Association between metal implants and urinary chromium levels in US adults: a cross-sectional study from NHANES.

Metal implants play a significant role in orthopedics, commonly used for treating fractures, joint replacement surgeries, spinal procedures, and more. Chromium (Cr), crucial in these implants, may raises health concerns. However, the relationship between metal implants and urine Cr levels remains uncertain. We aimed to evaluate this relationship. We conducted a cross-sectional study on 1419 individuals aged 40 years or older using data from the National Health and Nutrition Examination Survey (NHANES) spanning the years 2017 to 2020. Multivariate linear regression models and subgroup analysis were applied to assess associations between metal implants and urine Cr levels. Among the 1419 participants, 402 [28.3%] self-reported having metal objects in their bodies. After adjusting for potential confounding factors, metal implants were positively correlated with the accumulation of urine Cr (ß = 0.41, 95% CI 0.04-0.77, p = 0.028). However, the positive correlation of metal implants with urine Cr was only present in females (ß = 0.81, 95% CI 0.08-1.53, p = 0.029), but not in males. Our study revealed higher urine Cr levels in individuals with metal implants, with noticeable gender differences. Additionally, those with metal implants exhibited a more pronounced elevation in urine Cr levels with increasing age compared to individuals without implants.

Pseudorabies virus encephalitis in humans: a case series study.

Pseudorabies virus (PRV) is known to cause severe encephalitis in juvenile pigs and various non-native hosts; recent evidences suggest that PRV might cause encephalitis in humans. In a multicenter cohort study in China, next-generation sequencing of cerebrospinal fluid (CSF) was performed to detect pathogens in all patients with clinically suspected central nervous system infections. This study involved all the patients whose CSF samples were positive for PRV-DNA; their clinical features were evaluated, and species-specific PCR and serological tests were sequentially applied for validation. Among the 472 patients tested from June 1, 2016, to December 1, 2018, six were positive for PRV-DNA, which were partially validated by PCR and serological tests. Additionally, we retrospectively examined another case with similar clinical and neuroimaging appearance and detected the presence of PRV-DNA. These patients had similar clinical manifestations, including a rapid progression of panencephalitis, and similar neuroimaging features of symmetric lesions in the basal ganglia and bilateral hemispheres. Six of the patients were engaged in occupations connected with swine production. PRV infection should be suspected in patients with rapidly progressive panencephalitis and characteristic neuroimaging features, especially with exposure to swine.

Next-Generation Sequencing of Cerebrospinal Fluid for the Diagnosis of Neurocysticercosis.

Background: Neurocysticercosis (NCC) is the most common helminthic infection of the central nervous system (CNS). The diagnosis of NCC is sometimes challenging due to its heterogenous clinical manifestations and the variable sensitivity and specificity of neuroimaging and serological tests. Methods: Next-generation sequencing (NGS) of cerebrospinal fluid (CSF) was used to detect pathogens in patients with clinically suspected CNS infections. A series of patients diagnosed with NCC is reviewed here. Results: Using NGS of CSF, four patients were diagnosed with NCC. The reads corresponding to Taenia solium ranged from 478 to 117,362, with genomic coverage of 0.0564-11.15%. Reads corresponding to T. solium were not found in non-template controls and far exceeded those of the background microorganisms in patients with NCC, facilitating the interpretation of the NGS results. Conclusions: This case series demonstrates that NGS of CSF is promising in the diagnosis of NCC in difficult to diagnose cases. Larger studies are needed in the future.

[Multi-task least-squares support vector regression machines and their applications in NIR spectral analysis].

In near infrared spectral quantitative analysis, many models consider separately each component when modeling sample composition content, disregarding the underlying relatedness among sample compositions. To address this problem, the present paper views modeling each sample composition content as a task, thus one can transform the problem that models simultaneously analyze all sample compositions' contents to a multi-task learning problem. On the basis of the LS-SVR, a multi-task LS-SVR (MTLS-SVR) model is proposed. Furthermore, an efficient large-scale algorithm is given. The broomcorn samples are taken as experimental material, and corresponding quantitative analysis models are constructed for three sample composition contents (protein, lysine and starch) with LS-SVR, PLS, multiple dependent variables LS-SVR (MLS-SVR) and MTLS-SVR For the MTLS-SVR model, the average relative errors between actual values and predicted ones for the three sample compositions contents are 1.52%, 3.04% and 1.01%, respectively, and the correlation coefficients are 0.993 1, 0.894 0 and 0.940 6, respectively. Experimental results show MTLS-SVR model outperforms significantly the three others, which verifies the feasibility and efficiency of the MTLS-SVR model.