PITPNC1 Suppress CD8+ T cell immune function and promote radioresistance in rectal cancer by modulating FASN/CD155.

BACKGROUND: Radioresistance is a primary factor contributing to the failure of rectal cancer treatment. Immune suppression plays a significant role in the development of radioresistance. We have investigated the potential role of phosphatidylinositol transfer protein cytoplasmic 1 (PITPNC1) in regulating immune suppression associated with radioresistance. METHODS: To elucidate the mechanisms by which PITPNC1 influences radioresistance, we established HT29, SW480, and MC38 radioresistant cell lines. The relationship between radioresistance and changes in the proportion of immune cells was verified through subcutaneous tumor models and flow cytometry. Changes in the expression levels of PITPNC1, FASN, and CD155 were determined using immunohistochemistry and western blotting techniques. The interplay between these proteins was investigated using immunofluorescence co-localization and immunoprecipitation assays. Additionally, siRNA and lentivirus-mediated gene knockdown or overexpression, as well as co-culture of tumor cells with PBMCs or CD8+ T cells and establishment of stable transgenic cell lines in vivo, were employed to validate the impact of the PITPNC1/FASN/CD155 pathway on CD8+ T cell immune function. RESULTS: Under irradiation, the apoptosis rate and expression of apoptosis-related proteins in radioresistant colorectal cancer cell lines were significantly decreased, while the cell proliferation rate increased. In radioresistant tumor-bearing mice, the proportion of CD8+ T cells and IFN-γ production within immune cells decreased. Immunohistochemical analysis of human and animal tissue specimens resistant to radiotherapy showed a significant increase in the expression levels of PITPNC1, FASN, and CD155. Gene knockdown and rescue experiments demonstrated that PITPNC1 can regulate the expression of CD155 on the surface of tumor cells through FASN. In addition, co-culture experiments and in vivo tumor-bearing experiments have shown that silencing PITPNC1 can inhibit FASN/CD155, enhance CD8+ T cell immune function, promote colorectal cancer cell death, and ultimately reduce radioresistance in tumor-bearing models. CONCLUSIONS: PITPNC1 regulates the expression of CD155 through FASN, inhibits CD8+ T cell immune function, and promotes radioresistance in rectal cancer.

Text mining and portal development for gene-specific publications on Alzheimer's disease and other neurodegenerative diseases.

BACKGROUND: Tremendous research efforts have been made in the Alzheimer's disease (AD) field to understand the disease etiology, progression and discover treatments for AD. Many mechanistic hypotheses, therapeutic targets and treatment strategies have been proposed in the last few decades. Reviewing previous work and staying current on this ever-growing body of AD publications is an essential yet difficult task for AD researchers. METHODS: In this study, we designed and implemented a natural language processing (NLP) pipeline to extract gene-specific neurodegenerative disease (ND) -focused information from the PubMed database. The collected publication information was filtered and cleaned to construct AD-related gene-specific publication profiles. Six categories of AD-related information are extracted from the processed publication data: publication trend by year, dementia type occurrence, brain region occurrence, mouse model information, keywords occurrence, and co-occurring genes. A user-friendly web portal is then developed using Django framework to provide gene query functions and data visualizations for the generalized and summarized publication information. RESULTS: By implementing the NLP pipeline, we extracted gene-specific ND-related publication information from the abstracts of the publications in the PubMed database. The results are summarized and visualized through an interactive web query portal. Multiple visualization windows display the ND publication trends, mouse models used, dementia types, involved brain regions, keywords to major AD-related biological processes, and co-occurring genes. Direct links to PubMed sites are provided for all recorded publications on the query result page of the web portal. CONCLUSION: The resulting portal is a valuable tool and data source for quick querying and displaying AD publications tailored to users' interested research areas and gene targets, which is especially convenient for users without informatic mining skills. Our study will not only keep AD field researchers updated with the progress of AD research, assist them in conducting preliminary examinations efficiently, but also offers additional support for hypothesis generation and validation which will contribute significantly to the communication, dissemination, and progress of AD research.

DSCN: Double-target selection guided by CRISPR screening and network.

Cancer is a complex disease with usually multiple disease mechanisms. Target combination is a better strategy than a single target in developing cancer therapies. However, target combinations are generally more difficult to be predicted. Current CRISPR-cas9 technology enables genome-wide screening for potential targets, but only a handful of genes have been screend as target combinations. Thus, an effective computational approach for selecting candidate target combinations is highly desirable. Selected target combinations also need to be translational between cell lines and cancer patients. We have therefore developed DSCN (double-target selection guided by CRISPR screening and network), a method that matches expression levels in patients and gene essentialities in cell lines through spectral-clustered protein-protein interaction (PPI) network. In DSCN, a sub-sampling approach is developed to model first-target knockdown and its impact on the PPI network, and it also facilitates the selection of a second target. Our analysis first demonstrated a high correlation of the DSCN sub-sampling-based gene knockdown model and its predicted differential gene expressions using observed gene expression in 22 pancreatic cell lines before and after MAP2K1 and MAP2K2 inhibition (R2 = 0.75). In DSCN algorithm, various scoring schemes were evaluated. The 'diffusion-path' method showed the most significant statistical power of differentialting known synthetic lethal (SL) versus non-SL gene pairs (P = 0.001) in pancreatic cancer. The superior performance of DSCN over existing network-based algorithms, such as OptiCon and VIPER, in the selection of target combinations is attributable to its ability to calculate combinations for any gene pairs, whereas other approaches focus on the combinations among optimized regulators in the network. DSCN's computational speed is also at least ten times fast than that of other methods. Finally, in applying DSCN to predict target combinations and drug combinations for individual samples (DSCNi), DSCNi showed high correlation between target combinations predicted and real synergistic combinations (P = 1e-5) in pancreatic cell lines. In summary, DSCN is a highly effective computational method for the selection of target combinations.

CGPE: an integrated online server for Cancer Gene and Pathway Exploration.

SUMMARY: Cancer Gene and Pathway Explorer (CGPE) is developed to guide biological and clinical researchers, especially those with limited informatics and programming skills, performing preliminary cancer-related biomedical research using transcriptional data and publications. CGPE enables three user-friendly online analytical and visualization modules without requiring any local deployment. The GenePub HotIndex applies natural language processing, statistics and association discovery to provide analytical results on gene-specific PubMed publications, including gene-specific research trends, cancer types correlations, top-related genes and the WordCloud of publication profiles. The OnlineGSEA enables Gene Set Enrichment Analysis (GSEA) and results visualizations through an easy-to-follow interface for public or in-house transcriptional datasets, integrating the GSEA algorithm and preprocessed public TCGA and GEO datasets. The preprocessed datasets ensure gene sets analysis with appropriate pathway alternation and gene signatures. The CellLine Search presents evidence-based guidance for cell line selections with combined information on cell line dependency, gene expressions and pathway activity maps, which are valuable knowledge to have before conducting gene-related experiments. In a nutshell, the CGPE webserver provides a user-friendly, visual, intuitive and informative bioinformatics tool that allows biomedical researchers to perform efficient analyses and preliminary studies on in-house and publicly available bioinformatics data. AVAILABILITY AND IMPLEMENTATION: The webserver is freely available online at https://cgpe.soic.iupui.edu. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Mycobacterium tuberculosis co-infection is associated with increased surrogate marker of the HIV reservoir.

BACKGROUND: Tuberculosis (Tb) is the most frequent opportunistic infection among people living with HIV infection. The impact of Tb co-infection in the establishment and maintenance of the HIV reservoir is unclear. METHOD: We enrolled 13 HIV-infected patients with microbiologically confirmed Tb and 10 matched mono-HIV infected controls. Total HIV DNA in peripheral blood mononuclear cells (PBMCs), plasma interleukin-7 (IL-7) concentrations and the activities of indoleamine 2,3-dioxygenase (IDO) were measured for all the participants prior to therapy and after antiretroviral therapy (ART). RESULTS: After a duration of 16 (12, 22) months' ART, patients co-infected with Tb who were cured of Tb maintained higher levels of HIV DNA compared with mono-HIV infected patients [2.89 (2.65- 3.05) log10 copies/106 cells vs. 2.30 (2.11-2.84) log10 copies/106 cells, P = 0.008]. The levels of on-ART HIV DNA were positively correlated with the baseline viral load (r = 0.64, P = 0.02) in Tb co-infected group. However, neither plasma IL-7 concentration nor plasma IDO activity was correlated with the level of on-ART HIV DNA. CONCLUSIONS: Tb co-infection was associated with the increased surrogate marker of the HIV reservoir, while its mechanism warrants further examination.

An effective method to reduce the interplay effects between respiratory motion and a uniform scanning proton beam irradiation for liver tumors: A case study.

PURPOSE: For scanning particle beam therapy, interference between scanning patterns and interfield organ motion may result in suboptimal dose within target volume. In this study, we developed a simple offline correction technique for uniform scanning proton beam (USPB) delivery to compensate for the interplay between scanning patterns and respiratory motion and demonstrate the effectiveness of our technique in treating liver cancer. METHODS: The computed tomography (CT) and respiration data of two patients who had received stereotactic body radiotherapy for hepatocellular carcinoma were used. In the simulation, the relative beam weight delivered to each respiratory phase is calculated for each beam layer after treatment of each fraction. Respiratory phases with beam weights higher than 50% of the largest weight are considered "skipped phases" for the next fraction. For the following fraction, the beam trigger is regulated to prevent beam layers from starting irradiation in skipped phases by extending the interval between each layer. To calculate dose-volume histogram (DVH), the dose of the target volume at end-exhale (50% phase) was calculated as the sum of each energy layer, with consideration of displacement due to respiratory motion and relative beam weight delivered per respiratory phase. RESULTS: For a single fraction, D1% , D99% , and V100% were 114%, 88%, and 32%, respectively, when 8 Gy/min of dose rate was simulated. Although these parameters were improved with multiple fractions, dosimetric inhomogeneity without motion management remained even at 30 fractions, with V100% 86.9% at 30 fractions. In contrast, the V100% values with adaptation were 96% and 98% at 20 and 30 fractions, respectively. We developed an offline correction technique for USPB therapy to compensate for the interplay effects between respiratory organ motion and USPB beam delivery. CONCLUSIONS: For liver tumor, this adaptive therapy technique showed significant improvement in dose uniformity even with fewer treatment fractions than normal USPB therapy.

A patient-oriented clinical decision support system for CRC risk assessment and preventative care.

BACKGROUND: Colorectal Cancer (CRC) is the third leading cause of cancer death among men and women in the United States. Research has shown that the risk of CRC associates with genetic and lifestyle factors. It is possible to prevent or minimize certain CRC risks by adopting a healthy lifestyle. Existing Clinical Decision Support Systems (CDSS) mainly targeted physicians as the CDSS users. As a result, the availability of patient-oriented CDSS is limited. Our project is to develop patient-oriented CDSS for active CRC management. METHODS: We implemented an online patient-oriented CRC CDSS for the public to learn about CRC, assess CRC risk levels, understand personalized CRC risk factors, and seek professional advices for people with CRC concerns. The system is implemented based on the Django Model-View-Controller (MVC) framework with an extensible background MySQL database. A CRC absolute risk prediction model is applied to calculate the personalized CRC risk score with a user-friendly web survey. An interactive dashboard using advanced data visualization technics will display and interpret the risk scores and factors. Based on the risk assessment, a structured decision tree algorithm will provide the recommendations on customized CRC screening methods. The CDSS also provides a search function for preferred providers and hospitals based on geographical information and patient preferences. RESULTS: A prototype of the patient-oriented CRC CDSS has been developed. It provides an open assessment of potential CRC risks via an online survey. The CRC risk predictive model has been implemented. The prediction outcomes of risk levels and factors are presented to the users through a personalized interactive visualization interface, to guide the public on how to reduce the CRC risks by changing their living styles (such as smoking and drinking) and diet characteristics (such as consumptions of red meat and milk). The CDSS will also provide customized recommendations on screening methods based on the corresponding risk factors. For users seeking professional clinicians, the CDSS also provides a convenient tool for searching nearby hospitals and available doctors based on the location preferences and providers characteristics (such as gender, language, and specialty). CONCLUSIONS: This CRC CDSS prototype provides a patient-friendly interface for CRC risk assessment and gives a personalized interpretation on important CRC risk factors. It is a useful tool to educate the public on CRC, to provide guidance on minimizing CRC risks, and to promote early CRC screening that reduces the CRC occurrences.

Reconciling disparate information in continuity of care documents: Piloting a system to consolidate structured clinical documents.

BACKGROUND: Due to the nature of information generation in health care, clinical documents contain duplicate and sometimes conflicting information. Recent implementation of Health Information Exchange (HIE) mechanisms in which clinical summary documents are exchanged among disparate health care organizations can proliferate duplicate and conflicting information. MATERIALS AND METHODS: To reduce information overload, a system to automatically consolidate information across multiple clinical summary documents was developed for an HIE network. The system receives any number of Continuity of Care Documents (CCDs) and outputs a single, consolidated record. To test the system, a randomly sampled corpus of 522 CCDs representing 50 unique patients was extracted from a large HIE network. The automated methods were compared to manual consolidation of information for three key sections of the CCD: problems, allergies, and medications. RESULTS: Manual consolidation of 11,631 entries was completed in approximately 150h. The same data were automatically consolidated in 3.3min. The system successfully consolidated 99.1% of problems, 87.0% of allergies, and 91.7% of medications. Almost all of the inaccuracies were caused by issues involving the use of standardized terminologies within the documents to represent individual information entries. CONCLUSION: This study represents a novel, tested tool for de-duplication and consolidation of CDA documents, which is a major step toward improving information access and the interoperability among information systems. While more work is necessary, automated systems like the one evaluated in this study will be necessary to meet the informatics needs of providers and health systems in the future.

Utilizing Electronic Dental Records to Predict Neuro-Degenerative Diseases in a Dental Setting: A Pilot Study.

Limited research demonstrates the possible correlations between dental diseases and neurodegenerative diseases like Alzheimer's disease (AD) and Parkinson's disease (PD). Nevertheless, dental diseases are often overlooked while assessing the risk of AD and PD in clinical settings. It is unknown whether AD/PD risk can be predicted using electronic dental record (EDR) data collected in a routine dental setting. This pilot study determined the feasibility of predicting AD/PD using 84 features routinely captured in the EDR. We utilized the Temple University School of Dentistry clinic data of 27,138 patients. Using a natural language processing (NLP) approach (accuracy=97%), we identified patients with AD/PD and their matched controls (matched by age and gender). XGBoost machine learning model with 10-fold cross-validation was applied for prediction. With 77% accuracy, we found 53 features significantly associated with AD/PD that could be utilized to predict the risk of AD/PD. Further studies are warned to confirm these findings.

Correlation of Glycolysis-immune-related Genes in the Follicular Microenvironment of Endometriosis Patients with ART Outcomes.

Endometriosis (EMT) -related infertility has been a challenge for clinical research. Many studies have confirmed that abnormal alterations in the immune microenvironment and glycolysis are instrumental in causing EMT-related infertility. Recently, our research team identified several key glycolysis-immune-related genes in the endometrial cells of EMT patients. This study aimed to further investigate the expression patterns of pyruvate dehydrogenase kinase 3 (PDK3), glypican-3 (GPC3), and alcohol dehydrogenase 6 (ADH6), which are related to glycolysis and immunity, in the follicular microenvironment of infertile patients with EMT using enzyme-linked immunosorbent assay (ELISA) and quantitative real-time polymerase chain reaction (qRT-PCR) assays. According to the results, compared to the patients with tubal factor infertility, the concentrations of PDK3 and GPC3 were considerably increased in the follicular environment of EMT patients, while ADH6 expression was significantly reduced. The number of oocytes retrieved, the transferable embryo rate, and the cumulative clinical pregnancy rate of EMT patients were significantly reduced, and there was a correlation with the level of PDK3, GPC3, and ADH6 in Follicular Fluid (FF). The area under the receiver operating characteristic (ROC) curve for predicting clinical pregnancy in infertile patients with EMT for PDK3, GPC3, ADH6, and their combination was 0.732, 0.705, 0.855, and 0.879, respectively (P < 0.05). In conclusion, our research indicates that glycolysis-immune-related genes may contribute to infertility in EMT patients through immune infiltration, and disruption of mitochondrial and oocyte functions. The combined detection of PDK3, GPC3, and ADH6 in FF helps to predict clinical pregnancy outcomes in infertile patients with EMT.

The Inflammatory State of Follicular Fluid Combined with Negative Emotion Indicators can Predict Pregnancy Outcomes in Patients with PCOS.

Polycystic ovary syndrome (PCOS) is a complex endocrine disorder syndrome with an incidence of 6% to 10% in women of reproductive age. Women with PCOS not only exhibit abnormal follicular development and fertility disorders, but also have a greater tendency to develop anxiety and depression. Our aim was to evaluate the ability of inflammatory factors in follicular fluid to predict embryonic developmental potential and pregnancy outcome and to construct a machine learning model that can predict IVF pregnancy outcomes based on indicators such as basic sex hormones, embryonic morphology, the follicular microenvironment, and negative emotion. In this study, inflammatory factors (CRP, IL-6, and TNF-α) in follicular fluid samples obtained from 225 PCOS and 225 non-PCOS women were detected via ELISA. For patients with PCOS, the levels of CRP and IL-6 in the follicular fluid in the pregnant group were significantly lower than those in the nonpregnant group. For non-patients with PCOS, only the level of IL-6 in the follicular fluid was significantly lower in the pregnant group than in the nonpregnant group. In addition, for both PCOS and non-patients with PCOS, compared with those in the pregnant group, patients in the nonpregnant group showed more pronounced signs of anxiety and depression. Finally, the factors that were significantly different between the two subgroups (pregnancy and nonpregnancy) of patients with or without PCOS were identified by an independent sample t test first and further analysed by multilayer perceptron (MLP) and random forest (RF) models to distinguish the two clinical pregnancy outcomes according to the classification function. The accuracy of the RF model in predicting pregnancy outcomes in patients with or without PCOS was 95.6% and 91.1%, respectively. The RF model is more suitable than the MLP model for predicting pregnancy outcomes in IVF patients. This study not only identified inflammatory factors that can affect embryonic development and assessed the anxiety and depression tendencies of PCOS patients, but also constructed an AI model that predict pregnancy outcomes through machine learning methods, which is a beneficial clinical tool.

Phenotype Systemic Lupus Erythematosus Patients from EPIC Cosmos.

Systemic Lupus Erythematosus (SLE) is a widespread autoimmune disease for which early diagnosis is paramount in improving clinical outcomes. In this project, we used the de-identified patients from Epic Cosmos to retrieve the ICD code for SLE, checked data quality based on the EULAR/ACR classification systems, created an approach to determine the SLE patients, and performed statistical analyses on lab tests and clinical characteristics. Our preliminary results showed that clinical notes must be reviewed to improve the completeness, as structured EHR data fields provide limited information in determining if a patient meets the established classification criteria.

Digital twins for health: a scoping review.

The use of digital twins (DTs) has proliferated across various fields and industries, with a recent surge in the healthcare sector. The concept of digital twin for health (DT4H) holds great promise to revolutionize the entire healthcare system, including management and delivery, disease treatment and prevention, and health well-being maintenance, ultimately improving human life. The rapid growth of big data and continuous advancement in data science (DS) and artificial intelligence (AI) have the potential to significantly expedite DT research and development by providing scientific expertise, essential data, and robust cybertechnology infrastructure. Although various DT initiatives have been underway in the industry, government, and military, DT4H is still in its early stages. This paper presents an overview of the current applications of DTs in healthcare, examines consortium research centers and their limitations, and surveys the current landscape of emerging research and development opportunities in healthcare. We envision the emergence of a collaborative global effort among stakeholders to enhance healthcare and improve the quality of life for millions of individuals worldwide through pioneering research and development in the realm of DT technology.

Automatic Identification of Self-Reported COVID-19 Vaccine Information from Vaccine Adverse Events Reporting System.

BACKGROUND: The short time frame between the coronavirus disease 2019 (COVID-19) pandemic declaration and the vaccines authorization led to concerns among public regarding the safety and efficacy of the vaccines. The Food and Drug Administration uses the Vaccine Adverse Events Reporting System (VAERS) where general population can report their vaccine side effects in the text box. This information could be utilized to determine self-reported vaccine side effects. OBJECTIVE: To develop a supervised and unsupervised natural language processing (NLP) pipeline to extract self-reported COVID-19 vaccination side effects, location of the side effects, medications, and possibly false/misinformation seeking further investigation in a structured format for analysis and reporting. METHODS: We utilized the VAERS dataset of COVID-19 vaccine reports from November 2020 to August 2022 of 725,246 individuals. We first developed a gold-standard (GS) dataset of randomly selected 1,500 records. Second, the GS was split into training, testing, and validation sets. The training dataset was used to develop the NLP applications (supervised and unsupervised) and testing and validation datasets were used to test the performances of the NLP application. RESULTS: The NLP application automatically extracted vaccine side effects, body locations of the side effects, medication, and possibly misinformation with moderate to high accuracy (84% sensitivity, 82% specificity, and 83% F-1 measure). We found that 23% people (386,270) faced arm soreness, 31% body swelling (226,208), 23% fatigue/body weakness (168,160), and 22% (159,873) cold/flue-like symptoms. Most of the complications occurred in the body locations such as the arm, back, chest, neck, face, and head. Over-the-counter pain medications such as Tylenol and Ibuprofen and allergy medication like Benadryl were most reported self-reported medications. Death due to COVID-19, changes in the DNA, and infertility were possible false/misinformation reported by people. CONCLUSION: Some self-reported side effects such as syncope, arthralgia, and blood clotting need further clinical investigations. Our NLP application may help in extracting information from big free-text electronic datasets to help policy makers and other researchers with decision making.

Global spatiotemporal trends of cardiovascular diseases due to temperature in different climates and socio-demographic index regions from 1990 to 2019.

With the rapidly changing climate, assessing the global trends of cardiovascular diseases (CVDs) attributed to high and low temperatures in different climate zones and under varying socio-demographic levels is crucial for regulations, preparation, intervention, and clinical practice for CVD. Our study included 204 countries with global CVD data ranging from 1990 to 2019. We obtained the age-standardized mortality rate (ASMR); disability-adjusted life rate of CVD attributed to high, low, and non-optimal temperatures; and socio-demographic index (SDI) data from the Global Health Data Exchange. We also downloaded the temperature data from the Climatic Research Unit. These 204 countries were divided into five climate zones and five SDI levels according to the annual average temperature data and SDI in 2019. The temporal trends of CVD burden attributed to high, low, and non-optimal temperatures were estimated by using the cubic regression spline and the generalized additive mixed model (GAMM). The total burden of temperature-related CVD has been declining in the last 30 years. However, the burden of CVD attributed to high temperature showed an increasing trend. Among different climate regions, the ASMRs of CVD attributed to high temperature were the highest in the tropical regions, followed by subtropical regions, and the lowest in the boreal regions. In the past 30 years, the burden of CVD attributed to high temperatures has shown a significant increasing trend, while declining trends are observed for non-optimal and low temperatures. The CVD burden attributed to high temperatures is particularly pronounced in warmer and low-SDI regions with an increasing trend of CVD burden due to high temperature.

A discovery-based proteomics approach identifies protein disulphide isomerase (PDIA1) as a biomarker of ß cell stress in type 1 diabetes.

BACKGROUND: Stress responses within the ß cell have been linked with both increased ß cell death and accelerated immune activation in type 1 diabetes (T1D). At present, information on the timing and scope of these responses as well as disease-related changes in islet ß cell protein expression during T1D development is lacking. METHODS: Data independent acquisition-mass spectrometry was performed on islets collected longitudinally from NOD mice and NOD-SCID mice rendered diabetic through T cell adoptive transfer. FINDINGS: In islets collected from female NOD mice at 10, 12, and 14 weeks of age, we found a time-restricted upregulation of proteins involved in stress mitigation and maintenance of ß cell function, followed by loss of expression of protective proteins that heralded diabetes onset. EIF2 signalling and the unfolded protein response, mTOR signalling, mitochondrial function, and oxidative phosphorylation were commonly modulated pathways in both NOD mice and NOD-SCID mice rendered acutely diabetic by T cell adoptive transfer. Protein disulphide isomerase A1 (PDIA1) was upregulated in NOD islets and pancreatic sections from human organ donors with autoantibody positivity or T1D. Moreover, PDIA1 plasma levels were increased in pre-diabetic NOD mice and in the serum of children with recent-onset T1D compared to non-diabetic controls. INTERPRETATION: We identified a core set of modulated pathways across distinct mouse models of T1D and identified PDIA1 as a potential human biomarker of ß cell stress in T1D. FUNDING: NIH (R01DK093954, DK127308, U01DK127786, UC4DK104166, R01DK060581, R01GM118470, and 5T32DK101001-09). VA Merit Award I01BX001733. JDRF (2-SRA-2019-834-S-B, 2-SRA-2018-493-A-B, 3-PDF-20016-199-A-N, 5-CDA-2022-1176-A-N, and 3-PDF-2017-385-A-N).

A line-profile based double partial fusion method for acquiring planning CT of oversized patients in radiation treatment.

True 3D CT dataset for treatment planning of an oversized patient is difficult to acquire due to the bore size and field of view (FOV) reconstruction. This project aims to provide a simple approach to reconstruct true CT data for oversize patients using CT scanner with limited FOV by acquiring double partial CT (left and right side) images. An efficient line profile-based method has been developed to minimize the difference of the CT numbers in the overlapping region between the right and left images and to generate a complete true 3D CT dataset in the natural state. New image processing modules have been developed and integrated to the Insight Segmentation & Registration Toolkit (ITK 3.6) package. For example, different modules for image cropping, line profile generation, line profile matching, and optimized partial image fusion have been developed. The algorithm has been implemented for images containing the bony structure of the spine and tested on 3D CT planning datasets from both phantom and real patients with satisfactory results in both cases. The proposed optimized line profile-based partial registration method provides a simple and accurate method for acquiring a complete true 3D CT dataset for an oversized patient using CT scanning with small bore size, that can be used for accurate treatment planning.

A Data-Driven Assessment of the U.S. Health Informatics Programs and Job Market.

BACKGROUND: Health Informatics (HI) is an interdisciplinary field, integrating health sciences, computer science, information science, and cognitive science to assist health information management, analysis, and utilization. As the HI field is broad, it is impossible that a student will be able to master all the diverse HI topics. Thus, it is important to train the HI students based on the offering of the various HI programs and needs of the current market. This project will study the U.S. HI programs, training materials, HI job market, the skillset required by the employers, competencies taught in HI programs, and comparisons between them. METHODS: We collected the training information for the 238 U.S. universities that offered MS, PhD, or postbaccalaureate certificate programs in HI or related professions. Next, we explored the HI job market by randomly checking 200 jobs and their required skillsets and domain knowledge. Then, we compared these skillsets with those offered by the HI programs and identified the gaps and overlaps for program enhancements. RESULTS: Among the 238 U.S. universities, 94 universities offer HI programs: 92 universities with MS (Master of Science), 43 with doctoral, 42 with both MS and doctoral, and 54 with certificate programs. The most offered HI courses are related to practicum, data analytics, research, and ethics. For the HI job postings, the three most technical skillsets required in HI job posting are data analysis, database management, and knowledge of electronic health records. However, only 58% of HI programs offer courses in database management and analytics. Compared with American Medical Informatics Association's recommended 10 fundamental domains, the HI curriculum generally lacks training in socio-technical systems, social-behavioral aspects of health, and interprofessional collaborative practice. CONCLUSION: There are gaps between the industry expectations of HI and the training received in HI programs. Advance level technical courses are needed in HI programs to meet industry expectations.

Developing Automated Computer Algorithms to Phenotype Periodontal Disease Diagnoses in Electronic Dental Records.

OBJECTIVE: Our objective was to phenotype periodontal disease (PD) diagnoses from three different sections (diagnosis codes, clinical notes, and periodontal charting) of the electronic dental records (EDR) by developing two automated computer algorithms. METHODS: We conducted a retrospective study using EDR data of patients (n = 27,138) who received care at Temple University Maurice H. Kornberg School of Dentistry from January 1, 2017 to August 31, 2021. We determined the completeness of patient demographics, periodontal charting, and PD diagnoses information in the EDR. Next, we developed two automated computer algorithms to automatically diagnose patients' PD statuses from clinical notes and periodontal charting data. Last, we phenotyped PD diagnoses using automated computer algorithms and reported the improved completeness of diagnosis. RESULTS: The completeness of PD diagnosis from the EDR was as follows: periodontal diagnosis codes 36% (n = 9,834), diagnoses in clinical notes 18% (n = 4,867), and charting information 80% (n = 21,710). After phenotyping, the completeness of PD diagnoses improved to 100%. Eleven percent of patients had healthy periodontium, 43% were with gingivitis, 3% with stage I, 36% with stage II, and 7% with stage III/IV periodontitis. CONCLUSIONS: We successfully developed, tested, and deployed two automated algorithms on big EDR datasets to improve the completeness of PD diagnoses. After phenotyping, EDR provided 100% completeness of PD diagnoses of 27,138 unique patients for research purposes. This approach is recommended for use in other large databases for the evaluation of their EDR data quality and for phenotyping PD diagnoses and other relevant variables.

Enhancing an AI-Empowered Periodontal CDSS and Comparing with Traditional Perio-risk Assessment Tools.

Periodontal disease (PD) is one of the most prevalent dental diseases. Fortunately, it can be prevented if identified early, especially for high-risk patients. Dental electronic health records (EHRs) could help develop a data-driven personalized prediction model using advanced machine learning development of clinical decision support system (CDSS) as in our Phase I, II AMIA-AI showcase. In phase II, we created a CDSS, the Perio-Risk Scoring system (PRSS), to help clinicians generate perio-scores and diagnoses and identify the influential factors. In Phase III (this study), we implemented and compared the patient's risk factors information in five periodontal risk assessment tools [periodontal risk assessment (PRA), PreViser, Sonicare, Cigna, and Periodontal Risk Scoring System (PRSS)]. We examined 1) agreement between the risk scores provided by each of the five risk assessment tools of 20 patients' information and 2) compare the risk scores provided by each tool to the original outcomes (five years outcomes). Fleiss Kappa, Cohen's Kappa, and percentage agreements were performed to determine the agreements between risk scores and original outcomes. We found a -1.24 Kappa value which indicates disagreement between the risk scores provided by five risk assessment tools. Compared to the original outcomes (five-year disease outcomes), PRSS provided the most accurate prediction (70%), followed by Previser (55%), PRA (35%), Phillips (35%), and Cigna (25%). We conclude that using advanced state-of-the-art informatics methods could help us utilize EHR data optimally to represent the current patient populations and their risk factors to provide the most accurate disease risk score. This may promote preventive strategies at the chairside, hoping to reduce PD prevalence, improve quality of life, and reduce healthcare costs.