About

Charles W. Nichols, MD, is an Emeritus Associate Professor of Ophthalmology at the University of Pennsylvania School of Medicine. He serves as the Chief of Ophthalmology in the Biomedical Control Program at Tektite1 and is an ophthalmologist affiliated with the Department of Ophthalmology at the Hospital of the University of Pennsylvania. Dr. Nichols completed his undergraduate education at Williams College in 1960 and earned his medical degree from Thomas Jefferson University in 1964. His professional work includes a focus on ophthalmology with contributions to understanding infectious and inflammatory eye conditions, as evidenced by his publications on topics such as cytomegalovirus retinitis, uveitis, and ocular infections in immunocompromised patients.

Research topics

• Medicine
• Ophthalmology
• Surgery
• Chemistry
• Dermatology

Selected publications

• Specific Potentiation of the TMEM16A Chloride Channel Can Rescue Impaired Mucociliary Transport in Human Airway Models of Cystic Fibrosis

  American Journal of Respiratory and Critical Care Medicine · 2025-05-01

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  Abstract RATIONALE: Cystic fibrosis is caused by loss of function mutations in the CFTR chloride channel. The loss of CFTR function impairs airway defense functions such as innate antimicrobial activity and mucociliary transport to remove infectious and allergic entities. Highly effective modulator therapies (HEMT) that target some dysfunctional CFTR genotypes have been introduced in recent years. However, there are limitations: 1) they are not applicable to the 8% of patients that have class I mutations (early stop codons); 2) not all potentially appropriate CF patients respond to HEMTs; 3) long-term effectiveness is unknown; and 4) persistent inflammation may be needed for HEMTs to function. In light of this, we investigated the possibility of selectively potentiating an alternative chloride channel in the airway in order to improve mucus function. We previously showed that a small protein, the Calcium Activated Chloride Channel Regulator 1 VWA domain (CLCA1 VWA), could selectively potentiate the TMEM16A chloride channel in cell lines. Here we investigate the ability of CLCA1 VWA to potentiate TMEM16A and modulate mucociliary transport (MCT) in human models of CF airway disease. METHODS: We used X-ray crystallography to determine the structure of CLCA1 VWA. We used whole cell patch clamp and Ussing chamber electrophysiology to investigate CLCA1 VWA potentiation of TMEM16A in primary human CF airway cells and ALI cultures. We used PCR to identify and quantitate expression of TMEM16A isoforms in primary CF airway cells. We used airway slices from CF lungs to carry out MCT assays with fluorescent beads. RESULTS: We determined the crystal structure of the CLCA1 VWA, revealing a metal ion dependent adhesion site (MIDAS) in the open high affinity conformation, likely stabilized by a unique disulfide. We found CLCA1 VWA could potentiate TMEM16A chloride currents and enhance transepithelial conductance when applied to cells of various CFTR genotypes. We used immunohistochemistry in CF tissues to show TMEM16A was expressed in secretory cells and highly expressed in submucosal glands. We found that the TMEM16Aacd isoform was more highly expressed in CF lung than normal donors (TMEM16Aabc). We found CLCA1 VWA potentiation of TMEM16A rescues MCT in CF airways by measuring bead velocity. CONCLUSIONS: The TMEM16Aacd isoform is upregulated in CF (and potentially other airway diseases) and may be a more active channel. Specific potentiation of TMEM16A in CF can rescue MCT. TMEM16A could represent a target in CF and other muco-obstructive diseases to improve mucus function.

  PublisherDOI

• A multi-omics approach using lipids and proteins for early detection in individuals with signs and symptoms of ovarian cancer.

  Journal of Clinical Oncology · 2025-05-28

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  5581 Background: Late-stage ovarian cancer (OC) is diagnosed in 80% of patients, leading to a five-year survival rate below 30% and ranking OC as the fifth leading cause of cancer-related deaths in women. Non-specific abdominal symptoms overlap with benign disorders, delaying diagnosis. Testing symptomatic individuals can detect low disease burden, enabling high complete cytoreduction rates. However, current diagnostic tools lack sensitivity and specificity for early-stage OC, underscoring the critical need for novel biomarkers and approaches. Methods: We conducted a multi-omics analysis of serum from two independent, clinically annotated cohorts. Specimens were analyzed using UHPLC-MS untargeted lipidomics and a protein biomarker panel. Cohort #1 (N=544) from the University of Colorado Gynecologic Tissue and Fluid Bank and commercial vendors included patients diagnosed with OC (N=219: 80 early-stage I/II, 139 late-stage III/IV), and non-cancerous controls (N=325) for biomarker discovery. Cohort #2 (N=423) from Manchester University NHS Foundation Trust and commercial vendors included prospectively enrolled individuals with signs and symptoms of OC. Samples included patients diagnosed with OC (N=109 total: 52 stage I/II, 57 stage III/IV), and non-cancerous controls (N=314). Cohorts were processed independently. Results: Over 1000 features were identified in both cohorts. There was a significant overlap in common features confirming importance in indication for use population. The top features confirmed in both cohorts enabled machine learning-based modeling. Biomarker classes were modeled separately (lipids only, proteins only) and in combination (lipids and proteins), employing 20-fold cross validation. Models containing multi-omic features consistently exhibit the highest AUC compared to individual biomarker classes. AUC for the top-performing model applied to both cohorts was 95% (CI 94-96) for all controls vs. all OC, and 92% (CI 89-95) for all controls vs. early-stage OC. When compared with normal individuals, the AUC vs all OC across stages and sub-types was 97% (CI 96-98). Conclusions: Our top-performing models contain >50 multi-omic features common across two independent cohorts, comprised of 967 unique individuals. Combining LC-MS-based lipidomic profiling of serum with proteins represents a promising new approach as a clinical diagnostic for detecting OC in this complex patient population. Early detection in women with signs and symptoms of OC and faster triage to specialty care may lead to improved patient outcomes.

  PublisherDOI

• Traction Loading Human Coracohumeral Ligaments With 20 and 40 Newton Forces and Sustained Creep Deformation: A Preliminary Cadaveric Investigation

  Journal of Manipulative and Physiological Therapeutics · 2025-01-01

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  PublisherDOI

• Identification of tumor-marker ganglioside in serum for detection of ovarian cancer

  Gynecologic Oncology · 2025-09-01

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  PublisherDOI

• Abstract LB253: Utilizing serum-derived lipidomics with protein biomarkers and machine learning for early detection of ovarian cancer in the symptomatic population

  Cancer Research · 2025-04-25

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  Abstract Ovarian cancer (OC) is the fifth leading cause of cancer-related deaths among women. Unfortunately, for most patients, detection of OC occurs at late stages (III/IV) when five-year survival is <30%. This is due in part to patients presenting with vague abdominal symptoms common in a variety of non-cancerous disorders (ex. gastrointestinal and gynecological conditions) that confound diagnosis. In addition, diagnostic methods for OC lack sensitivity and specificity for early-stage disease. Novel approaches and new biomarkers are urgently needed. We conducted a multi-omics analysis of serum from two independent, clinically annotated biomarker discovery cohorts using UHPLC-MS untargeted lipidomics and a panel of protein biomarkers detected by manual immunoassay. Cohort #1 (N=544) specimens were obtained from the University of Colorado Gynecologic Tissue and Fluid Bank and commercial vendors. Samples included patients diagnosed with OC across subtypes and stages (N=219 total: 80 early-stage, 139 late-stage) and non-cancerous controls designed to mimic the symptomatic population. Controls included healthy donors (N=82), benign gynecological disorders (endometriosis, benign masses, etc. N=168), borderline tumors (N=25), and gastrointestinal disorders (irritable bowel syndrome, etc.: N=50). Cohort #2 (N=423) specimens were collected from a prospectively enrolled symptomatic population through Manchester University NHS Foundation Trust and supplemented with commercial vendor specimens. Samples included patients diagnosed with OC across subtypes and stages (N=109 total: 52 early-stage, 57 late-stage), individuals with benign gynecological disorders (N=86), borderline tumors (N=20), and symptomatic but otherwise normal individuals (N=208). Each cohort was processed independently. Common lipid features were identified across the datasets to enable machine learning-based modeling on the individual and combined cohorts. Biomarker classes were modeled separately (lipids only, proteins only) and in combination (lipids and proteins), employing 20-fold cross validation. We consistently observed that a multi-omic model exhibits the highest AUC compared to individual biomarker classes. The 20-fold cross-validated AUC for the top-performing model applied to both cohorts was 95.0% (CI 93.5-96.5) for all controls vs. all OC, and 97.3% (CI 96.1-98.4) for all healthy vs. all OC. In summary, LC-MS-based lipidomic profiling of serum combined with proteins represents a powerful diagnostic strategy, with multiple lipid species contributing to a diverse feature space. Combining multi-omics and machine learning offers a promising new approach as a clinical diagnostic for detecting OC in this complex patient population. Future development efforts are aimed at narrowing biomarkers and validating performance in a prospectively collected cohort of symptomatic women. Citation Format: Rachel Culp-Hill, Charles Nichols, Brendan Giles, Robert A. Law, Kian Behbakht, Benjamin G. Bitler, Emma J. Crosbie, Vuna Fa, James R. White, Abigail McElhinny. Utilizing serum-derived lipidomics with protein biomarkers and machine learning for early detection of ovarian cancer in the symptomatic population [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 2 (Late-Breaking, Clinical Trial, and Invited Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_2):Abstract nr LB253.

  PublisherDOI

• Abstract 1925: Development of a multi-omics diagnostic approach for the early detection of ovarian cancer in asymptomatic women

  Cancer Research · 2025-04-21 · 1 citations

  article

  Abstract Objectives: Ovarian cancer (OC) is the fifth leading cause of cancer-related deaths among women. Unfortunately, for most patients, detection of OC occurs at late stages (III/IV) when the five-year survival rate is <30%. To change this paradigm, screening methods must be developed that are minimally invasive, highly sensitive, and disease specific. We have determined that utilizing multi-omics, which include novel classes of lipid and protein biomarkers with machine-learning, enables the robust detection of ovarian cancer across stages and subtypes, while requiring <500µL of serum. Methods: We utilized a multi-omics approach to characterize a clinically annotated cohort comprising serum samples from individuals with ovarian cancer (N=235) and normal donors (N=82). The cohort was obtained from the University of Colorado Gynecologic Tissue and Fluid Bank (IRB #07-935 and 21-4787) and commercial vendors. UHPLC-MS data were generated from 20µL of extracted serum to profile lipids and fatty acids, while manual immunoassays were performed for a panel of protein biomarkers using unextracted serum. Machine learning (ML) model training was performed using the biomarker classes separately and in combination to identify top-performing models, using 20-fold cross validation. Results: We profiled a total of 611 features of the lipidome by UHPLC-MS and protein biomarkers by immunoassay. The main molecular drivers contributing to best performing initial OC-specific signatures included a combination of lipids, fatty acids, and proteins together: multi-omic model consistently exhibited highest AUC when compared to individual biomarker classes. The AUC for High Grade Serous OC (HGSOC) was 0.98. At 98.2% specificity, sensitivity was 93.1% for all stages and subtypes of OC (93.8% sensitivity for all subtypes of early-stage (I/II) OC). The sensitivity for early stage HGSOC was 92.6%. Conclusions: Early detection of OC is critical to improve patient outcomes, but current screening tools for OC lack early-stage sensitivity and specificity. The application of a multi-omics, machine learning approach for the screening of post-menopausal asymptomatic women offers significantly improved performance over CA125 for the detection of early-stage HGSOC. Future research will validate the performance of this approach in a large prospective cohort. Citation Format: Rachel Culp-Hill, Charles Nichols, Collin Hill, Brendan Giles, Robert A. Law, Enkhtuya Radnaa, Kian Behbakht, Benjamin G. Bitler, Maria Wong, Connor Hansen, Mattie Goldberg, Vuna Fa, Violeta Guthrie, James R. White, Anna Jeter, Abigail McElhinny. Development of a multi-omics diagnostic approach for the early detection of ovarian cancer in asymptomatic women [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 1925.

  PublisherDOI

• Global lipidomics analysis of early- and late-stage ovarian cancer serum.

  Journal of Clinical Oncology · 2025-05-28

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  e17587 Background: Ovarian cancer (OC) is the fifth leading cause of cancer-related deaths among women. For most patients, detection occurs at late stages (III/IV) when five-year survival is <30%. Currently, there is an unmet medical need for effective diagnostic tools to detect OC at earlier stages (I/II) when survival rates are greatly improved. The use of LCMS-based lipidomics has become an invaluable technology for the characterization of the serum lipid profile from normal to early- and late-stage disease. Understanding these alterations can offer a unique insight into molecular mechanisms of disease progression, tumor biology, and metabolic pathways with the potential for diagnostic and therapeutic targeting. Here, we have defined the serum lipid profile of patients with OC, spanning all stages and multiple subtypes. Methods: We conducted untargeted lipidomics using UHPLC-MS (Exploris 240, Thermo Scientific) on a cohort from the University of Colorado Gynecologic Tissue and Fluid Bank and commercial vendors (N=301). Samples included pre- and post-menopausal patients with OC across subtypes and stages (N=219 total: 80 early-stage, 139 late-stage) and normal donors with representative demographics (N=82). Feature assignments and relative quantitation were performed using internal standards, characteristic fragment moieties, and comparison against MS/MS spectral databases. Results: 38 lipid classes were detected, comprising 1570 unique lipid compounds with significant aberrations in both early- and late-stage OC when compared against normal serum. Significantly decreased classes across all OC stages included cardiolipins, triglycerides, and phospholipid classes such as LPA, LPC, LPE, PE, and PI. Conversely, significantly increased classes across all OC stages included bile acids, carnitines, free fatty acids, gangliosides, sphingomyelins, and sterols. Additionally, several classes contained species that were both upregulated and downregulated, such as MG, DG, PA, PC, PS, LPG, and LPS. All OC stages, regardless of subtype, exhibit clear differences as compared to normal serum. Conclusions: In summary, we have identified an altered lipid profile in both early- and late-stage OC serum. Similarity between early- and late-stage disease suggests early-stage OC serum is distinguishable from normal serum. Consistent with this, preliminary modeling yielded an AUC of 0.94 (0.91-0.97) for all stages and subtypes of OC vs. normal. Excitingly, early stage (I/II) vs. normal AUC was 0.93 (0.89-0.97), and early-stage high grade serous (HGS) OC vs normal was 0.94 (0.88-0.99.) This novel class of lipid biomarkers offers a promising diagnostic avenue with greater sensitivity than currently available options. Future studies will interrogate the mechanism underlying this unique lipid profile and build a robust statistical model that effectively differentiates non-cancer from OC with high sensitivity and specificity.

  PublisherDOI

• Understanding Yin-Yang Philosophic Concept Behind Tai Chi Practice

  Holistic Nursing Practice · 2023-08-18 · 13 citations

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  Yin-yang theorizes that everything in the world is interoppositionally unified with 2 dynamic opposites (yin and yang), interrooted, interchangeable, and interconvertible. Tai chi (TC) movements and postures are essentially yin-yang concept-based. However, there is still a lack of understanding of yin-yang concepts and applications among people practicing TC. So, in this concept review, we aimed to provide basic understanding of the yin-yang concept and characteristics behind TC practice. Terms derived from the yin-yang concept in TC practice may include blood/qi (energy), stability/mobility, closing/opening moves, expiration/inspiration, solid/empty stance, and defensive/offensive hand movements and postures. These yin-yang attributes are interrestricted and dependent on maintaining a dynamic mind-body harmony. With the yin-yang application, TC can be considered a self-controlled balance perturbation exercise to challenge the stability-mobility (yin-yang) to a new level of harmony. As a health promotion holistic intervention, TC can facilitate the flow in blood/qi pathways or meridians to improve medical conditions. As an integrative mind-body exercise, TC can activate different body parts and brain regions to participate in and coordinate the combined physical and mental activities.

  PublisherDOI

• Phase 2 Study of Belantamab Mafodotin As Pre- and Post-Stem Cell Transplant Consolidation and Maintenance (BLAST study) for Multiple Myeloma Patients in First Remission

  Blood · 2022-11-15 · 2 citations

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  PublisherDOI

• Complete Adipose Replacement of Bilateral Medial Gastrocnemius Muscle - A Cadaveric Case Report

  Academia Anatomica International · 2020-07-05

  articleOpen access1st authorCorresponding

  Sarcopenia and infiltration of intermuscular adipose tissue are often seen in older adults due to aging process, but a muscle is completely replaced by adipose tissue is rarely reported. In this cadaveric case, we describe an observation of bilateral symmetric adipose replacement of the medial gastrocnemius muscles (GM) on both left and right leg in an 82-year old Caucasian female, whose cause of death was advanced dementia. The white adipose tissue replaced the entire medial GM muscle belly with pennate-like arrangement, indicating adipose tissue infiltration very likely into the original muscle cells. Where and how these adipose tissues come from are discussed.

  PublisherOA PDFDOI

Recent grants

• NIH Grant R01HL054171

  NIH · $5.3M · 2018

• NIH Grant R01HL045742

  NIH · $4.9M · 2017

• NIH Grant R01HL071232

  NIH · $612k · 2007

Frequent coauthors

• Mohammed Mohiuddin

  Comilla University

  48 shared

• Cherie J. Hayostek

  47 shared

• Edith P. Mitchell

  46 shared

• Nader Hanna

  Cleveland Clinic Florida

  37 shared

• Christopher G. Willett

  Cancer Institute (WIA)

  37 shared

• Albert Yuen

  RTOG Foundation

  36 shared

• Kathryn Winter

  NRG Oncology

  36 shared

• Robert Shane

  Duke University Hospital

  36 shared

Labs

• OphthalmologyPI