Infections of the central nervous system (CNS) can lead to severe outcomes if not accurately diagnosed and treated. The broad spectrum of pathogens involved in CNS infections can make diagnosis challenging. Polymerase chain reaction (PCR)-based multiplex molecular diagnostic panels can rapidly and simultaneously detect multiple neuropathogens in cerebrospinal fluid (CSF). This study was aimed to assess the Bio-Speedy Meningitis/Encephalitis RT-PCR MX-17 panel (Bioeksen, & Idot;stanbul, T & uuml;rkiye), a novel multiplex PCR test, in diagnosing CNS infections. The panel can detect a range of CNS pathogens simultaneously and rapidly (< 1 hour), including Escherichia coli K1, Haemophilus influenzae, , Listeria monocytogenes, , Neisseria meningitidis, , Streptococcus pneumoniae, , Streptococcus agalactiae, , enterovirus (EV), herpes simplex virus (HSV) 1 and 2, HHV-6, HHV-7, HHV-8, human parechovirus (HPeV), varicella zoster virus (VZV), cytomegalovirus (CMV) and Cryptococcus gatti/neoformans / neoformans in CSF samples. This retrospective study included 128 CSF samples from 128 patients sent to Bursa Uluda & gbreve; University Health Application and Research Center Microbiology Laboratory between June 2022 and July 2023 to search for CNS infectious agents. Patient clinical, radiological and laboratory data were collected from the Hospital Information Record System (HIRS). Bacterial pathogens were identified through culture, while viral pathogens were detected in CSF samples using the Fast Track Diagnostics (FTD) multiplex RT-PCR panel (Fast Track Diagnostics Ltd., Luxembourg) for HSV-1, HSV-2, VZV, EV, mumps virus and HPeV. The stored CSF samples were then tested using the Bio-Speedy panel and the results were compared with those of the culture and the FTD panel. Pathogens that were detected were considered positive if they were consistent with the patient's symptoms and CSF characteristics according to infectious disease and pediatric infectious disease specialists. Pathogens detected but not supported by the patient's symptoms and CSF characteristics were classified as uncertain clinical relevance (UCR). Out of the 128 patients tested for CNS infectious agents, 44 (34.4%) were diagnosed with a CNS infection. The overall pathogen detection rate with all methods was 43.2% (19/44). The Bio-Speedy panel identified pathogens in 29.5% (13/44) of the patients, followed by the FTD panel (20.5%, 9/44) and culture (9.1%, 4/44). Four bacteria were identified with culture, three of which were also detected by the Bio-Speedy panel. Additionally, six bacteria were identified with Bio-Speedy panel, that were not identified by culture. The FTD panel identified nine viruses, four of which were also identified by Bio-Speedy. In total, the Bio-Speedy panel detected 13 of the 19 positive pathogens (nine bacteria and four viruses: [ S.pneumoniae (n= 3), VZV (n= 3), N.meningitidis (n= 2), H.influenzae (n= 2), L.monocytogenes (n= 1), E.coli (n= 1) ve EV (n= 1)]. However, the Bio-Speedy panel identified 15 pathogens [ S.pneumoniae (n= 1), E.coli (n= 1), C.gatti/neoformans / neoformans (n= 1), CMV (n= 8), HHV-6 (n= 3) ve HHV-7 (n= 1)] considered as UCR. The Bio-Speedy identified the causative pathogens in the highest percentage (29.5%) of patients with confirmed CNS infections. Nevertheless, test results should be interpreted based on patient characteristics to ensure appropriate patient management. Using multiple methods and multiplex tests may improve diagnostic accuracy for CNS infections.

Objectives: The VaCoMRI study tracked a healthcare worker cohort from 2020 through June 2024 to characterize the long-term sustainability of vaccine- and infection-induced SARS-CoV-2 immunity. Methods: Following an initial anti-nucleocapsid (N) IgG screening of 4,554 employees in early 2020, participants were monitored regularly from the BNT162b2 rollout (December 2020). Serum collected at predefined intervals after vaccination and breakthrough infection (BTI) was analyzed for anti-N, quantitative anti-spike (anti-S) IgG, and surrogate viral neutralizing antibody (sVNT) titers. Results: Over a median follow-up of 1,180 days, 142 healthcare workers contributed longitudinal samples; 66.2% experienced one BTI and 14.1% a second. In previously seronegative individuals, anti-N titers decreased 2.3-fold between 3 and 6 months post-first BTI, with seropositivity dropping from 82% to 40.4% (median time to seronegativity: 179 days). Conversely, prior anti-N seropositivity was associated with 4.5-fold higher anti-N IgG levels during follow-up, and a second BTI led to 3-fold higher titers at 3 months. Before BTI, the third vaccine dose significantly enhanced antibody persistence versus the second dose; at 9 months, titers remained 7.2-fold higher for sVNT and 3.9-fold higher for anti-S IgG. Initial seropositivity also predicted 2.9-fold higher sVNT levels during follow-up. Conclusions: Repeated vaccination and infection synergistically induced robust, durable spike-directed hybrid immunity. Conversely, rapid anti-N IgG waning severely limits its reliability for the retrospective serosurveillance of SARS-CoV-2 exposure in occupational settings.

Objectives: The VaCoMRI study tracked a cohort of healthcare workers from 2020 through June 2024 to characterize the long-term sustainability of hybrid and vaccine-induced SARS-CoV-2 immunity. Methods: An initial anti-nucleocapsid (N) IgG screening of 4554 health-care workers in early 2020 identified participants who had been infected during the first wave. Anti-N-positive and anti-N-negative individuals then received BNT162b2 through the institutional vaccination program, yielding groups with hybrid and vaccine-induced immunity. Both groups were monitored regularly for nearly four years for breakthrough infections (BTI). Serum collected regularly after vaccination and after BTI was analyzed for anti-N, quantitative anti-spike (anti-S) IgG, and surrogate viral neutralizing antibody (sVNT) titers. Results: Over a median follow-up of 1180 days, 142 healthcare workers contributed longitudinal samples; 66.2% experienced one BTI and 14.1% a second. In initially seronegative individuals, anti-N titers decreased 2.3-fold between 3 and 6 months after the first BTI, with seropositivity dropping from 82% to 40.4% (median time to seronegativity: 179 days). Prior anti-N seropositivity was associated with 4.5-fold higher anti-N IgG levels after BTI, with elevated levels persisting through 9 months. In both groups, the third vaccine dose significantly enhanced anti-S antibody persistence after 9 months compared to the second dose. In infection-naïve individuals, anti-S IgG titers remained 4.2-fold and sVNT titers 7.2-fold higher. Hybrid immunity was similarly durable: two vaccine doses after prior anti-N seropositivity yielded anti-S IgG and sVNT levels comparable to three-dose vaccination. Conclusions: This study demonstrates a rapid waning of anti-N IgG that severely limits its reliability for retrospective serosurveillance of SARS-CoV-2 exposure in occupational settings. The combination of vaccination and infection induced robust and durable spike-directed antibody responses. However, these were determined using assays based on the ancestral spike protein. This may limit the prediction of protection from SARS-CoV-2 variants, which usually cause BTIs.

Background: A critical lesson from the public health crisis caused by the COVID-19 pandemic is that pandemic preparedness requires rapid identification of early biomarkers to predict need of hospitalization. Furthermore, while therapy for COVID-19 is available, it must be administered early after infection and targeted specifically to patients in need. Currently, patient demographics and pre-existing clinical conditions are used to determine treatment eligibility. However, these factors do not provide accurate predictions, and previous research on COVID-19 severity biomarkers has focused on hospitalized patients. Here, we identify point-of-care biomarkers capable of accurately predicting COVID-19-related hospitalization risk and disease progression shortly after a positive diagnosis.Methods: Previously uninfected and non-vaccinated individuals (N=201) were recruited at, or shortly after, SARS-CoV-2 diagnosis. Biochemistry and serum cytokine levels were longitudinally measured. Biomarker combinations were tested by multi-dimensional clustering followed by dimension reduction.Findings: The combination of high levels of LDH or serum IL-6 showed the most accurate prediction (sensitivity=100%, specificity=94.5%) for hospitalization risk. The combination of four cytokines (IFN-a, IFN-g, IL-6, IL-8) was the most accurate predictor for moderate versus mild disease. Age and BMI showed significantly lower predictive values for hospitalization risk and were not predictive of mild versus moderate symptoms.Interpretation: Early accurate prediction of COVID-19 hospitalization and severity is possible using biomarkers feasibly measurable at point-of-care, thus allowing optimizing personalized treatment and public health management. The methodology used here sets out a paradigm for severity prediction in the framework of pandemic preparedness.

Respiratory tract infections are a leading cause of hospitalization in patients under 18 years of age and remain a major contributor to pediatric mor