Glucocorticoid therapy for sepsis in the AI era: a survey on current and future approaches.

Sepsis, a life-threatening medical condition, manifests as new or worsening organ failures due to a dysregulated host response to infection. Many patients with sepsis have manifested a hyperinflammatory phenotype leading to the identification of inflammatory modulation by corticosteroids as a key treatment modality. However, the optimal use of corticosteroids in sepsis treatment remains a contentious subject, necessitating a deeper understanding of their physiological and pharmacological effects. Our study conducts a comprehensive review of randomized controlled trials (RCTs) focusing on traditional corticosteroid treatment in sepsis, alongside an analysis of evolving clinical guidelines. Additionally, we explore the emerging role of artificial intelligence (AI) in medicine, particularly in diagnosing, prognosticating, and treating sepsis. AI's advanced data processing capabilities reveal new avenues for enhancing corticosteroid therapeutic strategies in sepsis. The integration of AI in sepsis treatment has the potential to address existing gaps in knowledge, especially in the application of corticosteroids. Our findings suggest that combining corticosteroid therapy with AI-driven insights could lead to more personalized and effective sepsis treatments. This approach holds promise for improving clinical outcomes and presents a significant advancement in the management of this complex and often fatal condition.

DP-GAN+B: A lightweight generative adversarial network based on depthwise separable convolutions for generating CT volumes.

X-rays, commonly used in clinical settings, offer advantages such as low radiation and cost-efficiency. However, their limitation lies in the inability to distinctly visualize overlapping organs. In contrast, Computed Tomography (CT) scans provide a three-dimensional view, overcoming this drawback but at the expense of higher radiation doses and increased costs. Hence, from both the patient's and hospital's standpoints, there is substantial medical and practical value in attempting the reconstruction from two-dimensional X-ray images to three-dimensional CT images. In this paper, we introduce DP-GAN+B as a pioneering approach for transforming two-dimensional frontal and lateral lung X-rays into three-dimensional lung CT volumes. Our method innovatively employs depthwise separable convolutions instead of traditional convolutions and introduces vector and fusion loss for superior performance. Compared to prior models, DP-GAN+B significantly reduces the generator network parameters by 21.104 M and the discriminator network parameters by 10.82 M, resulting in a total reduction of 31.924 M (44.17%). Experimental results demonstrate that our network can effectively generate clinically relevant, high-quality CT images from X-ray data, presenting a promising solution for enhancing diagnostic imaging while mitigating cost and radiation concerns.

DEVELOPMENT AND INTERNAL-EXTERNAL VALIDATION OF THE ACCI-SOFA MODEL FOR PREDICTING IN-HOSPITAL MORTALITY OF PATIENTS WITH SEPSIS-3 IN THE ICU: A MULTICENTER RETROSPECTIVE COHORT STUDY.

ABSTRACT: Objective: To achieve a better prediction of in-hospital mortality, the Sequential Organ Failure Assessment (SOFA) score needs to be adjusted and combined with comorbidities. This study aims to enhance the prediction of SOFA score for in-hospital mortality in patients with Sepsis-3. Methods: This study adjusted the maximum SOFA score within the first 3 days (Max Day3 SOFA) in relation to in-hospital mortality using logistic regression and incorporated the age-adjusted Charlson Comorbidity Index (aCCI) as a continuous variable to build the age-adjusted Charlson Comorbidity Index-Sequential Organ Failure Assessment (aCCI-SOFA) model. The outcome was in-hospital mortality. We developed, internally validated, and externally validated the aCCI-SOFA model using cohorts of Sepsis-3 patients from the MIMIC-IV, MIMIC-III (CareVue), and the FAHWMU cohort. The predictive performance of the model was assessed through discrimination and calibration, which was assessed using the area under the receiver operating characteristic and calibration curves, respectively. The overall predictive effect was evaluated using the Brier score. Measurements and main results: Compared with the Max Day3 SOFA, the aCCI-SOFA model showed significant improvement in area under the receiver operating characteristic with all cohorts: development cohort (0.81 vs 0.75, P < 0.001), internal validation cohort (0.81 vs 0.76, P < 0.001), MIMIC-III (CareVue) cohort (0.75 vs 0.68, P < 0.001), and FAHWMU cohort (0.72 vs 0.67, P = 0.001). In sensitivity analysis, it was suggested that the application of aCCI-SOFA in early nonseptic shock patients had greater clinical value, with significant differences compared with the original SOFA scores in all cohorts ( P < 0.05). Conclusion: For septic patients in intensive care unit, the aCCI-SOFA model exhibited superior predictive performance. The application of aCCI-SOFA in early nonseptic shock patients had greater clinical value.

MRI advances in the imaging diagnosis of tuberculous meningitis: opportunities and innovations.

Tuberculous meningitis (TBM) is not only one of the most fatal forms of tuberculosis, but also a major public health concern worldwide, presenting grave clinical challenges due to its nonspecific symptoms and the urgent need for timely intervention. The severity and the rapid progression of TBM underscore the necessity of early and accurate diagnosis to prevent irreversible neurological deficits and reduce mortality rates. Traditional diagnostic methods, reliant primarily on clinical findings and cerebrospinal fluid analysis, often falter in delivering timely and conclusive results. Moreover, such methods struggle to distinguish TBM from other forms of neuroinfections, making it critical to seek advanced diagnostic solutions. Against this backdrop, magnetic resonance imaging (MRI) has emerged as an indispensable modality in diagnostics, owing to its unique advantages. This review provides an overview of the advancements in MRI technology, specifically emphasizing its crucial applications in the early detection and identification of complex pathological changes in TBM. The integration of artificial intelligence (AI) has further enhanced the transformative impact of MRI on TBM diagnostic imaging. When these cutting-edge technologies synergize with deep learning algorithms, they substantially improve diagnostic precision and efficiency. Currently, the field of TBM imaging diagnosis is undergoing a phase of technological amalgamation. The melding of MRI and AI technologies unquestionably signals new opportunities in this specialized area.

COVID-19 mortality prediction using ensemble learning and grey wolf optimization.

COVID-19 is now often moderate and self-recovering, but in a significant proportion of individuals, it is severe and deadly. Determining whether individuals are at high risk for serious disease or death is crucial for making appropriate treatment decisions. We propose a computational method to estimate the mortality risk for patients with COVID-19. To develop the model, 4,711 reported cases confirmed as SARS-CoV-2 infections were used for model development. Our computational method was developed using ensemble learning in combination with a genetic algorithm. The best-performing ensemble model achieves an AUCROC (area under the receiver operating characteristic curve) value of 0.7802. The best ensemble model was developed using only 10 features, which means it requires less medical information so that the diagnostic cost may be reduced while the prognostic time may be improved. The results demonstrate the robustness of the used method as well as the efficiency of the combination of machine learning and genetic algorithms in developing the ensemble model.

An enhanced ant colony optimizer with Cauchy-Gaussian fusion and novel movement strategy for multi-threshold COVID-19 X-ray image segmentation.

The novel coronavirus pneumonia (COVID-19) is a respiratory disease of great concern in terms of its dissemination and severity, for which X-ray imaging-based diagnosis is one of the effective complementary diagnostic methods. It is essential to be able to separate and identify lesions from their pathology images regardless of the computer-aided diagnosis techniques. Therefore, image segmentation in the pre-processing stage of COVID-19 pathology images would be more helpful for effective analysis. In this paper, to achieve highly effective pre-processing of COVID-19 pathological images by using multi-threshold image segmentation (MIS), an enhanced version of ant colony optimization for continuous domains (MGACO) is first proposed. In MGACO, not only a new move strategy is introduced, but also the Cauchy-Gaussian fusion strategy is incorporated. It has been accelerated in terms of convergence speed and has significantly enhanced its ability to jump out of the local optimum. Furthermore, an MIS method (MGACO-MIS) based on MGACO is developed, where it applies the non-local means, 2D histogram as the basis, and employs 2D Kapur's entropy as the fitness function. To demonstrate the performance of MGACO, we qualitatively analyze it in detail and compare it with other peers on 30 benchmark functions from IEEE CEC2014, which proves that it has a stronger capability of solving problems over the original ant colony optimization for continuous domains. To verify the segmentation effect of MGACO-MIS, we conducted a comparison experiment with eight other similar segmentation methods based on real pathology images of COVID-19 at different threshold levels. The final evaluation and analysis results fully demonstrate that the developed MGACO-MIS is sufficient to obtain high-quality segmentation results in the COVID-19 image segmentation and has stronger adaptability to different threshold levels than other methods. Therefore, it has been well-proven that MGACO is an excellent swarm intelligence optimization algorithm, and MGACO-MIS is also an excellent segmentation method.

Pretreatment with Eupatilin Attenuates Inflammation and Coagulation in Sepsis by Suppressing JAK2/STAT3 Signaling Pathway.

Purpose: Sepsis is an aggressive and life-threatening organ dysfunction induced by infection. Excessive inflammation and coagulation contribute to the negative outcomes for sepsis, resulting in high morbidity and mortality. In this study, we explored whether Eupatilin could alleviate lung injury, reduce inflammation and coagulation during sepsis. Methods: We constructed an in vitro sepsis model by stimulating RAW264.7 cells with 1 µg/mL lipopolysaccharide (LPS) for 6 hours. The cells were divided into control group, LPS group, LPS+ Eupatilin (Eup) group, and Eup group to detect their cell activity and inflammatory cytokines and coagulation factor levels. Cells in LPS+Eup and Eup group were pretreated with Eupatilin (10µM) for 2 hours. In vivo, mice were divided into sham operation group, cecal ligation and puncture (CLP) group and Eup group. Mice in the CLP and Eup groups were pretreated with Eupatilin (10mg/kg) for 2 hours by gavage. Lung tissue and plasma were collected and inflammatory cytokines, coagulation factors and signaling were measured. Results: In vitro, tumor necrosis factor (TNF)-α, interleukin (IL)-1ß, IL-6, and tissue factor (TF) expression in LPS-stimulated RAW264.7 cells was downregulated by Eupatilin (10µM). Furthermore, Eupatilin inhibited phosphorylation of the JAK2/STAT3 signaling pathway and suppressed p-STAT3 nuclear translocation. In vivo, Eupatilin increased the survival rate of the mice. In septic mice, plasma concentrations of TNF-α, IL-1ß and IL-6, as well as TF, plasminogen activator inhibitor 1 (PAI-1), D-dimer, thrombin-antithrombin complex (TAT) and fibrinogen were improved by Eupatilin. Moreover, Eupatilin alleviated lung injury by improving the expression of inflammatory cytokines and TF, fibrin deposition and macrophage infiltration in lung tissue. Conclusion: Our results revealed that Eupatilin may modulate inflammation and coagulation indicators as well as improve lung injury in sepsis via the JAK2/STAT3 signaling pathway.

A missed case of hereditary hemorrhagic telangiectasia: A case report.

Hereditary hemorrhagic telangiectasia is a rare autosomal dominant disorder characterized by abnormal blood vessel formation. When an abnormal vascular architecture affects the lungs and central nervous system, serious complications can occur. We report a missed case of hereditary hemorrhagic telangiectasia with pulmonary arteriovenous malformations and cerebral arteriovenous malformations. A 22-year-old Chinese female was taken to the emergency room because of unconsciousness. Emergency head contrast-enhanced computed tomography and transthoracic contrast echocardiography showed that she had cerebral arteriovenous malformations and pulmonary arteriovenous malformations. The patient experienced multiple spontaneous epistaxis since childhood, for which she was treated at a local hospital for a brief period. Her mother also had pulmonary arteriovenous malformations. The patient was diagnosed with hereditary hemorrhagic telangiectasia according to the consensus Curaçao diagnostic criteria and eventually died of hereditary hemorrhagic telangiectasia. The case report highlights the importance of early diagnosis and intervention for hereditary hemorrhagic telangiectasia. Given that hereditary hemorrhagic telangiectasia is frequently undiagnosed, increasing the physician's awareness of hereditary hemorrhagic telangiectasia can play an important role in the timely diagnosis and treatment of these patients.

Horizontal and vertical search artificial bee colony for image segmentation of COVID-19 X-ray images.

The artificial bee colony algorithm (ABC) has been successfully applied to various optimization problems, but the algorithm still suffers from slow convergence and poor quality of optimal solutions in the optimization process. Therefore, in this paper, an improved ABC (CCABC) based on a horizontal search mechanism and a vertical search mechanism is proposed to improve the algorithm's performance. In addition, this paper also presents a multilevel thresholding image segmentation (MTIS) method based on CCABC to enhance the effectiveness of the multilevel thresholding image segmentation method. To verify the performance of the proposed CCABC algorithm and the performance of the improved image segmentation method. First, this paper demonstrates the performance of the CCABC algorithm itself by comparing CCABC with 15 algorithms of the same type using 30 benchmark functions. Then, this paper uses the improved multi-threshold segmentation method for the segmentation of COVID-19 X-ray images and compares it with other similar plans in detail. Finally, this paper confirms that the incorporation of CCABC in MTIS is very effective by analyzing appropriate evaluation criteria and affirms that the new MTIS method has a strong segmentation performance.

Application of Machine Learning in Intelligent Medical Image Diagnosis and Construction of Intelligent Service Process.

The introduction of digital technology in the healthcare industry is marked by ongoing difficulties with implementation and use. Slow progress has been made in unifying different healthcare systems, and much of the globe still lacks a fully integrated healthcare system. As a result, it is critical and advantageous for healthcare providers to comprehend the fundamental ideas of AI in order to design and deliver their own AI-powered technology. AI is commonly defined as the capacity of machines to mimic human cognitive functions. It can tackle jobs with equivalent or superior performance to humans by combining computer science, algorithms, machine learning, and data science. The healthcare system is a dynamic and evolving environment, and medical experts are constantly confronted with new issues, shifting duties, and frequent interruptions. Because of this variation, illness diagnosis frequently becomes a secondary concern for healthcare professionals. Furthermore, clinical interpretation of medical information is a cognitively demanding endeavor. This applies not just to seasoned experts, but also to individuals with varying or limited skills, such as young assistant doctors. In this paper, we proposed the comparative analysis of various state-of-the-art methods of deep learning for medical imaging diagnosis and evaluated various important characteristics. The methodology is to evaluate various important factors such as interpretability, visualization, semantic data, and quantification of logical relationships in medical data. Furthermore, the glaucoma diagnosis system is discussed in detail via qualitative and quantitative approaches. Finally, the applications and future prospects were also discussed.

Radiomics Is Effective for Distinguishing Coronavirus Disease 2019 Pneumonia From Influenza Virus Pneumonia.

Objectives: To develop and validate a radiomics model for distinguishing coronavirus disease 2019 (COVID-19) pneumonia from influenza virus pneumonia. Materials and Methods: A radiomics model was developed on the basis of 56 patients with COVID-19 pneumonia and 90 patients with influenza virus pneumonia in this retrospective study. Radiomics features were extracted from CT images. The radiomics features were reduced by the Max-Relevance and Min-Redundancy algorithm and the least absolute shrinkage and selection operator method. The radiomics model was built using the multivariate backward stepwise logistic regression. A nomogram of the radiomics model was established, and the decision curve showed the clinical usefulness of the radiomics nomogram. Results: The radiomics features, consisting of nine selected features, were significantly different between COVID-19 pneumonia and influenza virus pneumonia in both training and validation data sets. The receiver operator characteristic curve of the radiomics model showed good discrimination in the training sample [area under the receiver operating characteristic curve (AUC), 0.909; 95% confidence interval (CI), 0.859-0.958] and in the validation sample (AUC, 0.911; 95% CI, 0.753-1.000). The nomogram was established and had good calibration. Decision curve analysis showed that the radiomics nomogram was clinically useful. Conclusions: The radiomics model has good performance for distinguishing COVID-19 pneumonia from influenza virus pneumonia and may aid in the diagnosis of COVID-19 pneumonia.

Administration of bone marrow stromal cells in sepsis attenuates sepsis-related coagulopathy.

INTRODUCTION: Coagulopathy plays an important role in sepsis. The aim of this study was to determine whether bone marrow stromal cell (BMSC) administration could attenuate coagulopathy in sepsis. MATERIALS AND METHODS: In vitro: endothelial cells were cultured with/without BMSCs for 6 h following LPS stimulation and were collected for thrombomodulin (TM) and endothelial protein C receptor (EPCR) measurements. In vivo: Thirty-six mice were randomized into sham, sepsis, and sepsis + BMSC groups (n = 12 each group). Sepsis was induced through cecal ligation and puncture (CLP). BMSC infusion was started at 6 h after CLP. Lung tissues and plasma samples were collected at 24 h after CLP for enzyme-linked immunosorbent assay (ELISA), quantitative real-time RT-PCR, western blot, and immunohistochemistry analysis. RESULTS: In vitro: BMSCs attenuated the decrease in TM and EPCR mRNA and protein expression levels in LPS-stimulated endothelial cells. In vivo: BMSC treatment decreased lung injury and mesenteric perfusion impairment, and ameliorated coagulopathy, as suggested by the reduction in elevated TF, vWF, and TAT circulation levels. BMSC infusion decreased TF mRNA transcription and protein expression levels in lung tissues, and increased TM and EPCR mRNA transcription and expression levels. DISCUSSION: BMSC administration attenuated coagulopathy, and decreased lung injury and mesenteric perfusion impairment in sepsis. Key messages BMSCs increased the expression of TM and EPCR from endothelium cells exposed to LPS in vitro. BMSC treatment attenuated lung injury and coagulopathy in the mice cecal ligation and puncture (CLP) model. BMSC administration-attenuated coagulopathy is related to the reduced expression of TF and increased expression of TM and EPCR.

Synergistic interaction between MEK inhibitor and gefitinib in EGFR-TKI-resistant human lung cancer cells.

With the increasing use of epidermal growth factor receptor tyrosine kinase inhibitor (EGFR-TKIs) in patients with advanced non-small cell lung cancer (NSCLC), acquired resistance has become a major clinical problem. A combination of different signaling pathway inhibitors is a promising strategy to overcome this. In the present study, the mitogen-activated protein kinase kinase 1/2 inhibitor, AZD6244, was used in combination with gefitinib to investigate the efficacy of this treatment in NSCLC cell lines, particularly in gefitinib-resistant cells. The EGFR-TKI-sensitive PC-9 (mutant EGFR/wild-type K-Ras) and EGFR-TKI-resistant A549 (wild-type EGFR/mutant K-Ras) human NSCLC cell lines were treated with AZD6244 alone, gefitinib alone or the combination of the two drugs, and the effects were evaluated using cell proliferation assays, with alterations in signaling pathways analyzed by western blotting. It was found that the growth inhibitory effect of combination treatment with gefitinib and AZD6244 was greater than that of gefitinib alone in the EGFR-TKI-resistant A549 cells. Treatment of A549 cells with gefitinib alone reduced the expression level of the activated form of Akt, and the combination of the two drugs showed stronger inhibition of phosphorylated-Akt and phosphorylated-extracellular signal-regulated kinases. The data showed that the combination of AZD6244 and gefitinib exhibited dose-dependent synergism in gefitinib-resistant NSCLC cells. Thus, a preclinical rationale exists for the use of AZD6244 to enhance the efficacy of gefitinib in patients with EGFR-TKI-resistant NSCLC.

Bone Marrow Mesenchymal Stem Cells Alleviate Extracellular Kynurenine Levels, as Detected by High-Performance Liquid Chromatography.

Endothelial cell dysfunction plays an important role in the occurrence and development of sepsis, which is a consequence of the interaction between coagulation and inflammation. Kynurenine (KYN) is an endothelium-derived relaxing factor that makes a large contribution to sepsis pathophysiology. In this study, we investigated the influence of bone marrow mesenchymal stem cells (BMSCs) on KYN production by cultured endothelial cells. KYN and tryptophan (TRP) concentrations in cell supernatants were simultaneously measured with a high-performance liquid chromatography (HPLC) system equipped with a fluorescence detector (FLD) and an ultraviolet detector (UVD). Our results revealed that lipopolysaccharide-stimulated endothelial cells produced more KYN, which was accompanied by a parallel decrease in TRP. When co-cultured with BMSCs, KYN and TRP production were significantly decreased compared to lipopolysaccharide (LPS)-induction alone. Our results suggest that BMSCs can attenuate endothelial cell damage by decreasing KYN as detected with HPLC. This method is the first to be capable of capturing functional changes in the cells and is simple, fast, and suitable for cellular level research purposes.

[Experimental therapeutic study for activated coagulation by hemorrhagic shock in rats].

The study was aimed to investigate the changes of blood coagulation factors during hemorrhagic shock in rats and the effects of various of resuscitation fluids on expression of blood coagulation factors in rats with hemorrhagic shock and to clarify its possible mechanism. 50 SD rats were randomly divided into 5 groups: control, sham operation, shock, resuscitation 1 (infusion with Ringer's lactate) and resuscitation 2 (infusion with 6% VOLUVEN), 10 rats per group. The rats in resuscitation 1 and resuscitation 2 groups were subjected to hemorrhagic shock, after hemorrhage shock for 1 hour resuscitation was performed with Ringer's lactate and 6% VOLUVEN. After resuscitation for 2 hours the changes of t-PA, PAI-1, TF were measured. At the same time, the rats in shock and the sham operation groups were blooded out so as to test. The results showed that the levels of plasma t-PA, t-PA/PAI, TF in the shock and resuscitation 1 groups were significantly higher than that in control and sham operation groups (P<0.01). The levels of plasma t-PA, t-PA/PAI in resuscitation 1 group were higher than that in shock group (P<0.01), the levels of plasma t-PA, t-PA/PAI and TF in the resuscitation 2 group were significantly lower than that in shock and resuscitation 1 groups (P<0.01). It is concluded that hemorrhagic shock may trigger the coagulation cascade reaction, results in hyperfunctioning of fiberinolysis and activation of platelets and coagulation system, and so the coagulation factor is greatly consumed. Unbalance of coagulation system plays an important role in the progress of shock. Efficacy of resuscitation with 6% VOLUVEN plus Ringer's lactate may be better than Ringer's lactate alone in regulating blood coagulation after hemorrhagic shock in rats.