Audiological profile of adult Long COVID patients.

INTRODUCTION: Hearing loss is one of the self-reported symptoms of Long COVID patients, however data from objective and subjective audiological tests demonstrating diminished hearing in Long COVID patients has not been published. MATERIALS AND METHODS: Respondents of a large Long COVID online survey were invited to the ENT-department for an otologic exam. The participants were split into three groups based on their history of SARS-CoV-2 infection and persistence of symptoms. Respondents with a history of a SARS-CoV-2 infection were allocated to the Long COVID group, if they reported persistent symptoms and to the Ex COVID group, if they had regained their previous level of health. Participants without a history of SARS-CoV-2 infection made up the No COVID control group. In total, 295 ears were examined with otoscopy, tympanograms, pure tone audiometry and otoacoustic emissions. Ears with known preexisting hearing loss or status post ear surgery, as well as those with abnormal otoscopic findings, non-type A tympanograms or negative Rinne test were excluded. RESULTS: Compared to the No COVID and Ex COVID groups, we did not find a clinically significant difference in either hearing thresholds or frequency specific TEOAEs. However, at 500 Hz the data from the left ear, but not the right ear showed a significantly better threshold in the Ex COVID group, compared to Long COVID and No COVID groups. Any of the other tested frequencies between 500 Hz and 8 kHz were not significantly different between the different groups. There was a significantly lower frequency-specific signal-to-noise-ratio of the TEOAEs in the Long COVID compared to the No COVID group at 2.8 kHz. At all other frequencies, there were no significant differences between the three groups in the TEOAE signal-to-noise-ratio. CONCLUSION: This study detected no evidence of persistent cochlear damage months after SARS-CoV-2 infection in a large cohort of Long COVID patients, as well as those fully recovered.

Cluster analysis of long COVID symptoms for deciphering a syndrome and its long-term consequence.

The long-term symptoms of COVID-19 are the subject of public and scientific discussions. Understanding how those long COVID symptoms co-occur in clusters of syndromes may indicate the pathogenic mechanisms of long COVID. Our study objective was to cluster the different long COVID symptoms. We included persons who had a COVID-19 and assessed long-term symptoms (at least 4 weeks after first symptoms). Hierarchical clustering was applied to the symptoms as well as to the participants based on the Euclidean distance h of the log-values of the answers on symptom severity. The distribution of clusters within our cohort is shown in a heat map.From September 2021 to November 2023, 2371 persons with persisting long COVID symptoms participated in the study. Self-assessed long COVID symptoms were assigned to three symptom clusters. Cluster A unites rheumatological and neurological symptoms, cluster B includes neuro-psychological symptoms together with cardiorespiratory symptoms, and a third cluster C shows an association of general infection signs, dermatological and otology symptoms. A high proportion of the participants (n = 1424) showed symptoms of all three clusters. Clustering of long COVID symptoms reveals similarities to the symptomatology of already described syndromes such as the Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS) or rheumatological autoinflammatory diseases. Further research may identify serological parameters or clinical risk factors associated with the shown clusters and might improve our understanding of long COVID as a systemic disease. Furthermore, multimodal treatments can be developed and scaled for symptom clusters and associated impairments.

A diagnostic support system based on pain drawings: binary and k-disease classification of EDS, GBS, FSHD, PROMM, and a control group with Pain2D.

BACKGROUND AND OBJECTIVE: The diagnosis of rare diseases (RDs) is often challenging due to their rarity, variability and the high number of individual RDs, resulting in a delay in diagnosis with adverse effects for patients and healthcare systems. The development of computer assisted diagnostic decision support systems could help to improve these problems by supporting differential diagnosis and by prompting physicians to initiate the right diagnostic tests. Towards this end, we developed, trained and tested a machine learning model implemented as part of the software called Pain2D to classify four rare diseases (EDS, GBS, FSHD and PROMM), as well as a control group of unspecific chronic pain, from pen-and-paper pain drawings filled in by patients. METHODS: Pain drawings (PDs) were collected from patients suffering from one of the four RDs, or from unspecific chronic pain. The latter PDs were used as an outgroup in order to test how Pain2D handles more common pain causes. A total of 262 (59 EDS, 29 GBS, 35 FSHD, 89 PROMM, 50 unspecific chronic pain) PDs were collected and used to generate disease specific pain profiles. PDs were then classified by Pain2D in a leave-one-out-cross-validation approach. RESULTS: Pain2D was able to classify the four rare diseases with an accuracy of 61-77% with its binary classifier. EDS, GBS and FSHD were classified correctly by the Pain2D k-disease classifier with sensitivities between 63 and 86% and specificities between 81 and 89%. For PROMM, the k-disease classifier achieved a sensitivity of 51% and specificity of 90%. CONCLUSIONS: Pain2D is a scalable, open-source tool that could potentially be trained for all diseases presenting with pain.

A strict mask policy for hospital staff effectively prevents nosocomial influenza infections and mortality: monocentric data from five consecutive influenza seasons.

BACKGROUND: Influenza infections acquired in hospital show increased mortality, especially in elderly patients with risk factors. Nevertheless, vaccination rates are low among both high-risk patients and healthcare workers (HCWs). AIM: To more effectively prevent influenza infections in the hospital during the influenza season, a strict mouth-nose protection (MNP) requirement was introduced for all staff throughout the shift on the affected wards as an intervention and its effect on nosocomial infection rates was studied. METHODS: The present data were obtained in a retrospective, monocentric analysis over a period of four consecutive influenza seasons from 2015 to 2019. MNP for all staff during the whole shift as an intervention was introduced in 2017 and for the following seasons if at least three influenza patients were in the ward at the same time. Data from hospitalized influenza patients before and after intervention were compared with regard to nosocomial incidences and mortality. FINDINGS: In the years with strict mandatory MNP (2017-2019), the nosocomial influenza incidence fell nearly 50% (odds ratio: 0.40; 95% confidence interval: 0.28-0.56; P < 0.001) accompanied by a significant reduction in nosocomial mortality by 85% (0.15; 0.02-0.70; P = 0.007). The infectious pressure indicated by influenza incidences and patient-days at risk were comparable before and after intervention, as was the low rate of vaccine uptake by nurses. CONCLUSION: Mandatory MNP for HCWs effectively protects patients from nosocomial influenza infections and mortality.

Haploflow: Strain-resolved de novo assembly of viral genomes.

In viral infections often multiple related viral strains are present, due to coinfection or within-host evolution. We describe Haploflow, a de Bruijn graph-based assembler for de novo genome assembly of viral strains from mixed sequence samples using a novel flow algorithm. We assessed Haploflow across multiple benchmark data sets of increasing complexity, showing that Haploflow is faster and more accurate than viral haplotype assemblers and generic metagenome assemblers not aiming to reconstruct strains. Haplotype reconstructed high-quality strain-resolved assemblies from clinical HCMV samples and SARS-CoV-2 genomes from wastewater metagenomes identical to genomes from clinical isolates.

MS-specific noise model reveals the potential of iTRAQ in quantitative proteomics.

MOTIVATION: Mass spectrometry (MS) data are impaired by noise similar to many other analytical methods. Therefore, proteomics requires statistical approaches to determine the reliability of regulatory information if protein quantification is based on ion intensities observed in MS. RESULTS: We suggest a procedure to model instrument and workflow-specific noise behaviour of iTRAQ reporter ions that can provide regulatory information during automated peptide sequencing by LC-MS/MS. The established mathematical model representatively predicts possible variations of iTRAQ reporter ions in an MS data-dependent manner. The model can be utilized to calculate the robustness of regulatory information systematically at the peptide level in so-called bottom-up proteome approaches. It allows to determine the best fitting regulation factor and in addition to calculate the probability of alternative regulations. The result can be visualized as likelihood curves summarizing both the quantity and quality of regulatory information. Likelihood curves basically can be calculated from all peptides belonging to different regions of proteins if they are detected in LC-MS/MS experiments. Therefore, this approach renders excellent opportunities to detect and statistically validate dynamic post-translational modifications usually affecting only particular regions of the whole protein. The detection of known phosphorylation events at protein kinases served as a first proof of concept in this study and underscores the potential for noise models in quantitative proteomics.

[SARS-CoV-2 antibodies in ICU and clinic staff : From Germany's region with the highest infection rate]. / SARS-CoV-2-Antikörper bei Intensiv- und Klinikpersonal : Aus der am höchsten durchseuchten Region Deutschlands.

BACKGROUND: Healthcare workers are a high-risk population for SARS-CoV­2 infection. For capacity planning of healthcare providers and to optimize protection of healthcare workers (HCW) in SARS-CoV­2 pandemics, it is essential to know the risk of infection and potential immunity status of staff dealing with COVID-19 patients. MATERIALS AND METHODS: We examined seropravalence of SARS-CoV­2 IgM/IgG antibodies (AB) in HCW of a region with the highest rate of infection (1570/100,000) during COVID-19 pandemic in Germany, 4 months after its start. Employees of a nonmedical company (MU) served as control group. Demographic data, medical history and working situation were recorded. RESULTS: A total of 1838 HCW and 986 MU volunteered to participate. Seroprevalence for SARS-CoV­2 in HCW was 15.1% and 3.7% in MU. Among HCWs, nurses had a seropositivity of 20.0%, ICU personnel 20.3%, housekeepers 19.3%, physicians 12.0%, medical services (e.g., radiology, physiotherapy) 11.3%, administration 7.1% and technical services 6%. Symptoms typical for COVID-19 were not experienced by 10% of seropositive HCWs. CONCLUSION: Seroprevalence of SARS-CoV­2 antibodies in HCW of a region heavily affected by COVID-19 is with 15.1% significantly higher than in a control group of nonmedical staff with 3.7%. Infection rate in HCW was higher in staff with close contact to infected patients. Seropositivity in ICU personnel is higher than in other clinical professions. The occupational risk for housekeepers seems to be underestimated.

Bacterial load and pathogenic species on healthcare personnel attire: implications of alcohol hand-rub use, profession, and time of duty.

BACKGROUND: Since hand hygiene might prevent the risk of bacterial transmission from healthcare personnel attire (HCPA), the present study investigates the effect of alcohol-based hand rub (ABHR) use on bacterial load and pathogenic species on HCPA. METHODS: HCPA from doctors and nurses was investigated for bacterial contamination post duty. Samples from distinct areas of HCPA were obtained and analysed for bacterial load and species. A standardized questionnaire was performed regarding time of duty and profession, and ABHR from each ward was calculated according to a national standard. FINDINGS: Bacterial load on HCPA (700 samples from 200 HCPA) was found to be up to four-fold higher when wearing for more than one shift. Moreover, doctors had a lower bacterial load on attire compared to nurses. In a multivariate linear regression model, negative correlations with bacterial load on HCPA were found for ABHR (t = -2.080, P = 0.0379) and being a doctor (t = -6.009, P < 0.0001), and a positive correlation for the time of duty (t = 10.572; P < 0.0001). Detection of Staphylococcus aureus as the most prominent pathogen found on HCPA was influenced by the time of duty (odds ratio: 3.27; 95% confidence interval: 1.93-5.72; P < 0.0001) but not by ABHR (1.22; 0.30-3.42). CONCLUSION: ABHR, profession, and time of duty significantly affect the bacterial load on HCPA. Since the time of duty has the strongest impact on bacterial load, a daily change of HCPA is recommended.

Neural fuzzy logic programming.

A foundational development of propositional fuzzy logic programs is presented. Fuzzy logic programs are structured knowledge bases including uncertainties in rules and facts. The precise specifications of uncertainties have a great influence on the performance of the knowledge base. It is shown how fuzzy logic programs can be transformed to neural networks, where adaptations of uncertainties in the knowledge base increase the reliability of the program and are carried out automatically.

Theoretical analysis of the effects of mitotic crossover in large yeast populations.

In a diploid yeast population which is heterozygous for a given marker, A1A2, mitotic crossover (mit. c.o.) between the centromere and the marker will give rise to homozygous daughter cells, A1A1 and A2A2. Since this causes a decrease in the frequency of A1A2 cells, mit. c.o. is an important population genetic process in vegetatively propagated yeast cultures. The effect of mit. c.o. is counteracted by mutations and, in the case of heterosis, by selection. We present a mathematical analysis of these interactions.

Transcription regulatory region analysis using signal detection and fuzzy clustering.

MOTIVATION: Presently available programs for the recognition of potential transcription factor binding sites in genomic sequences generally yield a huge amount of output. These output lists have to be filtered to obtain biologically significant elements, which is highly laborious work to be done manually. RESULTS: We developed a strategy for systematic verification and improvement of the underlying profiles, and for their contextual analysis by a fuzzy clustering approach using non-redundant libraries of search profiles as a prerequisite. AVAILABILITY: The tools mentioned in the paper are available upon request. CONTACT: ewi@gbf.de