Primary amoebic meningoencephalitis (PAM), caused by Naegleria fowleri, is a rare protozoan infectious disease in China. A fatality rate of over 95% had been reported due to extremely rapid disease progression in the USA and other countries. Rapid and precise identification of the causative agent is very important to clinicians for guiding their choices for administering countermeasures in the clinic. In this report, we applied the next-generation sequencing (NGS) method to rapidly show that N. fowleri was the causative agent of a fatal case involving a 42-year-old man with severe PAM disease, the first reported in mainland China. A 42-year old male in a deep coma was admitted to Shenzhen Third People’s Hospital, a special medical care unit with expertise in infectious diseases. Increased intracranial pressure was detected. The cerebrospinal fluid (CSF) sample was found to be red and cloudy with increased leukocyte and protein levels. While bacterial cultures with CSF were negative, N. fowleri was determined to be the causative agent with NGS. Amphotericin B (AmB), a drug with anti-amoeba activity, was used immediately, but the treatment came too late and the patient died 2 days after the NGS confirmation. In this paper, we reported a case of PAM disease for the first time in mainland China. NGS was used for rapid diagnosis and provided guidance for prescribing medications. However, the patient died due to a late admission amid advanced PAM disease. Early detection of N. fowleri is necessary in order to select effective drug treatments and control the disease progression. Despite the negative survival outcome, NGS was shown to be a promising method of rapid and precise identification of N. fowleri.

Identifying patients at highest risk of serious adverse prognostic events (AE) in neurosyphilis could enable risk-stratified treatment beyond clinical judgment. We developed machine-learning models using electronic health records from six Chinese infectious-diseases hospitals, with two centers for external validation and four for discovery. Five models incorporated demographic, clinical, laboratory, and treatment variables from 602 observations (402 discovery, 200 validation). AE occurred in 20.90% and 20.50%, respectively. DEFEAT-NS-M1 achieved AUROC 0.975 (95% CI 0.949–0.995) internally and 0.863 (0.801–0.920) externally, with Brier scores 0.027 and 0.128. Decision curve analysis demonstrated favorable clinical utility; treating 1–2 high-risk patients prevents one AE. DEFEAT-NS-M1 supports population-level risk estimation and stratified care, potentially guiding targeted monitoring and therapy. Further external validation and health-economic assessment are warranted.