Metagenomics reveals the resistance patterns of electrochemically treated erythromycin fermentation residue.

Erythromycin fermentation residue (EFR) represents a typical hazardous waste produced by the microbial pharmaceutical industry. Although electrolysis is promising for EFR disposal, its microbial threats remain unclear. Herein, metagenomics was coupled with the random forest technique to decipher the antibiotic resistance patterns of electrochemically treated EFR. Results showed that 95.75% of erythromycin could be removed in 2 hr. Electrolysis temporarily influenced EFR microbiota, where the relative abundances of Proteobacteria and Actinobacteria increased, while those of Fusobacteria, Firmicutes, and Bacteroidetes decreased. A total of 505 antibiotic resistance gene (ARG) subtypes encoding resistance to 21 antibiotic types and 150 mobile genetic elements (MGEs), mainly including plasmid (72) and transposase (52) were assembled in EFR. Significant linear regression models were identified among microbial richness, ARG subtypes, and MGE numbers (r2=0.50-0.81, p< 0.001). Physicochemical factors of EFR (Total nitrogen, total organic carbon, protein, and humus) regulated ARG and MGE assembly (%IncMSE value = 5.14-14.85). The core ARG, MGE, and microbe sets (93.08%-99.85%) successfully explained 89.71%-92.92% of total ARG and MGE abundances. Specifically, gene aph(3')-I, transposase tnpA, and Mycolicibacterium were the primary drivers of the resistance dissemination system. This study also proposes efficient resistance mitigation measures, and provides recommendations for future management of antibiotic fermentation residue.

Quantitative impacts of meteorology and emissions on the long-term trend of O3 in the Yangtze River Delta (YRD), China from 2015 to 2022.

Extensive spatiotemporal analyses of long-trend surface ozone in the Yangtze River Delta (YRD) region and its meteorology-related and emission-related have not been systematically analyzed. In this study, by using 8-year-long (2015-2022) surface ozone observation data, we attempted to reveal the variation of multiple timescale components using the Kolmogorov-Zurbenko filter, and the effects of meteorology and emissions were quantitatively isolated using multiple linear regression with meteorological variables. The results showed that the short-term, seasonal, and long-term components accounted for daily maximum 8-hr average O3 (O3-8 hr) concentration, 46.4%, 45.9%, and 1.0%, respectively. The meteorological impacts account for an average of 71.8% of O3-8 hr, and the YRD's eastern and northern sections are meteorology-sensitive areas. Based on statistical analysis technology with empirical orthogonal function, the contribution of meteorology, local emission, and transport in the long-term component of O3-8 hr were 0.21%, 0.12%, and 0.6%, respectively. The spatiotemporal analysis indicated that a distinct decreasing spatial pattern could be observed from coastal cities towards the northwest, influenced by the monsoon and synoptic conditions. The central urban agglomeration north and south of the YRD was particularly susceptible to local pollution. Among the cities studied, Shanghai, Anqing, and Xuancheng, located at similar latitudes, were significantly impacted by atmospheric transmission-the contribution of Shanghai, the maximum accounting for 3.6%.

Carbonyl-amine condensation coupled ozonolysis of dipropylamine and styrene: Decay kinetics, reaction mechanism, secondary organic aerosol formation and cytotoxicity.

Oxidation of organic amines (OAs) or aromatic hydrocarbons (AHs) produces carbonyls, which further react with OAs to form carbonyl-amine condensation products, threatening environmental quality and human health. However, there is still a lack of systematic understanding of the carbonyl-amine condensation reaction processes of OAs or between OAs and AHs, and subsequent environmental health impact. This work systematically investigated the carbonyl-amine condensation coupled ozonolysis kinetics, reaction mechanism, secondary organic aerosol (SOA) formation and cytotoxicity from the mixture of dipropylamine (DPA) and styrene (STY) by a combined method of product mass spectrometry identification, particle property analysis and cell exposure evaluation. The results from ozonolysis of DPA and STY mixture revealed that STY inhibited the ozonolysis of DPA to different degrees to accelerate its own decay rate. The barycenter of carbonyl-amine condensation reactions was shifted from inside of DPA to between DPA and STY, which accelerated STY ozonolysis, but slowed down DPA ozonolysis. For the first time, ozonolysis of DPA and STY mixture to complex carbonyl-amine condensation products through the reactions of DPA with its carbonyl products, DPA with STY's carbonyl products and DPA's bond breakage product with STY's carbonyl products was confirmed. These condensation products significantly contributed to the formation and growth of SOA. The SOA containing particulate carbonyl-amine condensation products showed definite cytotoxicity. These findings are helpful to deeply and comprehensively understand the transformation, fate and environmental health effects of mixed organics in atmospheric environment.

Impact of coking plant to heavy metal characteristics in groundwater of surrounding areas: Spatial distribution, source apportionment and risk assessments.

Coking industry is a potential source of heavy metals (HMs) pollution. However, its impacts to the groundwater of surrounding residential areas have not been well understood. This study investigated the pollution characteristics and health risks of HMs in groundwater nearby a typical coking plant. Nine HMs including Fe, Zn, Mo, As, Cu, Ni, Cr, Pb and Cd were analyzed. The average concentration of total HMs was higher in the nearby area (244.27 µg/L) than that of remote area away the coking plant (89.15 µg/L). The spatial distribution of pollution indices including heavy metal pollution index (HPI), Nemerow index (NI) and contamination degree (CD), all demonstrated higher values at the nearby residential areas, suggesting coking activity could significantly impact the HMs distribution characteristics. Four sources of HMs were identified by Positive Matrix Factorization (PMF) model, which indicated coal washing and coking emission were the dominant sources, accounted for 40.4%, and 31.0%, respectively. Oral ingestion was found to be the dominant exposure pathway with higher exposure dose to children than adults. Hazard quotient (HQ) values were below 1.0, suggesting negligible non-carcinogenic health risks, while potential carcinogenic risks were from Pb and Ni with cancer risk (CR) values > 10-6. Monte Carlo simulation matched well with the calculated results with HMs concentrations to be the most sensitive parameters. This study provides insights into understanding how the industrial coking activities can impact the HMs pollution characteristics in groundwater, thus facilitating the implement of HMs regulation in coking industries.

MRPL13 is a metastatic and prognostic marker of breast cancer: A silico analysis accompanied with experimental validation.

BACKGROUND: Although progress has been made in accurate diagnosis and targeted treatments, breast cancer (BC) patients with metastasis still present a grim prognosis. With the continuous emergence and development of new personalized and precision medicine targeting specific tumor biomarkers, there is an urgent need to find new metastatic and prognostic biomarkers for BC patients. METHODS: We were dedicated to identifying genes linked to metastasis and prognosis in breast cancer through a combination of in silico analysis and experimental validation. RESULTS: A total of 25 overlap differentially expressed genes were identified. Ten hub genes (namely MRPL13, CTR9, TCEB1, RPLP0, TIMM8B, METTL1, GOLT1B, PLK2, PARL and MANBA) were identified and confirmed. MRPL13, TCEB1 and GOLT1B were shown to be associated with the worse overall survival (OS) and were optionally chosen for further verification by western blot. Only MRPL13 was found associated with cell invasion, and the expression of MRPL13 in metastatic BC was significantly higher than in primary BC. CONCLUSION: We proposed MRPL13 could be a potential novel biomarker for the metastasis and prognosis of breast cancer.

Integration of network pharmacology, UHPLC-Q exactive orbitrap HRMS technique and metabolomics to elucidate the active ingredients and mechanisms of compound danshen pills in treating hypercholesterolemic rats.

ETHNOPHARMACOLOGICAL RELEVANCE: Hypercholesterolemia (HLC) was a key risk factor for cardiovascular disease (CVD) characterized by elevated cholesterol levels, particularly LDL. While traditional Chinese medicine preparations Compound Danshen Pills(CDP) has been clinically used for hypercholesterolemia and coronary heart disease, its specific therapeutic effect on HLC remains understudied, necessitating further investigation into its mechanisms. AIM OF THE STUDY: The aim of this study was to explore the potential of CDP in treating HLC and elucidate its underlying mechanisms and active components. MATERIALS AND METHODS: A hypercholesterolemic lipemia rat model induced by a high-fat diet was employed. Network pharmacology combined with UHPLC-Q exactive orbitrap HRMS technique was used to predict the active components, targets and mechanisms of CDP for HLC. Histological analysis and serum biochemical assays were used to assess the therapeutic effect of CDP and its main active ingredient Sa B on hypercholesterolemic lipemia rat model. Immunofluorescence assays and western blotting were used to verify the mechanism of CDP and Sa B in the treatment of HLC. Metabolomics approach was used to demonstrate that CDP and Sa B affected the metabolic profile of HLC. RESULTS: Our findings demonstrated that both CDP and its main active ingredient Sa B significantly ameliorated hypercholesterolemic lipemic lesions, reducing levels of TC, LDL, AST, ALT, and ALP. Histological analysis revealed a decrease in lipid droplet accumulation and collagen fiber deposition in the liver, as well as reduced collagen fiber deposition in the aorta. Network pharmacology predicted potential targets such as PPARα and CYP27A1. Immunofluorescence assays and western blotting confirmed that CDP and Sa B upregulated the expression of Adipor1, PPARα and CYP27A1. Metabolomics analyses further indicated improvements in ABC transporters metabolic pathways, with differential metabolites such as riboflavin, taurine, and choline showed regression in levels after CDP treatment and riboflavin, L-Threonine, Thiamine, L-Leucine, and Adenosine showed improved expression after Sa B treatment. CONCLUSION: CDP and Sa B have been shown to alleviate high-fat diet-induced hypercholesterolemia by activating the PPAR pathway and improving hepatic lipid metabolism. Our study demonstrated, for the first time, the complex mechanism of CDP, Sa B in the treatment of hypercholesterolemia at the protein and metabolic levels and provided a new reference that could elucidate the pharmacological effects of traditional Chinese medicine on hypercholesterolemia from multiple perspectives.

Role of per- and polyfluoroalkyl substances in the cardiorenal system: Unraveling crosstalk from the network of pollutants and phenotypes.

Although per- and polyfluoroalkyl substances (PFAS) have been frequently linked to cardiovascular and renal disease separately, evidence remains scarce regarding their systematic effect. Therefore, we recruited 546 newly diagnosed acute coronary syndrome (ACS) patients and detected seven myocardial enzymes and six kidney function biomarkers. Twelve PFAS were also assessed with ultra-high-performance liquid chromatography-tandem mass spectrometry. Generalized linear model and restricted cubic spline model were applied to single pollutant analysis. Quantile g-computation was used for mixture analysis. Network model was utilized to identify central and bridge nodes of pollutants and phenotypes. In the present study, perfluorohexane sulfonic acid was positively associated with uric acid (UA) (ß= 0.04, 95% confidence interval (CI): 0.01, 0.07), and perfluorobutanoic acid was negatively associated with estimated glomerular filtration rate (ß= -0.04, 95% CI: -0.07, -0.01) but positively associated with UA (ß= 0.03, 95% CI: 0.01, 0.06). In mixture analysis, each quantile increase in the PFAS mixture was significantly associated with UA (ß= 0.08, 95% CI: 0.04, 0.11). Network analysis revealed that perfluorooctanoate, UA, and myoglobin were denoted as bridge nodes, and the first principal component of lactate dehydrogenase and creatine kinase- myocardial band was identified as the node with the highest strength and expected influence. This study investigates the systematic impact of PFAS exposure through cardiorenal interaction network, which highlights that PFAS may serve as an upstream approach in UA-modulated cardiorenal network to affect cardiorenal system comprehensively.

Enhanced anticancer efficacy of TRAIL-conjugated and odanacatib-loaded PLGA nanoparticles in TRAIL resistant cancer.

Tumor necrosis factor-related apoptosis-inducing ligand (TRAIL) demonstrates unique characteristics in anticancer therapies as it selectively induces apoptosis in cancer cells. However, most cancer cells are TRAIL-resistant. Odanacatib (ODN), a cathepsin K inhibitor, is considered a novel sensitizer for cancer treatment. Combination therapy between TRAIL and sensitizers is considered a potent platform that improves TRAIL-based anticancer therapies beyond TRAIL monotherapy. Herein, we developed ODN loaded poly(lactic-co-glycolic) nanoparticles conjugated to GST-TRAIL (TRAIL-ODN-PLGA-NPs) to target and treat TRAIL-resistant cancer. TRAIL-ODN-PLGA-NPs demonstrated a significant increase in cellular uptake via death receptors (DR5 and DR4) on surface of cancer cells. TRAIL-ODN-PLGA-NPs exposure destroyed more TRAIL-resistant cells compared to a single treatment with free drugs. The released ODN decreased the Raptor protein, thereby increasing damage to mitochondria by elevating reactive oxygen species (ROS) generation. Additionally, Bim protein stabilization improved TRAIL-resistant cell sensitization to TRAIL-induced apoptosis. The in vivo biodistribution study revealed that TRAIL-ODN-PLGA-NPs demonstrated high location and retention in tumor sites via the intravenous route. Furthermore, TRAIL-ODN-PLGA-NPs significantly inhibited xenograft tumor models of TRAIL-resistant Caki-1 and TRAIL-sensitive MDA-MB-231 cells.The inhibition was associated with apoptosis activation, Raptor protein stabilizing Bim protein downregulation, Bax accumulation, and mitochondrial ROS generation elevation. Additionally, TRAIL-ODN-PLGA-NPs affected the tumor microenvironment by increasing tumor necrosis factor-α and reducing interleukin-6. In conclusion, we evealed that our formulation demonstrated synergistic effects against TRAIL compared with the combination of free drug in vitro and in vivo models. Therefore, TRAIL-ODN-PLGA-NPs may be a novel candidate for TRAIL-induced apoptosis in cancer treatment.

Tumor-targeted delivery of hyaluronic acid/polydopamine-coated Fe2+-doped nano-scaled metal-organic frameworks with doxorubicin payload for glutathione depletion-amplified chemodynamic-chemo cancer therapy.

Chemodynamic therapy (CDT), an emerging cancer treatment modality, uses multivalent metal elements to convert endogenous hydrogen peroxide (H2O2) to toxic hydroxyl radicals (•OH) via a Fenton or Fenton-like reaction, thus eliciting oxidative damage of cancer cells. However, the antitumor potency of CDT is largely limited by the high glutathione (GSH) concentration and low catalytic efficiency in the tumor sites. The combination of CDT with chemotherapy provides a promising strategy to overcome these limitations. In this work, to enhance antitumor potency by tumor-targeted and GSH depletion-amplified chemodynamic-chemo therapy, the hyaluronic acid (HA)/polydopamine (PDA)-decorated Fe2+-doped ZIF-8 nano-scaled metal-organic frameworks (FZ NMs) were fabricated and utilized to load doxorubicin (DOX), a chemotherapy drug, via hydrophobic, π-π stacking and charge interactions. The attained HA/PDA-covered DOX-carrying FZ NMs (HPDFZ NMs) promoted DOX and Fe2+ release in weakly acidic and GSH-rich milieu and exhibited acidity-activated •OH generation. Through efficient CD44-mediated endocytosis, the HPDFZ NMs internalized by CT26 cells not only prominently enhanced •OH accumulation by consuming GSH via PDA-mediated Michael addition combined with Fe2+/Fe3+ redox couple to cause mitochondria damage and lipid peroxidation, but also achieved intracellular DOX release, thus eliciting apoptosis and ferroptosis. Importantly, the HPDFZ NMs potently inhibited CT26 tumor growth in vivo at a low DOX dose and had good biosafety, thereby showing promising potential in tumor-specific treatment.

Material-driven immunomodulation and ECM remodeling reverse pulmonary fibrosis by local delivery of stem cell-laden microcapsules.

Recent progress in stem cell therapy has demonstrated the therapeutic potential of intravenous stem cell infusions for treating the life-threatening lung disease of pulmonary fibrosis (PF). However, it is confronted with limitations, such as a lack of control over cellular function and rapid clearance by the host after implantation. In this study, we developed an innovative PF therapy through tracheal administration of microfluidic-templated stem cell-laden microcapsules, which effectively reversed the progression of inflammation and fibrotic injury. Our findings highlight that hydrogel microencapsulation can enhance the persistence of donor mesenchymal stem cells (MSCs) in the host while driving MSCs to substantially augment their therapeutic functions, including immunoregulation and matrix metalloproteinase (MMP)-mediated extracellular matrix (ECM) remodeling. We revealed that microencapsulation activates the MAPK signaling pathway in MSCs to increase MMP expression, thereby degrading overexpressed collagen accumulated in fibrotic lungs. Our research demonstrates the potential of hydrogel microcapsules to enhance the therapeutic efficacy of MSCs through cell-material interactions, presenting a promising yet straightforward strategy for designing advanced stem cell therapies for fibrotic diseases.

Assessment of internal exposure risk from metals pollution of occupational and non-occupational populations around a non-ferrous metal smelting plant.

Non-ferrous metal smelting poses significant risks to public health. Specifically, the copper smelting process releases arsenic, a semi-volatile metalloid, which poses an emerging exposure risk to both workers and nearby residents. To comprehensively understand the internal exposure risks of metal(loid)s from copper smelting, we explored eighteen metal(loid)s and arsenic metabolites in the urine of both occupational and non-occupational populations using inductively coupled plasma mass spectrometry with high-performance liquid chromatography and compared their health risks. Results showed that zinc and copper (485.38 and 14.00 µg/L), and arsenic, lead, cadmium, vanadium, tin and antimony (46.80, 6.82, 2.17, 0.40, 0.44 and 0.23 µg/L, respectively) in workers (n=179) were significantly higher compared to controls (n=168), while Zinc, tin and antimony (412.10, 0.51 and 0.15 µg/L, respectively) of residents were significantly higher than controls. Additionally, workers had a higher monomethyl arsenic percentage (MMA%), showing lower arsenic methylation capacity. Source appointment analysis identified arsenic, lead, cadmium, antimony, tin and thallium as co-exposure metal(loid)s from copper smelting, positively relating to the age of workers. The hazard index (HI) of workers exceeded 1.0, while residents and control were approximately at 1.0. Besides, all three populations had accumulated cancer risks exceeding 1.0 × 10-4, and arsenite (AsIII) was the main contributor to the variation of workers and residents. Furthermore, residents living closer to the smelting plant had higher health risks. This study reveals arsenic exposure metabolites and multiple metals as emerging contaminants for copper smelting exposure populations, providing valuable insights for pollution control in non-ferrous metal smelting.

Toward understanding the role of genomic repeat elements in neurodegenerative diseases.

Neurodegenerative diseases cause great medical and economic burdens for both patients and society; however, the complex molecular mechanisms thereof are not yet well understood. With the development of high-coverage sequencing technology, researchers have started to notice that genomic repeat regions, previously neglected in search of disease culprits, are active contributors to multiple neurodegenerative diseases. In this review, we describe the association between repeat element variants and multiple degenerative diseases through genome-wide association studies and targeted sequencing. We discuss the identification of disease-relevant repeat element variants, further powered by the advancement of long-read sequencing technologies and their related tools, and summarize recent findings in the molecular mechanisms of repeat element variants in brain degeneration, such as those causing transcriptional silencing or RNA-mediated gain of toxic function. Furthermore, we describe how in silico predictions using innovative computational models, such as deep learning language models, could enhance and accelerate our understanding of the functional impact of repeat element variants. Finally, we discuss future directions to advance current findings for a better understanding of neurodegenerative diseases and the clinical applications of genomic repeat elements.

Deciphering the prognostic and therapeutic significance of BAG1 and BAG2 for predicting distinct survival outcome and effects on liposarcoma.

Liposarcoma (LPS) is the second most common kind of soft tissue sarcoma, and a heterogeneous malignant tumor derived from adipose tissue. Up to now, the prognostic value of BAG1 or BAG2 in LPS has not been defined yet. Expression profiling data of LPS patients were collected from TCGA and GEO database. Survival curves were plotted to verify the outcome differences of patients based on BAG1 or BAG2 expression. Univariate and multivariate Cox regression models were used to analyze the prognostic ability of BAG1 or BAG2. Chaperone's regulators BAG1 and BAG2 were identified as prognostic biomarkers for LPS patients, which exhibited distinct expression patterns and survival outcome prediction performances. Patients with high BAG2 expression and/or low BAG1 expression had worse prognosis. Enrichment analysis showed that BAG1 was involved in negative regulation of TGF-ß signaling. Low expression of BAG1 was associated with high abundance of regulatory T cells (Tregs). The 2-gene signature model further confirmed the improved risk assessment performance of BAG1 and BAG2: high risk patients displayed poor prognosis. BAG1 and BAG2 are supposed to be potential prognostic biomarkers for LPS and have impacts on liposarcomagenesis and immune infiltration in distinctive manners, which may function as potential therapy targets (BAG1 agonists/BAG2 inhibitors) for LPS.

Pan-cancer analysis of the prognostic and immunological roles of SHP-1/ptpn6.

SHP-1, a nonreceptor protein tyrosine phosphatase encoded by ptpn6, has been regarded as a regulatory protein of hematopoietic cell biology for years. However, there is now increasing evidence to support its role in tumors. Thus, the role of ptpn6 for prognosis and immune regulation across 33 tumors was investigated, aiming to explore its functional heterogeneity and clinical significance in pan-cancer. Differential expression of ptpn6 was found between cancer and adjacent normal tissues, and its expression was significantly correlated with the prognosis of tumor patients. In most cancers, ptpn6 expression was significantly associated with immune infiltration. This was further confirmed by ptpn6-related genes/proteins enrichment analysis. Additionally, genetic alterations in ptpn6 was observed in most cancers. As for epigenetic changes, it's phosphorylation levels significantly altered in 6 tumors, while methylation levels significantly altered in 12 tumors. Notably, the methylation levels of ptpn6 were significantly decreased in 11 tumors, accompanied by its increased expression in 8 of them, suggesting that the hypomethylation may be related to its increased expression. Our results show that ptpn6 plays a specific role in tumor immunity and exerts a pleiotropic effect in a variety of tumors. It can serve as a prognostic factor for some cancers. Especially in LGG, KIRC, UCS and TGCT, the increased expression of ptpn6 is associated with poor prognosis and high immune infiltration. This aids in understanding the role of ptpn6 in tumor biology, and can provide insight into presenting a potential biomarker for poor prognosis and immune infiltration in cancers.

DeepResBat: Deep residual batch harmonization accounting for covariate distribution differences.

Pooling MRI data from multiple datasets requires harmonization to reduce undesired inter-site variabilities, while preserving effects of biological variables (or covariates). The popular harmonization approach ComBat uses a mixed effect regression framework that explicitly accounts for covariate distribution differences across datasets. There is also significant interest in developing harmonization approaches based on deep neural networks (DNNs), such as conditional variational autoencoder (cVAE). However, current DNN approaches do not explicitly account for covariate distribution differences across datasets. Here, we provide mathematical results, suggesting that not accounting for covariates can lead to suboptimal harmonization. We propose two DNN-based covariate-aware harmonization approaches: covariate VAE (coVAE) and DeepResBat. The coVAE approach is a natural extension of cVAE by concatenating covariates and site information with site- and covariate-invariant latent representations. DeepResBat adopts a residual framework inspired by ComBat. DeepResBat first removes the effects of covariates with nonlinear regression trees, followed by eliminating site differences with cVAE. Finally, covariate effects are added back to the harmonized residuals. Using three datasets from three continents with a total of 2787 participants and 10,085 anatomical T1 scans, we find that DeepResBat and coVAE outperformed ComBat, CovBat and cVAE in terms of removing dataset differences, while enhancing biological effects of interest. However, coVAE hallucinates spurious associations between anatomical MRI and covariates even when no association exists. Future studies proposing DNN-based harmonization approaches should be aware of this false positive pitfall. Overall, our results suggest that DeepResBat is an effective deep learning alternative to ComBat. Code for DeepResBat can be found here: https://github.com/ThomasYeoLab/CBIG/tree/master/stable_projects/harmonization/An2024_DeepResBat.

Combined influence of physical activity and C-reactive protein to albumin ratio on mortality among older cancer survivors in the United States: a prospective cohort study.

BACKGROUND: Although a high C-reactive protein-to-albumin ratio (CAR) is believed to increase mortality risk, the association between the physical activity (PA), CAR, and mortality among cancer survivors has not been investigated. This study aimed to examine this association among cancer survivors in the United States. METHODS: This cohort study used data from the National Health and Nutrition Examination Survey from 1999 to 2010. PA was self-reported using the Global Physical Activity Questionnaire, and C-reactive protein and albumin levels were obtained from laboratory data files. Mortality data were obtained by linkage of the cohort database to the National Death Index as of December 31, 2019. The analysis was conducted from November 1 to December 31, 2023. We used Cox proportional hazards multivariable regression to assess hazard ratios (HRs) and 95% confidence interval (CIs) for total and cancer-specific mortality risks attributable to PA and CAR. RESULTS: Among 2,232 cancer survivors, 325 (14.6%) reported no PA with a high CAR. During a follow-up of up to 20.75 years (median, 12.3 years; 27,453 person-years), 1,174 deaths occurred (cancer, 335; other, 839). A high CAR was observed to be consistently associated with the highest risks of total (HR, 1.59; 95% CI, 1.37-1.85) and cancer-specific (HR, 2.06; 95% CI, 1.55-2.73) mortality compared with a low CAR in a series of adjusted models. Multivariable models showed that PA was associated with a lower risk of all-cause (HR, 0.60; 95% CI, 0.52-0.69) and cancer-specific (HR, 0.64; 95% CI, 0.49-0.84) mortality compared with no PA. In the joint analyses, survivors with PA ≥ 600 metabolic equivalent min/wk and a low CAR were more likely to reduce the risk of total (HR, 0.41; 95% CI, 0.32-0.51) and cancer-specific (HR, 0.32; 95% CI, 0.20-0.50) mortality by 59% and 68% compared with those with no PA and a high CAR. CONCLUSION: The pairing of adequate PA and a low CAR was significantly associated with reduced all-cause and cancer-related mortality risks.

The Value of Topological Radiomics Analysis in Predicting Malignant Risk of Pulmonary Ground-Glass Nodules: A Multi-Center Study.

BACKGROUND: Early detection and accurate differentiation of malignant ground-glass nodules (GGNs) in lung CT scans are crucial for the effective treatment of lung adenocarcinoma. However, existing imaging diagnostic methods often struggle to distinguish between benign and malignant GGNs in the early stages. This study aims to predict the malignancy risk of GGNs observed in lung CT scans by applying two radiomics methods: topological data analysis and texture analysis. METHODS: A retrospective analysis was conducted on 3223 patients from two centers between January 2018 and June2023. The dataset was divided into training, testing, and validation sets to ensure robust model development and validation. We developed topological features applied to GGNs using radiomics analysis based on homology. This innovative approach emphasizes the integration of topological information, capturing complex geometric and spatial relationships within GGNs. By combining machine learning and deep learning algorithms, we established a predictive model that integrates clinical parameters, previous radiomics features, and topological radiomics features. RESULTS: Incorporating topological radiomics into our model significantly enhanced the ability to distinguish between benign and malignant GGNs. The topological radiomics model achieved areas under the curve (AUC) of 0.85 and 0.862 in two independent validation sets, outperforming previous radiomics models. Furthermore, this model demonstrated higher sensitivity compared to models based solely on clinical parameters, with sensitivities of 80.7% in validation set 1 and 82.3% in validation set 2. The most comprehensive model, which combined clinical parameters, previous radiomics features, and topological radiomics features, achieved the highest AUC value of 0.879 across all datasets. CONCLUSION: This study validates the potential of topological radiomics in improving the predictive performance for distinguishing between benign and malignant GGNs. By integrating topological features with previous radiomics and clinical parameters, our comprehensive model provides a more accurate and reliable basis for developing treatment strategies for patients with GGNs.

Genetic and clinical correlates of two neuroanatomical AI dimensions in the Alzheimer's disease continuum.

Alzheimer's disease (AD) is associated with heterogeneous atrophy patterns. We employed a semi-supervised representation learning technique known as Surreal-GAN, through which we identified two latent dimensional representations of brain atrophy in symptomatic mild cognitive impairment (MCI) and AD patients: the "diffuse-AD" (R1) dimension shows widespread brain atrophy, and the "MTL-AD" (R2) dimension displays focal medial temporal lobe (MTL) atrophy. Critically, only R2 was associated with widely known sporadic AD genetic risk factors (e.g., APOE ε4) in MCI and AD patients at baseline. We then independently detected the presence of the two dimensions in the early stages by deploying the trained model in the general population and two cognitively unimpaired cohorts of asymptomatic participants. In the general population, genome-wide association studies found 77 genes unrelated to APOE differentially associated with R1 and R2. Functional analyses revealed that these genes were overrepresented in differentially expressed gene sets in organs beyond the brain (R1 and R2), including the heart (R1) and the pituitary gland, muscle, and kidney (R2). These genes were enriched in biological pathways implicated in dendritic cells (R2), macrophage functions (R1), and cancer (R1 and R2). Several of them were "druggable genes" for cancer (R1), inflammation (R1), cardiovascular diseases (R1), and diseases of the nervous system (R2). The longitudinal progression showed that APOE ε4, amyloid, and tau were associated with R2 at early asymptomatic stages, but this longitudinal association occurs only at late symptomatic stages in R1. Our findings deepen our understanding of the multifaceted pathogenesis of AD beyond the brain. In early asymptomatic stages, the two dimensions are associated with diverse pathological mechanisms, including cardiovascular diseases, inflammation, and hormonal dysfunction-driven by genes different from APOE-which may collectively contribute to the early pathogenesis of AD. All results are publicly available at https://labs-laboratory.com/medicine/ .

A fair individualized polysocial risk score for identifying increased social risk in type 2 diabetes.

Racial and ethnic minorities bear a disproportionate burden of type 2 diabetes (T2D) and its complications, with social determinants of health (SDoH) recognized as key drivers of these disparities. Implementing efficient and effective social needs management strategies is crucial. We propose a machine learning analytic pipeline to calculate the individualized polysocial risk score (iPsRS), which can identify T2D patients at high social risk for hospitalization, incorporating explainable AI techniques and algorithmic fairness optimization. We use electronic health records (EHR) data from T2D patients in the University of Florida Health Integrated Data Repository, incorporating both contextual SDoH (e.g., neighborhood deprivation) and person-level SDoH (e.g., housing instability). After fairness optimization across racial and ethnic groups, the iPsRS achieved a C statistic of 0.71 in predicting 1-year hospitalization. Our iPsRS can fairly and accurately screen patients with T2D who are at increased social risk for hospitalization.