Background. In the general population, acute myocardial infarction (AMI) represents a significant cause of mortality. This study is aimed at identifying novel diagnostic biomarkers to aid in treating and diagnosing AMI. Methods. The Gene Expression Omnibus (GEO) database was explored to extract two microarray datasets, GSE66360 and GSE48060, which were subsequently merged into a single cohort. Both AMI and control samples were analyzed for differentially expressed genes (DEGs), which were subsequently subjected to weighed gene coexpression network analysis (WGCNA) to identify the most significant module. Gene Ontology (GO) and pathway analyses subsequently carried out the most significant gene modules along with construction of a protein‐protein interaction network (PPI). Cytoscape plugin cytoHubba allowed for the prediction of the top 4 key genes according to the network maximal clique centrality (MCC) algorithm. The expression levels and diagnostic value of the four key genes were additionally verified in the GSE62646 dataset. Results. A WCGNA analysis revealed 878 DEGs which were clustered into 6 modules. The module with the most significance in AMI was colored blue. Subsequent GO and KEGG pathway enrichment analysis on blue module genes revealed that they were primarily enriched in the inflammation‐related pathways. These findings, in combination with PPI and coexpression networks, resulted in the identification of the top four genes by cytoHubba, which included leukocyte immunoglobulin‐like receptor B2 (LILRB2), toll‐like receptor 2 (TLR2), neutrophil cytosolic factor 2 (NCF2), and S100A9. Among them, LILRB2, NCF2, and S100A9 were validated in the GSE62646 dataset. Conclusions. The results suggested that LILRB2, NCF2, and S100A9 could be potential gene biomarkers for AMI.

Background: For medical diagnosis, clinicians typically begin with a patient’s chief concerns, followed by questions about symptoms and medical history, physical examinations, and requests for necessary auxiliary examinations to gather comprehensive medical information. This complex medical investigation process has yet to be modeled by existing artificial intelligence (AI) methodologies. Objective: The aim of this study was to develop an AI-driven medical inquiry assistant for clinical diagnosis that provides inquiry recommendations by simulating clinicians’ medical investigating logic via reinforcement learning. Methods: We compiled multicenter, deidentified outpatient electronic health records from 76 hospitals in Shenzhen, China, spanning the period from July to November 2021. These records consisted of both unstructured textual information and structured laboratory test results. We first performed feature extraction and standardization using natural language processing techniques and then used a reinforcement learning actor-critic framework to explore the rational and effective inquiry logic. To align the inquiry process with actual clinical practice, we segmented the inquiry into 4 stages: inquiring about symptoms and medical history, conducting physical examinations, requesting auxiliary examinations, and terminating the inquiry with a diagnosis. External validation was conducted to validate the inquiry logic of the AI model. Results: This study focused on 2 retrospective inquiry-and-diagnosis tasks in the emergency and pediatrics departments. The emergency departments provided records of 339,020 consultations including mainly children (median age 5.2, IQR 2.6-26.1 years) with various types of upper respiratory tract infections (250,638/339,020, 73.93%). The pediatrics department provided records of 561,659 consultations, mainly of children (median age 3.8, IQR 2.0-5.7 years) with various types of upper respiratory tract infections (498,408/561,659, 88.73%). When conducting its own inquiries in both scenarios, the AI model demonstrated high diagnostic performance, with areas under the receiver operating characteristic curve of 0.955 (95% CI 0.953-0.956) and 0.943 (95% CI 0.941-0.944), respectively. When the AI model was used in a simulated collaboration with physicians, it notably reduced the average number of physicians’ inquiries to 46% (6.037/13.26; 95% CI 6.009-6.064) and 43% (6.245/14.364; 95% CI 6.225-6.269) while achieving areas under the receiver operating characteristic curve of 0.972 (95% CI 0.970-0.973) and 0.968 (95% CI 0.967-0.969) in the scenarios. External validation revealed a normalized Kendall τ distance of 0.323 (95% CI 0.301-0.346), indicating the inquiry consistency of the AI model with physicians. Conclusions: This retrospective analysis of predominantly respiratory pediatric presentations in emergency and pediatrics departments demonstrated that an AI-driven diagnostic assistant had high diagnostic performance both in stand-alone use and in simulated collaboration with clinicians. Its investigation process was found to be consistent with the clinicians’ medical investigation logic. These findings highlight the diagnostic assistant’s promise in assisting the decision-making processes of health care professionals.

Klebsiella pneumoniae invasion syndrome (KPIS) is a critical multi-site infection that is usually caused by highly virulent Klebsiella pneumonia. It is relatively common in Asian patients with diabetes and leads to sepsis, which has a high mortality rate. We report the case of a man in his early 40s who presented to the hospital with blurred vision in his left eye of 7 days’ duration and fever of 1 day’s duration. After a complete examination, he was diagnosed with KPIS on the basis of his liver abscessation, lung abscessation, endophthalmitis of the left eye and brain abscessation. After needle puncture and drainage of the left eye and liver abscess and anti-bacterial treatment with meropenem, the patient recovered well. When KPIS is suspected, attention should be paid to the sites of infection and the selection of the most appropriate antibiotics, but the most important aim should be to drain the lesions in a timely manner to improve the patient’s prognosis.

Candidemia leads to higher mortality and longer hospital-stay. While the studies about the clinical manifestations of candidemia caused by different Candida species and the relationship between the antifungal drugs and prognosis were rarely performed. This retrospective study enrolled all 94 patients diagnosed as candidemia from January 2020 to July 2023 in BTCH. Demographic information, comorbidities, laboratory parameters, medications and prognosis were collected and analyzed. C. albicans was the most common specie of candidemia. There was no significant difference in age, gender and hospital-mortality in different species groups. Higher-level and longer duration of broad-spectrum antibiotic use, lower BMI, hypoalbuminemia, longer duration of PN and history of G+ coccemia were conclusive about mortality. The C.tropicalis group had higher SCRE levels (F = 8.40, P = 0.03) and shorter TTP (F = 5.03, P < 0.01) than other species. No distinction was found in different antifungal drugs groups including triazoles and echinocandins after 7 days treatment (χ2 = 0.05, P = 0.81). The efficacy was no difference between triazoles and echinocandins in the different species groups. (χ12 = 1.20, P1 = 0.75; χ22 = 0.05, P2 = 0.81). C. albicans accounts the most among candida induecd candidemia.The C.tropicalis group had higher SCRE levels and shorter TTP than other groups. Elder, hypoproteinemia, lower BMI, longer duration and higher-level of broad-spectrum antibiotic use, longer PN support and G+ coccemia increase risks for candidemia. The efficacy of triazoles and echinocandins are the same when blood culture turned negative in 7 days.

Spontaneous renal rupture is a rare disease in the clinic. The causes of spontaneous renal rupture include extrarenal factors, intrarenal factors, and idiopathic factors. Reports on infection secondary to spontaneous renal rupture and the complications of spontaneous renal rupture are scarce. Furthermore, there are few patients with spontaneous renal rupture who present only with fever. We present the case of a 52-year-old female patient who was admitted to our hospital. She presented only with fever, and the cause of the disease was unclear. She underwent a contrast-enhanced computed tomography (CT) scan, which showed that the left renal capsule had a crescent-shaped, low-density shadow; the perirenal fat was blurred, and exudation was visible with no sign of calculi, malignancies, instrumentation, or trauma. Under ultrasound guidance, a pigtail catheter was inserted into the hematoma, and fluid was drained and used for the bacterial test, which proved the presence of Klebsiella pneumoniae. Two months later, abdominal CT showed that the hematoma was absorbed, so the drainage tube was removed. The abdominal CT was normal after 4 mo. Spontaneous renal rupture due to intrarenal factors causes a higher proportion of shock and is more likely to cause anemia.

A 33-plex digital PCR assay with an optimized DTT-based pretreatment protocol enables rapid absolute quantification and dynamic monitoring of bacteria, viruses, fungi, and atypical pathogens in bronchoalveolar lavage fluid.