EUS-Guided Diagnosis of Gastric Subepithelial Lesions, What Is New?

Gastric subepithelial lesions (SELs) are intramural lesions that arise underneath the gastric mucosa. SELs can be benign, but can also be malignant or have malignant potential. Therefore, correct diagnosis is crucial. Endosonography has been established as the diagnostic gold standard. Although the identification of some of these lesions can be carried out immediately, solely based on their echo characteristics, for certain lesions histological examination is necessary. Sometimes histology can be inconclusive, especially for smaller lesions. Therefore, new methods have been developed in recent years to assist decision making, such as contrast enhanced endosonography, EUS elastography, and artificial intelligence systems. In this narrative review we provide a complete overview of the gastric SELs and summarize the new data of the last ten years concerning the diagnostic advances of endosonography on this topic.

Multi-Fold Computational Analysis to Discover Novel Putative Inhibitors of Isethionate Sulfite-Lyase (Isla) from Bilophila wadsworthia: Combating Colorectal Cancer and Inflammatory Bowel Diseases

A glycal radical enzyme called isethionate sulfite-lyase (Isla) breaks the C-S bond in isethionate to produce acetaldehyde and sulfite. This enzyme was found in the Gram-negative, colonial Bilophila wadsworthia bacteria. Sulfur dioxide, acetate, and ammonia are produced by the anaerobic respiration route from (sulfonate isethionate). Strong genotoxic H₂S damages the colon's mucous lining, which aids in the development of colorectal cancer. H₂S production also contributes to inflammatory bowel diseases such as colitis. Here, we describe the structure-based drug designing for the Isla using an in-house database of naturally isolated compounds and synthetic derivatives. In structure-based drug discovery, a combination of methods was used, including molecular docking, pharmacokinetics properties evaluation, binding free energy calculations by the molecular mechanics/generalized born surface area (MM/GBSA) method, and protein structure dynamics exploration via molecular dynamic simulations, to retrieve novel and putative inhibitors for the Isla protein. Based on the docking score, six compounds show significant binding interaction with the Isla active site crucial residues and exhibit drug-like features, good absorption, distribution, metabolism, and excretion profile with no toxicity. The binding free energy reveals that these compounds have a strong affinity with the Isla. In addition, the molecular dynamics simulations reveal that these compounds substantially affect the protein structure dynamics. As per our knowledge, this study is the first attempt to discover Isla potential inhibitors. The compounds proposed in the study using a multi-fold computational technique may be verified in vitro as possible inhibitors of Isla and possess the potential for the future development of new medications that target Isla.

Trends in research related to ophthalmic microperimetry from 1992 to 2022: A bibliometric analysis and knowledge graph study

Background

Microperimetry is a novel technology to assess macular function. The aim of the study was to explore the global research status and trends in microperimetry.

Methods

Documents related to microperimetry in ophthalmology from 1992 to 2022 were extracted from the Science Citation Index Expanded (SCI-E) database of the Web of Science Core Collection (WOSCC). Raw data were analyzed using the VOSviewer and CiteSpace software. Bibliometric parameters included annual publication quantity, countries, authors, journals, international cooperation, and keywords.

Results

A total of 1,217 peer-reviewed documents were retrieved. Annual research output has increased significantly since 2005, especially since 2013. Holz F, Rubin G, and Guymer R contributed most to the number of articles published about microperimetry. Rubin G, Fitzke F, and Holz F, respectively, received the most citations for their study. The countries publishing most were the USA, Italy, and the UK, while the USA, the UK, and Germany received the most citation frequency. Univ Bonn, UCL, and Moorfields Eye Hosp were the top three productive institutions for microperimetry research in the world. The top three journals that publish articles about microperimetry were Retina-The Journal of Retinal and Vitreous Diseases, Investigative Ophthalmology and Visual Science, and the American Journal of Ophthalmology. The top 10 common keywords included microperimetry, optical coherence tomography, eye, retinal sensitivity, macular degeneration, fundus autofluorescence, scanning laser ophthalmoscope, visual acuity, sensitivity, and degeneration. Keywords "optical coherence tomography angiography," "retinitis pigmentosa," and "internal limiting membrane" burst in the last 3 years.

Conclusion

The bibliometric and knowledge graph analysis of research status and trends in microperimetry provided global researchers with valuable information to propose future cooperation and track cutting-edge progress.

Predictive keywords: Using machine learning to explain document characteristics

When exploring the characteristics of a discourse domain associated with texts, keyword analysis is widely used in corpus linguistics. However, one of the challenges facing this method is the evaluation of the quality of the keywords. Here, we propose casting keyword analysis as a prediction problem with the goal of discriminating the texts associated with the target corpus from the reference corpus. We demonstrate that, when using linear support vector machines, this approach can be used not only to quantify the discrimination between the two corpora, but also extract keywords. To evaluate the keywords, we develop a systematic and rigorous approach anchored to the concepts of usefulness and relevance used in machine learning. The extracted keywords are compared with the recently proposed text dispersion keyness measure. We demonstrate that that our approach extracts keywords that are highly useful and linguistically relevant, capturing the characteristics of their discourse domain.

Title

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Trends in Research of Prenatal Stress From 2011 to 2021: A Bibliometric Study

BACKGROUND

Maternal stress during pregnancy can raise the risk of mental disorders in offspring. The continuous emergence of clinical concepts and the introduction of new technologies are great challenges. In this study, through bibliometric analysis, the research trends and hotspots on prenatal stress (PS) were explored to comprehend clinical treatments and recommend future scientific research directions.

METHODS

Studies on PS published on the Web of Science Core Collection (WoSCC) database between 2011 and 2021 were reviewed. Bibliometric analysis was conducted according to the number of publications, keywords, journals, citations, affiliations, and countries. With the data collected from the WoSCC, visualization of geographic distribution; clustering analysis of keywords, affiliations, and authors; and descriptive analysis and review of PS were carried out.

RESULTS

A total of 7,087 articles published in 2011-2021 were retrieved. During this period, the number of publications increased. Psychoneuroendocrinology is the leading journal on PS. The largest contributor was the United States. The University of California system was leading among institutions conducting relevant research. Wang H, King S, and Tain YL were scholars with significant contributions. Hotspots were classified into four clusters, namely, pregnancy, prenatal stress, oxidative stress, and growth.

CONCLUSION

The number of studies on PS increased. Journals, countries, institutions, researchers with the most contributions, and most cited articles worldwide were extracted. Studies have mostly concentrated on treating diseases, the application of new technologies, and the analysis of epidemiological characteristics. Multidisciplinary integration is becoming the focus of current development. Epigenetics is increasingly used in studies on PS. Thus, it constitutes a solid foundation for future clinical medical and scientific research.

Influence of Healthcare Organization Factors on Cardiovascular Diseases Mortality

One serious pandemic can nullify years of efforts to extend life expectancy and reduce disability. The coronavirus pandemic has been a perturbing factor that has provided an opportunity to assess not only the effectiveness of health systems for cardio-vascular diseases (CVD), but also their sustainability. The goal of our research is to analyze the influence of public health factors on the mortality from circulatory diseases using machine learning methods. We analysed a very large dataset that consisted of the information collected from the national registers in Russia. We included data from 2015 to 2021. It included 340 factors that characterize organization of healthcare in Russia. The resulting area under receiver operating characteristic curve (AUC of ROC) of the Random Forest based regression model was 92% with a testing dataset. The models allow for automated retraining as time passes and epidemiological and other situations change. They also allow additional characteristics of regions and health care organizations to be added to existing training datasets depending on the target. The developed models allow the calculation of the probability of the target for 6-12 months with an error of 8%. Moreover, the models allow to calculate scenarios and the value of the target indicator when other indicators of the region change.

Effective Training Data Extraction Method to Improve Influenza Outbreak Prediction from Online News Articles: Deep Learning Model Study

Background

Each year, influenza affects 3 to 5 million people and causes 290,000 to 650,000 fatalities worldwide. To reduce the fatalities caused by influenza, several countries have established influenza surveillance systems to collect early warning data. However, proper and timely warnings are hindered by a 1- to 2-week delay between the actual disease outbreaks and the publication of surveillance data. To address the issue, novel methods for influenza surveillance and prediction using real-time internet data (such as search queries, microblogging, and news) have been proposed. Some of the currently popular approaches extract online data and use machine learning to predict influenza occurrences in a classification mode. However, many of these methods extract training data subjectively, and it is difficult to capture the latent characteristics of the data correctly. There is a critical need to devise new approaches that focus on extracting training data by reflecting the latent characteristics of the data.

Objective

In this paper, we propose an effective method to extract training data in a manner that reflects the hidden features and improves the performance by filtering and selecting only the keywords related to influenza before the prediction.

Methods

Although word embedding provides a distributed representation of words by encoding the hidden relationships between various tokens, we enhanced the word embeddings by selecting keywords related to the influenza outbreak and sorting the extracted keywords using the Pearson correlation coefficient in order to solely keep the tokens with high correlation with the actual influenza outbreak. The keyword extraction process was followed by a predictive model based on long short-term memory that predicts the influenza outbreak. To assess the performance of the proposed predictive model, we used and compared a variety of word embedding techniques.

Results

Word embedding without our proposed sorting process showed 0.8705 prediction accuracy when 50.2 keywords were selected on average. Conversely, word embedding using our proposed sorting process showed 0.8868 prediction accuracy and an improvement in prediction accuracy of 12.6%, although smaller amounts of training data were selected, with only 20.6 keywords on average.

Conclusions

The sorting stage empowers the embedding process, which improves the feature extraction process because it acts as a knowledge base for the prediction component. The model outperformed other current approaches that use flat extraction before prediction.

Workplace Health in Kentucky: A Statewide Comparison

The objectives of this study were to assess the state of Kentucky’s workplace health promotion and occupational safety and health programs, to ensure the ability to comprehend any possible trends over the past six years in the state’s progress in offering workplace health promotion and health protection programs, to compare the results of this survey with the 2013 Kentucky state-wide assessment, and to identify gaps in Kentucky’s workplace health promotion and occupational safety and health based on Total Worker Health^® (TWH) concepts. Using Qualtrics research software, the Workplace Health in America assessment was sent to companies located in Kentucky and having 10 or more employees. Participants were identified using Dun and Bradstreet’s Hoover’s database. The results showed that, as with the 2013 survey, larger workplaces significantly were more likely to offer workplace health promotion programs than smaller companies (X² = 24.30; p < 0.001). However, more companies (78%) reported offering programs compared to the 2013 assessment (49%). Given the results of the current study as compared to the statewide assessment conducted in 2013, Kentucky’s WHP is moving in a positive direction; yet, there is still much to be done. There remains a strong need to provide cost-effective and accessible resources for all elements of TWH to small workplaces.

Title

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Speech-Based Activity Recognition for Trauma Resuscitation

We present a speech-based approach to recognize team activities in the context of trauma resuscitation. We first analyzed the audio recordings of trauma resuscitations in terms of activity frequency, noise-level, and activity-related keyword frequency to determine the dataset characteristics. We next evaluated different audio-preprocessing parameters (spectral feature types and audio channels) to find the optimal configuration. We then introduced a novel neural network to recognize the trauma activities using a modified VGG network that extracts features from the audio input. The output of the modified VGG network is combined with the output of a network that takes keyword text as input, and the combination is used to generate activity labels. We compared our system with several baselines and performed a detailed analysis of the performance results for specific activities. Our results show that our proposed architecture that uses Mel-spectrum spectral coefficients features with a stereo channel and activity-specific frequent keywords achieve the highest accuracy and average F1-score.

Analysis of Keywords Used in Internet Searches for Melanoma Information: Observational Study

Background

The internet is an accessible resource for health care information and is often used by patients to learn about melanoma. The keywords that are used in internet searches can reflect internet users' interest in specific topics and the public's awareness of health-related issues.

Objective

This study aims to describe the most frequently used keywords, questions, and corresponding websites in internet searches for melanoma.

Methods

This is an observational study using data retrieved from Google Trends, Alexa Internet, SEMrush, Ahrefs, and SE Ranking for the keywords "melanoma" and "skin cancer."

Results

Average search interest as per Google Trends was greater for the keyword "skin cancer" than for the keyword "melanoma." Searches for the top 25 keywords in 3 databases resulted in 34 unique melanoma keywords and 33 unique skin cancer keywords. Melanoma keywords were most frequently related to clinicopathologic classification (n=11, 32%), and skin cancer keywords were most frequently about diagnosis (n=14, 42%). Questions about the prognosis of melanoma appeared most frequently among the most popular melanoma questions, but general questions or questions about the diagnosis of melanoma contributed the greatest proportion of searches by search volume. Skin cancer question searches were most commonly about diagnosis. The highest proportion of searches for popular melanoma and skin cancer keywords most frequently sent traffic to websites from nonprofit organizations and media companies, respectively.

Conclusions

We identified common keywords, questions, and websites used to access information about melanoma on the internet. These data may help health care providers and public health professionals when educating and counseling patients and the public about skin cancer.

Title

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Alpelisib to treat breast cancer

Phosphatidylinositol 3-kinase (PI3K) catalytic subunit p110α (PIK3CA) mutations occur in approximately 40% of patients with hormone receptor-positive, human epidermal growth factor receptor 2 (HER2)-negative breast cancer. Alpelisib, a selective oral inhibitor of PI3K, with inhibitory activity predominantly against PIK3CA, has shown synergistic antitumor activity with endocrine therapy against hormone receptor-positive PIK3CA-mutated breast cancer cells in preclinical and early-phase clinical trials. The combination of alpelisib with fulvestrant or an aromatase inhibitor such as letrozole is safe and effective with reversible toxicities. Although clinical activity has been observed independently of PIK3CA mutation status, clinical improvement has been mostly seen in a higher proportion of patients with PIK3CA-mutated tumors. In this review I share current data on alpelisib in breast cancer treatment.

Unique features of high-density lipoproteins in the Japanese: in population and in genetic factors

Despite its gradual increase in the past several decades, the prevalence of atherosclerotic vascular disease is low in Japan. This is largely attributed to difference in lifestyle, especially food and dietary habits, and it may be reflected in certain clinical parameters. Plasma high-density lipoprotein (HDL) levels, a strong counter risk for atherosclerosis, are indeed high among the Japanese. Accordingly, lower HDL seems to contribute more to the development of coronary heart disease (CHD) than an increase in non-HDL lipoproteins at a population level in Japan. Interestingly, average HDL levels in Japan have increased further in the past two decades, and are markedly higher than in Western populations. The reasons and consequences for public health of this increase are still unknown. Simulation for the efficacy of raising HDL cholesterol predicts a decrease in CHD of 70% in Japan, greater than the extent by reducing low-density lipoprotein cholesterol predicted by simulation or achieved in a statin trial. On the other hand, a substantial portion of hyperalphalipoproteinemic population in Japan is accounted for by genetic deficiency of cholesteryl ester transfer protein (CETP), which is also commonly unique in East Asian populations. It is still controversial whether CETP mutations are antiatherogenic. Hepatic Schistosomiasis is proposed as a potential screening factor for historic accumulation of CETP deficiency in East Asia.

Evolving strategies for cancer and autoimmunity: back to the future

Although current thinking has focused on genetic variation between individuals and environmental influences as underpinning susceptibility to both autoimmunity and cancer, an alternative view is that human susceptibility to these diseases is a consequence of the way the immune system evolved. It is important to remember that the immunological genes that we inherit and the systems that they control were shaped by the drive for reproductive success rather than for individual survival. It is our view that human susceptibility to autoimmunity and cancer is the evolutionarily acceptable side effect of the immune adaptations that evolved in early placental mammals to accommodate a fundamental change in reproductive strategy. Studies of immune function in mammals show that high affinity antibodies and CD4 memory, along with its regulation, co-evolved with placentation. By dissection of the immunologically active genes and proteins that evolved to regulate this step change in the mammalian immune system, clues have emerged that may reveal ways of de-tuning both effector and regulatory arms of the immune system to abrogate autoimmune responses whilst preserving protection against infection. Paradoxically, it appears that such a detuned and deregulated immune system is much better equipped to mount anti-tumor immune responses against cancers.

Similar structures to the E-to-H helix unit in the globin-like fold are found in other helical folds

A protein in the globin-like fold contains six alpha-helices, A, B, E, F, G and H. Among them, the E-to-H helix unit (E, F, G and H helices) forms a compact structure. In this study, we searched similar structures to the E-to-H helix of leghomoglobin in the whole protein structure space using the Dali program. Several similar structures were found in other helical folds, such as KaiA/RbsU domain and Type III secretion system domain. These observations suggest that the E-to-H helix unit may be a common subunit in the whole protein 3D structure space. In addition, the common conserved hydrophobic residues were found among the similar structures to the E-to-H helix unit. Hydrophobic interactions between the conserved residues may stabilize the 3D structures of the unit. We also predicted the possible compact regions of the units using the average distance method.

Effect of combined high pressure and thermal treatment on myofibrillar proteins solubilization of beef muscle

The effects of high pressure (to 600 MPa) at different temperatures (20 to 60 °C) for 20 min on protein solubilization and electrophoretic pattern in beef post-rigor longissimus dorsi muscle were studied. The results showed that protein solubilization increased with increasing temperature, especially from 40 °C to 60 °C. A regular trend of protein solubilization was found when isolated myofibrils were subjected to high pressure at different temperatures, an increase was observed with increasing pressure up to about 400 MPa, solubility then decreasing to 600 MPa. Electrophoretic profiles showed that myosin light chains and actin thin filaments were sensitive to pressure, and were released from myofibrils subjected to 100 MPa and higher pressures at the different temperatures.

Information extraction from full text scientific articles: where are the keywords?

BACKGROUND

To date, many of the methods for information extraction of biological information from scientific articles are restricted to the abstract of the article. However, full text articles in electronic version, which offer larger sources of data, are currently available. Several questions arise as to whether the effort of scanning full text articles is worthy, or whether the information that can be extracted from the different sections of an article can be relevant.

RESULTS

In this work we addressed those questions showing that the keyword content of the different sections of a standard scientific article (abstract, introduction, methods, results, and discussion) is very heterogeneous.

CONCLUSIONS

Although the abstract contains the best ratio of keywords per total of words, other sections of the article may be a better source of biologically relevant data.