Search

Major Depressive Disorder (MDD) and Generalized Anxiety Disorder (GAD) pose significant burdens on individuals and society, necessitating accurate prediction methods. Machine learning (ML) algorithms utilizing electronic health records and survey data offer promising tools for forecasting these conditions. However, potential bias and inaccuracies inherent in subjective survey responses can undermine the precision of such predictions. This research investigates the reliability of five prominent ML algorithms—a Convolutional Neural Network (CNN), Random Forest, XGBoost, Logistic Regression, and Naive Bayes—in predicting MDD and GAD. A dataset rich in biomedical, demographic, and self-reported survey information is used to assess the algorithms’ performance under different levels of subjective response inaccuracies. These inaccuracies simulate scenarios with potential memory recall bias and subjective interpretations. While all algorithms demonstrate commendable accuracy with high-quality survey data, their performance diverges significantly when encountering erroneous or biased responses. Notably, the CNN exhibits superior resilience in this context, maintaining performance and even achieving enhanced accuracy, Cohen’s kappa score, and positive precision for both MDD and GAD. This highlights the CNN’s superior ability to handle data unreliability, making it a potentially advantageous choice for predicting mental health conditions based on self-reported data. These findings underscore the critical importance of algorithmic resilience in mental health prediction, particularly when relying on subjective data. They emphasize the need for careful algorithm selection in such contexts, with the CNN emerging as a promising candidate due to its robustness and improved performance under data uncertainties.

BackgroundClinical decision support systems (CDSSs) are important for the quality and safety of health care delivery. Although CDSS rules guide CDSS behavior, they are not routinely shared and reused. ObjectiveOntologies have the potential to promote the reuse of CDSS rules. Therefore, we systematically screened the literature to elaborate on the current status of ontologies applied in CDSS rules, such as rule management, which uses captured CDSS rule usage data and user feedback data to tailor CDSS services to be more accurate, and maintenance, which updates CDSS rules. Through this systematic literature review, we aim to identify the frontiers of ontologies used in CDSS rules. MethodsThe literature search was focused on the intersection of ontologies; clinical decision support; and rules in PubMed, the Association for Computing Machinery (ACM) Digital Library, and the Nursing & Allied Health Database. Grounded theory and PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) 2020 guidelines were followed. One author initiated the screening and literature review, while 2 authors validated the processes and results independently. The inclusion and exclusion criteria were developed and refined iteratively. ResultsCDSSs were primarily used to manage chronic conditions, alerts for medication prescriptions, reminders for immunizations and preventive services, diagnoses, and treatment recommendations among 81 included publications. The CDSS rules were presented in Semantic Web Rule Language, Jess, or Jena formats. Despite the fact that ontologies have been used to provide medical knowledge, CDSS rules, and terminologies, they have not been used in CDSS rule management or to facilitate the reuse of CDSS rules. ConclusionsOntologies have been used to organize and represent medical knowledge, controlled vocabularies, and the content of CDSS rules. So far, there has been little reuse of CDSS rules. More work is needed to improve the reusability and interoperability of CDSS rules. This review identified and described the ontologies that, despite their limitations, enable Semantic Web technologies and their applications in CDSS rules.

Bacillus anthracis has gained international attention as a deadly bacterium and a potentially deadly biological warfare agent. Dipicolinic acid (DPA) is the main component of the protective layer of anthracis spores, and is also an anthrax biomarker. Therefore, it is of great significance to explore an efficient and sensitive DPA detection method. Herein, a novel ratio hybrid probe (CQDs-PIL-Eu3+) was prepared by a simple one-step hydrothermal method using carbon quantum dots (CQDs) as an internal reference fluorescence and a covalent bond between CQDs and Eu3+ by using a polyionic liquid (PIL) as a bridge molecule. The ratiometric fluorescence probe was found to have the characteristics of sensitive fluorescence visual sensing in detecting DPA. The structure and the sensing properties of CQDs-PIL-Eu3+ were investigated in detail. In particular, the fluorescence intensity ratio of Eu3+ to CQDs (I616/I440) was linear with the concentration of DPA in the range of 0–50 μM, so the detection limit of the probe was as low as 32 nm, which was far lower than the DPA dose released by the number of anthrax spores in human body (60 μM) and, thus, can achieve sensitive detection. Therefore, the ratiometric fluorescence probe in this work has the characteristics of strong anti-interference, visual sensing, and high sensitivity, which provides a very promising scheme for the realization of anthrax biomarker DPA detection.

doi：10.3969/j.issn.1672⁃6731.2021.10.013

Abstract Background In real-world pragmatic administrative databases, patient reported remission is often missing. Objective We evaluate if, in administrative data, five features of antidepressant use patterns can replace patient-reported symptom remission. Method We re-examined data from Sequence Treatment Alternatives to Relieve Depression (STAR*D) study. Remission was measured using 50% reduction in Hamilton index. Pattern of antidepressant use was examined through five variables: (a) number of prior ineffective antidepressants, (b) duration of taking current antidepressant, (c) receiving therapeutic dose of the medication, and (d) switching to another medication, or (e) augmenting with another antidepressant. The likelihood ratio (LR) associated with each of these predictors was assessed in 90% of data (3329 cases) and evaluated in 10% of data (350 cases) set-aside for evaluation. The accuracy of predictions was calculated using Area under the Receiver Operating Curve (AROC). Results Patients who took antidepressants for 14 weeks (LR = 2.007) were more likely to have symptom remission. Prior use of 3 antidepressants reduced the odds of remission (LR = 0.771). Patients who received antidepressants below therapeutic dose were 5 times less likely to experience remission (LR = 0.204). Antidepressant that were augment or switched, almost never led to remission (LR = 0.008, LR = 0.002 respectively). Patterns of antidepressant use accurately (AROC = 0.93) predicted symptom remission. Conclusion Within the first 100 days, antidepressants use patterns could serve as a surrogate measure for patient-reported remission of symptoms.

Abstract Background Summarization networks are compact summaries of ontologies. The “Big Picture” view offered by summarization networks enables to identify sets of concepts that are more likely to have errors than control concepts. For ontologies that have outgoing lateral relationships, we have developed the "partial-area taxonomy" summarization network. Prior research has identified one kind of outlier concepts, concepts of small partials-areas within partial-area taxonomies. Previously we have shown that the small partial-area technique works successfully for four ontologies (or their hierarchies). Methods To improve the Quality Assurance (QA) scalability, a family-based QA framework, where one QA technique is potentially applicable to a whole family of ontologies with similar structural features, was developed. The 373 ontologies hosted at the NCBO BioPortal in 2015 were classified into a collection of families based on structural features. A meta-ontology represents this family collection, including one family of ontologies having outgoing lateral relationships. The process of updating the current meta-ontology is described. To conclude that one QA technique is applicable for at least half of the members for a family F, this technique should be demonstrated as successful for six out of six ontologies in F. We describe a hypothesis setting the condition required for a technique to be successful for a given ontology. The process of a study to demonstrate such success is described. This paper intends to prove the scalability of the small partial-area technique. Results We first updated the meta-ontology classifying 566 BioPortal ontologies. There were 371 ontologies in the family with outgoing lateral relationships. We demonstrated the success of the small partial-area technique for two ontology hierarchies which belong to this family, SNOMED CT’s Specimen hierarchy and NCIt’s Gene hierarchy. Together with the four previous ontologies from the same family, we fulfilled the “six out of six” condition required to show the scalability for the whole family. Conclusions We have shown that the small partial-area technique can be potentially successful for the family of ontologies with outgoing lateral relationships in BioPortal, thus improve the scalability of this QA technique.

Abstract Background Ontologies house various kinds of domain knowledge in formal structures, primarily in the form of concepts and the associative relationships between them. Ontologies have become integral components of many health information processing environments. Hence, quality assurance of the conceptual content of any ontology is critical. Relationships are foundational to the definition of concepts. Missing relationship errors (i.e., unintended omissions of important definitional relationships) can have a deleterious effect on the quality of an ontology. An abstraction network is a structure that overlays an ontology and provides an alternate, summarization view of its contents. One kind of abstraction network is called an area taxonomy, and a variation of it is called a subtaxonomy. A methodology based on these taxonomies for more readily finding missing relationship errors is explored. Methods The area taxonomy and the subtaxonomy are deployed to help reveal concepts that have a high likelihood of exhibiting missing relationship errors. A specific top-level grouping unit found within the area taxonomy and subtaxonomy, when deemed to be anomalous, is used as an indicator that missing relationship errors are likely to be found among certain concepts. Two hypotheses pertaining to the effectiveness of our Quality Assurance approach are studied. Results Our Quality Assurance methodology was applied to the Biological Process hierarchy of the National Cancer Institute thesaurus (NCIt) and SNOMED CT’s Eye/vision finding subhierarchy within its Clinical finding hierarchy. Many missing relationship errors were discovered and confirmed in our analysis. For both test-bed hierarchies, our Quality Assurance methodology yielded a statistically significantly higher number of concepts with missing relationship errors in comparison to a control sample of concepts. Two hypotheses are confirmed by these findings. Conclusions Quality assurance is a critical part of an ontology’s lifecycle, and automated or semi-automated tools for supporting this process are invaluable. We introduced a Quality Assurance methodology targeted at missing relationship errors. Its successful application to the NCIt’s Biological Process hierarchy and SNOMED CT’s Eye/vision finding subhierarchy indicates that it can be a useful addition to the arsenal of tools available to ontology maintenance personnel.

Purpose: Although research evidence indicates positive associations between stressful life events and postpartum depression, limited research assessed these associations in women with disabilities. This study examined the effects of stressful life events on postpartum depressive symptoms in women with disabilities. Methods: Data from the 2012–2017 Massachusetts Pregnancy Risk Assessment Monitoring System ( n = 8453) were used in this study. Women were asked if they experienced any life stressors (e.g. financial, traumatic, relational, and emotional) during the 12 months prior to giving birth. Disability was measured based on reports of emotional and physical functioning. Descriptive statistics, bivariate, and binary logistic regression analyses were conducted to estimate the effect of stressful life events on postpartum depressive symptoms among women with and without disabilities. Results: Findings show that 37.4% of women with disabilities had postpartum depressive symptoms, which was significantly higher than 8.79% of women without disabilities. Stressful life events were reported in 86.6% of women with disabilities, compared to 66.6% for women without disabilities. Prevalence of three or more stressful life events and postpartum depressive symptoms was greater among women with disabilities (50.8% and 62.9%, respectively) than women without disabilities (22.6% and 37.0%, respectively). Women with disabilities experiencing six or more stressful life events were more likely (odds ratio = 3.78, 95% confidence interval = [1.57–9.10]) to report postpartum depressive symptoms, compared to those with no stressful life events. Women with disabilities who experienced relational (odds ratio = 2.36, 95% confidence interval = [1.44–3.87]) and traumatic (odds ratio = 1.75, 95% confidence interval = [1.02–3.00]) life stressors had higher odds for postpartum depressive symptoms relative to those reporting no such life stressors. Conclusion: Women with disabilities are at an amplified risk for stressful life events and postpartum depressive symptoms. Relational and traumatic stressful life events particularly increase the odds for postpartum depressive symptoms among this group of mothers. Early prenatal and postnatal screening for life stressors and depressive symptoms, coupled with timely referral for appropriate prenatal and postnatal care, are vital to mitigate the harmful effects of depression among mothers with disabilities and the health of their children.

The existing adaptive growth technique deducted the optimization iteration formula according to the condition named Karush-Kuhn-Tucker condition. It is high efficiency and good, which is aimed to strain energy and bound to the volume against optimization problem of stiffener distribution for plate and shell structures. However, different optimization iteration formula, which deducted by different kinds of problem, is poor and difficult to generalize, especially for the optimization problems with more than one constraint. Against with the lack of adaptive growth technique, a new method that is moving asymptotes（MMA） is suggested by this article and the adaptive growth technique is applied to truss structure topology optimization design. The design of typical truss structures with single constraint and multiple constraints is discussed, and the multi load cases are studied. The results of numerical examples show that the proposed method can obtain clear information of rod distribution and dimension, and has high design efficiency and good applicability. It is convenient for practical machining and has a good application prospect.

This work investigated the effect of controlled malolactic fermentation starters (Lactobacillus plantarum NoVA and Oenococcus oeni Oenos) and an un-inoculated culture on cherry wine’s physical, biochemical and sensory properties. NoVA promoted MLF most efficiently (15d), resulted in a large increase in the concentration of many esters and aromatic alcohols, and reinforced the cherry wine’s ‘fruity’ note and overall aroma. MLF carried out by Oenos lasted for 18 days, accompanied by the increase of many volatile compounds (with 2-phenylethyl acetate and benzaldehyde as major representatives) and several biogenic amines. With respect to spontaneous MLF, Tatumella, Pantoea, Lactococcus, and Lactobacillus were identified as the dominant genera, with a fermentation period of 25 d and the total biogenic amine amount 1.6-fold than that at the end of alcoholic fermentation. This study has proved that the use of Viniflora NoVA to conduct MLF is a worthwhile alternative to the traditional cherry wine-making.

For patients with rare comorbidities, there are insufficient observations to accurately estimate the effectiveness of treatment. At the same time, all diagnosis, including rare diagnosis, are part of the International Classification of Disease (ICD). Grouping ICD into broader concepts (i.e., ontology adjustment) can not only increase accuracy of estimating antidepressant effectiveness for patients with rare conditions but also prevent overfitting in big data analysis. In this study, 3,678,082 depressed patients treated with antidepressants were obtained from OptumLabs® Data Warehouse (OLDW). For rare diagnoses, adjustments were made by using the likelihood ratio of the immediate broader concept in the ICD hierarchies. The accuracy of models in training (90%) and test (10%) sets was examined using the area under the receiver operating curves (AROC). The gap in training and test AROC shows how much random noise was modeled. If the gap is large, then the parameters of the model, including the reported effectiveness of the antidepressant for patients with rare conditions, are suspect. There was, on average, a 9.0% reduction in the AROC gap after using the ontological adjustment. Therefore, ontology adjustment can reduce model overfitting, leading to better parameter estimates from the training set.

Abstract Background Bio-ontologies are becoming increasingly important in knowledge representation and in the machine learning (ML) fields. This paper presents a ML approach that incorporates bio-ontologies and its application to the SEER-MHOS dataset to discover patterns of patient characteristics that impact the ability to perform activities of daily living (ADLs). Bio-ontologies are used to provide computable knowledge for ML methods to “understand” biomedical data. Results This retrospective study included 723 cancer patients from the SEER-MHOS dataset. Two ML methods were applied to create predictive models for ADL disabilities for the first year after a patient’s cancer diagnosis. The first method is a standard rule learning algorithm; the second is that same algorithm additionally equipped with methods for reasoning with ontologies. The models showed that a patient’s race, ethnicity, smoking preference, treatment plan and tumor characteristics including histology, staging, cancer site, and morphology were predictors for ADL performance levels one year after cancer diagnosis. The ontology-guided ML method was more accurate at predicting ADL performance levels (P

In order to obtain optimum packaging materials for peanut kernels, the effects of four types of packaging materials on peanut storage quality (coat color, acid value, germination rate, relative damage, and prevention of aflatoxin contamination) were examined. The results showed that packaging materials had a major influence on peanut storage quality indexes. The color of the peanut seed coat packaged in the polyester/aluminum/polyamide/polyethylene (PET/AL/PA/PE) composite film bag did not change significantly during the storage period. Color deterioration was slower with polyamide/polyethylene (PA/PE) packaging materials than with polyethylene (PE) film bags and was slower in PE bags than in the woven bags. The use of PET/AL/PA/PE and PA/PE bags maintained peanut quality and freshness for more than one year and both package types resulted in better germination rates. There were significant differences between the four types of packaging materials in terms of controlling insect pests. The peanuts packaged in the highly permeable woven bags suffered serious invasion from insect pests, while both PET/AL/PA/PE and PA/PE bags effectively prevented insect infection. Peanuts stored in PET/AL/PA/PE and PA/PE bags were also better at preventing and controlling aflatoxin contamination.

Bovine leukemia virus (BLV), a deltaretrovirus, causes B-cell leukemia/lymphoma in cattle and is prevalent in herds globally. A previous finding of antibodies against BLV in humans led us to examine the possibility of human infection with BLV. We focused on breast tissue because, in cattle, BLV DNA and protein have been found to be more abundant in mammary epithelium than in lymphocytes. In human breast tissue specimens, we identified BLV DNA by using nested liquid-phase PCR and DNA sequencing. Variations from the bovine reference sequence were infrequent and limited to base substitutions. In situ PCR and immunohistochemical testing localized BLV to the secretory epithelium of the breast. Our finding of BLV in human tissues indicates a risk for the acquisition and proliferation of this virus in humans. Further research is needed to determine whether BLV may play a direct role in human disease.

Liquefaction of cornstalk in sub-critical solution of ethanol without and with catalysts (K2CO3, Na2CO3 and ZnCl2) was performed in a stainless steel reactor (1 L) at temperatures of 200 to 300 oC. The cornstalk and the products of decomposition were divided into five lumps (gas, organic dissolved, heavy oil, volatile organic compounds, and residue). The effects of reaction temperature and the catalyst amount on the five lump yields were studied. The bio-oils produced with and without catalysts were characterized by GC/MS. Results showed that an increment in the temperature and the addition of catalysts had a synergetic effect on the lumps yield as compared to the non-catalytic experiments, and different catalytic procedures had an important effect on the lump yields and compounds of the bio-oils. The addition of the catalyst enhanced the gas yield and the total conversion rate. A high temperature, lower amount of Na2CO3, moderate amount of K2CO3, and a high amount of ZnCl2 were propitious to enhance the heavy oil. The formation of volatile organic compounds with the presence of ZnCl2 and K2CO3 was less than that in non-catalytic experiments at the higher temperatures. However, a higher conversion temperature had a negative impact on the bio-oils yield from liquefaction of cornstalk with and without catalysts.

Gene terminologies are playing an increasingly important role in the ever-growing field of genomic research. While errors in large, complex terminologies are inevitable, gene terminologies are even more susceptible to them due to the rapid growth of genomic knowledge and the nature of its discovery. It is therefore very important to establish quality- assurance protocols for such genomic-knowledge repositories. Different kinds of terminologies oftentimes require auditing methodologies adapted to their particular structures. In light of this, an auditing methodology tailored to the characteristics of the NCI Thesaurus’s (NCIT’s) Gene hierarchy is presented. The Gene hierarchy is of particular interest to the NCIT’s designers due to the primary role of genomics in current cancer research. This multiphase methodology focuses on detecting role-errors, such as missing roles or roles with incorrect or incomplete target structures, occurring within that hierarchy. The methodology is based on two kinds of abstraction networks, called taxonomies, that highlight the role distribution among concepts within the IS-A (subsumption) hierarchy. These abstract views tend to highlight portions of the hierarchy having a higher concentration of errors. The errors found during an application of the methodology

Age, reproductive history, hormones, genetics, and lifestyle are known risk factors for breast cancer, but the agents that initiate cellular changes from normal to malignant are not understood. We previously detected bovine leukemia virus (BLV), a common oncogenic virus of cattle, in the breast epithelium of humans. The objective of this study was to determine whether the presence of BLV DNA in human mammary epithelium is associated with breast cancer.This was a case-control study of archival formalin fixed paraffin embedded breast tissues from 239 donors, received 2002-2008 from the Cooperative Human Tissue Network. Case definition as breast cancer versus normal (women with no history of breast cancer) was established through medical records and examination of tissues by an anatomical pathologist. Breast exposure to BLV was determined by in situ-PCR detection of a biomarker, BLV DNA, localized within mammary epithelium.The frequency of BLV DNA in mammary epithelium from women with breast cancer (59%) was significantly higher than in normal controls (29%) (multiply- adjusted odds ratio = 3.07, confidence interval = 1.66-5.69, p = .0004, attributable risk = 37%). In women with premalignant breast changes the frequency of BLV DNA was intermediate (38%) between that of women with breast cancer and normal controls (p for trend < .001).Among the specimens in this study, the presence of amplified BLV DNA was significantly associated with breast cancer. The odds ratio magnitude was comparable to those of well-established breast cancer risk factors related to reproductive history, hormones, and lifestyle and was exceeded only by risk factors related to genetics (familial breast cancer), high dose ionizing radiation, and age. These findings have the potential for primary and secondary prevention of breast cancer.