Type 2 diabetes (T2D) presents a formidable global health challenge, highlighted by its escalating prevalence, underscoring the critical need for precision health strategies and early detection ...initiatives. Leveraging artificial intelligence, particularly eXtreme Gradient Boosting (XGBoost), we devise robust risk assessment models for T2D. Drawing upon comprehensive genetic and medical imaging datasets from 68,911 individuals in the Taiwan Biobank, our models integrate Polygenic Risk Scores (PRS), Multi-image Risk Scores (MRS), and demographic variables, such as age, sex, and T2D family history. Here, we show that our model achieves an Area Under the Receiver Operating Curve (AUC) of 0.94, effectively identifying high-risk T2D subgroups. A streamlined model featuring eight key variables also maintains a high AUC of 0.939. This high accuracy for T2D risk assessment promises to catalyze early detection and preventive strategies. Moreover, we introduce an accessible online risk assessment tool for T2D, facilitating broader applicability and dissemination of our findings.
The authors show that a five-gene signature is closely associated with outcome among patients who have undergone surgical resection of early-stage non–small-cell lung cancer. This investigation ...represents the final phase of work to devise molecular methods for staging tumors and formulating a prognosis. For the findings to be clinically directive, these kinds of signatures will need to be incorporated into prospective clinical trials of cancer treatment.
The authors show that a five-gene signature is closely associated with outcome among patients who have undergone surgical resection of early-stage non–small-cell lung cancer.
Lung cancer — predominantly non–small-cell lung cancer (NSCLC) — is the most common cause of death from cancer worldwide.
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The relapse rate among patients with early-stage NSCLC is 40% within 5 years after potentially curative treatment.
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The current staging system for NSCLC is inadequate for predicting the outcome of treatment.
Gene-expression profiling (see Glossary) by means of microarrays
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and reverse-transcriptase polymerase chain reaction (RT-PCR)
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is useful for classifying tumors and formulating a prognosis for patients with various types of cancer,
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including lung cancer.
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The use of microarrays in clinical practice is limited, however, by the large . . .
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the causal agent of COVID 19, continues to evolve since its first emergence in December 2019. Using the complete sequences of 1,932 ...SARS-CoV-2 genomes, various clustering analyses consistently identified six types of the strains. Independent of the dendrogram construction, 13 signature variations in the form of single nucleotide variations (SNVs) in protein coding regions and one SNV in the 5′ untranslated region (UTR) were identified and provided a direct interpretation for the six types (types I to VI). The six types of the strains and their underlying signature SNVs were validated in two subsequent analyses of 6,228 and 38,248 SARS-CoV-2 genomes which became available later. To date, type VI, characterized by the four signature SNVs C241T (5′UTR), C3037T (nsp3 F924F), C14408T (nsp12 P4715L), and A23403G (Spike D614G), with strong allelic associations, has become the dominant type. Since C241T is in the 5′ UTR with uncertain significance and the characteristics can be captured by the other three strongly associated SNVs, we focus on the other three. The increasing frequency of the type VI haplotype 3037T-14408T-23403G in the majority of the submitted samples in various countries suggests a possible fitness gain conferred by the type VI signature SNVs. The fact that strains missing one or two of these signature SNVs fail to persist implies possible interactions among these SNVs. Later SNVs such as G28881A, G28882A, and G28883C have emerged with strong allelic associations, forming new subtypes. This study suggests that SNVs may become an important consideration in SARS-CoV-2 classification and surveillance.
Since brain tissue is not readily accessible, a new focus in search of biomarkers for schizophrenia is blood-based expression profiling of non-protein coding genes such as microRNAs (miRNAs), which ...regulate gene expression by inhibiting the translation of messenger RNAs. This study aimed to identify potential miRNA signature for schizophrenia by comparing genome-wide miRNA expression profiles in patients with schizophrenia vs. healthy controls. A genome-wide miRNA expression profiling was performed using a Taqman array of 365 human miRNAs in the mononuclear leukocytes of a learning set of 30 cases and 30 controls. The discriminating performance of potential biomarkers was validated in an independent testing set of 60 cases and 30 controls. The expression levels of the miRNA signature were then evaluated for their correlation with the patients' clinical symptoms, neurocognitive performances, and neurophysiological functions. A seven-miRNA signature (hsa-miR-34a, miR-449a, miR-564, miR-432, miR-548d, miR-572 and miR-652) was derived from a supervised classification with internal cross-validation, with an area under the curve (AUC) of receiver operating characteristics of 93%. The putative signature was then validated in the testing set, with an AUC of 85%. Among these miRNAs, miR-34a was differentially expressed between cases and controls in both the learning (P = 0.005) and the testing set (P = 0.002). These miRNAs were differentially correlated with patients' negative symptoms, neurocognitive performance scores, and event-related potentials. The results indicated that the mononuclear leukocyte-based miRNA profiling is a feasible way to identify biomarkers for schizophrenia, and the seven-miRNA signature warrants further investigation.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Examining PM_(2.5) (atmospheric particulate matter with a maximum diameter of 2.5 micrometers), seasonal patterns is an important research area for environmental scientists. An improved understanding ...of PM_(2.5) seasonal patterns can help environmental protection agencies (EPAs) make decisions and develop complex models for controlling the concentration of PM_(2.5) in different regions. This work proposes an R Shiny App web-based interactive tool, namely a ＂model-based time series clustering＂ (MTSC) tool, for clustering PM_(2.5) time series using spatial and population variables and their temporal features, like seasonality. Our tool allows stakeholders to visualize important characteristics of PM_(2.5) time series, including temporal patterns and missing values, and cluster series by attribute groupings. We apply the MTSC tool to cluster Taiwan's PM_(2.5) time series based on air quality zones and types of monitoring stations. The tool clusters the series into four clusters that reveal several phenomena, including an improvement in Taiwan's air quality since 2017 in all regions, although at varying rates, an increasing pattern of PM_(2.5) concentration when moving from northern towards southern regions, winter/summer seasonal patterns that are more pronounced in certain types of areas (e.g., industrial), and unusual behavior in the southernmost region. The tool provides cluster-specific quantitative figures, like seasonal variations in PM_(2.5) concentration in different air quality zones of Taiwan, and identifies, for example, an annual peak in early January and February (maximum value around 120 μg m^(-3)). Our analysis identifies a region in southernmost Taiwan as different from other zones that are currently grouped together with it by Taiwan EPA (TEPA), and a northern region that behaves differently from its TEPA grouping. All these cluster-based insights help EPA experts implement short-term zone-specific air quality policies (e.g., fireworks and traffic regulations, school closures) as well as longer-term decision-making (e.g., transport control stations, fuel permits, old vehicle replacement, fuel type).
Ultrasound imaging is a widely used technique for fatty liver diagnosis as it is practically affordable and can be quickly deployed by using suitable devices. When it is applied to a patient, ...multiple images of the targeted tissues are produced. We propose a machine learning model for fatty liver diagnosis from multiple ultrasound images. The machine learning model extracts features of the ultrasound images by using a pre-trained image encoder. It further produces a summary embedding on these features by using a graph neural network. The summary embedding is used as input for a classifier on fatty liver diagnosis. We train the machine learning model on a ultrasound image dataset collected by Taiwan Biobank. We also carry out risk control on the machine learning model using conformal prediction. Under the risk control procedure, the classifier can improve the results with high probabilistic guarantees.
•The Article’s Scientific Prestige (ASP) metric can measure articles’ impact.•Unlike other metrics, ASP considers direct and indirect citations.•Science, Biology, and Geography articles have the ...largest ASP.•Arts, Law & Policy, and Education articles have the smallest ASP.•Journal grade cannot accurately reflect articles’ scientific impact.
We performed a citation analysis on the Web of Science publications consisting of more than 63 million articles and over a billion citations on 254 subjects from 1981 to 2020. We proposed the Article’s Scientific Prestige (ASP) metric and compared this metric to number of citations (#Cit) and journal grade in measuring the scientific impact of individual articles in the large-scale hierarchical and multi-disciplined citation network. In contrast to #Cit, ASP, that is computed based on the eigenvector centrality, considers both direct and indirect citations, and provides steady-state evaluation cross different disciplines. We found that ASP and #Cit are not aligned for most articles, with a growing mismatch amongst the less cited articles. While both metrics are reliable for evaluating the prestige of articles such as Nobel Prize winning articles, ASP tends to provide more persuasive rankings than #Cit when the articles are not highly cited. The journal grade, that is eventually determined by a few highly cited articles, is unable to properly reflect the scientific impact of individual articles. The number of references and coauthors are less relevant to scientific impact, but subjects do make a difference.
Inflammation plays a critical role in cancer progression. In this study we investigate the pro-tumorigenic activities and gene expression profiles of lung cancer cells after interaction with ...macrophages.
We measured intratumoral microvessel counts and macrophage density in 41 lung cancer tumor specimens and correlated these with the patients' clinical outcome. The interaction between macrophages and cancer cell lines was assessed using a transwell coculture system. The invasive potential was evaluated by in vitro invasion assay. The matrix-degrading activity was assayed by gelatin zymography. The microarray was applied to a large-scale analysis of the genes involved in the interaction, as well as to monitor the gene expression profiles of lung cancer cells responding to anti-inflammatory drugs in cocultures.
The macrophage density positively correlated with microvessel counts and negatively correlated with patient relapse-free survival (P < .05). After coculture with macrophages, lung cancer cell lines exhibited higher invasive potentials and matrix-degrading activities. We identified 50 genes by microarray that were upregulated more than two-fold in cancer cells after coculture. Northern blot analyses confirmed some gene expression such as interleukin-6, interleukin-8, and matrix metalloproteinase 9. The two-dimensional hierarchical clustering also demonstrated that the gene expression profiles of lung cancer cells responding to various anti-inflammatory drugs in cocultures are distinct.
The interaction of lung cancer cells and macrophages can promote the invasiveness and matrix-degrading activity of cancer cells. Our results also suggest that a great diversity of gene expression occurs in this interaction, which may assist us in understanding the process of cancer metastasis.
Carbon nanotubes are a nanomaterial that is extensively used in industry. The potential health risk of chronic carbon nanotubes exposure has been raised as of great public concern. In the present ...study, we have demonstrated that intratracheal instillation of 0.5 mg of single-walled carbon nanotubes (SWCNT) into male ICR mice (8 weeks old) induced alveolar macrophage activation, various chronic inflammatory responses, and severe pulmonary granuloma formation. We then used Affymetrix microarrays to investigate the molecular effects on the macrophages when exposed to SWCNT. A biological pathway analysis, a literature survey, and experimental validation suggest that the uptake of SWCNT into the macrophages is able to activate various transcription factors such as nuclear factor κB (NF-κB) and activator protein 1 (AP-1), and this leads to oxidative stress, the release of proinflammatory cytokines, the recruitment of leukocytes, the induction of protective and antiapoptotic gene expression, and the activation of T cells. The resulting innate and adaptive immune responses may explain the chronic pulmonary inflammation and granuloma formation in vivo caused by SWCNT.
Case-control genetic association studies typically ignore possible later disease onset in currently healthy subjects and assume that subjects with diseases equally contribute to the likelihood for ...inference, regardless of their onset age. Therefore, we used an event-history with risk-free model to simultaneously characterize alcoholism susceptibility and onset age in 65 independent non-Hispanic Caucasian males in the Collaborative Study on the Genetics of Alcoholism. Following data quality control, we analysed 22 single nucleotide polymorphisms (SNPs) on 12 candidate genes. The single-SNP analysis showed that the dominant minor allele of rs2134655 on DRD3 increases alcoholism susceptibility; the dominant minor allele of rs1439047 on NTRK2 delays the alcoholism onset age, but the additive minor allele of rs172677 on GRIN2B and the dominant minor allele of rs63319 on ALDH1A1 advance the alcoholism onset age; and the dominant minor allele of rs1079597 on DRD2 shortens the onset age range. Similarly, multiple-SNPs analysis revealed joint effects of rs2134655, rs172677 and rs1079597, with an adjustment for habitual smoking. This study provides a more comprehensive understanding of the genetics of alcoholism than previous case-control studies.
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