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.

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.

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.

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.

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.

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.

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).

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.

Developing and testing a prediction model for periodontal disease using machine learning and big electronic dental record data.

Despite advances in periodontal disease (PD) research and periodontal treatments, 42% of the US population suffer from periodontitis. PD can be prevented if high-risk patients are identified early to provide preventive care. Prediction models can help assess risk for PD before initiation and progression; nevertheless, utilization of existing PD prediction models is seldom because of their suboptimal performance. This study aims to develop and test the PD prediction model using machine learning (ML) and electronic dental record (EDR) data that could provide large sample sizes and up-to-date information. A cohort of 27,138 dental patients and grouped PD diagnoses into: healthy control, mild PD, and severe PD was generated. The ML model (XGBoost) was trained (80% training data) and tested (20% testing data) with a total of 74 features extracted from the EDR. We used a five-fold cross-validation strategy to identify the optimal hyperparameters of the model for this one-vs.-all multi-class classification task. Our prediction model differentiated healthy patients vs. mild PD cases and mild PD vs. severe PD cases with an average area under the curve of 0.72. New associations and features compared to existing models were identified that include patient-level factors such as patient anxiety, chewing problems, speaking trouble, teeth grinding, alcohol consumption, injury to teeth, presence of removable partial dentures, self-image, recreational drugs (Heroin and Marijuana), medications affecting periodontium, and medical conditions such as osteoporosis, cancer, neurological conditions, infectious diseases, endocrine conditions, cardiovascular diseases, and gastroenterology conditions. This pilot study demonstrated promising results in predicting the risk of PD using ML and EDR data. The model may provide new information to the clinicians about the PD risks and the factors responsible for the disease progression to take preventive approaches. Further studies are warned to evaluate the prediction model's performance on the external dataset and determine its usability in clinical settings.

Subpathway Analysis of Transcriptome Profiles Reveals New Molecular Mechanisms of Acquired Chemotherapy Resistance in Breast Cancer.

Chemoresistance has been a major challenge in the treatment of patients with breast cancer. The diverse omics platforms and small sample sizes reported in the current studies of chemoresistance in breast cancer limit the consensus regarding the underlying molecular mechanisms of chemoresistance and the applicability of these study findings. Therefore, we built two transcriptome datasets for patients with chemotherapy-resistant breast cancers­one comprising paired transcriptome samples from 40 patients before and after chemotherapy and the second including unpaired samples from 690 patients before and 45 patients after chemotherapy. Subsequent conventional pathway analysis and new subpathway analysis using these cohorts uncovered 56 overlapping upregulated genes (false discovery rate [FDR], 0.018) and 36 downregulated genes (FDR, 0.016). Pathway analysis revealed the activation of several pathways in the chemotherapy-resistant tumors, including those of drug metabolism, MAPK, ErbB, calcium, cGMP-PKG, sphingolipid, and PI3K-Akt, as well as those activated by Cushing's syndrome, human papillomavirus (HPV) infection, and proteoglycans in cancers, and subpathway analysis identified the activation of several more, including fluid shear stress, Wnt, FoxO, ECM-receptor interaction, RAS signaling, Rap1, mTOR focal adhesion, and cellular senescence (FDR < 0.20). Among these pathways, those associated with Cushing's syndrome, HPV infection, proteoglycans in cancer, fluid shear stress, and focal adhesion have not yet been reported in breast cancer chemoresistance. Pathway and subpathway analysis of a subset of triple-negative breast cancers from the two cohorts revealed activation of the identical chemoresistance pathways.

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.

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.

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.

Comparison of Ease of Use and Comfort in Fitness Trackers for Participants Impaired by Parkinson's Disease: An exploratory study.

Parkinson's disease (PD) is an incurable, fatal neurodegenerative disease, and only available treatment is to minimize symptoms. Anecdotal evidence suggests whole body workout can help to reduce PD severity; however, it is challenging to quantify its effect on PD. The increased availability of fitness trackers can help in quantifying the effect of whole-body workout on PD. Before using any over the counter fitness tracker, we must study the ease of use of the fitness trackers in PD patients. We interviewed 32 PD patients with six over the counter fitness trackers and determined their perceptions and attitude towards the fitness trackers. Although none of the fitness trackers received perfect scores for ease of use or comfort due to the presence of tremors, two trackers performed significantly better than the others. Further study is warranted to understand the potential for fitness trackers to be used by PD patients.

Word Embedding and Clustering for Patient-Centered Redesign of Appointment Scheduling in Ambulatory Care Settings.

Background. A key to a more efficient scheduling systems is to ensure appointments are designed to meet patient's needs and to design and simplify appointment scheduling less prone to error. Electronic Health Records (EHR) consist of valuable information about patient characteristics and their healthcare needs. The aim of this study is to utilize information from structured and unstructured EHR data to redesign appointment scheduling in community health clinics. Methods. We used Global Vectors for Word Representation, a word embedding approach, on free text field "scheduler note" to cluster patients into groups based on similarities of reasons for appointment. We then redesigned an appointment scheduling template with new types and durations based on the clusters. We compared the current appointment scheduling system and our proposed system by predicting and evaluating clinic performance measures such as patient time spent in-clinic and number of additional patients to accommodate. Results. We collected 17,722 encounters of an urban community health clinic in 2014 including 102 unique types recorded in the EHR. Following data processing, word embedding implementation, and clustering, appointment types were grouped into 10 clusters. The proposed scheduling template could open space to see overall an additional 716 patients per year and decrease patient in-clinic time by 3.6 minutes on average (p-value<0.0001). Conclusions. We found word embedding, that is an NLP approach, can be used to extract information from schedulers notes for improving scheduling systems. Unsupervised machine learning approach can be applied to simplify appointment scheduling in CHCs. Patient-centered appointment scheduling can be achieved by simplifying and redesigning appointment types and durations that could improve performance measures, such as increasing availability of time and patient satisfaction.

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.

Transcription factor expression as a predictor of colon cancer prognosis: a machine learning practice.

BACKGROUND: Colon cancer is one of the leading causes of cancer deaths in the USA and around the world. Molecular level characters, such as gene expression levels and mutations, may provide profound information for precision treatment apart from pathological indicators. Transcription factors function as critical regulators in all aspects of cell life, but transcription factors-based biomarkers for colon cancer prognosis were still rare and necessary. METHODS: We implemented an innovative process to select the transcription factors variables and evaluate the prognostic prediction power by combining the Cox PH model with the random forest algorithm. We picked five top-ranked transcription factors and built a prediction model by using Cox PH regression. Using Kaplan-Meier analysis, we validated our predictive model on four independent publicly available datasets (GSE39582, GSE17536, GSE37892, and GSE17537) from the GEO database, consisting of 925 colon cancer patients. RESULTS: A five-transcription-factors based predictive model for colon cancer prognosis has been developed by using TCGA colon cancer patient data. Five transcription factors identified for the predictive model is HOXC9, ZNF556, HEYL, HOXC4 and HOXC6. The prediction power of the model is validated with four GEO datasets consisting of 1584 patient samples. Kaplan-Meier curve and log-rank tests were conducted on both training and validation datasets, the difference of overall survival time between predicted low and high-risk groups can be clearly observed. Gene set enrichment analysis was performed to further investigate the difference between low and high-risk groups in the gene pathway level. The biological meaning was interpreted. Overall, our results prove our prediction model has a strong prediction power on colon cancer prognosis. CONCLUSIONS: Transcription factors can be used to construct colon cancer prognostic signatures with strong prediction power. The variable selection process used in this study has the potential to be implemented in the prognostic signature discovery of other cancer types. Our five TF-based predictive model would help with understanding the hidden relationship between colon cancer patient survival and transcription factor activities. It will also provide more insights into the precision treatment of colon cancer patients from a genomic information perspective.