In patients with acute respiratory infections (ARIs), routine diagnostic tests often fail to identify the microbial cause; thus, many ARIs have undetermined etiology. We investigated potential involvement of thermotolerant bacteria in ARIs among patients in Hong Kong, China, by incubating blood agar inoculated with respiratory specimens at 50°C for 5 days. Among 7,257 specimens analyzed, 58 specimens from 57 patients grew thermotolerant bacteria not identified by other methods. We identified Tepidimonas spp. in 42 isolates, 3 of which appear to be a novel Tepidimonas species (tentatively Tepidimonas hongkongensis sp. nov). Genomic analysis revealed various virulence, resistance, and stress-related genomes in the 3 isolates. Tepidimonas spp. bacteria were predominantly isolated from patients with chronic lung disease and malignancies. We also detected T. hongkongensis in hospital water samples but at a lower percentage than in respiratory specimens, suggesting colonization potential. Clinical implications of T. hongkongensis remain unknown; continued surveillance could determine its role in ARIs.

Co-infection with multiple severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants, though rare, may have clinical and public health implications, including facilitating variant recombination. Early detection of co-infections is, therefore, crucial. In this study, we report two probable cases of co-infection identified during routine genomic surveillance. Initially suspected as cross-contamination due to the presence of private mutations and nucleotide mixtures flagged by Nextclade and bammix, the samples were re-extracted and re-sequenced after workspace decontamination, yet the anomalies persisted. To investigate further, we developed a bioinformatics pipeline (Katmon) incorporating various tools such as Freyja, with lineage abundance results that illustrated the presence of multiple variants, and VirStrain, which confirmed inconsistent lineage assignments. We also visualized the alternative allele fractions for each lineage-defining mutation and amplicon, showing evidence of two variants, Delta and Omicron, co-existing within a single amplicon. Amplicon sorting effectively separated reads corresponding to the two variants, and the resulting consensus sequences aligned with their respective lineage assignments. These findings suggest that the first sample, PH-RITM-1395, involved a Delta-Omicron co-infection, while the second sample, PH-RITM-4146, probably contains both a co-infection and a recombinant variant. To further support the second sample's recombinant nature, we employed sc2rf, which identified Delta-Omicron breakpoints. Retrospective analysis of 1,078 samples from July 2021 to July 2022, encompassing the period of co-circulation of different variants in the Philippines, flagged four additional co-infection cases, including Delta-Omicron and Beta-Omicron, suggesting a lower bound co-infection prevalence of 0.27% and 0.19%, respectively. Furthermore, the pipeline was used to test previously identified co-infections of different variants from different countries. Our findings underscore the critical importance of real-time genomic surveillance and advanced bioinformatics pipelines in detecting SARS-CoV-2 co-infections and variant recombination.

The COVID-19 pandemic has drawn attention to the interconnected roles of environmental conditions and public health beyond conventional medical explanations. The One Health (OH) perspective offers a collaborative and interdisciplinary perspective that integrates humans, animals, plants, and their shared environment to achieve optimal health outcomes. The 12 cities in Hubei Province that experienced lockdown during the peak phase of COVID-19 (February 1 to March 4, 2020) provided unique samples. In this study, land cover was selected as the environmental variable, and the COVID-19 growth rate was used as the infectious disease indicator to examine their relationship, thereby investigating the potential role of environmental factors in epidemic control.

The Least Absolute Shrinkage and Selection Operator (LASSO) regression was used to identify the most influential variables for subsequent analyses. Spatial autocorrelation was assessed using Moran's I in RStudio, while spatial dependence was explicitly modeled through the Spatial Autoregressive (SAR) and Spatial Lag of X (SLX) models to evaluate the effects of explanatory variables while accounting for spatial interactions. All results were interpreted within the One Health perspective, considering the source of infection, routes of transmission, and susceptible populations.

LASSO regression identified wetland, cultivated land, orchard land, forest land, and population density as the main factors associated with the COVID-19 growth rate. Wetland coverage exhibited a significant positive association with growth rate, whereas cultivated land showed a negative but marginally significant relationship. Orchard land and forest land were associated with weak negative effects.

The statistical results indicate that variations in land cover influence the growth rate of COVID-19 cases, suggesting that environmental management, including wetland and wastewater control, agricultural landscape configuration, forest vegetation preservation, and control population density, may help mitigate infectious disease growth. From the One Health perspective, sustainable habitat design and planning strategies and land use policies were proposed for future research.

Public-private partnerships focused on infectious disease diagnostics have been increasing since the COVID-19 pandemic. These partnerships have resulted in new test development, increased testing capacity and services, technology development, and processes to enable faster collaborative response in the context of an outbreak. This paper explores the importance of public-private partnerships in response to infectious disease public health threats. Collaboration between federal partners and diagnostic test manufacturers has been critical to the COVID-19, mpox, and other responses in the United States, and these partnerships will be critical to future responses. Public-private partnerships pull together the pieces needed to rapidly develop and scale diagnostics necessary for responding to emerging infectious diseases. Developing partnerships during "peace time" further enables rapid action when outbreaks occur.

Hypertensive cognitive impairment is associated with increased iron deposition in deep gray matter. This study aimed to evaluate the potential clinical application of quantitative susceptibility mapping (QSM)-based radiomics for stratifying cognitive impairment in hypertensive patients.

We prospectively enrolled 178 hypertensive patients who underwent QSM examination and categorized them into cognitive impairment group and normal cognition group. The workflow included: (1) Precise 3D segmentation and susceptibility quantification of the basal ganglia; (2) Initial radiomics feature extraction from the target regions, followed by optimal feature selection via variance thresholding, maximum relevance minimum redundancy (mRMR) algorithm, and LASSO regression; (3) Construction of a multiparametric model integrating radiomics scores and clinical risk factors. The model's performance was assessed by receiver operating characteristic (ROC) curve and its clinical utility for stratification was further evaluated using decision curve analysis (DCA).

The multi-region radiomics model demonstrated superior diagnostic performance in the training cohort (AUC = 0.812), significantly outperforming the QSM and WMH models (AUCs = 0.620 and 0.688, respectively). In the validation cohort, clinically meaningful improvements were observed (ΔAUC = 0.117 and 0.136, respectively). The combined model, which integrated Radscore, susceptibility values, WMH scores, age, and education level, achieved the highest discriminability in both the training (AUC = 0.860) and validation cohorts (AUC = 0.872). DCA further indicated that the nomogram derived from the combined model provided the greatest net clinical benefit for stratifying hypertensive cognitive impairment.

A nomogram integrating QSM-based radiomics with clinical and imaging markers accurately stratified hypertensive cognitive impairment, offering an objective tool for early risk assessment.

The triglyceride-glucose (TyG) index is a simple and reliable marker of insulin resistance and is associated with cardiovascular risk. Epicardial adipose tissue (EAT) volume reflects local visceral fat burden and also correlates with cardiovascular events. While both markers have been studied individually, their combined predictive value for major adverse cardiovascular events (MACE) after coronary artery bypass grafting (CABG) remains unclear. This study evaluated whether TyG index and EAT volume, alone or in combination, can improve risk prediction of MACE following CABG and assessed their potential interaction.

We retrospectively analyzed 304 patients who underwent CABG between 2018 and 2022. TyG index and EAT volume were measured preoperatively. Patients were stratified based on optimal cut-off values derived from ROC analysis. Cox regression models were used to estimate associations with MACE. Interaction was assessed using relative excess risk due to interaction (RERI), attributable proportion (AP), and synergy index (SI). Model performance was evaluated using C-statistic, net reclassification improvement (NRI), and integrated discrimination improvement (IDI). Model fit was assessed with the Akaike information criterion (AIC), Bayesian information criterion (BIC).

During follow-up of 44 months, 82 patients experienced MACE. Both TyG index and EAT volume were independently associated with increased risk. Patients with elevations in both markers had a significantly higher risk (adjusted HR = 7.62, 95% CI: 3.27-17.76). A significant additive interaction was observed (RERI = 3.81; AP = 0.50; SI = 2.34). Adding both variables improved model discrimination and fit.

TyG index and EAT volume are independent predictors of MACE after CABG. Their combined assessment provides additional information for risk stratification, but the findings are preliminary and require validation in larger, prospective, multi-center studies.

Cardiovascular disease (CVD) remains the leading cause of death in the United States, with persistent disparities in access to preventive screening. This systematic review investigates how racial, geographic, and socioeconomic factors influence cardiovascular screening rates across Michigan, focusing on blood pressure and cholesterol screening across various groups. A systematic search of PubMed, Embase, Scopus, Web of Science, and Google Scholar was conducted for studies published through October 2025. Eligible studies reported Michigan-specific data on preventive cardiovascular screening, operationally defined as blood pressure measurement, and/or lipid testing in adults. Twelve studies met the inclusion criteria, and methodological quality was assessed using study-design-appropriate appraisal tools, including the NIH quality assessment instrument. Across studies, Black, rural, uninsured, and low-income populations consistently demonstrated lower screening rates compared with White, urban, and insured groups. Reported disparities in screening ranged from approximately 5%-15% points across racial and socioeconomic strata, depending on population and data source. Policy and community-based interventions, including Medicaid expansion and mobile screening initiatives, were associated with modest increases in screening, although effects were variable and not uniformly sustained. Overall, preventive cardiovascular screening in Michigan remains unevenly distributed, highlighting persistent equity gaps and the need for more consistent, statewide screening strategies.

Background Pulmonary arterial hypertension (PAH) is known to impact other pulmonary disease outcomes, but there is a lack of data showing the degree of its impact. This study aims to elucidate the burden that PAH brings to patients admitted due to asthma. Methods The National Inpatient Sample (NIS) Database 2016-2020 was used to identify patients admitted due to asthma exacerbation. Patients admitted with a primary diagnosis of asthma, with or without a secondary diagnosis of PAH, were identified using International Classification of Diseases, 10th Edition (ICD-10) codes. PAH was classified using ICD-10 codes, specifically Group 3 PAH. The primary outcome was all-cause in-hospital mortality. Secondary outcomes were length of stay, resource utilization, and the necessity for endotracheal intubation. STATA v.13 (StataCorp LLC, College Station, TX, USA) was used for univariate and multivariate analysis. Data were considered statistically significant at p < 0.05. Results From 2016 to 2020, 491,990 patients were admitted due to asthma, and 7,860 had PAH. Patients with asthma and PAH had a 226% higher chance of dying during the index admission (OR 2.26; 95% CI 1.27-4.03; p = 0.006), an additional 0.75 days of hospital stay (regression coefficient 0.75; 95% CI 0.54-0.95; p < 0.001), and an average of $11,558.41 more spent per hospital stay (regression coefficient 11,558.41; 95% CI 8,657.33-14,459.49; p < 0.001). These patients had no statistically significant difference in endotracheal intubation rates (OR 1.69; 95% CI 0.80-1.94; p = 0.42). Discussion Asthma patients with PAH experience higher all-cause in-hospital mortality compared to those with asthma alone. These results highlight the increased risk associated with the coexistence of PAH and asthma, potentially due to the added hemodynamic burden imposed by PAH, which exacerbates respiratory and cardiovascular compromise. Activation of the transcription factor nuclear factor of activated T cells (NFAT) is an important marker known to link PAH and asthma. Due to its activation in both diseases, it may serve as a critical pathway for developing more effective therapeutic strategies targeting both conditions. There is also some research suggesting that increased neutrophils in asthma are correlated with PAH. Some limitations of using the NIS database are that it is subject to coding errors, misclassification, and variability across institutions, potentially affecting diagnostic and procedural accuracy. Conclusion PAH significantly impacts in-hospital outcomes in patients admitted due to asthma; thus, physicians should be aware of this difference. Future studies are needed to differentiate PAH patients by severity and to verify whether PAH treatment reverses this impact.

Early diagnosis of oral and systemic diseases remains a major challenge due to limitations in conventional invasive procedures. This review addresses the critical need for non-invasive, accessible diagnostics by examining the potential of saliva as an alternative diagnostic fluid. The main objective is to synthesize current knowledge on salivary biomarkers and technological innovations that enhance early detection capabilities. Methodologically, the review integrates evidence from proteomic, genomic, and metabolomic studies, as well as recent advances in microfluidic point-of-care devices. Key findings highlight that saliva contains a rich array of biomarkers, including cytokines, microRNAs, and extracellular vesicles, that can accurately reflect both local oral pathologies, such as dental caries and oral cancer, and systemic conditions, including diabetes, cardiovascular diseases, autoimmune disorders, and neurodegenerative diseases. The analysis further emphasizes that portable biosensors and lab-on-chip platforms are rapidly improving diagnostic sensitivity and enabling decentralized testing. Notwithstanding these encouraging advancements, issues with biological variability, standardization, and regulatory validation still exist. This review underscores the significance of interdisciplinary collaboration to overcome these barriers and fully integrate salivary diagnostics into routine healthcare. The implications are profound: widespread adoption could transform preventive medicine by providing patient-friendly, real-time monitoring tools that improve health outcomes globally.

Heart failure (HF) is a primary cause of death in patients on maintenance hemodialysis (MHD), yet the role of microbial dysbiosis is poorly defined. This study characterized the salivary and gut microbiomes of MHD patients with heart failure with preserved ejection fraction (HFpEF), those without HF (NHF), and healthy controls (CON).

In this cross-sectional study (n=88), we compared the salivary and fecal microbiomes of HFpEF (n=30), NHF (n=30), and CON (n=28) groups using 16S rRNA gene sequencing. Microbial community structure and composition were analyzed.

Alpha diversity and Beta diversity analysis revealed a distinct salivary microbial structure, which effectively distinguished the MHD group from the Con group (P < 0.05). Conversely, the overall gut community structure showed no significant separation. At the genus level, both MHD groups showed depletion of salivary Veillonella and gut Faecalibacterium compared to controls. Notably, LEfSe analysis highlighted salivary Anaerocolumna as a promising candidate feature associated with the HFpEF group.

Our analyses suggest that HFpEF in MHD patients may be associated with structural alterations in the oral microbiome, which appear more pronounced than those in the gut. Specific oral microbial signatures, particularly the enrichment of Anaerocolumna, showed associations with the HFpEF cohort in our study. This preliminary evidence positions the oral microbiome as an area worthy of further investigation for its potential role in this high-risk population.