Gut Pathog. 2024 Jan 4;16(1):2. doi: 10.1186/s13099-023-00595-4.

ABSTRACT

BACKGROUND: The non-pharmaceutical interventions (NPIs) implemented to curb the spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) early in the coronavirus disease 2019 (COVID-19) pandemic, substantially disrupted the activity of other respiratory viruses. However, there is limited data from low-and-middle income countries (LMICs) to determine whether these NPIs also impacted the transmission of common enteric viruses. Here, we investigated the changes in the positivity rate of five enteric viruses among hospitalised children who presented with diarrhoea to a referral hospital in coastal Kenya, during COVID-19 pandemic period.

METHODS: A total of 870 stool samples from children under 13 years of age admitted to Kilifi County Hospital between January 2019, and December 2022 were screened for rotavirus group A (RVA), norovirus genogroup II (GII), astrovirus, sapovirus, and adenovirus type F40/41 using real-time reverse-transcription polymerase chain reaction. The proportions positive across the four years were compared using the chi-squared test statistic.

RESULTS: One or more of the five virus targets were detected in 282 (32.4%) cases. A reduction in the positivity rate of RVA cases was observed from 2019 (12.1%, 95% confidence interval (CI) 8.7-16.2%) to 2020 (1.7%, 95% CI 0.2-6.0%; p < 0.001). However, in the 2022, RVA positivity rate rebounded to 23.5% (95% CI 18.2%-29.4%). For norovirus GII, the positivity rate fluctuated over the four years with its highest positivity rate observed in 2020 (16.2%; 95% C.I, 10.0-24.1%). No astrovirus cases were detected in 2020 and 2021, but the positivity rate in 2022 was similar to that in 2019 (3.1% (95% CI 1.5%-5.7%) vs. 3.3% (95% CI 1.4-6.5%)). A higher case fatality rate was observed in 2021 (9.0%) compared to the 2019 (3.2%), 2020 (6.8%) and 2022 (2.1%) (p < 0.001).

CONCLUSION: Our study finds that in 2020 the transmission of common enteric viruses, especially RVA and astrovirus, in Kilifi Kenya may have been disrupted due to the COVID-19 NPIs. After 2020, local enteric virus transmission patterns appeared to return to pre-pandemic levels coinciding with the removal of most of the government COVID-19 NPIs.

PMID:38178245 | DOI:10.1186/s13099-023-00595-4

Diagn Pathol. 2024 Jan 4;19(1):4. doi: 10.1186/s13000-023-01414-9.

ABSTRACT

Ovarian cancer is the deadliest gynecologic cancer due to its high rate of recurrence and limited early diagnosis. For certain patients, particularly those with recurring disorders, standard treatment alone is insufficient in the majority of cases. Ferroptosis, an iron- and ROS (reactive oxygen species)-reliant cell death, plays a vital role in the occurrence of ovarian cancer. Herein, subjects from TCGA-OV were calculated for immune scores using the ESTIMATE algorithm and assigned into high- (N = 185) or low-immune (N = 193) score groups; 259 ferroptosis regulators and markers were analyzed for expression, and 64 were significantly differentially expressed between two groups. These 64 differentially expressed genes were applied for LASSO-regularized linear Cox regression for establishing ferroptosis regulators and a markers-based risk model, and a 10-gene signature was established. The ROC curve indicated that the risk score-based curve showed satisfactory predictive efficiency. Univariate and multivariate Cox risk regression analyses showed that age and risk score were risk factors for ovarian cancer patients' overall survival; patients in the high-risk score group obtained lower immune scores. The Nomogram analysis indicated that the model has a good prognostic performance. GO functional enrichment annotation confirmed again the involvement of these 10 genes in ferroptosis and immune activities. TIMER online analysis showed that risk factors and immune cells were significantly correlated. In conclusion, the risk model based on 10 ferroptosis regulators and markers has a good prognostic value for ovarian cancer patients.

PMID:38178187 | DOI:10.1186/s13000-023-01414-9

EMBO Rep. 2023 Dec 14. doi: 10.1038/s44319-023-00006-4. Online ahead of print.

ABSTRACT

Maternal mRNAs are essential for protein synthesis during oogenesis and early embryogenesis. To adapt translation to specific needs during development, maternal mRNAs are translationally repressed by shortening the polyA tails. While mRNA deadenylation is associated with decapping and degradation in somatic cells, maternal mRNAs with short polyA tails are stable. Here we report that the germline-specific eIF4E paralog, eIF4E1b, is essential for zebrafish oogenesis. eIF4E1b localizes to P-bodies in zebrafish embryos and binds to mRNAs with reported short or no polyA tails, including histone mRNAs. Loss of eIF4E1b results in reduced histone mRNA levels in early gonads, consistent with a role in mRNA storage. Using mouse and human eIF4E1Bs (in vitro) and zebrafish eIF4E1b (in vivo), we show that unlike canonical eIF4Es, eIF4E1b does not interact with eIF4G to initiate translation. Instead, eIF4E1b interacts with the translational repressor eIF4ENIF1, which is required for eIF4E1b localization to P-bodies. Our study is consistent with an important role of eIF4E1b in regulating mRNA dormancy and provides new insights into fundamental post-transcriptional regulatory principles governing early vertebrate development.

PMID:38177902 | DOI:10.1038/s44319-023-00006-4

Nat Comput Sci. 2023 May;3(5):403-417. doi: 10.1038/s43588-023-00453-y. Epub 2023 May 22.

ABSTRACT

Human diseases are traditionally studied as singular, independent entities, limiting researchers' capacity to view human illnesses as dependent states in a complex, homeostatic system. Here, using time-stamped clinical records of over 151 million unique Americans, we construct a disease representation as points in a continuous, high-dimensional space, where diseases with similar etiology and manifestations lie near one another. We use the UK Biobank cohort, with half a million participants, to perform a genome-wide association study of newly defined human quantitative traits reflecting individuals' health states, corresponding to patient positions in our disease space. We discover 116 genetic associations involving 108 genetic loci and then use ten disease constellations resulting from clustering analysis of diseases in the embedding space, as well as 30 common diseases, to demonstrate that these genetic associations can be used to robustly predict various morbidities.

PMID:38177845 | DOI:10.1038/s43588-023-00453-y

Nat Comput Sci. 2022 May;2(5):307-316. doi: 10.1038/s43588-022-00247-8. Epub 2022 May 23.

ABSTRACT

The close association between gut microbiota dysbiosis and human diseases is being increasingly recognized. However, contradictory results are frequently reported, as confounding effects exist. The lack of unbiased data integration methods is also impeding the discovery of disease-associated microbial biomarkers from different cohorts. Here we propose an algorithm, NetMoss, for assessing shifts of microbial network modules to identify robust biomarkers associated with various diseases. Compared to previous approaches, the NetMoss method shows better performance in removing batch effects. Through comprehensive evaluations on both simulated and real datasets, we demonstrate that NetMoss has great advantages in the identification of disease-related biomarkers. Based on analysis of pandisease microbiota studies, there is a high prevalence of multidisease-related bacteria in global populations. We believe that large-scale data integration will help in understanding the role of the microbiome from a more comprehensive perspective and that accurate biomarker identification will greatly promote microbiome-based medical diagnosis.

PMID:38177817 | DOI:10.1038/s43588-022-00247-8

Nat Struct Mol Biol. 2024 Jan 4. doi: 10.1038/s41594-023-01148-8. Online ahead of print.

ABSTRACT

Intrinsically disordered proteins and protein regions (IDPs) are prevalent in all proteomes and are essential to cellular function. Unlike folded proteins, IDPs exist in an ensemble of dissimilar conformations. Despite this structural plasticity, intramolecular interactions create sequence-specific structural biases that determine an IDP ensemble's three-dimensional shape. Such structural biases can be key to IDP function and are often measured in vitro, but whether those biases are preserved inside the cell is unclear. Here we show that structural biases in IDP ensembles found in vitro are recapitulated inside human-derived cells. We further reveal that structural biases can change in a sequence-dependent manner due to changes in the intracellular milieu, subcellular localization, and intramolecular interactions with tethered well-folded domains. We propose that the structural sensitivity of IDP ensembles can be leveraged for biological function, can be the underlying cause of IDP-driven pathology or can be used to design disorder-based biosensors and actuators.

PMID:38177684 | DOI:10.1038/s41594-023-01148-8

Nat Struct Mol Biol. 2024 Jan 4. doi: 10.1038/s41594-023-01141-1. Online ahead of print.

ABSTRACT

The cancer-specific fusion oncoprotein SS18-SSX1 disturbs chromatin accessibility by hijacking the BAF complex from the promoters and enhancers to the Polycomb-repressed chromatin regions. This process relies on the selective recognition of H2AK119Ub nucleosomes by synovial sarcoma X breakpoint 1 (SSX1). However, the mechanism underlying the selective recognition of H2AK119Ub nucleosomes by SSX1 in the absence of ubiquitin (Ub)-binding capacity remains unknown. Here we report the cryo-EM structure of SSX1 bound to H2AK119Ub nucleosomes at 3.1-Å resolution. Combined in vitro biochemical and cellular assays revealed that the Ub recognition by SSX1 is unique and depends on a cryptic basic groove formed by H3 and the Ub motif on the H2AK119 site. Moreover, this unorthodox binding mode of SSX1 induces DNA unwrapping at the entry/exit sites. Together, our results describe a unique mode of site-specific ubiquitinated nucleosome recognition that underlies the specific hijacking of the BAF complex to Polycomb regions by SS18-SSX1 in synovial sarcoma.

PMID:38177667 | DOI:10.1038/s41594-023-01141-1

Nat Comput Sci. 2023 Nov;3(11):946-956. doi: 10.1038/s43588-023-00544-w. Epub 2023 Nov 16.

ABSTRACT

Deep learning has become a popular tool to study cis-regulatory function. Yet efforts to design software for deep-learning analyses in regulatory genomics that are findable, accessible, interoperable and reusable (FAIR) have fallen short of fully meeting these criteria. Here we present elucidating the utility of genomic elements with neural nets (EUGENe), a FAIR toolkit for the analysis of genomic sequences with deep learning. EUGENe consists of a set of modules and subpackages for executing the key functionality of a genomics deep learning workflow: (1) extracting, transforming and loading sequence data from many common file formats; (2) instantiating, initializing and training diverse model architectures; and (3) evaluating and interpreting model behavior. We designed EUGENe as a simple, flexible and extensible interface for streamlining and customizing end-to-end deep-learning sequence analyses, and illustrate these principles through application of the toolkit to three predictive modeling tasks. We hope that EUGENe represents a springboard towards a collaborative ecosystem for deep-learning applications in genomics research.

PMID:38177592 | DOI:10.1038/s43588-023-00544-w

Nat Comput Sci. 2023 Jun;3(6):552-568. doi: 10.1038/s43588-023-00465-8. Epub 2023 Jun 26.

ABSTRACT

Graph representation learning methods opened new avenues for addressing complex, real-world problems represented by graphs. However, many graphs used in these applications comprise millions of nodes and billions of edges and are beyond the capabilities of current methods and software implementations. We present GRAPE (Graph Representation Learning, Prediction and Evaluation), a software resource for graph processing and embedding that is able to scale with big graphs by using specialized and smart data structures, algorithms, and a fast parallel implementation of random-walk-based methods. Compared with state-of-the-art software resources, GRAPE shows an improvement of orders of magnitude in empirical space and time complexity, as well as competitive edge- and node-label prediction performance. GRAPE comprises approximately 1.7 million well-documented lines of Python and Rust code and provides 69 node-embedding methods, 25 inference models, a collection of efficient graph-processing utilities, and over 80,000 graphs from the literature and other sources. Standardized interfaces allow a seamless integration of third-party libraries, while ready-to-use and modular pipelines permit an easy-to-use evaluation of graph-representation-learning methods, therefore also positioning GRAPE as a software resource that performs a fair comparison between methods and libraries for graph processing and embedding.

PMID:38177435 | DOI:10.1038/s43588-023-00465-8

Mol Syst Biol. 2023 Dec 19. doi: 10.1038/s44320-023-00003-8. Online ahead of print.

ABSTRACT

Although clinical applications represent the next challenge in single-cell genomics and digital pathology, we still lack computational methods to analyze single-cell or pathomics data to find sample-level trajectories or clusters associated with diseases. This remains challenging as single-cell/pathomics data are multi-scale, i.e., a sample is represented by clusters of cells/structures, and samples cannot be easily compared with each other. Here we propose PatIent Level analysis with Optimal Transport (PILOT). PILOT uses optimal transport to compute the Wasserstein distance between two individual single-cell samples. This allows us to perform unsupervised analysis at the sample level and uncover trajectories or cellular clusters associated with disease progression. We evaluate PILOT and competing approaches in single-cell genomics or pathomics studies involving various human diseases with up to 600 samples/patients and millions of cells or tissue structures. Our results demonstrate that PILOT detects disease-associated samples from large and complex single-cell or pathomics data. Moreover, PILOT provides a statistical approach to find changes in cell populations, gene expression, and tissue structures related to the trajectories or clusters supporting interpretation of predictions.

PMID:38177382 | DOI:10.1038/s44320-023-00003-8

Nat Microbiol. 2024 Jan 4. doi: 10.1038/s41564-023-01566-w. Online ahead of print.

NO ABSTRACT

PMID:38177300 | DOI:10.1038/s41564-023-01566-w

Nat Commun. 2024 Jan 4;15(1):276. doi: 10.1038/s41467-023-44409-2.

ABSTRACT

Dysfunctional extracellular matrices (ECM) contribute to aging and disease. Repairing dysfunctional ECM could potentially prevent age-related pathologies. Interventions promoting longevity also impact ECM gene expression. However, the role of ECM composition changes in healthy aging remains unclear. Here we perform proteomics and in-vivo monitoring to systematically investigate ECM composition (matreotype) during aging in C. elegans revealing three distinct collagen dynamics. Longevity interventions slow age-related collagen stiffening and prolong the expression of collagens that are turned over. These prolonged collagen dynamics are mediated by a mechanical feedback loop of hemidesmosome-containing structures that span from the exoskeletal ECM through the hypodermis, basement membrane ECM, to the muscles, coupling mechanical forces to adjust ECM gene expression and longevity via the transcriptional co-activator YAP-1 across tissues. Our results provide in-vivo evidence that coordinated ECM remodeling through mechanotransduction is required and sufficient to promote longevity, offering potential avenues for interventions targeting ECM dynamics.

PMID:38177158 | DOI:10.1038/s41467-023-44409-2

J Integr Neurosci. 2023 Nov 6;22(6):160. doi: 10.31083/j.jin2206160.

ABSTRACT

BACKGROUND: Population voltage imaging is used for studying brain physiology and brain circuits. Using a genetically encoded voltage indicator (GEVI), "VSFP" or "ASAP2s", or a voltage-sensitive dye, Di-4-Anepps, we conducted population voltage imaging in brain slices. The resulting optical signals, optical local field potentials (LFPs), were used to evaluate the performances of the 3 voltage indicators.

METHODS: In brain slices prepared from VSFP-transgenic or ASAP2s-transgenic mice, we performed multi-site optical imaging of evoked cortical depolarizations - compound excitatory postsynaptic potentials (cEPSPs). Optical signal amplitudes (ΔF/F) and cEPSP decay rates (OFF rates) were compared using analysis of variance (ANOVA) followed by unpaired Student's t test (31-104 data points per voltage indicator).

RESULTS: The ASAP2s signal amplitude (ΔF/F) was on average 3 times greater than Di-4-Anepps, and 7 times greater than VSFP. The optical cEPSP decay (OFF rate) was the slowest in Di-4-Anepps and fastest in ASAP2s. When ASAP2s expression was weak, we observed slow, label-free (autofluorescence, metabolic) optical signals mixed into the ASAP2s traces. Fast hyperpolarizations, that typically follow depolarizing cortical transients (afterhyperpolarizations), were prominent in ASAP2s but not present in the VSFP and Di-4-Anepps experiments.

CONCLUSIONS: Experimental applications for ASAP2s may potentially include systems neuroscience studies that require voltage indicators with large signal amplitude (ΔF/F), fast decay times (fast response time is needed for monitoring high frequency brain oscillations), and/or detection of brain patches in transiently hyperpolarized states (afterhyperpolarization).

PMID:38176939 | DOI:10.31083/j.jin2206160

Science. 2024 Jan 5;383(6678):24-26. doi: 10.1126/science.adn4211. Epub 2024 Jan 4.

NO ABSTRACT

PMID:38175880 | DOI:10.1126/science.adn4211

Bioinformatics. 2024 Jan 4:btad784. doi: 10.1093/bioinformatics/btad784. Online ahead of print.

ABSTRACT

MOTIVATION: Ultra-multiplexed fluorescence imaging has revolutionized our understanding of biological systems, enabling the simultaneous visualization and quantification of multiple targets within biological specimens. A recent breakthrough in this field is PICASSO, a mutual-information-based technique capable of demixing up to 15 fluorophores without their spectra, thereby significantly simplifying the application of ultra-multiplexed fluorescence imaging. However, this study has identified a limitation of mutual information-based techniques. They do not differentiate between spatial colocalization and spectral mixing. Consequently, mutual information-based demixing may incorrectly interpret spatially co-localized targets as non-colocalized, leading to overcorrection.

RESULTS: We found that selecting regions within a multiplex image with low spatial similarity for measuring spectroscopic mixing results in more accurate demixing. This method effectively minimizes overcorrections and promises to accelerate the broader adoption of ultra-multiplex imaging.

AVAILABILITY: The codes are available at https://github.com/xing-lab-pitt/mosaic-picasso.

SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

PMID:38175779 | DOI:10.1093/bioinformatics/btad784

Cell Rep. 2024 Jan 3;43(1):113657. doi: 10.1016/j.celrep.2023.113657. Online ahead of print.

ABSTRACT

Environmental factors influence an organism's reproductive ability by regulating germline development and physiology. While the reproductive adaptations in response to extrinsic stress cues offer fitness and survival advantages to individuals, the mechanistic understanding of these modifications remains unclear. Here, we find that parasitoid wasps' stress signaling regulates Drosophila melanogaster oogenesis. We show that fruit flies dwelling in the wasp-infested area elevate their fecundity, and the observed reproductive response is specific to Pachycrepoideus sp., a pupal parasitoid wasp. Pachycrepoideus-specific olfactory and visual cues recruit the signaling pathways that promote germline stem cell proliferation and accelerate follicle development, increasing egg production in Drosophila females. Downregulation of signaling engaged in oocyte development by shifting flies to a non-wasp-infested environment increases apoptosis of the developing follicles. Thus, this study establishes host germline responsiveness to parasitoid-specific signals and supports a predator strategy to increase hosts for infection.

PMID:38175752 | DOI:10.1016/j.celrep.2023.113657

STAR Protoc. 2024 Jan 2;5(1):102806. doi: 10.1016/j.xpro.2023.102806. Online ahead of print.

ABSTRACT

Whole-exome sequencing (WES) is a major approach to uncovering gene-disease associations and pinpointing effector genes. Here, we present a protocol for estimating genetic associations of rare and common variants in large-scale case-control WES studies using MAGICpipeline, an open-access analysis pipeline. We describe steps for assessing gene-based rare-variant association analyses by incorporating multiple variant pathogenic annotations and statistical techniques. We then detail procedures for identifying disease-related modules and hub genes using weighted correlation network analysis, a systems biology approach. For complete details on the use and execution of this protocol, please refer to Su et al. (2023).1.

PMID:38175747 | DOI:10.1016/j.xpro.2023.102806

Appl Microbiol Biotechnol. 2024 Dec;108(1):1-16. doi: 10.1007/s00253-023-12977-4. Epub 2024 Jan 4.

ABSTRACT

The discovery and characterization of bacterial carbohydrate-active enzymes is a fundamental component of biotechnology innovation, particularly for renewable fuels and chemicals; however, these studies have increasingly transitioned to exploring the complex regulation required for recalcitrant polysaccharide utilization. This pivot is largely due to the current need to engineer and optimize enzymes for maximal degradation in industrial or biomedical applications. Given the structural simplicity of a single cellulose polymer, and the relatively few enzyme classes required for complete bioconversion, the regulation of cellulases in bacteria has been thoroughly discussed in the literature. However, the diversity of hemicelluloses found in plant biomass and the multitude of carbohydrate-active enzymes required for their deconstruction has resulted in a less comprehensive understanding of bacterial hemicellulase-encoding gene regulation. Here we review the mechanisms of this process and common themes found in the transcriptomic response during plant biomass utilization. By comparing regulatory systems from both Gram-negative and Gram-positive bacteria, as well as drawing parallels to cellulase regulation, our goals are to highlight the shared and distinct features of bacterial hemicellulase-encoding gene regulation and provide a set of guiding questions to improve our understanding of bacterial lignocellulose utilization. KEY POINTS: • Canonical regulatory mechanisms for bacterial hemicellulase-encoding gene expression include hybrid two-component systems (HTCS), extracytoplasmic function (ECF)-σ/anti-σ systems, and carbon catabolite repression (CCR). • Current transcriptomic approaches are increasingly being used to identify hemicellulase-encoding gene regulatory patterns coupled with computational predictions for transcriptional regulators. • Future work should emphasize genetic approaches to improve systems biology tools available for model bacterial systems and emerging microbes with biotechnology potential. Specifically, optimization of Gram-positive systems will require integration of degradative and fermentative capabilities, while optimization of Gram-negative systems will require bolstering the potency of lignocellulolytic capabilities.

PMID:38175245 | DOI:10.1007/s00253-023-12977-4

iScience. 2023 Nov 29;27(1):108593. doi: 10.1016/j.isci.2023.108593. eCollection 2024 Jan 19.

ABSTRACT

Gene expression change is a dominant mode of evolution. Mutations, however, can affect gene expression in multiple cell types. Therefore, gene expression evolution in one cell type can lead to similar gene expression changes in another cell type. Here, we test this hypothesis by investigating dermal skin fibroblasts (SFs) and uterine endometrial stromal fibroblasts (ESFs). The comparative dataset consists of transcriptomes from cultured SF and ESF of nine mammalian species. We find that evolutionary changes in gene expression in SF and ESF are highly correlated. The experimental dataset derives from a SCID mouse strain selected for slow cancer growth leading to substantial gene expression changes in SFs. We compared the gene expression profiles of SF with that of ESF and found a significant correlation between them. We discuss the implications of these findings for the evolutionary correlation between placental invasiveness and vulnerability to metastatic cancer.

PMID:38174318 | PMC:PMC10762354 | DOI:10.1016/j.isci.2023.108593

World J Gastrointest Oncol. 2023 Dec 15;15(12):2169-2184. doi: 10.4251/wjgo.v15.i12.2169.

ABSTRACT

BACKGROUND: Gastroesophageal reflux disease (GERD) affects approximately 13% of the global population. However, the pathogenesis of GERD has not been fully elucidated. The development of metabolomics as a branch of systems biology in recent years has opened up new avenues for the investigation of disease processes. As a powerful statistical tool, Mendelian randomization (MR) is widely used to explore the causal relationship between exposure and outcome.

AIM: To analyze of the relationship between 486 blood metabolites and GERD.

METHODS: Two-sample MR analysis was used to assess the causal relationship between blood metabolites and GERD. A genome-wide association study (GWAS) of 486 metabolites was the exposure, and two different GWAS datasets of GERD were used as endpoints for the base analysis and replication and meta-analysis. Bonferroni correction is used to determine causal correlation features (P < 1.03 × 10-4). The results were subjected to sensitivity analysis to assess heterogeneity and pleiotropy. Using the MR Steiger filtration method to detect whether there is a reverse causal relationship between metabolites and GERD. In addition, metabolic pathway analysis was conducted using the online database based MetaboAnalyst 5.0 software.

RESULTS: In MR analysis, four blood metabolites are negatively correlated with GERD: Levulinate (4-oxovalerate), stearate (18:0), adrenate (22:4n6) and p-acetamidophenylglucuronide. However, we also found a positive correlation between four blood metabolites and GERD: Kynurenine, 1-linoleoylglycerophosphoethanolamine, butyrylcarnitine and guanosine. And bonferroni correction showed that butyrylcarnitine (odd ratio 1.10, 95% confidence interval: 1.05-1.16, P = 7.71 × 10-5) was the most reliable causal metabolite. In addition, one significant pathways, the "glycerophospholipid metabolism" pathway, can be involved in the pathogenesis of GERD.

CONCLUSION: Our study found through the integration of genomics and metabolomics that butyrylcarnitine may be a potential biomarker for GERD, which will help further elucidate the pathogenesis of GERD and better guide its treatment. At the same time, this also contributes to early screening and prevention of GERD. However, the results of this study require further confirmation from both basic and clinical real-world studies.

PMID:38173433 | PMC:PMC10758654 | DOI:10.4251/wjgo.v15.i12.2169