project

Hands-on synthetic laboratory experiences are critically important for undergraduate chemistry majors. A senior undergraduate research experience at the College of Wooster is presented in the current work. The inorganic complexes [Cu(phenan) 2 (NO 3 ) 2 ] (1; phenan = phenanthridine) and [Au(phenan)Cl 3 ](2) were prepared by metallation of the phenan ligand in a 1:1 CH 3 OH/CH 2 Cl 2 mixture at room temperature. Complex 1 is a new inorganic coordination compound. The structures were confirmed by X-ray crystallography. Complex 1 reveals a distorted octahedral geometry around the central Cu (II) metal ion. In complex 2, the Au(III) atom exhibits a square planar geometry. In addition, we investigated [Cu(dmeobpy)(H 2 O) 2 ][NO 3 ] 2 (3; dmeobpy = 4,4’-dimethoxy-2,2’-bipyridine), [Cu(bap)(NO 3 ) 2 ](4; bap = 2,6-bis(azaindole)pyridine) and [Re(CO) 3 (dmeobpy)Cl] (5). The cytoxocities of 1-5, phenan, dmeobpy, bap and cisplatin were evaluated against several human non-small cell lung cancer cell lines (NCI-H1975, HCC827, NCI-H460, and NCI-A549). Compound 1 exhibited modest antitumor activity against the HCC827 cell line, with an IC 50 value of 19 µM. Compound 3 exhibits lower cytotoxic activity than 1. Finally, the cytotoxic activity of complex 5 (IC 50 < 11 µM) is significantly greater than 1 and 3 against the H1975 cancer cell line.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗