Disseminated tuberculosis (TB) is a severe form of TB associated with high mortality typically occurring in individuals with impaired host defenses. It results from the lymphohematogenous dissemination of Mycobacterium tuberculosis from the primary infection site. Diagnosis can be particularly challenging when pulmonary symptoms are absent. We present a case of disseminated TB in a 73-year-old woman who presented with nausea, poor appetite, and low-grade fever. Imaging studies revealed multiple lesions in the lungs, sacroiliac joints, and brain. The nonspecific nature of her initial symptoms, which were inconsistent with the extent of disease involvement, posed significant diagnostic challenges. In this case, metagenomic next-generation sequencing played a pivotal role in confirming the diagnosis of disseminated TB.
Neonatal bacterial meningitis (NBM) is an extremely severe disease in the neonatal period. Early identification of high-risk infants is critical for timely intervention, yet prognostic assessment remains challenging due to nonspecific symptoms and variable clinical trajectories. While machine learning (ML) has shown promise in predicting outcomes for other neonatal conditions, its application for short-term adverse prognosis in NBM remains unexplored. This study aims to systematically evaluate ML models to screen for risk factors and identify the optimal predictive model to provide clinicians with a data-driven tool for the stratified management of NBM patients.
Clinical data of 433 term neonates with NBM hospitalized in the Department of Neonatology at the Capital Institute of Pediatrics between January 2013 and December 2023 were analyzed retrospectively. Based on discharge outcomes, patients were stratified into adverse (n = 84) and favorable prognosis (n = 349) groups. From an initial set of 32 clinical variables derived from clinical and laboratory data. Seventeen variables (15 via maximum Relevance Minimum Redundancy algorithm, two clinical-based) were selected. Nine machine learning models were evaluated using area under the curve (AUC), sensitivity, specificity, positive predictive value, and negative predictive value.
Among the nine models, the logistic regression model achieved optimal performance (AUC: 0.908, accuracy: 0.890, sensitivity: 0.541, specificity: 0.974, positive predictive value: 0.845, negative predictive value: 0.898). Key predictors included muscle tone abnormalities, seizures, cerebrospinal fluid (CSF) protein > 2000 mg/L, mechanical ventilation, hypotension requiring inotropes, CSF glucose < 2.0 mmol/L, bulging fontanelle, C-reactive protein, hepatomegaly, and positive blood culture.
Machine learning models can be reliable tools for predicting short-term adverse prognoses in patients with NBM. The logistic regression model demonstrated the best predictive performance, which can help clinicians identify high-risk patients.
This paper reports a case of Chlamydia psittaci pneumonia and pulmonary aspergillosis with concurrent liver injury. The patient was admitted to the hospital with coughing, expectoration, and fever for 5 days. Laboratory tests upon admission revealed abnormalities in liver function. The patient was treated with broad-spectrum antibiotics and liver-protection therapy, but symptoms did not significantly improve. Further nanopore sequencing detected Chlamydia psittaci and Aspergillus flavus. The treatment plan was adjusted to include combined anti-infection and antifungal therapies. After treatment, the patient's symptoms improved significantly; their liver function returned to normal, and their condition improved.
Human cytomegalovirus (HCMV), also known as human herpesvirus 5, infects the majority of human populations worldwide and causes a range of diseases, particularly in immunocompromised individuals. HCMV can establish life-long latency in infected hematopoietic cells, maintaining the viral genome as an episome that can reactivate to produce viral progeny upon appropriate stimulation. Understanding the establishment, maintenance, and reactivation of HCMV latency is crucial for developing targeted therapeutic strategies against HCMV infection and HCMV-induced diseases. Here, we discuss the recent advances in the mechanisms by which HCMV maintains its genome in infected cells, the cellular factors or viral antigens that modulate its reactivation, and the development of anti-HCMV therapeutics or vaccines. Overall, these insights may pave the way for the development of novel therapies that specifically and efficiently target HCMV-associated diseases.
Circulating tumor cell (CTC) analysis, as a form of liquid biopsy, has become a powerful tool for the early diagnosis of cancer, monitoring disease progression, and evaluating treatment efficacy. In recent decades, significant progress has been made in DNA-based technology for capturing and detecting CTCs. However, effectively releasing captured CTCs remains challenging. This article reviews the latest progress in CTC detection, capture, and release methods based on DNA materials and provides insights into current development trends and future research directions. We give a comprehensive overview of various strategies and discuss their design principles, characteristics, advantages, and limitations and the challenges faced by the CTC research field.
Extrachromosomal circular DNA (eccDNA) has recently become a focus of cardiovascular disease (CVD) research as it is possibly involved in disease mechanisms and may function as a biomarker. The literature suggests that eccDNAs may modify gene expression patterns and contribute to the pathogenesis of diseases such as pulmonary arterial hypertension (PAH). Many eccDNAs have been reported to be upregulated in PAH patients with promisingly high diagnostic sensitivity and specificity values. The advent of eccDNA detection from plasma samples using high-throughput sequencing technologies has opened new avenues for non-invasive diagnosis. In this review, we focus on the role of eccDNA in CVDs, concentrating on its prospects for use as a biomarker of disease development and stage. However, further studies should be conducted to explicate the functional aspects of eccDNAs in cardiovascular health and disease. This work may eventually lead to the development of novel treatment options and improved clinical outcomes for CVD patients.
Electrical impedance tomography (EIT) is an emerging medical imaging technology that involves injecting excitation current into a patient's skin through electrodes and then reconstructing the internal conductivity distribution from the voltage data collected by the different electrodes. EIT boasts several notable advantages over other imaging methods, including its non-invasive nature that does not utilize ionizing radiation, making it safer for patients. Additionally, it offers high temporal resolution, allowing for real-time monitoring of physiological changes, and its low cost and high portability make it suitable for real-time monitoring in emergency rescue and bedside situations. However, EIT also has some technical limitations that lead to poor spatial resolution. The possibility of designing wearable devices incorporating EIT has recently propelled this technology. In this article, we review the principles, hardware design, and main clinical applications of EIT. Wireless wearable EIT technology extends beyond clinical use, proving equally effective in emergency response scenarios, and is adaptable to various operational environments because of its portable monitoring capabilities.
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