Extracellular Protease and DNase Activities in Clinical and Environmental Isolates of Cryptococcus neoformans Species Complex from Central India

Enzymes are important not only for the growth and multiplication of the microorganism but also in the infection, penetration of the host tissue and encountering host defense mechanisms. This study aims to investigate extracellular protease and DNase activity in clinical (20) and 120 environmental isolates of C. neoformans species complex collected from different localities of central India.  DNase test agar containing toluidine blue and Yeast Carbon Base (YCB) agar medium supplemented with 0.1% BSA + 0.01% polypeptone was employed for the screening of DNase and protease production respectively. DNase and protease production was detected by the appearance of clear zones around the colonies. On the basis of enzymatic activity and their Pz values, high protease production (Pz≤0.6) was observed by 14 (11.6 %) environmental and 4 (11.6 %) clinical strains on 5th day, whereas 35 (29.16 %) environmental and 8 (40 %) clinical strains were screened on 8th day of incubation. Similarly 13 (10.83%) environmental and 3 (15 %) clinical strains on the 5th day, however 32 (26.66%) environmental and 8 (40 %) clinical strains on the 8th day of incubation were found to be high DNase producing strains with low Pz value (Pz≤0.6). In the case of protease activity, no significant difference was observed whereas a significant difference has shown by clinical C. neoformans and C. gattii strains on the 5th day of DNase production (p < .001). Extracellular enzymes play a vital role in the pathogenicity and virulence of C. neoformans species complex, therefore, enzymes are considered as worthy targets for developing therapeutics. Keywords: Cryptococcus neoformans species complex, Extracellular enzymes, DNase, protease, virulence, Pz valu

Clinical Validation of Integrated Nucleic Acid and Protein Detection on an Electrochemical Biosensor Array for Urinary Tract Infection Diagnosis

BACKGROUND: Urinary tract infection (UTI) is a common infection that poses a substantial healthcare burden, yet its definitive diagnosis can be challenging. There is a need for a rapid, sensitive and reliable analytical method that could allow early detection of UTI and reduce unnecessary antibiotics. Pathogen identification along with quantitative detection of lactoferrin, a measure of pyuria, may provide useful information towards the overall diagnosis of UTI. Here, we report an integrated biosensor platform capable of simultaneous pathogen identification and detection of urinary biomarker that could aid the effectiveness of the treatment and clinical management. METHODOLOGY/PRINCIPAL FINDINGS: The integrated pathogen 16S rRNA and host lactoferrin detection using the biosensor array was performed on 113 clinical urine samples collected from patients at risk for complicated UTI. For pathogen detection, the biosensor used sandwich hybridization of capture and detector oligonucleotides to the target analyte, bacterial 16S rRNA. For detection of the protein biomarker, the biosensor used an analogous electrochemical sandwich assay based on capture and detector antibodies. For this assay, a set of oligonucleotide probes optimized for hybridization at 37°C to facilitate integration with the immunoassay was developed. This probe set targeted common uropathogens including E. coli, P. mirabilis, P. aeruginosa and Enterococcus spp. as well as less common uropathogens including Serratia, Providencia, Morganella and Staphylococcus spp. The biosensor assay for pathogen detection had a specificity of 97% and a sensitivity of 89%. A significant correlation was found between LTF concentration measured by the biosensor and WBC and leukocyte esterase (p<0.001 for both). CONCLUSION/SIGNIFICANCE: We successfully demonstrate simultaneous detection of nucleic acid and host immune marker on a single biosensor array in clinical samples. This platform can be used for multiplexed detection of nucleic acid and protein as the next generation of urinary tract infection diagnostics

Trichomonas vaginalis Detection in Urogenital Specimens from Symptomatic and Asymptomatic Men and Women by Use of the cobas TV/MG Test

Trichomonas vaginalis is a prevalent sexually transmitted infection (STI). Diagnosis has historically relied on either microscopic analysis or culture, the latter being the previous gold standard. However, these tests are not readily available for male diagnosis, generally only perform well for symptomatic women, and are not as sensitive as nucleic acid amplification tests (NAATs). Men are largely asymptomatic but carry the organism and transmit to their sexual partners. This multicenter, prospective study evaluated the performance of the cobas T. vaginalis/Mycoplasma genitalium (TV/MG) assay for detection of T. vaginalis DNA compared with patient infection status (PIS) defined by a combination of commercially available NAATs and culture using urogenital specimens. A total of 2,064 subjects (984 men and 1,080 women, 940 [45.5%] symptomatic, 1,124 [54.5%] asymptomatic) were evaluable. In women, sensitivity ranged from 99.4% (95% confidence interval [CI] 96.8 to 99.9%) using vaginal samples to 94.7% (95% CI 90.2 to 97.2%) in PreservCyt samples. Specificity ranged from 98.9 to 96.8% (95% CI 95.4 to 97.8%). In men, the cobas TV/MG assay was 100% sensitive for the detection of T. vaginalis in both male urine samples and meatal swabs, with specificity of 98.4% in urine samples and 92.5% in meatal swabs. The cobas TV/MG is a suitable diagnostic test for the detection of T. vaginalis, which could support public health efforts toward infection control and complement existing STI programs

Federated learning enables big data for rare cancer boundary detection.

Although machine learning (ML) has shown promise across disciplines, out-of-sample generalizability is concerning. This is currently addressed by sharing multi-site data, but such centralization is challenging/infeasible to scale due to various limitations. Federated ML (FL) provides an alternative paradigm for accurate and generalizable ML, by only sharing numerical model updates. Here we present the largest FL study to-date, involving data from 71 sites across 6 continents, to generate an automatic tumor boundary detector for the rare disease of glioblastoma, reporting the largest such dataset in the literature (n = 6, 314). We demonstrate a 33% delineation improvement for the surgically targetable tumor, and 23% for the complete tumor extent, over a publicly trained model. We anticipate our study to: 1) enable more healthcare studies informed by large diverse data, ensuring meaningful results for rare diseases and underrepresented populations, 2) facilitate further analyses for glioblastoma by releasing our consensus model, and 3) demonstrate the FL effectiveness at such scale and task-complexity as a paradigm shift for multi-site collaborations, alleviating the need for data-sharing

Author Correction: Federated learning enables big data for rare cancer boundary detection.

10.1038/s41467-023-36188-7NATURE COMMUNICATIONS14

Prospective, multicentre study of screening, investigation and management of hyponatraemia after subarachnoid haemorrhage in the UK and Ireland

Background: Hyponatraemia often occurs after subarachnoid haemorrhage (SAH). However, its clinical significance and optimal management are uncertain. We audited the screening, investigation and management of hyponatraemia after SAH. Methods: We prospectively identified consecutive patients with spontaneous SAH admitted to neurosurgical units in the United Kingdom or Ireland. We reviewed medical records daily from admission to discharge, 21 days or death and extracted all measurements of serum sodium to identify hyponatraemia (&lt;135 mmol/L). Main outcomes were death/dependency at discharge or 21 days and admission duration &gt;10 days. Associations of hyponatraemia with outcome were assessed using logistic regression with adjustment for predictors of outcome after SAH and admission duration. We assessed hyponatraemia-free survival using multivariable Cox regression. Results: 175/407 (43%) patients admitted to 24 neurosurgical units developed hyponatraemia. 5976 serum sodium measurements were made. Serum osmolality, urine osmolality and urine sodium were measured in 30/166 (18%) hyponatraemic patients with complete data. The most frequently target daily fluid intake was &gt;3 L and this did not differ during hyponatraemic or non-hyponatraemic episodes. 26% (n/N=42/164) patients with hyponatraemia received sodium supplementation. 133 (35%) patients were dead or dependent within the study period and 240 (68%) patients had hospital admission for over 10 days. In the multivariable analyses, hyponatraemia was associated with less dependency (adjusted OR (aOR)=0.35 (95% CI 0.17 to 0.69)) but longer admissions (aOR=3.2 (1.8 to 5.7)). World Federation of Neurosurgical Societies grade I–III, modified Fisher 2–4 and posterior circulation aneurysms were associated with greater hazards of hyponatraemia. Conclusions: In this comprehensive multicentre prospective-adjusted analysis of patients with SAH, hyponatraemia was investigated inconsistently and, for most patients, was not associated with changes in management or clinical outcome. This work establishes a basis for the development of evidence-based SAH-specific guidance for targeted screening, investigation and management of high-risk patients to minimise the impact of hyponatraemia on admission duration and to improve consistency of patient care

Federated Learning Enables Big Data for Rare Cancer Boundary Detection

Although machine learning (ML) has shown promise across disciplines, out-of-sample generalizability is concerning. This is currently addressed by sharing multi-site data, but such centralization is challenging/infeasible to scale due to various limitations. Federated ML (FL) provides an alternative paradigm for accurate and generalizable ML, by only sharing numerical model updates. Here we present the largest FL study to-date, involving data from 71 sites across 6 continents, to generate an automatic tumor boundary detector for the rare disease of glioblastoma, reporting the largest such dataset in the literature (n = 6, 314). We demonstrate a 33% delineation improvement for the surgically targetable tumor, and 23% for the complete tumor extent, over a publicly trained model. We anticipate our study to: 1) enable more healthcare studies informed by large diverse data, ensuring meaningful results for rare diseases and underrepresented populations, 2) facilitate further analyses for glioblastoma by releasing our consensus model, and 3) demonstrate the FL effectiveness at such scale and task-complexity as a paradigm shift for multi-site collaborations, alleviating the need for data-sharing