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This paper presents a comprehensive workflow for integrating revolving events into the transitive sequential pattern mining (tSPM+) algorithm and Machine Learning for Health Outcomes (MLHO) framework, emphasizing best practices and pitfalls in its application. We emphasize feature engineering and visualization techniques, demonstrating their efficacy in capturing temporal relationships. Applied to an EGFR lung cancer cohort, our approach showcases reliable temporal insights even in a small dataset. This work highlights the importance of temporal nuances in healthcare data analysis, paving the way for improved disease understanding and patient care.

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Open chromatin regions are the genomic regions associated with basic cellular physiological activities, while chromatin accessibility is reported to affect gene expressions and functions. A basic computational problem is to efficiently estimate open chromatin regions, which could facilitate both genomic and epigenetic studies. Currently, ATAC-seq and cfDNA-seq (plasma cell-free DNA sequencing) are two popular strategies to detect OCRs. As cfDNA-seq can obtain more biomarkers in one round of sequencing, it is considered more effective and convenient. However, in processing cfDNA-seq data, due to the dynamically variable chromatin accessibility, it is quite difficult to obtain the training data with pure OCRs or non-OCRs, and leads to a noise problem for either feature-based approaches or learning-based approaches. In this paper, we propose a learning-based OCR estimation approach with a noise-tolerance design. The proposed approach, named OCRFinder, incorporates the ideas of ensemble learning framework and semi-supervised strategy to avoid potential overfitting of noisy labels, which are the false positives on OCRs and non-OCRs. Compared to different noise control strategies and state-of-the-art approaches, OCRFinder achieved higher accuracies and sensitivities in the experiments. In addition, OCRFinder also has an excellent performance in ATAC-seq or DNase-seq comparison experiments.

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Cytotoxic CD8+ T lymphocytes (CTL) eliminate infected cells or transformed tumor cells by releasing perforin-containing cytotoxic granules at the immunological synapse. The secretion of such granules depends on Ca2+-influx through store operated Ca2+ channels, formed by STIM (stromal interaction molecule)-activated Orai proteins. Whereas molecular mechanisms of the secretion machinery are well understood, much less is known about the molecular machinery that regulates the efficiency of Ca2+-dependent target cell killing. CTL killing efficiency is of high interest considering the number of studies on CD8+ T lymphocytes modified for clinical use. Here, we isolated total RNA from primary human cells: natural killer (NK) cells, non-stimulated CD8+ T-cells, and from Staphylococcus aureus enterotoxin A (SEA) stimulated CD8+ T-cells (SEA-CTL) and conducted whole genome expression profiling by microarray experiments. Based on differential expression analysis of the transcriptome data and analysis of master regulator genes, we identified 31 candidates which potentially regulate Ca2+-homeostasis in CTL. To investigate a putative function of these candidates in CTL cytotoxicity, we transfected either SEA-stimulated CTL (SEA-CTL) or antigen specific CD8+ T-cell clones (CTL-MART-1) with siRNAs specific against the identified candidates and analyzed the killing capacity using a real-time killing assay. In addition, we complemented the analysis by studying the effect of inhibitory substances acting on the candidate proteins if available. Finally, to unmask their involvement in Ca2+ dependent cytotoxicity, candidates were also analyzed under Ca2+-limiting conditions. Overall, we identified four hits, CCR5 (C-C chemokine receptor type five), KCNN4 (potassium calcium-activated channel subfamily N), RCAN3 (regulator of calcineurin) and BCL (B-cell lymphoma) 2 which clearly affect the efficiency of Ca2+ dependent cytotoxicity in CTL-MART-1 cells, CCR5, BCL2, and KCNN4 in a positive manner, and RCAN3 in a negative way.

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BACKGROUND: Vitamin D is recognized in bone health and the prevention of rickets and osteomalacia. OBJECTIVE: This study aimed to assess vitamin D status of people in Canada and to identify factors associated with vitamin D inadequacy and deficiency. METHODS: Serum 25-hydroxyvitamin D (25(OH)D) from the Canadian Health Measures Survey (cycles 3-6, n = 21,770, 3-79 y) were evaluated for geometric means and proportions <40 (inadequate) and <30 (risk of deficiency) nmol/L. Factors associated with inadequacy or deficiency were tested using logistic regression. RESULTS: Mean serum 25(OH)D was 57.9 (95% CI: 55.4, 60.5) nmol/L; the prevalence of inadequacy was 19.0% (95% CI: 15.7, 22.3) and risk of deficiency was 8.4% (95% CI: 6.5, 10.3). Prominent dietary factors associated with inadequacy in adults included: not consuming fish compared with ≥1/wk (adjusted ORadj: 1.60; 95% CI: 1.21, 2.11), none compared with ≥1/d for cow's milk (ORadj: 1.41; 95% CI: 1.02, 1.94) or margarine (ORadj: 1.42; 95% CI: 1.08, 1.88); or nonuser compared with user of vitamin D supplements (ORadj: 5.21; 95% CI: 3.88, 7.01). Notable demographic factors included: younger adults compared with 71 to 79 y (19-30 y ORadj: 2.33; 95% CI: 1.66, 3.29); BMI ≥30 compared with <25 kg/m2 (ORadj: 2.30; 95% CI: 1.79, 2.95); lower household income quartile 1 compared with 4 (ORadj: 1.46; 95% CI: 1.00, 2.15); and self-reported Black (ORadj: 8.06; 95% CI: 4.71, 13.81), East/Southeast Asian (ORadj: 3.83; 95% CI: 2.14, 6.85), Middle Eastern (ORadj: 4.57; 95% CI: 3.02, 6.92), and South Asian (ORadj: 4.63; 95% CI: 2.62, 8.19) race compared with White. Similar factors were observed in children and for deficiency. CONCLUSIONS: Most people in Canada have adequate vitamin D status; nonetheless, racialized groups have an elevated prevalence of inadequacy. Further research is required to evaluate if current strategies to improve vitamin D status, including increasing vitamin D in fortified foods and supplements, and dietary guidance to include a source of vitamin D every day help to reduce health inequality in Canada.

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The inability to quantitatively integrate scattered data regarding potential threats posed by the increasing total amount and diversity of chemical substances in our environment limits our ability to understand whether existing regulations and management actions sufficiently protect wildlife. Systematic literature reviews and meta-analyses are great scientific tools to build upon the current push for accessibility under the Open Science and FAIR movements. Despite the potential of such integrative analyses, the emergence of innovative findings in wildlife ecology and ecotoxicology is still too rare relative to the potential that is hidden within the entirety of the available scattered data. To promote the reuse of wildlife ecotoxicology data, we propose the ATTAC workflow which comprises five key steps (Access, Transparency, Transferability, Add-ons, and Conservation sensitivity) along the chain of collecting, homogenizing, and integrating data for subsequent meta-analyses. The ATTAC workflow brings together guidelines supporting both the data prime movers and re-users. As such, the ATTAC workflow could promote an open and collaborative wildlife ecotoxicology able to reach a major objective in this applied field, namely, providing strong scientific support for regulations and management actions to protect and preserve wildlife species.

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Prior MALDI mass spectrometry imaging (MALDI-MSI) studies reported significant changes in phosphatidylcholines (PCs), lysophosphatidylcholines (LPCs), and sphingomyelins (SMs) in ischemic rat brains yet overlooked the information on other classes of PLs and SLs and provided very little or no validation on the detected lipid markers. Relative quantitation of four classes of PLs and two classes of SLs in the ischemic and normal temporal cortex (TCX), parietal cortex (PCX), and striatum (ST) of rats was performed with hydrophilic interaction chromatography (HILIC)-tandem mass spectrometry (MS/MS) analyses, and the marker lipid species was identified by multivariate data analysis and validated with additional tissue cohorts. The acquired lipid information was sufficient in differentiating individual anatomical regions under different pathological states, identifying region-specific ischemic brain lipid markers and revealing additional PL and SL markers not reported previously. Validation of orthogonal partial least square discriminating analysis (OPLS-DA) identified ischemic brain lipid markers yielded much higher classification accuracy, precision, specificity, sensitivity, and lower false positive and false negative rates than those from the volcano plot analyses using conventional statistical significance and a fold change of two as the cutoff and provided a wider prospective to ischemia-associated brain lipid changes.

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BACKGROUND: A virus-infected cell triggers a signalling cascade, resulting in the secretion of interferons (IFNs), which in turn induces the upregulation of the IFN-stimulated genes (ISGs) that play a role in antipathogen host defence. Here, we conducted analyses on large-scale data relating to evolutionary gene expression, sequence composition, and network properties to elucidate factors associated with the stimulation of human genes in response to IFN-α. RESULTS: We find that ISGs are less evolutionary conserved than genes that are not significantly stimulated in IFN experiments (non-ISGs). ISGs show obvious depletion of GC content in the coding region. This influences the representation of some compositions following the translation process. IFN-repressed human genes (IRGs), downregulated genes in IFN experiments, can have similar properties to the ISGs. Additionally, we design a machine learning framework integrating the support vector machine and novel feature selection algorithm that achieves an area under the receiver operating characteristic curve (AUC) of 0.7455 for ISG prediction. Its application in other IFN systems suggests the similarity between the ISGs triggered by type I and III IFNs. CONCLUSIONS: ISGs have some unique properties that make them different from the non-ISGs. The representation of some properties has a strong correlation with gene expression following IFN-α stimulation, which can be used as a predictive feature in machine learning. Our model predicts several genes as putative ISGs that so far have shown no significant differential expression when stimulated with IFN-α in the cell/tissue types in the available databases. A web server implementing our method is accessible at http://isgpre.cvr.gla.ac.uk/. The docker image at https://hub.docker.com/r/hchai01/isgpre can be downloaded to reproduce the prediction.

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This study aimed to evaluate the predictive value of diabetic retinopathy (DR) and its stages with the incidence of major cardiovascular events and all-cause mortality in type 2 diabetes mellitus (T2DM) persons in our large primary healthcare database from Catalonia (Spain). A retrospective cohort study with pseudo-anonymized routinely collected health data from SIDIAP was conducted from 2008 to 2016. We calculated incidence rates of major cardiovascular events [coronary heart disease (CHD), stroke, or both-macrovascular events] and all-cause mortality for subjects with and without DR and for different stages of DR. The proportional hazards regression analysis was done to assess the probability of occurrence between DR and the study events. About 22,402 T2DM subjects with DR were identified in the database and 196,983 subjects without DR. During the follow-up period among the subjects with DR, we observed the highest incidence of all-cause mortally. In the second place were the macrovascular events among the subjects with DR. In the multivariable analysis, fully adjusted for DR, sex, age, body mass index (BMI), tobacco, duration of T2DM, an antiplatelet or antihypertensive drug, and HbA1c, we observed that subjects with any stage of DR had higher risks for all of the study events, except for stroke. We observed the highest probability of all-cause death events (adjusted hazard ratios, AHRs: 1.34, 95% CI: 1.28; 1.41). In conclusion, our results show that DR is related to CHD, macrovascular events, and all-cause mortality among persons with T2DM.

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INTRODUCTION: In microbial metabolomics, the use of multivariate data analysis (MDVA) has not been comprehensively explored regarding the different techniques available and the information that each gives about the metabolome. To overcome these limitations, here we show the use of Fusarium oxysporum cultured in the presence of exogenous alkaloids as a model system to demonstrate a comprehensive strategy for metabolic profiling. MATHERIALS AND METHODS: F. oxysporum was harvested on different days of incubation after alkaloidal addition, and the chemical profiles were compared using LC-MS data and MDVA. We show significant innovation to evaluate the chemical production of microbes during their life cycle by utilizing the full capabilities of Partial Least Square (PLS) with microbial-specific modeling that considers incubation days, media culture availability, and growth rate in solid media. RESULTS AND DISCUSSCION: Results showed that the treatment of the Y-data and the use of both PLS regression and discrimination (PLSr and PLS-DA) inferred complemental chemical information. PLSr revealed the metabolites that are produced/consumed during fungal growth, whereas PLS-DA focused on metabolites that are only consumed/produced at a specific period. Both regression and classificatory analysis were equally important to identify compounds that are regulated and/or selectively produced as a response to the presence of the alkaloids. Lastly, we report the annotation of analogs from the piperidine alkaloids biotransformed by F. oxysporum as a defense response to the toxic plant metabolites. These molecules do not show the antimicrobial potential of their precursors in the fungal extracts and were rapidly produced and consumed within 4 days of microbial growth.

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Introduction: Promoting standardization and quality assurance (QA) in oncology on the strength of real-world data is essential to ensure better patient outcomes. Wide excision after primary tumor biopsy is a fundamental step in the therapeutic pathway for cutaneous malignant melanoma (CMM). The aim of this population-based cohort study is to assess adherence to wide local excision in a cohort of patients diagnosed with CMM and the impact of this recommended procedure on overall and disease-specific survival. Materials and Methods: This retrospective cohort study concerns CMM patients diagnosed in the Veneto region (north-east Italy) in 2017, included in the high-resolution Veneto Cancer Registry, and followed up through linkage with the regional mortality registry up until February 29th, 2020. Using population-level real-world data, linking patient-level cancer registry data with administrative records of clinical procedures may shed light on the real-world treatment of CMM patients in accordance with current guidelines. After excluding TNM stage IV patients, a Cox regression analysis was performed to test whether the completion of a wide local excision was associated with a difference in melanoma-specific and overall survival, after adjusting for other covariates. Results: No wide excision after the initial biopsy was performed in 9.7% of cases in our cohort of 1,305 patients. After adjusting for other clinical prognostic characteristics, Cox regression revealed that failure to perform a wide local excision raised the hazard ratio of death in terms of overall survival (HR = 4.80, 95% CI: 2.05-11.22, p < 0.001) and melanoma-specific survival (HR = 2.84, 95% CI: 1.04-7.76, p = 0.042). Conclusion: By combining clinical and administrative data, this study on real-world clinical practice showed that almost one in ten CMM patients did not undergo wide local excision surgery. Monitoring how diagnostic-therapeutic protocols are actually implemented in the real world may contribute significantly to promoting quality improvements in the management of oncological patients.

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Developing new long-acting products of well-characterized contraceptive drugs is one way to address some of the reasons for unmet need for modern methods of family planning among women in low- and middle-income countries. Development and approval of such products traditionally follow a conventional paradigm that includes large Phase 3 clinical trials to evaluate efficacy (pregnancy prevention) and safety of the investigational product. Exposure-bracketing is a concept that applies known pharmacokinetics and pharmacodynamics of a drug substance to inform its safe and efficacious use in humans. Several therapeutic areas have applied this concept by leveraging established drug concentration-response relationships for approved products to expedite development and shorten the timeline for the approval of an investigational product containing the same drug substance. Based on discussions at a workshop hosted by the Bill & Melinda Gates Foundation in December 2020, it appears feasible to apply exposure-bracketing to develop novel contraceptive products using well-characterized drugs.

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BACKGROUND: Breast cancer is a well-recognised late adverse effect in female childhood cancer survivors (CCSs), especially after chest radiotherapy; information on subsequent male breast cancer (SMBC) is limited. We summarised the existing evidence on SMBC after childhood cancer in a systematic review and investigated the risk of SMBC among males in a Pan-European cohort. METHODS: We searched Medline/PubMed for cohort studies and case reports/series that assessed SMBC after childhood cancer (≤21 years). Furthermore, we analysed data on SMBC in the PanCareSurFup cohort, reporting standardised incidence ratios (SIRs), absolute excess risks (AERs), and 5- and 10-year survival rates. RESULTS: The systematic review included 38 of 7080 potentially eligible articles. Cohort-specific SMBC frequencies were 0-0.40% (31 studies). SMBC occurred after a follow-up ranging from 24.0 to 42.0 years. Nine case reports/series described 11 SMBC cases, occurring 11.0-42.5 years after primary childhood cancer. In the PanCareSurFup cohort (16 SMBC/37,738 males; 0.04%), we observed a 22.3-fold increased risk of SMBC relative to the general male population (95% CI 12.7-36.2; absolute excess risk/100,000 person-years: 2.3, 95% CI 1.3-3.7). The five- and ten-year survival rates after SMBC diagnosis were 60.3% (95% CI 35.6%-85.0%) and 43.0% (95% CI 16.1%-69.9%), respectively. Clear evidence of risk factors did not emerge from these comprehensive efforts. CONCLUSIONS: Compared to the general population, male CCSs have an elevated risk of developing subsequent breast cancer, although the absolute risk is low. Health care providers should be aware of this rare yet serious late effect; male CCSs with symptoms potentially related to SMBC warrant careful examination.

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Circular dichroism (CD) spectroscopy is a widely-used method for characterizing the secondary structures of proteins. The well-established and highly used analysis website, DichroWeb (located at: http://dichroweb.cryst.bbk.ac.uk/html/home.shtml) enables the facile quantitative determination of helix, sheet, and other secondary structure contents of proteins based on their CD spectra. DichroWeb includes a range of reference datasets and algorithms, plus graphical and quantitative methods for determining the quality of the analyses produced. This article describes the current website content, usage and accessibility, as well as the many upgraded features now present in this highly popular tool that was originally created nearly two decades ago.

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[This corrects the article DOI: 10.3389/fvets.2021.633977.].

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BACKGROUND: Chronic myelogenous leukemia (CML) is a clonal stem cell disorder accounting for 15% of adult leukemias. We aimed to determine if machine learning models could predict CML using blood cell counts prior to diagnosis. METHODS: We identified patients with a diagnostic test for CML (BCR-ABL1) and at least 6 consecutive prior years of differential blood cell counts between 1999 and 2020 in the largest integrated health care system in the United States. Blood cell counts from different time periods prior to CML diagnostic testing were used to train, validate, and test machine learning models. RESULTS: The sample included 1,623 patients with BCR-ABL1 positivity rate 6.2%. The predictive ability of machine learning models improved when trained with blood cell counts closer to time of diagnosis: 2 to 5 years area under the curve (AUC), 0.59 to 0.67, 0.5 to 1 years AUC, 0.75 to 0.80, at diagnosis AUC, 0.87 to 0.92. CONCLUSIONS: Blood cell counts collected up to 5 years prior to diagnostic workup of CML successfully predicted the BCR-ABL1 test result. These findings suggest a machine learning model trained with blood cell counts could lead to diagnosis of CML earlier in the disease course compared to usual medical care.

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Asymmetries compromise performance in powerlifting and Paralympic powerlifting, but its quantification can be complex. Previous studies consider average or peak values to quantify asymmetries, however this approach does not consider the pattern of movement like velocity profiles. Here we demonstrate that conducting a functional analysis of variance (FANOVA) permits to quantify asymmetries in bench press performance by Paralympic powerlifting at different submaximal intensities. Kinematic data were collected from 10 Paralympic powerlifting athletes performing in bench press at submaximal intensities (50% and 90% of the one-repetition maximum). Linear velocity was quantified considering mean values and the entire waveform. Mean values were compared by analysis of variance (ANOVA) and the waveforms were compared by FANOVA. FANOVA identified asymmetry profiles that ANOVA did not recognize at the highest intensity, which is the closest to a competition. This way, FANOVA can bring advantages to the analysis of competitive performance. FANOVA data analysis identifies asymmetries at higher intensity of effort considering the whole pattern of movement. Therefore, we consider that the FANOVA's approach may benefit the biomechanical assessment of the Paralympic powerlifting.

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The biggest change brought about by the "era of big data" to health in general, and epidemiology in particular, relates arguably not to the volume of data encountered, but to its variety. An increasing number of new data sources, including many not originally collected for health purposes, are now being used for epidemiological inference and contextualization. Combining evidence from multiple data sources presents significant challenges, but discussions around this subject often confuse issues of data access and privacy, with the actual technical challenges of data integration and interoperability. We review some of the opportunities for connecting data, generating information, and supporting decision-making across the increasingly complex "variety" dimension of data in population health, to enable data-driven surveillance to go beyond simple signal detection and support an expanded set of surveillance goals.

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Dust-related occupational diseases are common in the mining sector. It is important to identify employees who have high potential for these diseases and to investigate the factors affecting disease formation. For this reason, dust and dust-related occupational diseases should be carefully investigated in mining operations. In this study, dust-related occupational diseases in an open-pit lignite mine were investigated. Firstly, dust measurements were performed and then a health check of all employees was implemented. The obtained data set was categorized by taking into account the occupation, age, experience and level of dust exposure of the employees. While the logistic regression analysis was performed to determine the probability of the diseases, a hierarchical loglinear model was established to investigate the factors in the occurrence of these diseases for those employees with the diseases. Therefore, the most important factors for the development of the diseases were determined by the hierarchical loglinear model.

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The exposure of mining workers to crystalline particles, e.g., alpha quartz in respirable dust, is a ubiquitous global problem in occupational safety and health at surface and underground operations. The challenge of rapid in-field monitoring for direct assessment and adoption of intervention has not been solved satisfactorily to date, as conventional analytical methods such as X-ray diffraction and infrared spectroscopy require laboratory environments, complex system handling, tedious sample preparation, and are limited by, e.g., addressable particle size. A novel monitoring approach was developed for potential in-field application enabling the quantification of crystalline particles in the respirable regime based on transmission infrared spectroscopy. This on-site approach analyzes samples of dust in ambient air collected onto PVC filters using respirable dust sampling devices. In the present study, we demonstrate that portable Fourier transform infrared (FT-IR) spectroscopy in combination with multivariate data analysis provides a versatile tool for the identification and quantification of minerals in complex real-world matrices. Without further sample preparation, the loaded filters are immediately analyzed via transmission infrared spectroscopy, and the mineral amount is quantified in real-time using a partial least squares regression algorithm. Due to the inherent molecular selectivity for crystalline as well as organic matrix components, infrared spectroscopy uniquely allows to precisely determine the particle composition even in complex samples such as dust from coal mines or clay-rich environments. For establishing a robust partial least squares regression model, a method was developed for generating calibration samples representative in size and composition for respirable mine dust via aerodynamic size separation. Combined with experimental design strategies, this allows tailoring the calibration set to the demands of air quality management in underground mining scenarios, i.e., the respirable particle size regime and the matrix of the target analyte.

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Metagenomics and marker gene approaches, coupled with high-throughput sequencing technologies, have revolutionized the field of microbial ecology. Metagenomics is a culture-independent method that allows the identification and characterization of organisms from all kinds of samples. Whole-genome shotgun sequencing analyses the total DNA of a chosen sample to determine the presence of micro-organisms from all domains of life and their genomic content. Importantly, the whole-genome shotgun sequencing approach reveals the genomic diversity present, but can also give insights into the functional potential of the micro-organisms identified. The marker gene approach is based on the sequencing of a specific gene region. It allows one to describe the microbial composition based on the taxonomic groups present in the sample. It is frequently used to analyse the biodiversity of microbial ecosystems. Despite its importance, the analysis of metagenomic sequencing and marker gene data is quite a challenge. Here we review the primary workflows and software used for both approaches and discuss the current challenges in the field.

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