Impact of Inherited Genetic Variants on Critically Ill Septic Children

Sepsis remains an important source of morbidity and mortality in children, despite the development of standardized care. In the last decades, there has been an increased interest in genetic and genomic approaches to early recognition and development of treatments to manipulate the host inflammatory response. This review will present a summary of the normal host response to infection and progression to sepsis, followed by highlighting studies with a focus on gene association studies, epigenetics, and genome-wide expression profiling. The susceptibility (or outcome) of sepsis in children has been associated with several polymorphisms of genes broadly involved in inflammation, immunity, and coagulation. More recently, gene expression profiling has been focused on identifying novel biomarkers, pathways and therapeutic targets, and gene expression-based subclassification. Knowledge of a patient’s individual genotype may, in the not-too-remote future, be used to guide tailored treatment for sepsis. However, at present, the impact of genomics remains far from the bedside of critically ill children.

Artificial Intelligence-Based Solution in Personalized Computer-Aided Arthroscopy of Shoulder Prostheses

Background: Early recognition of prostheses before reoperation can reduce perioperative morbidity and mortality. Because of the intricacy of the shoulder biomechanics, accurate classification of implant models before surgery is fundamental for planning the correct medical procedure and setting apparatus for personalized medicine. Expert surgeons usually use X-ray images of prostheses to set the patient-specific apparatus. However, this subjective method is time-consuming and prone to errors. Method: As an alternative, artificial intelligence has played a vital role in orthopedic surgery and clinical decision-making for accurate prosthesis placement. In this study, three different deep learning-based frameworks are proposed to identify different types of shoulder implants in X-ray scans. We mainly propose an efficient ensemble network called the Inception Mobile Fully-Connected Convolutional Network (IMFC-Net), which is comprised of our two designed convolutional neural networks and a classifier. To evaluate the performance of the IMFC-Net and state-of-the-art models, experiments were performed with a public data set of 597 de-identified patients (597 shoulder implants). Moreover, to demonstrate the generalizability of IMFC-Net, experiments were performed with two augmentation techniques and without augmentation, in which our model ranked first, with a considerable difference from the comparison models. A gradient-weighted class activation map technique was also used to find distinct implant characteristics needed for IMFC-Net classification decisions. Results: The results confirmed that the proposed IMFC-Net model yielded an average accuracy of 89.09%, a precision rate of 89.54%, a recall rate of 86.57%, and an F1.score of 87.94%, which were higher than those of the comparison models. Conclusion: The proposed model is efficient and can minimize the revision complexities of implants.

Circulating Exosomal circRNAs Contribute to Potential Diagnostic Value of Large Artery Atherosclerotic Stroke

Large artery atherosclerotic (LAA) stroke is closely associated with atherosclerosis, characterized by the accumulation of immune cells. Early recognition of LAA stroke is crucial. Circulating exosomal circRNAs profiling represents a promising, noninvasive approach for the detection of LAA stroke. Exosomal circRNA sequencing was used to identify differentially expressed circRNAs between LAA stroke and normal controls. From a further validation stage, the results were validated using RT-qPCR. We then built logistic regression models of exosomal circRNAs based on a large replication stage, and receiver operating characteristic (ROC) curves were constructed to assess the diagnostic efficacy. Using exosomal circRNA sequencing, large sample validation, and diagnostic model construction revealed that exosomal circ_0043837 and circ_ 0001801were independent predictive factors for LAA stroke, and had better diagnostic efficacy than plasma circRNAs. In the atherosclerotic group (AS), we developed a nomogram for clinical use that integrated the two-circRNA-based risk factors to predict which patients might have the risk of plaque rupture. Circulating exosomal circRNAs profiling identifies novel predictive biomarkers for the LAA stroke and plaque rupture, with superior diagnostic value than plasma circRNAs. It might facilitate the prevention and better management of this disease.

Serum Lactate for Predicting Cardiac Arrest in the Emergency Department

Objectives: Early recognition and prevention of in-hospital cardiac arrest (IHCA) play an increasingly important role in the Chain of Survival. However, clinical tools for predicting IHCA in the emergency department (ED) are scanty. We sought to evaluate the role of serum lactate in predicting ED-based IHCA. Methods: Data were retrieved from 733,398 ED visits over a 7-year period in a tertiary medical centre. We selected one ED visit per person and excluded out-of-hospital cardiac arrest, children, or those without lactate measurements. Patient demographics, computerised triage information, and serum lactate levels were extracted. The initial serum lactate levels were grouped into normal (≤2 mmol/L), moderately elevated (2 < lactate ≤ 4), and highly elevated (>4 mmol/L) categories. The primary outcome was ED-based IHCA. Results: A total of 17,392 adult patients were included. Of them, 342 (2%) developed IHCA. About 50% of the lactate levels were normal, 30% were moderately elevated, and 20% were highly elevated. In multivariable analysis, the group with highly elevated lactate had an 18-fold increased risk of IHCA (adjusted odds ratio [OR], 18.0; 95% confidence interval [CI], 11.5–28.2), compared with the normal lactate group. In subgroup analysis, the poor lactate-clearance group (<2.5%/h) was associated with a 7.5-fold higher risk of IHCA (adjusted OR, 7.5; 95%CI, 3.7–15.1) compared with the normal clearance group. Conclusions: Elevated lactate levels and poor lactate clearance were strongly associated with a higher risk of ED-based IHCA. Clinicians may consider a more liberal sampling of lactate in patients at higher risk of IHCA with follow-up of abnormal levels.

Wellens’ Syndrome: Incidence, Characteristics, and Long-Term Clinical Outcomes

Background: Sparse study of large sample size are available on patients with Wellens’ syndrome. We sought to assess the current incidence, risk factors, clinical presentation and long-term outcomes of this population. Methods: Among a total of 3528 patients with ACS underwent angioplasty from 2017 to 2019 in our center, 2127 NSTE-ACS patients with culprit vessel of LAD were enrolled in this study. According to electrocardiographic criteria, they were divided into Wellens’ group (n = 200) and non-Wellens’ group (n =1927). The primary endpoint was cardiac death; the secondary endpoint was MACCE, a composite of all-cause death, cardiac death, recurrent myocardial infarction, target lesion revascularization, heart failure and stroke. Results: The incidence of Wellens’ syndrome was 5.7% (200 of 3528) in all the ACS patients. Wellens’ syndrome was more often manifested as NSTEMI (69% vs 17.5%, P＜0.001). Percent of preexisting coronary heart disease (39.6% vs 23%) and previous PCI (19.5% vs 9%) were significantly higher in the non-Wellens’ group than in Wellens’ group (all P<0.001). More importantly, the proportion of early PCI was higher in Wellens’ group (68% vs 59.3%, P=0.017). At a median follow-up of 24 months, Wellens’ syndrome was not a factor that affects the prognosis of MACCE (P=0.05) and cardiac death (P=0.188). Conclusions: In patients with NSTE-ACS, Wellens’ syndrome does not affect the prognosis. The presence of age≥65years, diabetes, NSTEMI, eGFR< 60ml/min and left main disease were associated with an incidence of cardiac death. Early recognition and aggressive intervention are critical as they may help to attenuate adverse outcomes.

Clinical Features and Early Recognition of 242 Cases of Autoimmune Encephalitis

Objective: To analyze the clinical features of common autoimmune encephalitis and evaluate the sensitivity of antibodies contributing to focal epilepsy signs and symptoms (ACES) score.Methods: Collecting and analyzing the data of 242 patients with autoimmune encephalitis (AE) diagnosed in the First Affiliated Hospital of Zhengzhou University from August 2015 to December 2020 in this retrospective study. The six items of the ACES score (cognitive symptoms, behavioral changes, autonomic symptoms, speech problems, autoimmune diseases, temporal MRI hyperintensities) were screened in patients with complete clinical data.Results: (1) In total, 242 patients were included, with 147 cases of anti-N-methyl-D-aspartate receptor encephalitis, 47 cases of anti-γ-aminobutyric acid type B (GABA-B) receptor encephalitis, and 48 cases of anti-leucine-rich glioma inactivating protein 1 (LGI1) encephalitis. The most common clinical symptoms are cognitive impairment (77%), behavioral changes (79%), and seizures (71%). In total, 129 cases (54%) combined with autonomic dysfunction, such as gastrointestinal dysmotility, sinus tachycardia, and central hypoventilation. Twelve patients had autoimmune diseases, most of which were of thyroid diseases. (2) One hundred and twenty-seven patients with complete clinical data evaluated ACES score, 126 cases of whom (126/127, 99.2%) were equal to or &gt;2 points, 1 case (1/127, 0.8%) was of &lt;2 points.Interpretation: (1) Cognitive impairment, abnormal behavior, and seizures are the most common manifestations of AE and autonomic symptoms. Thyroid disease is the most autoimmune disease in AE. Clinically, for patients of suspected AE, increasing the knowledge and testing of thyroid function and rheumatism is necessary. (2) ACES score is a simple, effective, and easy-to-operate score, with a certain screening value for most patients suspected of AE.

Follow-up Strategies in Focal Liver Lesions and Treatment Methods

Today, advances in cross-sectional imaging have led to the detection and early recognition of incidental/focal liver lesions (FCL). In approximately 17,000 cases of chest CT, incidental liver lesions were found in 6% [1]. In general, FCL consists of hepatocytes, biliary epithelium, mesenchymal tissue, connective tissue, or metastasized cells from distant sites. Most incidental lesions are benign, some may require careful management and treatment. In evaluating the lesion, the patient's clinical history, underlying disease and age factor should be considered. FCL can be detected at a rate of 10-30% in normal healthy and chronic liver disease patients, and even in oncology patients with malignancy, FCLs can be highly benign (50-80%)

Analysis of Exotic Parasitic Infection in a Large Group of Imported Dogs Following an Animal Welfare Investigation

Background: Increased dog relocation can cause dissemination of parasite and vector populations and this is being recognised in countries across Northern Europe, including the UK. Data regarding the prevalence of exotic infections entering the UK would be beneficial to vets to help assess pets entering the UK from abroad, and to help calculate risk of establishment of novel pathogens. One such group of dogs were seized as part of an RSPCA-led animal welfare investigation and blood tested for exotic pathogens. Methods: As part of the RSPCA investigation, 151 dogs were removed from the site. Blood tests were performed for Babesia. canis, Ehrlichia canis, Hepatozoon canis and Leishmania infantum by PCR, Br.canis by antibody serology and D.immitis by blood antigen. In addition to pathogen screening, a serology titre for Rabies was measured for each dog. A clinical examination was performed by a veterinary surgeon and clinical signs recorded. Clinical signs data were analysed by the Fisher-Freeman-Halton Exact TestResults: Overall, 24% of the dogs tested positive for an infection. Two dogs were positive for Br.canis antibodies and had no clinical signs indicative of infection. Leishmania infantum was identified in 10.5% of dogs with all but two cases being diagnosed in dogs whose microchip originated in Romania. Hepatozoon canis was identified in 9.6% of dogs, all of which had a Romanian microchip. Dirofilaria immitis was identified in 4.1% of dogs, B.canis in 2.3% of dogs and E.canis was only present in 1.5% of dogs tested. Only four dogs were found to have co-infections. No significant association was found between the pathogens detected and presenting clinical signs. Conclusions: This study demonstrates a range of exotic pathogens entering the UK including Br.canis and demonstrates the importance of screening imported dogs. The emphasis for early recognition of exotic pathogens in imported dogs has relied on screening based on relevant clinical signs and the country of origin. While these factors are useful, this study demonstrated no significant association between presenting clinical signs and the pathogens carried.

Immunotherapy toxicity: identification and management

The widespread adoption of immunotherapy has revolutionized the treatment of various cancer types, including metastatic triple-negative breast cancer (TNBC), which has long been associated with poor prognostic outcomes. In particular, immune checkpoint inhibitors (ICIs) that target and inhibit programmed cell death-1 (PD-1) and programmed cell death ligand-1 (PD-L1), have shown promising results in the treatment of patients with metastatic TNBC. However, while manipulating the immune system to induce antitumor response, ICIs can also lead to a unique set of immune-related adverse events (IRAEs), which differ from standard chemotherapy toxicities due to their immune-based origin. These toxicities require highly specific management, including guidance from multidisciplinary specialists. The primary treatment strategy against IRAEs is systemic corticosteroid use, but additional treatment approaches may also involve supportive care, additional immunosuppression, and concurrent treatment delay or discontinuation. Given the rising prevalence of ICI therapy, it is essential to educate clinicians on the presentation and management of these potentially life-threatening events so that they are identified early and treated appropriately. Using data from recent clinical trials, this review will focus on known IRAEs, particularly those seen in patients with breast cancer, and will summarize their prevalence, severity, and outcomes. We will discuss optimal strategies for early recognition and management, as well as approaches toward cautious retreatment following resolution of IRAEs.

StackBC: Deep learning and transfer learning techniques based stacking approach for accurate Invasive Ductal Carcinoma classification using histology images

Artificial intelligence (AI) based computer-aided diagnostic (CAD) systems can effectively diagnose critical disease. AI-based detection of breast cancer (BC) through images data is more efficient and accurate than professional radiologists. However, the existing AI-based BC diagnosis methods have complexity in low prediction accuracy and high computation time. Due to these reasons, medical professionals are not employing the current proposed techniques in E-Healthcare to effectively diagnose the BC. To diagnose the breast cancer effectively need to incorporate advanced AI techniques based methods in diagnosis process. In this work, we proposed a deep learning based diagnosis method (StackBC) to detect breast cancer in the early stage for effective treatment and recovery. In particular, we have incorporated deep learning models including Convolutional neural network (CNN), Long short term memory (LSTM), and Gated recurrent unit (GRU) for the classification of Invasive Ductal Carcinoma (IDC). Additionally, data augmentation and transfer learning techniques have been incorporated for data set balancing and for effective training the model. To further improve the predictive performance of model we used stacking technique. Among the three base classifiers (CNN, LSTM, GRU) the predictive performance of GRU are better as compared to individual model. The GRU is selected as a meta classifier to distinguish between Non-IDC and IDC breast images. The method Hold-Out has been incorporated and the data set is split into 90% and 10% for training and testing of the model, respectively. Model evaluation metrics have been computed for model performance evaluation. To analyze the efficacy of the model, we have used breast histology images data set. Our experimental results demonstrated that the proposed StackBC method achieved improved performance by gaining 99.02% accuracy and 100% area under the receiver operating characteristics curve (AUC-ROC) compared to state-of-the-art methods. Due to the high performance of the proposed method, we recommend it for early recognition of breast cancer in E-Healthcare.