Occurrence and risks of pharmaceuticals, personal care products, and endocrine-disrupting compounds in Chinese surface waters.

The continuous and rapid increase of chemical pollution in surface waters has become a pressing and widely recognized global concern. As emerging contaminants (ECs) in surface waters, pharmaceutical and personal care products (PPCPs), and endocrine-disrupting compounds (EDCs) have attracted considerable attention due to their wide occurrence and potential threat to human health. Therefore, a comprehensive understanding of the occurrence and risks of ECs in Chinese surface waters is urgently required. This study summarizes and assesses the environmental occurrence concentrations and ecological risks of 42 pharmaceuticals, 15 personal care products (PCPs), and 20 EDCs frequently detected in Chinese surface waters. The ECs were primarily detected in China's densely populated and highly industrialized regions. Most detected PPCPs and EDCs had concentrations between ng/L to µg/L, whereas norfloxacin, caffeine, and erythromycin had relatively high contamination levels, even exceeding 2000 ng/L. Risk evaluation based on the risk quotient method revealed that 34 PPCPs and EDCs in Chinese surface waters did not pose a significant risk, whereas 4-nonylphenol, 4-tert-octylphenol, 17α-ethinyl estradiol, 17ß-estradiol, and triclocarban did. This review provides a comprehensive summary of the occurrence and associated hazards of typical PPCPs and EDCs in Chinese surface waters over the past decade, and will aid in the regulation and control of these ECs in Chinese surface waters.

Target repositioning using multi-layer networks and machine learning: The case of prostate cancer.

The discovery of novel therapeutic targets, defined as proteins which drugs can interact with to induce therapeutic benefits, typically represent the first and most important step of drug discovery. One solution for target discovery is target repositioning, a strategy which relies on the repurposing of known targets for new diseases, leading to new treatments, less side effects and potential drug synergies. Biological networks have emerged as powerful tools for integrating heterogeneous data and facilitating the prediction of biological or therapeutic properties. Consequently, they are widely employed to predict new therapeutic targets by characterizing potential candidates, often based on their interactions within a Protein-Protein Interaction (PPI) network, and their proximity to genes associated with the disease. However, over-reliance on PPI networks and the assumption that potential targets are necessarily near known genes can introduce biases that may limit the effectiveness of these methods. This study addresses these limitations in two ways. First, by exploiting a multi-layer network which incorporates additional information such as gene regulation, metabolite interactions, metabolic pathways, and several disease signatures such as Differentially Expressed Genes, mutated genes, Copy Number Alteration, and structural variants. Second, by extracting relevant features from the network using several approaches including proximity to disease-associated genes, but also unbiased approaches such as propagation-based methods, topological metrics, and module detection algorithms. Using prostate cancer as a case study, the best features were identified and utilized to train machine learning algorithms to predict 5 novel promising therapeutic targets for prostate cancer: IGF2R, C5AR, RAB7, SETD2 and NPBWR1.

Pesticides in wastewater treatment plant effluents in the Yeongsan River Basin, Korea: Occurrence and environmental risk assessment.

Pesticides are among the main drivers posing risks to aquatic environments, with effluents from wastewater treatment plants (WWTPs) serving as a major source. This study aimed to identify the primary pesticides for which there was a risk of release into aquatic environments through WWTP effluents, thereby enabling more effective contamination management in public water bodies. In this study, monitoring, risk assessment, and risk-based prioritization of 87 pesticides in effluents from three WWTPs in the Yeongsan River Basin, Korea, were conducted. A total of 59 pesticides were detected at concentrations from 0.852 ng/L to 82.044 µg/L and exhibited variable patterns across different WWTP locations. An environmental risk assessment based on the risk quotient (RQ) of individual pesticides identified 13 substances implicated in significant ecotoxicological risks, as they exceeded RQ values of 1 at least once. An optimized risk (RQf)-based prioritization, considering the frequency of the measured environmental concentration (MEC) exceeding the predicted environmental concentration (PNEC), was conducted to identify pesticides that potentially posed risks and thus should be managed as a priority. Four pesticides had an RQf value >1; metribuzin exhibited the highest RQf value of 4.951, followed by 3-phenoxybenzoic acid, atrazin-2-hydroxy, and atrazine. Additionally, five pesticides (terbuthylazine, methabenzthiazuron, diuron, thiacloprid, and fipronil) and another four pesticides (propazine, imidacloprid, hexaconazole, and hexazione) had RQf values >0.1 and > 0.01, respectively. By calculating the contributions of individual pesticides to the RQf of these mixtures (RQf, mix) based on the concentration addition model, it was determined that >95 % of the sum of RQf, mix was driven by the top seven pesticides. These findings highlight the importance of prioritizing pesticides for effective management of contamination sources.

Non-target liquid chromatography high-resolution mass spectrometry screening to prioritize unregulated micropollutants that persist through domestic wastewater treatment.

Efforts to regulate and monitor emerging contaminants are insufficient because new chemicals are continually brought to market, and many are unregulated and potentially harmful. Domestic wastewater treatment plants are not designed to remove micropollutants and are important sources of emerging contaminants in the aquatic environment. In this study, non-target screening, an unbiased method for analyzing compounds without prior information, was used to identify compounds that may be emitted in wastewater treatment plant effluent and should be monitored. Nine wastewater treatment plants using different treatment methods were studied, and a non-target screening data-processing method was used. The frequencies at which the contaminants were detected and contaminant persistence through the treatment processes were considered, and then the contaminants were prioritized. The predicted no-effect concentration of each prioritized contaminant was used to determine whether further analysis and monitoring of the contaminant was necessary. Quantitative analyses of five compounds (amantadine, atenolol, benzotriazole, diphenhydramine, and sulpiride) were performed using reference standards. Probable molecular formulae and structures were proposed for 17 contaminants, and the risks posed by the contaminants were estimated using predicted no-effect concentrations. The results provide valuable insights into how unregulated micropollutants can be identified and prioritized for monitoring in future studies.

pVACview: an interactive visualization tool for efficient neoantigen prioritization and selection.

Background: Neoantigen targeting therapies including personalized vaccines have shown promise in the treatment of cancers, particularly when used in combination with checkpoint blockade therapy. At least 100 clinical trials involving these therapies are underway globally. Accurate identification and prioritization of neoantigens is highly relevant to designing these trials, predicting treatment response, and understanding mechanisms of resistance. With the advent of massively parallel DNA and RNA sequencing technologies, it is now possible to computationally predict neoantigens based on patient-specific variant information. However, numerous factors must be considered when prioritizing neoantigens for use in personalized therapies. Complexities such as alternative transcript annotations, various binding, presentation and immunogenicity prediction algorithms, and variable peptide lengths/registers all potentially impact the neoantigen selection process. There has been a rapid development of computational tools that attempt to account for these complexities. While these tools generate numerous algorithmic predictions for neoantigen characterization, results from these pipelines are difficult to navigate and require extensive knowledge of the underlying tools for accurate interpretation. This often leads to over-simplification of pipeline outputs to make them tractable, for example limiting prediction to a single RNA isoform or only summarizing the top ranked of many possible peptide candidates. In addition to variant detection, gene expression and predicted peptide binding affinities, recent studies have also demonstrated the importance of mutation location, allele-specific anchor locations, and variation of T-cell response to long versus short peptides. Due to the intricate nature and number of salient neoantigen features, presenting all relevant information to facilitate candidate selection for downstream applications is a difficult challenge that current tools fail to address. Results: We have created pVACview, the first interactive tool designed to aid in the prioritization and selection of neoantigen candidates for personalized neoantigen therapies including cancer vaccines. pVACview has a user-friendly and intuitive interface where users can upload, explore, select and export their neoantigen candidates. The tool allows users to visualize candidates across three different levels, including variant, transcript and peptide information. Conclusions: pVACview will allow researchers to analyze and prioritize neoantigen candidates with greater efficiency and accuracy in basic and translational settings The application is available as part of the pVACtools pipeline at pvactools.org and as an online server at pvacview.org.

A novel digital PCR assay for detection and comprehensive characterization of Molluscum contagiosum virus genotypes MOCV1, MOCV2, and MOCV3 and recombinant lineages.

Molluscum contagiosum virus (MOCV) is an important human pathogen causing a high disease burden worldwide. It is the last exclusively human-infecting poxvirus still circulating in its natural reservoir-a valuable model of poxviral evolution. Unfortunately, MOCV remains neglected, and little is known about its evolutionary history and circulating genomic variants, especially in non-privileged countries. The design weaknesses of available MOCV detection/genotyping assays surfaced with recent accumulation of abundant sequence information: all existing MOCV assays fail at accurate genotyping and capturing sub-genotype level diversity. Because complete MOCV genome characterization is an expensive and labor-intensive task, it makes sense to prioritize samples for whole-genome sequencing by diversity triage screening. To meet this demand, we developed a novel assay for accurate MOCV detection and genotyping, and comprehensive sub-genotype qualification to the level of phylogenetic groups (PGs). The assay included a novel set of oligonucleotide primers and probes, and it was implemented using digital polymerase chain reaction (dPCR). It offers sensitive, specific, and accurate detection, genotyping (MOCV1-MOCV3), and PG qualification (PG1-6) of MOCV DNA from clinical samples. The novel dPCR assay is suitable for MOCV diversity triage screening and prioritization of samples for complete MOCV genome characterization.

Enhancing nursing students' clinical competency using a multi-patient simulation learning model: A randomized controlled study.

BACKGROUND: For nurses, clinical competency is paramount in ensuring that patients receive safe, high-quality care. Multi-patient simulation (MPS) in nursing education is gaining attention, and evidence shows its suitability for real-life situations. MPS can be an effective solution for nurses' continuing clinical education. OBJECTIVES: This project compares the effectiveness of MPS (involving both a standardized patient and a high fidelity simulator) and a single high-fidelity simulation (single HFS; only involving a high fidelity simulator) for enhancing the clinical competency of nursing students. DESIGN: A stratified, permuted, block randomized controlled study design was used. SETTINGS AND PARTICIPANTS: Sixty undergraduate nursing students in years 3, 4, and 5 were selected to participate. Subgroups with each comprising three undergraduate nursing students from different years were formed. METHODS: The participants were randomized to receive either an MPS (intervention group) or single HFS (control group) for 1 day; they later received the same intervention after a 30-day washout period. One objectively measured questionnaire and two self-reported questionnaires were used to measure clinical competency: the Creighton Competency Evaluation Instrument (CCEI), Clinical Competence Questionnaire (CCQ), and Simulation Effectiveness Tool - Modified Questionnaire (SET-M). RESULTS: The results revealed significant between-group differences. Specifically, the intervention group showed greater improvement than the control group in both the CCQ (linear contrast [d] = 71.4; 95 % confidence interval [CI] = 53.407, 89.393; P < 0.001) and CCEI total scores (d = 7.17; 95 % CI = 5.837, 8.503; P < 0.001). The SET-M results indicated that 85 % of the participants (n = 51) strongly agreed that they felt more confident about performing a patient handover to the healthcare team after the simulation. CONCLUSIONS: The study findings indicated that both the MPS and single HFS effectively enhanced students' clinical competency. However, MPSs have superior educational outcomes relative to traditional single HFSs.

AmiCa: Atlas of miRNA-gene correlations in cancer.

The increasing availability of RNA sequencing data has opened up numerous opportunities to analyze various RNA interactions, including microRNA-target interactions (MTIs). In response to the necessity for a specialized tool to study MTIs in cancer and normal tissues, we developed AmiCa (https://amica.omics.si/), a web server designed for comprehensive analysis of mature microRNA (miRNA) and gene expression in 32 cancer types. Data from 9498 tumor samples and 626 normal samples from The Cancer Genome Atlas were obtained through the Genomic Data Commons and used to calculate differential expression and miRNA-target gene (MTI) correlations. AmiCa provides data on differential expression of miRNAs/genes for cancers for which normal tissue samples were available. In addition, the server calculates and presents correlations separately for tumor and normal samples for cancers for which normal samples are available. Furthermore, it enables the exploration of miRNA/gene expression in all cancer types with different miRNA/gene expression. In addition, AmiCa includes a ranking system for genes and miRNAs that can be used to identify those that are particularly highly expressed in certain cancers compared to other cancers, facilitating targeted and cancer-specific research. Finally, the functionality of AmiCa is illustrated by two case studies.

Conservation Action Tracker: A tool to identify and monitor conservation actions for tree species.

Premise: The GlobalTree Portal, hosted by Botanic Gardens Conservation International, provides access to information on the approximately 58,000 tree species worldwide. Included in the GlobalTree Portal is the Conservation Action Tracker, a dynamic and collaborative database to identify and monitor conservation actions for tree species globally. Methods: The Conservation Action Tracker collates conservation action information at the species level, including species recovery/action plans, ex situ collections, propagation protocols, in situ management, species protection policy, and education/awareness campaigns. Results: To date, the Conservation Action Tracker contains conservation action information for 4126 tree species, including 2161 threatened species, of which 659 are classified as Vulnerable, 783 as Endangered, and 719 as Critically Endangered. It covers conservation action information for at least one tree species in every country; however, more information is needed for 89% of Vulnerable, 87% of Endangered, and 77% of Critically Endangered tree species. Discussion: Monitoring species conservation actions can support the prioritization and scaling up of conservation practices by sharing knowledge, increasing collaboration, enabling the identification of conservation gaps, and making the information available to be used by decision-makers. Tracking conservation actions at the species level is, therefore, essential to guide future conservation efforts. Increasing the amount of data in the Conservation Action Tracker will improve the tool's ability to guide future conservation efforts and avoid the extinction of tree species.

Occurrence and Distribution of Antibacterial Quaternary Ammonium Compounds in Chinese Estuaries Revealed by Machine Learning-Assisted Mass Spectrometric Analysis.

Antimicrobial resistance (AMR) undermines the United Nations Sustainable Development Goals of good health and well-being. Antibiotics are known to exacerbate AMR, but nonantibiotic antimicrobials, such as quaternary ammonium compounds (QACs), are now emerging as another significant driver of AMR. However, assessing the AMR risks of QACs in complex environmental matrices remains challenging due to the ambiguity in their chemical structures and antibacterial activity. By machine learning prediction and high-resolution mass spectrometric analysis, a list of antibacterial QACs (n = 856) from industrial chemical inventories is compiled, and it leads to the identification of 50 structurally diverse antibacterial QACs in sediments, including traditional hydrocarbon-based compounds and new subclasses that bear additional functional groups, such as choline, ester, betaine, aryl ether, and pyridine. Urban wastewater, aquaculture, and hospital discharges are the main factors influencing QAC distribution patterns in estuarine sediments. Toxic unit calculations and metagenomic analysis revealed that these QACs can influence antibiotic resistance genes (particularly sulfonamide resistance genes) through cross- and coresistances. The potential to influence the AMR is related to their environmental persistence. These results suggest that controlling the source, preventing the co-use of QACs and sulfonamides, and prioritizing control of highly persistent molecules will lead to global stewardship and sustainable use of QACs.

From unstructured accident reports to a hybrid decision support system for occupational risk management: The consensus converging approach.

INTRODUCTION: Workplace accidents in the petroleum industry can cause catastrophic damage to people, property, and the environment. Earlier studies in this domain indicate that the majority of the accident report information is available in unstructured text format. Conventional techniques for the analysis of accident data are time-consuming and heavily dependent on experts' subject knowledge, experience, and judgment. There is a need to develop a machine learning-based decision support system to analyze the vast amounts of unstructured text data that are frequently overlooked due to a lack of appropriate methodology. METHOD: To address this gap in the literature, we propose a hybrid methodology that uses improved text-mining techniques combined with an un-bias group decision-making framework to combine the output of objective weights (based on text mining) and subjective weights (based on expert opinion) of risk factors to prioritize them. Based on the contextual word embedding models and term frequencies, we extracted five important clusters of risk factors comprising more than 32 risk sub-factors. A heterogeneous group of experts and employees in the petroleum industry were contacted to obtain their opinions on the extracted risk factors, and the best-worst method was used to convert their opinions to weights. CONCLUSIONS AND PRACTICAL APPLICATIONS: The applicability of our proposed framework was tested on the data compiled from the accident data released by the petroleum industries in India. Our framework can be extended to accident data from any industry, to reduce analysis time and improve the accuracy in classifying and prioritizing risk factors.

Identification and Prioritization of Organic Pollutants in Human Milk from the Yangtze River Delta, China.

Pollutants in human milk are critical for evaluating maternal internal exposure and infant external exposure. However, most studies have focused on a limited range of pollutants. Here, 15 pooled samples (prepared from 467 individual samples) of human milk from three areas of the Yangtze River Delta (YRD) in China were analyzed by gas chromatography quadrupole time-of-flight mass spectrometry. In total, 171 compounds of nine types were preliminarily identified. Among these, 16 compounds, including 2,5-di-tert-butylhydroquinone and 2-tert-butyl-1,4-benzoquinone, were detected in human milk for the first time. Partial least-squares discriminant analysis identified ten area-specific pollutants, including 2-naphthylamine, 9-fluorenone, 2-isopropylthianthrone, and benzo[a]pyrene, among pooled human milk samples from Shanghai (n = 3), Jiangsu Province (n = 6), and Zhejiang Province (n = 6). Risk index (RI) values were calculated and indicated that legacy polycyclic aromatic hydrocarbons (PAHs) contributed only 20% of the total RIs for the identified PAHs and derivatives, indicating that more attention should be paid to PAHs with various functional groups. Nine priority pollutants in human milk from the YRD were identified. The most important were 4-tert-amylphenol, caffeine, and 2,6-di-tert-butyl-p-benzoquinone, which are associated with apoptosis, oxidative stress, and other health hazards. The results improve our ability to assess the health risks posed by pollutants in human milk.

Genetic assessment of efficacy and safety profiles of coagulation cascade proteins identifies Factors II and XI as actionable anticoagulant targets.

Aims: Anticoagulants are routinely used by millions of patients worldwide to prevent blood clots. Yet, problems with anticoagulant therapy remain, including a persistent and cumulative bleeding risk in patients undergoing prolonged anticoagulation. New safer anticoagulant targets are needed. Methods and results: To prioritize anticoagulant targets with the strongest efficacy [venous thromboembolism (VTE) prevention] and safety (low bleeding risk) profiles, we performed two-sample Mendelian randomization and genetic colocalization. We leveraged three large-scale plasma protein data sets (deCODE as discovery data set and Fenland and Atherosclerosis Risk in Communities as replication data sets] and one liver gene expression data set (Institut Universitaire de Cardiologie et de Pneumologie de Québec bariatric biobank) to evaluate evidence for a causal effect of 26 coagulation cascade proteins on VTE from a new genome-wide association meta-analysis of 44 232 VTE cases and 847 152 controls, stroke subtypes, bleeding outcomes, and parental lifespan as an overall measure of efficacy/safety ratio. A 1 SD genetically predicted reduction in F2 blood levels was associated with lower risk of VTE [odds ratio (OR) = 0.44, 95% confidence interval (CI) = 0.38-0.51, P = 2.6e-28] and cardioembolic stroke risk (OR = 0.55, 95% CI = 0.39-0.76, P = 4.2e-04) but not with bleeding (OR = 1.13, 95% CI = 0.93-1.36, P = 2.2e-01). Genetically predicted F11 reduction was associated with lower risk of VTE (OR = 0.61, 95% CI = 0.58-0.64, P = 4.1e-85) and cardioembolic stroke (OR = 0.77, 95% CI = 0.69-0.86, P = 4.1e-06) but not with bleeding (OR = 1.01, 95% CI = 0.95-1.08, P = 7.5e-01). These Mendelian randomization associations were concordant across the three blood protein data sets and the hepatic gene expression data set as well as colocalization analyses. Conclusion: These results provide strong genetic evidence that F2 and F11 may represent safe and efficacious therapeutic targets to prevent VTE and cardioembolic strokes without substantially increasing bleeding risk.

Statistics to prioritize rare variants in family-based sequencing studies with disease subtypes.

Family-based sequencing studies are increasingly used to find rare genetic variants of high risk for disease traits with familial clustering. In some studies, families with multiple disease subtypes are collected and the exomes of affected relatives are sequenced for shared rare variants (RVs). Since different families can harbor different causal variants and each family harbors many RVs, tests to detect causal variants can have low power in this study design. Our goal is rather to prioritize shared variants for further investigation by, for example, pathway analyses or functional studies. The transmission-disequilibrium test prioritizes variants based on departures from Mendelian transmission in parent-child trios. Extending this idea to families, we propose methods to prioritize RVs shared in affected relatives with two disease subtypes, with one subtype more heritable than the other. Global approaches condition on a variant being observed in the study and assume a known probability of carrying a causal variant. In contrast, local approaches condition on a variant being observed in specific families to eliminate the carrier probability. Our simulation results indicate that global approaches are robust to misspecification of the carrier probability and prioritize more effectively than local approaches even when the carrier probability is misspecified.

Chemistry domain of applicability evaluation against existing estrogen receptor high-throughput assay-based activity models.

Introduction: The U. S. Environmental Protection Agency's Endocrine Disruptor Screening Program (EDSP) Tier 1 assays are used to screen for potential endocrine system-disrupting chemicals. A model integrating data from 16 high-throughput screening assays to predict estrogen receptor (ER) agonism has been proposed as an alternative to some low-throughput Tier 1 assays. Later work demonstrated that as few as four assays could replicate the ER agonism predictions from the full model with 98% sensitivity and 92% specificity. The current study utilized chemical clustering to illustrate the coverage of the EDSP Universe of Chemicals (UoC) tested in the existing ER pathway models and to investigate the utility of chemical clustering to evaluate the screening approach using an existing 4-assay model as a test case. Although the full original assay battery is no longer available, the demonstrated contribution of chemical clustering is broadly applicable to assay sets, chemical inventories, and models, and the data analysis used can also be applied to future evaluation of minimal assay models for consideration in screening. Methods: Chemical structures were collected for 6,947 substances via the CompTox Chemicals Dashboard from the over 10,000 UoC and grouped based on structural similarity, generating 826 chemical clusters. Of the 1,812 substances run in the original ER model, 1,730 substances had a single, clearly defined structure. The ER model chemicals with a clearly defined structure that were not present in the EDSP UoC were assigned to chemical clusters using a k-nearest neighbors approach, resulting in 557 EDSP UoC clusters containing at least one ER model chemical. Results and Discussion: Performance of an existing 4-assay model in comparison with the existing full ER agonist model was analyzed as related to chemical clustering. This was a case study, and a similar analysis can be performed with any subset model in which the same chemicals (or subset of chemicals) are screened. Of the 365 clusters containing >1 ER model chemical, 321 did not have any chemicals predicted to be agonists by the full ER agonist model. The best 4-assay subset ER agonist model disagreed with the full ER agonist model by predicting agonist activity for 122 chemicals from 91 of the 321 clusters. There were 44 clusters with at least two chemicals and at least one agonist based upon the full ER agonist model, which allowed accuracy predictions on a per-cluster basis. The accuracy of the best 4-assay subset ER agonist model ranged from 50% to 100% across these 44 clusters, with 32 clusters having accuracy ≥90%. Overall, the best 4-assay subset ER agonist model resulted in 122 false-positive and only 2 false-negative predictions compared with the full ER agonist model. Most false positives (89) were active in only two of the four assays, whereas all but 11 true positive chemicals were active in at least three assays. False positive chemicals also tended to have lower area under the curve (AUC) values, with 110 out of 122 false positives having an AUC value below 0.214, which is lower than 75% of the positives as predicted by the full ER agonist model. Many false positives demonstrated borderline activity. The median AUC value for the 122 false positives from the best 4-assay subset ER agonist model was 0.138, whereas the threshold for an active prediction is 0.1. Conclusion: Our results show that the existing 4-assay model performs well across a range of structurally diverse chemicals. Although this is a descriptive analysis of previous results, several concepts can be applied to any screening model used in the future. First, the clustering of the chemicals provides a means of ensuring that future screening evaluations consider the broad chemical space represented by the EDSP UoC. The clusters can also assist in prioritizing future chemicals for screening in specific clusters based on the activity of known chemicals in those clusters. The clustering approach can be useful in providing a framework to evaluate which portions of the EDSP UoC chemical space are reliably covered by in silico and in vitro approaches and where predictions from either method alone or both methods combined are most reliable. The lessons learned from this case study can be easily applied to future evaluations of model applicability and screening to evaluate future datasets.

Transcript and protein signatures derived from shared molecular interactions across cancers are associated with mortality.

BACKGROUND: Characterization of shared cancer mechanisms have been proposed to improve therapy strategies and prognosis. Here, we aimed to identify shared cell-cell interactions (CCIs) within the tumor microenvironment across multiple solid cancers and assess their association with cancer mortality. METHODS: CCIs of each cancer were identified by NicheNet analysis of single-cell RNA sequencing data from breast, colon, liver, lung, and ovarian cancers. These CCIs were used to construct a shared multi-cellular tumor model (shared-MCTM) representing common CCIs across cancers. A gene signature was identified from the shared-MCTM and tested on the mRNA and protein level in two large independent cohorts: The Cancer Genome Atlas (TCGA, 9185 tumor samples and 727 controls across 22 cancers) and UK biobank (UKBB, 10,384 cancer patients and 5063 controls with proteomics data across 17 cancers). Cox proportional hazards models were used to evaluate the association of the signature with 10-year all-cause mortality, including sex-specific analysis. RESULTS: A shared-MCTM was derived from five individual cancers. A shared gene signature was extracted from this shared-MCTM and the most prominent regulatory cell type, matrix cancer-associated fibroblast (mCAF). The signature exhibited significant expression changes in multiple cancers compared to controls at both mRNA and protein levels in two independent cohorts. Importantly, it was significantly associated with mortality in cancer patients in both cohorts. The highest hazard ratios were observed for brain cancer in TCGA (HR [95%CI] = 6.90[4.64-10.25]) and ovarian cancer in UKBB (5.53[2.08-8.80]). Sex-specific analysis revealed distinct risks, with a higher mortality risk associated with the protein signature score in males (2.41[1.97-2.96]) compared to females (1.84[1.44-2.37]). CONCLUSION: We identified a gene signature from a comprehensive shared-MCTM representing common CCIs across different cancers and revealed the regulatory role of mCAF in the tumor microenvironment. The pathogenic relevance of the gene signature was supported by differential expression and association with mortality on both mRNA and protein levels in two independent cohorts.

From genetic associations to genes: methods, applications, and challenges.

Genome-wide association studies (GWASs) have identified numerous genetic loci associated with human traits and diseases. However, pinpointing the causal genes remains a challenge, which impedes the translation of GWAS findings into biological insights and medical applications. In this review, we provide an in-depth overview of the methods and technologies used for prioritizing genes from GWAS loci, including gene-based association tests, integrative analysis of GWAS and molecular quantitative trait loci (xQTL) data, linking GWAS variants to target genes through enhancer-gene connection maps, and network-based prioritization. We also outline strategies for generating context-dependent xQTL data and their applications in gene prioritization. We further highlight the potential of gene prioritization in drug repurposing. Lastly, we discuss future challenges and opportunities in this field.

Occurrence, removal, and prioritization of organic micropollutants in four full-scale wastewater treatment plants in Korea.

This study investigated the occurrence, removal rate, and potential risks of 43 organic micropollutants (OMPs) in four municipal wastewater treatment plants (WWTPs) in Korea. Results from two-year intensive monitoring confirmed the presence of various OMPs in the influents, including pharmaceuticals such as acetaminophen (pain relief), caffeine (stimulants), cimetidine (H2-blockers), ibuprofen (non-steroidal anti-inflammatory drugs- NSAIDs), metformin (antidiabetics), and naproxen (NSAIDs) with median concentrations of >1 µg/L. Some pharmaceuticals (carbamazepine-anticonvulsants, diclofenac-NSAIDs, propranolol-ß-blockers), corrosion inhibitors (1H-benzotriazole-BTR, 4-methyl-1H-benzotriazole-4-TTR), and perfluorinated compounds (PFCs) were negligibly removed during WWTP treatment. The OMP concentrations in the influents and effluents were mostly lower in August than those of other months (p-value <0.05) possibly due to wastewater dilution by high precipitation or enhanced biodegradation under high-temperature conditions. The anaerobic-anoxic-oxic process (A2O) with a membrane bioreactor exhibited higher OMP removal than other processes, such as A2O with sedimentation or the conventional activated sludge process (p-value <0.05). Pesticides (DEET and atrazine), corrosion inhibitors (4-TTR and BTR), and metformin were selected as priority OMPs in toxicity-driven prioritization, whereas PFCs were determined as priority OMPs given their persistence and bioaccumulation properties. Overall, our results contribute to an important database on the occurrence, removal, and potential risks of OMPs in Korean WWTPs.

The robustness of phylogenetic diversity indices to extinctions.

Phylogenetic diversity indices provide a formal way to apportion evolutionary history amongst living species. Understanding the properties of these measures is key to determining their applicability in conservation biology settings. In this work, we investigate some questions posed in a recent paper by Fischer et al. (Syst Biol 72(3):606-615, 2023). In that paper, it is shown that under certain extinction scenarios, the ranking of the surviving species by their Fair Proportion index scores may be the complete reverse of their ranking beforehand. Our main results here show that this behaviour extends to a large class of phylogenetic diversity indices, including the Equal-Splits index. We also provide a necessary condition for reversals of Fair Proportion rankings to occur on phylogenetic trees whose edge lengths obey the ultrametric constraint. Specific examples of rooted phylogenetic trees displaying these behaviours are given and the impact of our results on the use of phylogenetic diversity indices more generally is discussed.

Do physicians' attitudes toward prioritization predict poor-health patients' access to care?

Physicians often face tight resource constraints, meaning they have to make trade-offs between which patients they care for and the amount of care received. Studies show that patients requiring many resources disproportionately suffer a loss of care when resources are constrained. This study uncovers whether physicians' attitudes toward prioritization of healthcare predicts poor-health patients' access to care. We combine unique survey data on Danish GPs' preferred prioritization principle with register data on their patients' contacts in general practice. We consider different types of contacts as the required effort could impact the need for prioritization. Our results show variation in GPs' prioritization principles, where a majority prefers a principle that may lead to an unequal distribution of services. We further find that GPs' attitudes toward prioritization predict some poor-health patients' access to general practice. GPs who state they prefer the principle of prioritizing patients in the poorest health state when resources tightened provide more contacts to poor-health patients. The additional contacts are typically high-effort contacts such as annual status meetings and home visits, but also low-effort contacts such as emails. Our findings indicate inequity in poor-health patients' access to care across general practices.