Cureus. 2024 Dec 20;16(12):e76089. doi: 10.7759/cureus.76089. eCollection 2024 Dec.

ABSTRACT

Shock is a state of inadequate perfusion that affects vital organs. Cardiogenic shock (CS) predisposes patients to various arrhythmias. The adverse effect depends on intervention and pharmacogenomics. This narrative review sheds light on treatment strategies for arrhythmias caused by milrinone and dobutamine when managing CS. Dobutamine, through beta-1 agonism, and milrinone, by phosphodiesterase-3 inhibition, increase cardiac contractility by enhancing the availability of calcium to the myocardium. Dobutamine is also a beta-2 agonist, and milrinone is a phosphodiesterase-3 inhibitor; both result in peripheral vasodilation, leading to their use preferentially in patients with CS with normotensive blood pressure. To narrow down relevant literature, various electronic databases, including PubMed, Google Scholar, and Cochrane Library, were searched. The review revealed limited evidence favoring either milrinone or dobutamine as the preferred inotropic agent for managing CS, but it did reveal that though hospital stays using dobutamine were shorter, mortality from its induced arrhythmias led to an increase in all-cause mortality rates. Both proarrhythmic agents triggered ventricular and supraventricular tachyarrhythmias, some requiring cardioversion while others are non-sustained and managed medically or symptomatically. Though neither agent has a specific reversal agent, the effect of dobutamine was seen to be successfully aborted using intravenous ultrashort half-life beta-blockers (such as esmolol). The findings accentuated the critical need for a tailored approach to managing these iatrogenic arrhythmias, emphasizing clinical vigilance and individualized patient care.

PMID:39835019 | PMC:PMC11743927 | DOI:10.7759/cureus.76089

Front Immunol. 2025 Jan 6;15:1505752. doi: 10.3389/fimmu.2024.1505752. eCollection 2024.

ABSTRACT

BACKGROUND: Factors leading to severe COVID-19 remain partially known. New biomarkers predicting COVID-19 severity that are also causally involved in disease pathogenesis could improve patient management and contribute to the development of innovative therapies. Autophagy, a cytosolic structure degradation pathway is involved in the maintenance of cellular homeostasis, degradation of intracellular pathogens and generation of energy for immune responses. Acyl-CoA binding protein (ACBP) is a key regulator of autophagy in the context of diabetes, obesity and anorexia. The objective of our work was to assess whether circulating ACBP levels are associated with COVID-19 severity, using proteomics data from the plasma of 903 COVID-19 patients.

METHODS: Somalogic proteomic analysis was used to detect 5000 proteins in plasma samples collected between March 2020 and August 2021 from hospitalized participants in the province of Quebec, Canada. Plasma samples from 903 COVID-19 patients collected during their admission during acute phase of COVID-19 and 295 hospitalized controls were assessed leading to 1198 interpretable proteomic profiles. Levels of anti-SARS-CoV-2 IgG were measured by ELISA and a cell-binding assay.

RESULTS: The median age of the participants was 59 years, 46% were female, 65% had comorbidities. Plasma ACBP levels correlated with COVID-19 severity, in association with inflammation and anti-SARS-CoV-2 antibody levels, independently of sex or the presence of comorbidities. Samples collected during the second COVID-19 wave in Quebec had higher levels of plasma ACBP than during the first wave. Plasma ACBP levels were negatively correlated with biomarkers of T and NK cell responses interferon-γ, tumor necrosis factor-α and interleukin-21, independently of age, sex, and severity.

CONCLUSIONS: Circulating ACBP levels can be considered a biomarker of COVID-19 severity linked to inflammation. The contribution of extracellular ACBP to immunometabolic responses during viral infection should be further studied.

PMID:39835130 | PMC:PMC11743960 | DOI:10.3389/fimmu.2024.1505752

Cureus. 2024 Dec 19;16(12):e76002. doi: 10.7759/cureus.76002. eCollection 2024 Dec.

ABSTRACT

Aquagenic syringeal acrokeradermatoma (ASA) is a dermatological condition characterized by the transient appearance of edematous, white, translucent papules on the palms, typically triggered by water exposure. While ASA is most commonly associated with cystic fibrosis (CF) and predominantly affects young females, there has been a significant increase in ASA cases since the most recent update in 2015. The COVID-19 pandemic increased the number of patients diagnosed with ASA following exposure to the viral infection. The growing body of literature suggests a multifactorial etiology for ASA, with potential links to CF, medication use, and possibly COVID-19-related behavioral changes. Due to the recent increase in cases of ASA, an updated review seeks to quantify the existing literature that has been published on the prevalence of this condition. This review sought to find those newly diagnosed cases between the years 2014 and 2024. Through a literature review, we were able to find 57 cases of ASA since the last significant update to the total number of cases found in the literature. This review includes the prevalence of CF, a known etiology of ASA, as well as demographic information and known status of exposure to COVID-19.

PMID:39835050 | PMC:PMC11743320 | DOI:10.7759/cureus.76002

Respir Med Case Rep. 2024 Dec 27;53:102159. doi: 10.1016/j.rmcr.2024.102159. eCollection 2025.

ABSTRACT

INTRODUCTION: Acute fibrinous and organizing pneumonia (AFOP) is a severe form of acute lung injury which can occur after lung transplantation. Treatment is empiric, based on immunosuppressive regimens and the mortality rate is very high.

CASE PRESENTATION: We report the case of a young lung transplant (LT) recipient who developed AFOP following a respiratory viral infection while on suboptimal maintenance immunosuppression due to adherence issues. Diagnosis was confirmed by cryobiopsies showing intra-alveolar fibrin balls. Despite high dose systemic corticosteroids, the patient developed severe respiratory failure requiring mechanical ventilation. IV infliximab and tocilizumab were administered. The patient was extubated 11 days later and discharged to home 42 days after intubation with 1L/min O2. She developed severe pleuritic pain needing opioid treatment and died 4 months later.

CONCLUSION: While high-dose systemic corticosteroids remain the first line of treatment, the use of anti TNF-α has shown promising results in case reports. Furthermore, we propose prompt realization of a cytokine panel analysis in both blood and bronchoalveolar lavage to better guide the adjuvant administration of a targeted anti-inflammatory therapy.

PMID:39834689 | PMC:PMC11743898 | DOI:10.1016/j.rmcr.2024.102159

Cureus. 2025 Jan 19;17(1):e77681. doi: 10.7759/cureus.77681. eCollection 2025 Jan.

ABSTRACT

Bronchiectasis is a well-recognized chronic respiratory disease characterized by a productive cough and multi-microbial activation syndrome (MMAS) of various respiratory infections due to what can be the permanent dilatation of the bronchi. Bronchiectasis represents an ongoing challenge to conventional antibiotic treatment as the damaged bronchial environment remains conducive to ongoing opportunistic infections and microbial mutations, leading to multi-drug resistance. Standard treatment guidelines are designed to promptly identify and address the primary infection. Despite the strong focus on identification of the primary infection in each new episode, by combining clinical history, and high-resolution computed tomography (HRCT), a high proportion of patients remain classified as "idiopathic". Important underlying infections, such as Aspergillus and other mold infections, Pseudomonas aeruginosa, Mycobacterium, Mycoplasma, and various viruses, are frequently not identified for prolonged periods of time, and selected broad-spectrum antibiotics are often ineffective. The introduction of Induced Native Phage Therapy in 2021 and Induced Native Phage cocktails in 2024 provides a new treatment alternative that induces naturally occurring phages to eliminate specifically targeted acute and chronic mixed infections even in cases of multi-drug resistant infections as seen in chronic bronchiectasis. This article will present the successful long-term results in a case study demonstrating the speed, gentleness, and effectiveness of induced native phage cocktails in a 45-year-old male with life-long asthma resulting in multi-microbial activation syndrome in severe non-cystic fibrosis bronchiectasis for the last 20 years.

PMID:39834667 | PMC:PMC11744022 | DOI:10.7759/cureus.77681

Front Public Health. 2025 Jan 6;12:1442114. doi: 10.3389/fpubh.2024.1442114. eCollection 2024.

ABSTRACT

INTRODUCTION: Diabetic retinopathy grading plays a vital role in the diagnosis and treatment of patients. In practice, this task mainly relies on manual inspection using human visual system. However, the human visual system-based screening process is labor-intensive, time-consuming, and error-prone. Therefore, plenty of automated screening technique have been developed to address this task.

METHODS: Among these techniques, the deep learning models have demonstrated promising outcomes in various types of machine vision tasks. However, most of the medical image analysis-oriented deep learning approaches are built upon the convolutional operations, which might neglect the global dependencies between long-range pixels in the medical images. Therefore, the vision transformer models, which can unveil the associations between global pixels, have been gradually employed in medical image analysis. However, the quadratic computation complexity of attention mechanism has hindered the deployment of vision transformer in clinical practices. Bearing the analysis above in mind, this study introduces an integrated self-attention mechanism with both softmax and linear modules to guarantee efficiency and expressiveness, simultaneously. To be specific, a portion of query and key tokens, which are much less than the original query and key tokens, are adopted in the attention module by adding a set of proxy tokens. Note that the proxy tokens can fully utilize both the advantages of softmax and linear attention.

RESULTS: To evaluate the performance of the presented approach, the comparison experiments between state-of-the-art algorithms and the proposed approach are conducted. Experimental results demonstrate that the proposed approach achieves superior outcome over the state-of-the-art algorithms on the publicly available datasets.

DISCUSSION: Accordingly, the proposed approach can be taken as a potentially valuable instrument in clinical practices.

PMID:39835306 | PMC:PMC11743363 | DOI:10.3389/fpubh.2024.1442114

Front Med (Lausanne). 2025 Jan 6;11:1510792. doi: 10.3389/fmed.2024.1510792. eCollection 2024.

ABSTRACT

Sepsis remains a leading cause of morbidity and mortality worldwide due to its rapid progression and heterogeneous nature. This review explores the potential of Artificial Intelligence (AI) to transform sepsis management, from early detection to personalized treatment and real-time monitoring. AI, particularly through machine learning (ML) techniques such as random forest models and deep learning algorithms, has shown promise in analyzing electronic health record (EHR) data to identify patterns that enable early sepsis detection. For instance, random forest models have demonstrated high accuracy in predicting sepsis onset in intensive care unit (ICU) patients, while deep learning approaches have been applied to recognize complications such as sepsis-associated acute respiratory distress syndrome (ARDS). Personalized treatment plans developed through AI algorithms predict patient-specific responses to therapies, optimizing therapeutic efficacy and minimizing adverse effects. AI-driven continuous monitoring systems, including wearable devices, provide real-time predictions of sepsis-related complications, enabling timely interventions. Beyond these advancements, AI enhances diagnostic accuracy, predicts long-term outcomes, and supports dynamic risk assessment in clinical settings. However, ethical challenges, including data privacy concerns and algorithmic biases, must be addressed to ensure fair and effective implementation. The significance of this review lies in addressing the current limitations in sepsis management and highlighting how AI can overcome these hurdles. By leveraging AI, healthcare providers can significantly enhance diagnostic accuracy, optimize treatment protocols, and improve overall patient outcomes. Future research should focus on refining AI algorithms with diverse datasets, integrating emerging technologies, and fostering interdisciplinary collaboration to address these challenges and realize AI's transformative potential in sepsis care.

PMID:39835096 | PMC:PMC11743359 | DOI:10.3389/fmed.2024.1510792

Front Med (Lausanne). 2025 Jan 6;11:1478842. doi: 10.3389/fmed.2024.1478842. eCollection 2024.

ABSTRACT

BACKGROUND: The conventional treatment for locally advanced head and neck squamous cell carcinoma (LA-HNSCC) is surgery; however, the efficacy of definitive chemoradiotherapy (CRT) remains controversial.

OBJECTIVE: This study aimed to evaluate the ability of deep learning (DL) models to identify patients with LA-HNSCC who can achieve organ preservation through definitive CRT and provide individualized adjuvant treatment recommendations for patients who are better suited for surgery.

METHODS: Five models were developed for treatment recommendations. Their performance was assessed by comparing the difference in overall survival rates between patients whose actual treatments aligned with the model recommendations and those whose treatments did not. Inverse probability treatment weighting (IPTW) was employed to reduce bias. The effect of the characteristics on treatment plan selection was quantified through causal inference.

RESULTS: A total of 7,376 patients with LA-HNSCC were enrolled. Balanced Individual Treatment Effect for Survival data (BITES) demonstrated superior performance in both the CRT recommendation (IPTW-adjusted hazard ratio (HR): 0.84, 95% confidence interval (CI), 0.72-0.98) and the adjuvant therapy recommendation (IPTW-adjusted HR: 0.77, 95% CI, 0.61-0.85), outperforming other models and the National Comprehensive Cancer Network guidelines (IPTW-adjusted HR: 0.87, 95% CI, 0.73-0.96).

CONCLUSION: BITES can identify the most suitable treatment option for an individual patient from the three most common treatment options. DL models facilitate the establishment of a valid and reliable treatment recommendation system supported by quantitative evidence.

PMID:39835092 | PMC:PMC11744519 | DOI:10.3389/fmed.2024.1478842

Cureus. 2024 Dec 20;16(12):e76095. doi: 10.7759/cureus.76095. eCollection 2024 Dec.

ABSTRACT

Preeclampsia is one of the leading causes of maternal and perinatal morbidity and mortality. Early prediction is the need of the hour so that interventions like aspirin prophylaxis can be started. Nowadays, machine learning (ML) is increasingly being used to predict the disease and its prognosis. This review explores the methodologies, predictors, and performance of ML models for preeclampsia prediction, emphasizing their comparative advantages, challenges, and clinical applicability. We conducted a systematic search of databases including PubMed, Cochrane, and Scopus for studies published in the last 10 years using terms such as "preeclampsia", "risk factors", "machine learning", "artificial intelligence", and "deep learning". Words and phrases were selected using MeSH, a controlled vocabulary. Appropriate articles were selected using Boolean operators "OR" and "AND". The database search yielded 325 records. After removing duplicates and non-English articles, and completing a title and abstract search 55 research articles were assessed for eligibility of which 11 were included in this review. The risk of bias was found to be high in three of the studies and low in the rest. Clinicodemographic characteristics, laboratory reports, Doppler ultrasound, and some innovative ones like genotypic data and fundal images were predictors used to train ML models. More than ten different ML models were used in the 11 studies from diverse countries like the United States, the United Kingdom, China, and Korea. The area under the curve varied from 0.76 to 0.97. ML algorithms such as extreme gradient boosting (XGBoost), random forest, and neural networks consistently demonstrated superior predictive accuracy Non-interpretable or black box ML models may not find clinical application on ethical grounds. The future of preeclampsia prediction using ML lies in balancing model performance with interpretability. Human oversight remains indispensable in implementing and interpreting these models to achieve better maternal outcomes. Further research and validation across diverse populations are critical to establishing the universal applicability of these promising ML-based approaches.

PMID:39834976 | PMC:PMC11743919 | DOI:10.7759/cureus.76095

Front Oncol. 2025 Jan 6;14:1428929. doi: 10.3389/fonc.2024.1428929. eCollection 2024.

ABSTRACT

OBJECTIVE: This meta-analysis aims to evaluate the diagnostic accuracy of magnetic resonance imaging (MRI) based radiomic features for predicting epidermal growth factor receptor (EGFR) mutation status in non-small cell lung cancer (NSCLC) patients with brain metastases.

METHODS: We systematically searched PubMed, Embase, Cochrane Library, Web of Science, Scopus, Wanfang, and China National Knowledge Infrastructure (CNKI) for studies published up to April 30, 2024. We included those studies that utilized MRI-based radiomic features to detect EGFR mutations in NSCLC patients with brain metastases. Sensitivity, specificity, positive and negative likelihood ratios (PLR, NLR), and area under the curve (AUC) were calculated to evaluate the accuracy. Quality assessment was performed using the quality assessment of prognostic accuracy studies 2 (QUADAS-2) tool. Meta-analysis was conducted using random-effects models.

RESULTS: A total of 13 studies involving 2,348 patients were included. The pooled sensitivity and specificity of MRI-based radiomic features for detecting EGFR mutations were 0.86 (95% CI: 0.74-0.93) and 0.83 (95% CI: 0.72-0.91), respectively. The PLR and NLR were calculated as 5.14 (3.09, 8.55) and 0.17 (0.10, 0.31), respectively. Substantial heterogeneity was observed, with I² values exceeding 50% for all parameters. The AUC for the receiver operating characteristic analysis was 0.91 (95% CI: 0.88-0.93). Subgroup analysis indicated that deep learning models and studies conducted in Asian showed higher diagnostic accuracy compared to their respective counterparts.

CONCLUSIONS: MRI-based radiomic features demonstrate a high potential for accurately detecting EGFR mutations in NSCLC patients with brain metastases, particularly when advanced deep learning techniques were employed. However, the variability in diagnostic performance across different studies underscores the need for standardized radiomic protocols to enhance reproducibility and clinical utility.

SYSTEMATIC REVIEW REGISTRATION: https://www.crd.york.ac.uk/prospero/, identifier CRD42024544131.

PMID:39834943 | PMC:PMC11743156 | DOI:10.3389/fonc.2024.1428929

Front Oncol. 2025 Jan 6;14:1423549. doi: 10.3389/fonc.2024.1423549. eCollection 2024.

ABSTRACT

BACKGROUND: Improvements in the clinical diagnostic use of magnetic resonance imaging (MRI) for the identification of liver disorders have been made possible by gadolinium ethoxybenzyl diethylenetriamine pentaacetic acid (Gd-EOB-DTPA). Gd-EOB-DTPA-enhanced magnetic resonance imaging (MRI) technology is in high demand.

OBJECTIVES: The purpose of the study is to segment the liver using an enhanced multi-gradient deep convolution neural network (EMGDCNN) and to identify and categorize a localized liver lesion using a Gd-EOB-DTPA-enhanced MRI.

METHODS: We provided the classifier images of the liver in five states (unenhanced, arterial, portal venous, equilibrium, and hepatobiliary) and labeled them with localized liver diseases (hepatocellular carcinoma, metastasis, hemangiomas, cysts, and scarring). The Shanghai Public Health Clinical Center ethics committee recruited 132 participants between August 2021 and February 2022. Fisher's exact test analyses liver lesion Gd-EOB-DTPA-enhanced MRI data.

RESULTS: Our method could identify and classify liver lesions at the same time. On average, 25 false positives and 0.6 real positives were found in the test instances. The percentage of correct answers was 0.790. AUC, sensitivity, and specificity evaluate the procedure. Our technique outperforms others in extensive testing.

CONCLUSION: EMGDCNN may identify and categorize a localized hepatic lesion in Gd-EOB-DTPA-enhanced MRI. We found that one network can detect and classify. Radiologists need higher detection capability.

PMID:39834934 | PMC:PMC11743610 | DOI:10.3389/fonc.2024.1423549

Front Artif Intell. 2025 Jan 6;7:1501184. doi: 10.3389/frai.2024.1501184. eCollection 2024.

ABSTRACT

Traditional Chinese medicine (TCM) has long utilized tongue diagnosis as a crucial method for assessing internal visceral condition. This study aims to modernize this ancient practice by developing an automated system for analyzing tongue images in relation to the five organs, corresponding to the heart, liver, spleen, lung, and kidney-collectively known as the "five viscera" in TCM. We propose a novel tongue image partitioning algorithm that divides the tongue into four regions associated with these specific organs, according to TCM principles. These partitioned regions are then processed by our newly developed OrganNet, a specialized neural network designed to focus on organ-specific features. Our method simulates the TCM diagnostic process while leveraging modern machine learning techniques. To support this research, we have created a comprehensive tongue image dataset specifically tailored for these five visceral pattern assessment. Results demonstrate the effectiveness of our approach in accurately identifying correlations between tongue regions and visceral conditions. This study bridges TCM practices with contemporary technology, potentially enhancing diagnostic accuracy and efficiency in both TCM and modern medical contexts.

PMID:39834879 | PMC:PMC11743429 | DOI:10.3389/frai.2024.1501184

Front Cardiovasc Med. 2025 Jan 6;11:1473482. doi: 10.3389/fcvm.2024.1473482. eCollection 2024.

ABSTRACT

INTRODUCTION: The risk of mortality associated with cardiac arrhythmias is considerable, and their diagnosis presents significant challenges, often resulting in misdiagnosis. This situation highlights the necessity for an automated, efficient, and real-time detection method aimed at enhancing diagnostic accuracy and improving patient outcomes.

METHODS: The present study is centered on the development of a portable deep learning model for the detection of arrhythmias via electrocardiogram (ECG) signals, referred to as CardioAttentionNet (CANet). CANet integrates Bi-directional Long Short-Term Memory (BiLSTM) networks, Multi-head Attention mechanisms, and Depthwise Separable Convolution, thereby facilitating its application in portable devices for early diagnosis. The architecture of CANet allows for effective processing of extended ECG patterns and detailed feature extraction without a substantial increase in model size.

RESULTS: Empirical results indicate that CANet outperformed traditional models in terms of predictive performance and stability, as confirmed by comprehensive cross-validation. The model demonstrated exceptional capabilities in detecting cardiac arrhythmias, surpassing existing models in both cross-validation and external testing scenarios. Specifically, CANet achieved high accuracy in classifying various arrhythmic events, with the following accuracies reported for different categories: Normal (97.37 ± 0.30%), Supraventricular (98.09 ± 0.25%), Ventricular (92.92 ± 0.09%), Atrial Fibrillation (99.07 ± 0.13%), and Unclassified arrhythmias (99.68 ± 0.06%). In external evaluations, CANet attained an average accuracy of 94.41%, with the area under the curve (AUC) for each category exceeding 99%, thereby demonstrating its substantial clinical applicability and significant advancements over traditional models.

DISCUSSION: The deep learning model proposed in this study has the potential to enhance the accuracy of early diagnosis for various types of arrhythmias. Looking ahead, this technology is anticipated to provide improved medical services for patients with heart disease through continuous, non-invasive monitoring and timely intervention.

PMID:39834732 | PMC:PMC11744002 | DOI:10.3389/fcvm.2024.1473482

Proc IEEE Int Symp Biomed Imaging. 2024 May;2024. doi: 10.1109/isbi56570.2024.10635530. Epub 2024 Aug 22.

ABSTRACT

Real-time dynamic MRI is important for visualizing time-varying processes in several applications, including cardiac imaging, where it enables free-breathing images of the beating heart without ECG gating. However, current real-time MRI techniques commonly face challenges in achieving the required spatio-temporal resolutions due to limited acceleration rates. In this study, we propose a deep learning (DL) technique for improving the estimation of stationary outer-volume signal from shifted time-interleaved undersampling patterns. Our approach utilizes the pseudo-periodic nature of the ghosting artifacts arising from the moving organs. Subsequently, this estimated outer-volume signal is subtracted from individual timeframes of the real-time MR time series, and each timeframe is reconstructed individually using physics-driven DL methods. Results show improved image quality at high acceleration rates, where conventional methods fail.

PMID:39834646 | PMC:PMC11742269 | DOI:10.1109/isbi56570.2024.10635530

Front Surg. 2025 Jan 6;11:1424716. doi: 10.3389/fsurg.2024.1424716. eCollection 2024.

ABSTRACT

INTRODUCTION: To develop an intelligent system based on artificial intelligence (AI) deep learning algorithms using deep learning tools, aiming to assist in the diagnosis of lumbar degenerative diseases by identifying lumbar spine magnetic resonance images (MRI) and improve the clinical efficiency of physicians.

METHODS: The PP-YOLOv2 algorithm, a deep learning technique, was used to design a deep learning program capable of automatically identifying the spinal diseases (lumbar disc herniation or lumbar spondylolisthesis) based on the lumbar spine MR images. A retrospective analysis was conducted on lumbar spine MR images of patients who visited our hospital from January 2017 to January 2022. The collected images were divided into a training set and a testing set. The training set images were used to establish and validate the deep learning program's algorithm. The testing set images were annotated, and the experimental results were recorded by three spinal specialists. The training set images were also validated using the deep learning program, and the experimental results were recorded. Finally, a comparison of the accuracy of the deep learning algorithm and that of spinal surgeons was performed to determine the clinical usability of deep learning technology based on the PP-YOLOv2 algorithm. A total of 654 patients were included in the final study, with 604 cases in the training set and 50 cases in the testing set.

RESULTS: The mean average precision (mAP) value of the deep learning algorithm reached 90.08% based on the PP-YOLOv2 algorithm. Through classification of the testing set, the deep learning algorithm showed higher sensitivity, specificity, and accuracy in diagnosing lumbar spine MR images compared to manual identification. Additionally, the testing time of the deep learning program was significantly shorter than that of manual identification, and the differences were statistically significant (P < 0.05).

CONCLUSIONS: Deep learning technology based on the PP-YOLOv2 algorithm can be used to identify normal intervertebral discs, lumbar disc herniation, and lumbar spondylolisthesis from lumbar MRI images. Its diagnostic performance is significantly higher than that of most spinal surgeons and can be practically applied in clinical settings.

PMID:39834502 | PMC:PMC11743461 | DOI:10.3389/fsurg.2024.1424716

Anal Methods. 2025 Jan 21. doi: 10.1039/d4ay01599a. Online ahead of print.

ABSTRACT

The study aims to evaluate and compare two advanced proteomic techniques, nanoLC-MALDI-MS/MS and nanoLC-TIMS-MS/MS, in characterizing extracellular vesicles (EVs) from the bronchoalveolar lavage fluid (BALF) of patients with asthma and idiopathic pulmonary fibrosis (IPF). Pulmonary diseases, driven by pollutants and infections, often necessitate detailed analysis of BALF to identify diagnostic biomarkers and therapeutic targets. EVs, which include exosomes, microvesicles, and apoptotic bodies, are isolated using filtration and ultracentrifugation, and their morphology, concentration, and size distribution are assessed through transmission electron microscopy (TEM) and nanoparticle tracking analysis (NTA). The proteomic profiles of these EVs are then analyzed using the aforementioned techniques, highlighting their unique and common proteins. The study found that nanoLC-TIMS-MS/MS identified significantly more proteins compared to nanoLC-MALDI-MS/MS. Functional analysis via Gene Ontology revealed pathways related to inflammation and cell signaling, underscoring the role of EVs in disease pathophysiology. The findings suggest that EVs in BALF can serve as valuable biomarkers and therapeutic targets in respiratory diseases, providing a foundation for future research and clinical applications.

PMID:39835386 | DOI:10.1039/d4ay01599a

Front Med (Lausanne). 2025 Jan 6;11:1497530. doi: 10.3389/fmed.2024.1497530. eCollection 2024.

ABSTRACT

BACKGROUND: Idiopathic pulmonary fibrosis (IPF) is an interstitial lung disease characterized by chronic inflammation and progressive fibrosis. The blood urea nitrogen-to-albumin ratio (BAR) is a comprehensive parameter associated with inflammation status; however, it is unknown whether the BAR can predict the prognosis of IPF.

METHODS: This retrospective study included 176 patients with IPF, and 1-year all-cause mortality of these patients was recorded. A receiver operating characteristic (ROC) curve was used to explore the diagnostic value of BAR for 1-year all-cause mortality in IPF patients, and the survival rate was further estimated using the Kaplan-Meier survival curve. Cox proportional hazards regression model and forest plot were used to assess the association between the BAR and 1-year all-cause mortality in IPF patients.

RESULTS: The BAR of IPF patients was significantly higher in the non-survivor group than in the survivor group [0.16 (0.13-0.23) vs. 0.12 (0.09-0.17) mmol/g, p = 0.002]. The area under the ROC curve for predicting 1-year all-cause mortality in IPF patients was 0.671, and the optimal cut-off value was 0.12 mmol/g. The Kaplan-Meier survival curve showed that the 1-year cumulative survival rate of IPF patients with a BAR ≥0.12 was significantly decreased compared with the patients with a BAR <0.12. The Cox regression model and forest plot showed that the BAR was an independent prognostic biomarker for 1-year all-cause mortality in IPF patients (HR = 2.778, 95% CI 1.020-7.563, p = 0.046).

CONCLUSION: The BAR is a significant predictor of 1-year all-cause mortality of IPF patients, and high BAR values may indicate poor clinical outcomes.

PMID:39835108 | PMC:PMC11743257 | DOI:10.3389/fmed.2024.1497530

Front Immunol. 2025 Jan 6;15:1488745. doi: 10.3389/fimmu.2024.1488745. eCollection 2024.

ABSTRACT

INTRODUCTION: Serum levels of interleukin-6 (IL-6) are increased in COVID-19 patients. IL-6 is an effective therapeutic target in inflammatory diseases and tocilizumab, a monoclonal antibody that blocks signaling via the IL-6 receptor (IL-6R), is used to treat patients with severe COVID-19. However, the IL-6R exists in membrane-bound and soluble forms (sIL-6R), and the sIL-6R in combination with soluble glycoprotein 130 (sgp130) forms an IL-6-neutralizing buffer system capable of neutralizing small amounts of IL-6.

METHODS: In this study, we analyzed serum levels of IL-6, sIL-6R and sgp130 in the serum of COVID-19 convalescent individuals with a history of mild COVID-19 disease and in acute severely ill COVID-19 patients compared to uninfected control subjects. Furthermore, we used single cell RNA sequencing data in order to determine which immune cell types are sources and targets of the individual cytokines and whether their expression is altered in severe COVID-19 patients.

RESULTS: We find that sIL-6R levels are not only increased in acute severely ill patients, but also in convalescents after a mild COVID-19 infection. We show that this increase in sIL-6R results in an enhanced capacity of the sIL-6R/sgp130 buffer system, but that significantly enhanced free IL-6 is still present due to an overload of the buffer. Further, we identify IL-6 serum levels, age and the number of known pre-existing medical conditions as crucial determinants of disease outcome for the patients. We also show that IL-11 has no major systemic role in COVID-19 patients and that sCD25 is only increased in acute severely ill COVID-19 patients, but not in mild convalescent individuals.

DISCUSSION: In conclusion, our study shows long-lasting alterations of the IL-6 system after COVID-19 disease, which might be relevant when applying anti-IL-6 or anti-IL-6R therapy.

PMID:39835136 | PMC:PMC11743636 | DOI:10.3389/fimmu.2024.1488745

Front Immunol. 2025 Jan 6;15:1468969. doi: 10.3389/fimmu.2024.1468969. eCollection 2024.

ABSTRACT

INTRODUCTION: Extracellular vesicles (EVs) can potently inhibit inflammation yet there is a lack of understanding about the impact of donor characteristics on the efficacy of EVs. The goal of this study was to determine whether the sex and age of donor platelet-derived EVs (PEV) affected their ability to inhibit viral myocarditis.

METHODS: PEV, isolated from men and women of all ages, was compared to PEV obtained from women under 50 years of age, which we termed premenopausal PEV (pmPEV). Because of the protective effect of estrogen against myocardial inflammation, we hypothesized that pmPEV would be more effective than PEV at inhibiting myocarditis. We injected PEV, pmPEV, or vehicle control in a mouse model of viral myocarditis and examined histology, gene expression, protein profiles, and performed proteome and microRNA (miR) sequencing of EVs.

RESULTS: We found that both PEV and pmPEV significantly inhibited myocarditis; however, PEV was more effective, which was confirmed by a greater reduction of inflammatory cells and proinflammatory and profibrotic markers determined using gene expression and immunohistochemistry. Proteome and miR sequencing of EVs revealed that PEV miRs specifically targeted antiviral, Toll-like receptor (TLR)4, and inflammasome pathways known to contribute to myocarditis while pmPEV contained general immunoregulatory miRs.

DISCUSSION: These differences in EV content corresponded to the differing anti-inflammatory effects of the two types of EVs on viral myocarditis.

PMID:39835120 | PMC:PMC11743460 | DOI:10.3389/fimmu.2024.1468969

iScience. 2024 Dec 5;28(1):111546. doi: 10.1016/j.isci.2024.111546. eCollection 2025 Jan 17.

ABSTRACT

The genome of Methanocaldococcus (Methanococcus) jannaschii DSM 2661 was the first Archaeal genome to be sequenced in 1996. Subsequent sequence-based annotation cycles led to its first metabolic reconstruction in 2005. Leveraging new experimental results and function assignments, we have now re-annotated M. jannaschii, creating an updated resource with novel information and testable predictions in a pathway-genome database available at BioCyc.org. This reannotation effort has resulted in 652 function assignments with enzyme roles, accounting for a third of the total protein-coding entries for this genome. The updated resource includes 883 reactions, 540 enzymes, and 142 individual pathways. Despite notable progress in computational genomics, more than a third of the genome remains functionally uncharacterized. The publicly available MjCyc pathway-genome database holds great potential for the wider community to conduct research on the biology of methanogenic Archaea.

PMID:39834858 | PMC:PMC11742838 | DOI:10.1016/j.isci.2024.111546