Chemotoxicity and Associated Risk Factors in Colorectal Cancer: A Systematic Review and Meta-Analysis.

BACKGROUND: Colorectal cancer (CRC) patients experience multiple types of chemotoxicity affecting treatment compliance, survival, and quality of life (QOL). Prior research shows clinician-reported chemotoxicity (i.e., grading scales or diagnostic codes) predicts rehospitalization and cancer survival. However, a comprehensive synthesis of clinician-reported chemotoxicity is still lacking. OBJECTIVES: We conducted a systematic review and meta-analysis to determine chemotoxicity's prevalence and risk factors in CRC. METHODS: A systematic search from 2009 to 2024 yielded 30 studies for review, with 25 included in the meta-analysis. RESULTS: Pooled prevalences of overall, non-hematological, and hematological moderate-to-severe toxicities were 45.7%, 39.2%, and 25.3%, respectively. The most common clinician-reported chemotoxicities were gastrointestinal (GI) toxicity (22.9%) and neuropathy or neutropenia (17.9%). Significant risk factors at baseline were malnutritional status, frailty, impaired immune or hepato-renal functions, short telomere lengths, low gut lactobacillus levels, age, female sex, aggressive chemotherapy, and low QOL. Age was associated with neutropenia (ß: -1.44) and GI toxicity (ß:1.85) (p-values < 0.01). Older adults (>65 y.o.) had higher prevalences of overall (OR: 1.14) and GI (OR: 1.65) toxicities, but a lower prevalence of neutropenia (OR: 0.65) than younger adults (p-values < 0.05). CONCLUSIONS: Our findings highlight the importance of closely monitoring and managing chemotoxicity in CRC patients receiving chemotherapy.

No genetic link between Parkinson's disease and SARS-CoV-2 infection: a two-sample Mendelian randomization study.

Objective: Existing literature has not clearly elucidated whether SARS-CoV-2 infection increases the incidence of Parkinson's disease or if Parkinson's disease patients are more susceptible to the effects of SARS-CoV-2 infection. To clarify the issue, this study employs a genetic epidemiological approach to investigate the association. Methods: This study utilizes a two-sample Mendelian randomization analysis. The primary analysis employs the inverse variance-weighted (IVW) method, supplemented by secondary analyses including MR-Egger regression, weighted median, IVW radial method, and weighted mode, to evaluate the bidirectional causal relationship between Parkinson's disease and SARS-CoV-2 infection. Results: IVW results showed no genetic causality between SARS-CoV-2 susceptibility, hospitalization rate and severity and Parkinson's disease. (IVW method: p = 0.408 OR = 1.10 95% CI: 0.87 ~ 1.39; p = 0.744 OR = 1.11 95% CI: 0.94 ~ 1.09; p = 0.436 OR = 1.05 95% CI: 0.93 ~ 1.17). Parkinson's disease was not genetically associated with susceptibility to new crown infections, hospitalization rates, and severity (IVW method: p = 0.173 OR = 1.01 95% CI: 0.99 ~ 1.03; p = 0.109 OR = 1.05 95% CI: 0.99 ~ 1.12; p = 0.209 OR = 1.03 95% CI: 0.99 ~ 1.07). MR-Egger regression, weighted median, IVW radial method, and weighted mode results are consistent with the results of the IVW method. Conclusion: This study does not support a genetic link between Parkinson's disease and SARS-CoV-2 infection, and the association observed in previous cohort studies and observational studies may be due to other confounding factors.

Using Social Media to Understand Primary Discussions in Gastrointestinal Cancers: Machine Learning Approach.

BACKGROUND: The incidence and mortality rates of gastrointestinal (GI) cancers are high in the United States as well as worldwide. The widespread use of social media provides unique opportunities to facilitate the dissemination of information, especially in the context of health. OBJECTIVE: We aim to characterize the public's primary discussions, including perceptions, concerns, and interests toward GI cancers, from prevention, diagnosis, and treatment to survivorship care through the social media platform Twitter, using tweets posted by Twitter users. METHODS: We analyzed 87 860 Twitter posts related to GI cancers. We used machine learning with natural language processing to identify salient topics and themes in the collected tweets. RESULTS: The most common themes across all GI cancer types included cancer risk prevention and awareness outreach programs, risk factors including lifestyles (primarily diet), and cancer survivorship-related discussions (primarily GI symptoms and quality of life). GI symptom-related tweets were prevalent in patients with colorectal and stomach cancers, whereas themes of newer clinical trials, end-of-life trials, palliative care trials, and disease prognosis were common in tweets related to liver/biliary and pancreatic cancers. CONCLUSIONS: Our research emphasizes the importance of individualized approaches in managing GI cancers, considering lifestyle and diet, the need for comprehensive survivorship care, raising awareness, delivering information, and improving targeted interventions related to GI cancers. IMPLICATIONS FOR PRACTICE: Our study suggests utilizing Twitter data to better understand the real-world interest and concerns about GI cancers among the public, which can guide future patient-centered research in this field.

Multiomics Analyses Explore the Immunometabolic Interplay in the Liver of White Crucian Carp (Carassius cuvieri) After Aeromonas veronii Challenge.

Aeromonas veronii is one of the predominant pathogenic species that can imperil the survival of farmed fish. However, the interactive networks of immune regulation and metabolic response in A. veronii-infected fish are still unclear. In this investigation, we aimed to explore immunometabolic interplay in white crucian carp (WCC) after the A. veronii challenge. Elevated levels of immune-related genes were observed in various tissues after A. veronii infection, along with the sharp alteration of disease-related enzymatic activities. Besides, decreased levels of antioxidant status were observed in the liver, but most metabolic gene expressions increased dramatically. Multiomics analyses revealed that metabolic products of amino acids, such as formiminoglutamic acid (FIGLU), L-glutamate (L-Glu), and 4-hydroxyhippuric acid, were considered the crucial liver biomarkers in A. veronii-infected WCC. In addition, A. veronii infection may dysregulate endoplasmic reticulum (ER) function to affect the metabolic process of lipids, carbohydrates, and amino acids in the liver of WCC. These results may have a comprehensive implication for understanding immunometabolic response in WCC upon A. veronii infection.

Enhancing molecular property prediction with auxiliary learning and task-specific adaptation.

Pretrained Graph Neural Networks have been widely adopted for various molecular property prediction tasks. Despite their ability to encode structural and relational features of molecules, traditional fine-tuning of such pretrained GNNs on the target task can lead to poor generalization. To address this, we explore the adaptation of pretrained GNNs to the target task by jointly training them with multiple auxiliary tasks. This could enable the GNNs to learn both general and task-specific features, which may benefit the target task. However, a major challenge is to determine the relatedness of auxiliary tasks with the target task. To address this, we investigate multiple strategies to measure the relevance of auxiliary tasks and integrate such tasks by adaptively combining task gradients or by learning task weights via bi-level optimization. Additionally, we propose a novel gradient surgery-based approach, Rotation of Conflicting Gradients ( RCGrad ), that learns to align conflicting auxiliary task gradients through rotation. Our experiments with state-of-the-art pretrained GNNs demonstrate the efficacy of our proposed methods, with improvements of up to 7.7% over fine-tuning. This suggests that incorporating auxiliary tasks along with target task fine-tuning can be an effective way to improve the generalizability of pretrained GNNs for molecular property prediction.Scientific contributionWe introduce a novel framework for adapting pretrained GNNs to molecular tasks using auxiliary learning to address the critical issue of negative transfer. Leveraging novel gradient surgery techniques such as RCGrad , the proposed adaptation framework represents a significant departure from the dominant pretraining fine-tuning approach for molecular GNNs. Our contributions are significant for drug discovery research, especially for tasks with limited data, filling a notable gap in the efficient adaptation of pretrained models for molecular GNNs.

Deep autoencoder as an interpretable tool for Raman spectroscopy investigation of chemical and extracellular vesicle mixtures.

Surface-enhanced Raman spectroscopy (SERS) is a powerful tool that provides valuable insight into the molecular contents of chemical and biological samples. However, interpreting Raman spectra from complex or dynamic datasets remains challenging, particularly for highly heterogeneous biological samples like extracellular vesicles (EVs). To overcome this, we developed a tunable and interpretable deep autoencoder for the analysis of several challenging Raman spectroscopy applications, including synthetic datasets, chemical mixtures, a chemical milling reaction, and mixtures of EVs. We compared the results with classical methods (PCA and UMAP) to demonstrate the superior performance of the proposed technique. Our method can handle small datasets, provide a high degree of generalization such that it can fill unknown gaps within spectral datasets, and even quantify relative ratios of cell line-derived EVs to fetal bovine serum-derived EVs within mixtures. This simple yet robust approach will greatly improve the analysis capabilities for many other Raman spectroscopy applications.

Multivariate mediation analysis with voxel-based morphometry revealed the neurodegeneration pathways from genetic variants to Alzheimer's Disease.

Neurodegenerative processes are increasingly recognized as potential causative factors in Alzheimer's disease (AD) pathogenesis. While many studies have leveraged mediation analysis models to elucidate the underlying mechanisms linking genetic variants to AD diagnostic outcomes, the majority have predominantly focused on regional brain measure as a mediator, thereby compromising the granularity of the imaging data. In our investigation, using the imaging genetics data from a landmark AD cohort, we contrasted both region-based and voxel-based brain measurements as imaging endophenotypes, and examined their roles in mediating genetic effects on AD outcomes. Our findings underscored that using voxel-based morphometry offers enhanced statistical power. Moreover, we delineated specific mediation pathways between SNP, brain volume, and AD outcomes, shedding light on the intricate relationship among these variables.

Enhancing drug and cell line representations via contrastive learning for improved anti-cancer drug prioritization.

Due to cancer's complex nature and variable response to therapy, precision oncology informed by omics sequence analysis has become the current standard of care. However, the amount of data produced for each patient makes it difficult to quickly identify the best treatment regimen. Moreover, limited data availability has hindered computational methods' abilities to learn patterns associated with effective drug-cell line pairs. In this work, we propose the use of contrastive learning to improve learned drug and cell line representations by preserving relationship structures associated with drug mechanisms of action and cell line cancer types. In addition to achieving enhanced performance relative to a state-of-the-art method, we find that classifiers using our learned representations exhibit a more balanced reliance on drug- and cell line-derived features when making predictions. This facilitates more personalized drug prioritizations that are informed by signals related to drug resistance.

Improving Anticancer Drug Selection and Prioritization via Neural Learning to Rank.

Personalized cancer treatment requires a thorough understanding of complex interactions between drugs and cancer cell lines in varying genetic and molecular contexts. To address this, high-throughput screening has been used to generate large-scale drug response data, facilitating data-driven computational models. Such models can capture complex drug-cell line interactions across various contexts in a fully data-driven manner. However, accurately prioritizing the most effective drugs for each cell line still remains a significant challenge. To address this, we developed multiple neural ranking approaches that leverage large-scale drug response data across multiple cell lines from diverse cancer types. Unlike existing approaches that primarily utilize regression and classification techniques for drug response prediction, we formulated the objective of drug selection and prioritization as a drug ranking problem. In this work, we proposed multiple pairwise and listwise neural ranking methods that learn latent representations of drugs and cell lines and then use those representations to score drugs in each cell line via a learnable scoring function. Specifically, we developed neural pairwise and listwise ranking methods, Pair-PushC and List-One on top of the existing methods, pLETORg and ListNet, respectively. Additionally, we proposed a novel listwise ranking method, List-All, that focuses on all the effective drugs instead of the top effective drug, unlike List-One. We also provide an exhaustive empirical evaluation with state-of-the-art regression and ranking baselines on large-scale data sets across multiple experimental settings. Our results demonstrate that our proposed ranking methods mostly outperform the best baselines with significant improvements of as much as 25.6% in terms of selecting truly effective drugs within the top 20 predicted drugs (i.e., hit@20) across 50% test cell lines. Furthermore, our analyses suggest that the learned latent spaces from our proposed methods demonstrate informative clustering structures and capture relevant underlying biological features. Moreover, our comprehensive evaluation provides a thorough and objective comparison of the performance of different methods (including our proposed ones).

Developmental Toxicity of Fine Particulate Matter: Multifaceted Exploration from Epidemiological and Laboratory Perspectives.

Particulate matter of size ≤ 2.5 µm (PM2.5) is a critical environmental threat that considerably contributes to the global disease burden. However, accompanied by the rapid research progress in this field, the existing research on developmental toxicity is still constrained by limited data sources, varying quality, and insufficient in-depth mechanistic analysis. This review includes the currently available epidemiological and laboratory evidence and comprehensively characterizes the adverse effects of PM2.5 on developing individuals in different regions and various pollution sources. In addition, this review explores the effect of PM2.5 exposure to individuals of different ethnicities, genders, and socioeconomic levels on adverse birth outcomes and cardiopulmonary and neurological development. Furthermore, the molecular mechanisms involved in the adverse health effects of PM2.5 primarily encompass transcriptional and translational regulation, oxidative stress, inflammatory response, and epigenetic modulation. The primary findings and novel perspectives regarding the association between public health and PM2.5 were examined, highlighting the need for future studies to explore its sources, composition, and sex-specific effects. Additionally, further research is required to delve deeper into the more intricate underlying mechanisms to effectively prevent or mitigate the harmful effects of air pollution on human health.

Tumor necrosis factor α1 decreases mucosal immune and antioxidant response in the midgut of hybrid fish (white crucian carp ♀ × red crucian carp ♂).

Tumor necrosis factor α1 (TNFα) is a pleiotropic cytokine involved in immune regulation and cellular homeostasis, but the crucial role of TNFα in fish gut remained unclear. The current study aimed to evaluate the immunoregulatory function of TNFα1 on gut barrier in a novel hybrid fish (WR), which was produced by crossing white crucian carp (Carassius cuvieri, ♀) with red crucian carp (Carassius auratus red var, ♂). In this study, WR-tnfα1 sequence was identified, and a high-level expression was detected in the intestine. Elevated levels of WR-tnfα1 expressions were detected in immune-related tissues and cultured fish cells on stimulation. The appearance of vacuolization and submucosal rupture was observed in TNFα1-treated midgut of WR, along with elevated levels of goblet cell atrophy, whereas no significant changes were detected in most expressions of tight-junction genes and mucin genes. In contrast, WR receiving gut perfusion with WR-TNFα1 showed a remarkable decrease in antioxidant status in midgut, whereas the expression levels of apoptotic genes and redox responsive genes increased sharply. These results suggested that TNFα1 could exhibit a detrimental effect on antioxidant defense and immune regulation in the midgut of WR.

Role of sleep in asthenospermia induced by di (2-ethyl-hexyl) phthalate.

Di (2-ethyl-hexyl) phthalate (DEHP) mainly enters the human body through the digestive tract, respiratory tract, and skin. At the same time, it has reproductive and developmental toxicity, neurotoxicity, and so on, which can cause the decrease of sperm motility. Asthenospermia is also known as low sperm motility, and the semen quality of men in some areas of China is declining year by year. Interestingly, previous studies have shown that sleep disorders can also lead to asthenospermia. However, the relationship between sleep, DEHP, and asthenospermia is still unclear. Analysis of the National Health and Nutrition Examination Survey (NHANES) population database showed that DEHP was associated with sleep disorders, and subsequent experiments in mice and Drosophila indicated that DEHP exposure had certain effects on sleep and asthenospermia. Furthermore, we analyzed the Comparative Toxicogenomics Database (CTD) to find out the common signaling pathway among the three: hypoxia-inducible factor 1(HIF-1). Then Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) was used to screen out the proteins that DEHP affected the HIF-1 pathway: glyceraldehyde-3-phosphate dehydrogenase (GAPDH), serine/threonine-protein kinase (AKT1), epidermal growth factor receptor (EGFR), and finally Western blot analysis was used to detect the expression levels of the three proteins. Compared with the control group, DEHP decreased the protein expression levels of GAPDH and AKT1 in the HIF-1 pathway, and caused sleep disorders and decreased sperm motility. This study provides preliminary evidence for exploring the mechanism among DEHP, sleep disorders, and asthenospermia.

Cloning, expression and functional characterization of recombinant tumor necrosis factor α1 (TNFα1) from white crucian carp in gut immune regulation.

TNFα is one of important cytokines belonging to TNF superfamily, which can exhibit a pleiotropic effect in immune modulation, homeostasis as well as pathogenesis. However, its immunoregulatory function on mucosal immunity in fish gut are still unclear. In this study, we aimed to investigated the immunoregulatory role of TNFα1 in midgut of white crucian carp (WCC). WCC-TNFα1 sequence and its deduced structure were firstly identified in WCC. Then, tissue-specific analysis revealed that high-level WCC-TNFα1 expression was detected in gill. After Aeromonas hydrophila and lipopolysaccharide (LPS) stimulated, increased trends of WCC-TNFα1 expressions were detected in immune-related tissues and cultured fish cells, respectively. WCC anal-intubated with WCC-TNFα1 fusion protein showed the increased levels of edema and fuzzy appearance in impaired villi, along with atrophy and reduction of goblet cells (GC). Moreover, the expression levels of tight junction (TJ) genes and mucin genes were consistently lower than those of the control (P < 0.05). WCC-TNFα1 treatment could sharply decrease antioxidant status in midgut, while the expression levels of caspase (CASP) genes, unfolded protein response (UPR) genes and redox response genes increased dramatically. Our results suggested that WCC-TNFα1 could exhibit a detrimental effect on antioxidant and mucosal immune regulation in midgut of WCC.

Modeling Path Importance for Effective Alzheimer's Disease Drug Repurposing.

Recently, drug repurposing has emerged as an effective and resource-efficient paradigm for AD drug discovery. Among various methods for drug repurposing, network-based methods have shown promising results as they are capable of leveraging complex networks that integrate multiple interaction types, such as protein-protein interactions, to more effectively identify candidate drugs. However, existing approaches typically assume paths of the same length in the network have equal importance in identifying the therapeutic effect of drugs. Other domains have found that same length paths do not necessarily have the same importance. Thus, relying on this assumption may be deleterious to drug repurposing attempts. In this work, we propose MPI (Modeling Path Importance), a novel network-based method for AD drug repurposing. MPI is unique in that it prioritizes important paths via learned node embeddings, which can effectively capture a network's rich structural information. Thus, leveraging learned embeddings allows MPI to effectively differentiate the importance among paths. We evaluate MPI against a commonly used baseline method that identifies anti-AD drug candidates primarily based on the shortest paths between drugs and AD in the network. We observe that among the top-50 ranked drugs, MPI prioritizes 20.0% more drugs with anti-AD evidence compared to the baseline. Finally, Cox proportional-hazard models produced from insurance claims data aid us in identifying the use of etodolac, nicotine, and BBB-crossing ACE-INHs as having a reduced risk of AD, suggesting such drugs may be viable candidates for repurposing and should be explored further in future studies.

Detection of Cognitive Impairment From eSAGE Metadata Using Machine Learning.

OBJECTIVE: Using the metadata collected in the digital version of the Self-Administered Gerocognitive Examination (eSAGE), we aim to improve the prediction of mild cognitive impairment (MCI) and dementia (DM) by applying machine learning methods. PATIENTS AND METHODS: A total of 66 patients had a diagnosis of normal cognition (NC), MCI, or DM, and eSAGE scores and metadata were used. eSAGE scores and metadata were obtained. Each eSAGE question was scored and behavioral features (metadata) such as the time spent on each test page, drawing speed, and average stroke length were extracted for each patient. Logistic regression (LR) and gradient boosting models were trained using these features to detect cognitive impairment (CI). Performance was evaluated using 10-fold cross-validation, with accuracy, precision, recall, F1 score, and receiver operating characteristic area under the curve (AUC) score as evaluation metrics. RESULTS: LR with feature selection achieved an AUC of 89.51%, a recall of 87.56%, and an F1 of 85.07% using both behavioral and scoring. LR using scores and metadata also achieved an AUC of 84.00% in detecting MCI from NC, and an AUC of 98.12% in detecting DM from NC. Average stroke length was particularly useful for prediction and when combined with 4 other scoring features, LR achieved an even better AUC of 92.06% in detecting CI. The study shows that eSAGE scores and metadata are predictive of CI. CONCLUSIONS: eSAGE scores and metadata are predictive of CI. With machine learning methods, the metadata could be combined with scores to enable more accurate detection of CI.

Modeling Path Importance for Effective Alzheimer's Disease Drug Repurposing.

Recently, drug repurposing has emerged as an effective and resource-efficient paradigm for AD drug discovery. Among various methods for drug repurposing, network-based methods have shown promising results as they are capable of leveraging complex networks that integrate multiple interaction types, such as protein-protein interactions, to more effectively identify candidate drugs. However, existing approaches typically assume paths of the same length in the network have equal importance in identifying the therapeutic effect of drugs. Other domains have found that same length paths do not necessarily have the same importance. Thus, relying on this assumption may be deleterious to drug repurposing attempts. In this work, we propose MPI (Modeling Path Importance), a novel network-based method for AD drug repurposing. MPI is unique in that it prioritizes important paths via learned node embeddings, which can effectively capture a network's rich structural information. Thus, leveraging learned embeddings allows MPI to effectively differentiate the importance among paths. We evaluate MPI against a commonly used baseline method that identifies anti-AD drug candidates primarily based on the shortest paths between drugs and AD in the network. We observe that among the top-50 ranked drugs, MPI prioritizes 20.0% more drugs with anti-AD evidence compared to the baseline. Finally, Cox proportional-hazard models produced from insurance claims data aid us in identifying the use of etodolac, nicotine, and BBB-crossing ACE-INHs as having a reduced risk of AD, suggesting such drugs may be viable candidates for repurposing and should be explored further in future studies.

Upregulation of oxidative stress by triphenyl phosphate (TPhP) exposure causes antioxidant insult and apoptotic process in Epithelioma papulosum cyprini (EPC) cells.

Triphenyl phosphate (TPhP) is the predominant compound of organophosphate flame retardants (OPFRs), which can elicit a toxicological effect on physiological response and tissue development of fish. In this study, we investigated the effect of TPhP exposure on cell viability, antioxidant capacities, and apoptosis in EPC cells. Current study revealed that TPhP exposure could decrease cell viability and promote intracellular oxidative stress in EPC cells. In addition, high-dose TPhP exposure could facilitate antioxidant insults and cause mitochondrial collapse in a dose-dependent manner, along with increased gene expressions involved in apoptosis and unfolded protein response (UPR). These results indicated that reactive oxygen species (ROS)-induced cytotoxic stress and cell death were involved in antioxidant insults and apoptotic activation in TPhP-exposed fish cells.

Several confirmed and probable zoonotic cases of toxigenic Corynebacterium ulcerans, Queensland, Australia.

Background: Toxigenic Corynebacterium ulcerans is an emerging zoonosis globally, causing both cutaneous and respiratory diphtheria-like illness. In Queensland, human infection with toxigenic C. ulcerans is rare, with only three cases reported before October 2015. This case series describes five subsequent cases of toxigenic C. ulcerans in Queensland with links to companion animals. Methods: All data were collected as part of routine public health response, and strains were whole genome sequenced for further characterisation. Household contacts were screened, treated with appropriate antibiotics, and received a diphtheria toxoid-containing vaccine if more than five years had elapsed since their last dose. Findings: No epidemiological or genomic links could be established between any of the five patients, including between the two cases notified from the same locality within eight days of each other. The C. ulcerans strains from Cases Two, Four and Five were closely related to the strains isolated from their respective pets by whole genome sequencing. Domestic dogs were identified as the most likely mode of transmission for Cases One and Three; however, this was unable to be laboratory confirmed, since Case One's dog was treated with antibiotics before it could be tested, and Case Three's dog was euthanised and cremated prior to case notification. Interpretation: These are the first reported Australian cases of this emerging zoonosis with links to companion animals. These cases demonstrate the likely transmission route between companion animals and humans, with no evidence of human-to-human transmission. The existing requirement in the Queensland Health Public Health Management Guidelines, of restrictions on cases and some contacts while awaiting swab results, is currently under review.