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Alzheimer’s disease (AD) is the most prevalent neurodegenerative disease causing dementia and poses significant health risks to middle-aged and elderly people. Brain magnetic resonance imaging (MRI) is the most widely used diagnostic method for AD. However, it is challenging to collect sufficient brain imaging data with high-quality annotations. Weakly supervised learning (WSL) is a machine learning technique aimed at learning effective feature representation from limited or low-quality annotations. In this paper, we propose a WSL-based deep learning (DL) framework (ADGNET) consisting of a backbone network with an attention mechanism and a task network for simultaneous image classification and image reconstruction to identify and classify AD using limited annotations. The ADGNET achieves excellent performance based on six evaluation metrics (Kappa, sensitivity, specificity, precision, accuracy, F1-score) on two brain MRI datasets (2D MRI and 3D MRI data) using fine-tuning with only 20% of the labels from both datasets. The ADGNET has an F1-score of 99.61% and sensitivity is 99.69%, outperforming two state-of-the-art models (ResNext WSL and SimCLR). The proposed method represents a potential WSL-based computer-aided diagnosis method for AD in clinical practice.
The incidence of FIX inhibitors in severe hemophilia B (SHB) is not well defined. Frequencies of 3-5% have been reported but most studies to date were small, including patients with different severities, and without prospective follow-up for inhibitor incidence. Study objective was to investigate inhibitor incidence in patients with SHB followed up to 500 exposure days (ED), the frequency of allergic reactions, and the relationship with genotypes. Consecutive previously untreated patients (PUPs) with SHB enrolled into the PedNet cohort were included. Detailed data was collected for the first 50 ED, followed by annual collection of inhibitor status and allergic reactions. Presence of inhibitors was defined by at least two consecutive positive samples. Additionally, data on factor IX gene mutation was collected. 154 PUPs with SHB were included; 75% were followed until 75 ED, and 43% until 500 ED. Inhibitors developed in 14 patients (7 high-titre). Median number of ED at inhibitor manifestation was 11 (IQR 6.5-36.5). Cumulative inhibitor incidence was 9.3% (95%CI 4.4-14.1) at 75 ED, and 10.2% (5.1-15.3) at 500 ED. Allergic reactions occurred in 4 (28.6%) inhibitor patients. Missense mutations were most frequent (46.8%) overall but not associated with inhibitors. Nonsense mutations and deletions with large structural changes comprised all mutations among inhibitor patients and were associated with an inhibitor risk of 26.9% and 33.3%, respectively. In an unselected, well-defined cohort of PUPs with SHB, cumulative inhibitor incidence was 10.2% at 500 ED. Nonsense mutations and large deletions were strongly associated with the risk of inhibitor development. The PedNet Registry is registered at clinicaltrials.gov; identifier: NCT02979119
The C-type lectin-like receptor NKG2D contributes to the immunosurveillance of virally infected and malignant cells by cytotoxic lymphocytes. A peculiar and puzzling feature of the NKG2D-based immunorecognition system is the high number of ligands for this single immunoreceptor. In humans, there are a total of eight NKG2D ligands (NKG2DL) comprising two members of the MIC (MICA, MICB) and six members of the ULBP family of glycoproteins (ULBP1 to ULBP6). While MICA has been extensively studied with regard to its biochemistry, cellular expression and function, very little is known about the NKG2DL ULBP4. This is, at least in part, due to its rather restricted expression by very few cell lines and tissues. Recently, constitutive ULBP4 expression by human monocytes was reported, questioning the view of tissue-restricted ULBP4 expression. Here, we scrutinized ULBP4 expression by human peripheral blood mononuclear cells and monocytes by analyzing ULBP4 transcripts and ULBP4 surface expression. In contrast to MICA, there was no ULBP4 expression detectable, neither by freshly isolated monocytes nor by PAMP-activated monocytes. However, a commercial antibody erroneously indicated surface ULBP4 on monocytes due to a non-ULBP4-specific binding activity, emphasizing the critical importance of validated reagents for life sciences. Collectively, our data show that ULBP4 is not expressed by monocytes, and likely also not by other peripheral blood immune cells, and therefore exhibits an expression pattern rather distinct from other human NKG2DL.
Attention selects relevant information regardless of whether it is physically present or internally stored in working memory. Perceptual research has shown that attentional selection of external information is better conceived as rhythmic prioritization than as stable allocation. Here we tested this principle using information processing of internal representations held in working memory. Participants memorized four spatial positions that formed the endpoints of two objects. One of the positions was cued for a delayed match-non-match test. When uncued positions were probed, participants responded faster to uncued positions located on the same object as the cued position than to those located on the other object, revealing object-based attention in working memory. Manipulating the interval between cue and probe at a high temporal resolution revealed that reaction times oscillated at a theta rhythm of 6 Hz. Moreover, oscillations showed an anti-phase relationship between memorized but uncued positions on the same versus other object as the cued position, suggesting that attentional prioritization fluctuated rhythmically in an object-based manner. Our results demonstrate the highly rhythmic nature of attentional selection in working memory. Moreover, the striking similarity between rhythmic attentional selection of mental representations and perceptual information suggests that attentional oscillations are a general mechanism of information processing in human cognition. These findings have important implications for current, attention-based models of working memory.
Background: The current COVID-19 pandemic has led to a surge of research activity. While this research provides important insights, the multitude of studies results in an increasing segmentation of information. To ensure comparability across projects and institutions, standard datasets are needed. Here, we introduce the “German Corona Consensus Dataset” (GECCO), a uniform dataset that uses international terminologies and health IT standards to improve interoperability of COVID-19 data.
Methods: Based on previous work (e.g., the ISARIC-WHO COVID-19 case report form) and in coordination with experts from university hospitals, professional associations and research initiatives, data elements relevant for COVID-19 research were collected, prioritized and consolidated into a compact core dataset. The dataset was mapped to international terminologies, and the Fast Healthcare Interoperability Resources (FHIR) standard was used to define interoperable, machine-readable data formats.
Results: A core dataset consisting of 81 data elements with 281 response options was defined, including information about, for example, demography, anamnesis, symptoms, therapy, medications or laboratory values of COVID-19 patients. Data elements and response options were mapped to SNOMED CT, LOINC, UCUM, ICD-10-GM and ATC, and FHIR profiles for interoperable data exchange were defined.
Conclusion: GECCO provides a compact, interoperable dataset that can help to make COVID-19 research data more comparable across studies and institutions. The dataset will be further refined in the future by adding domain-specific extension modules for more specialized use cases.
Background: In the pandemic, testing for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) by real-time polymerase chain reaction is one of the pillars on which countermeasures are based. Factors limiting the output of laboratories interfere with the effectiveness of public health measures. Conserving reagents by pooling samples in low-probability settings is proposed but may cause dilution and loss of sensitivity. Blood transfusion services had experience in performance of high throughput nucleic acid testing (NAT) analysis and can support the national health system by screening of the inhabitants for SARS-COV-2.
Methods: We evaluated a new approach of a multiple-swab method by simultaneously incubating multiple respiratory swabs in a single tube. Analytical sensitivity was constant up to a total number of 50 swabs. It was consequently applied in the testing of 50 symptomatic patients (5-sample pools) as well as 100 asymptomatic residents of a nursing home (10-sample pools).
Results: The novel method did not cause false-negative results with nonsignificantly differing cycle threshold values between single-swab and multiple-swab NAT. In two routine applications, all minipools containing positive patient samples were correctly identified.
Conclusions: The new method enables countries to increase the total number of testing significantly. The multiple-swab method is able to screen system relevant groups of employees frequently. The example in Germany shows that blood transfusion services can support general health systems with their experience in NAT and their high-throughput instruments. Screening of a huge number of inhabitants is currently the only option to prevent a second infection wave and enable exit strategies in many countries.
Background In the pandemic, testing for SARS-CoV-2 by RT-PCR in one of the pillars on which countermeasures are based. Factors limiting the output of laboratories interfere with the effectiveness of public health measures. Conserving reagents by pooling samples in low-probability settings is proposed, but may cause dilution and loss of sensitivity.
Methods We tested an alternate approach (FACT) by simultaneously incubating multiple respiratory swabs in a single tube. This protocol was evaluated by serial incubation of a respiratory swab in up to 10 tubes. The analytics validity of this concept was demonstrated in a five-sample mini pool set-up. It was consequently applied in the testing of 50 symptomatic patients (five-sample pools) as well as 100 asymptomatic residents of a nursing home (ten-sample pools).
Results Serial incubation of a respiratory swab in up to 10 tubes did not lead to a significant decline in viral concentration. The novel FACT-protocol did not cause a false negative result in a five-sample mini-pool setup, with non-significantly differing Ct values between single sample and mini-pool NAT. In two routine applications, all mini pools containing positive patient samples were correctly identified.
Conclusions Our proposed FACT-protocol did not cause a significant loss in analytic or diagnostic sensitivity compared to single sample testing in multiple setups. It reduced the amount of reagents needed by up to 40%, and also reduced hands-on time. This method could enhance testing efficiency, especially in groups with a low pretest-probability, such as systemically relevant professional groups.
Background: The current COVID-19 pandemic has led to a surge of research activity. While this research provides important insights, the multitude of studies results in an increasing fragmentation of information. To ensure comparability across projects and institutions, standard datasets are needed. Here, we introduce the “German Corona Consensus Dataset” (GECCO), a uniform dataset that uses international terminologies and health IT standards to improve interoperability of COVID-19 data, in particular for university medicine.
Methods: Based on previous work (e.g., the ISARIC-WHO COVID-19 case report form) and in coordination with experts from university hospitals, professional associations and research initiatives, data elements relevant for COVID-19 research were collected, prioritized and consolidated into a compact core dataset. The dataset was mapped to international terminologies, and the Fast Healthcare Interoperability Resources (FHIR) standard was used to define interoperable, machine-readable data formats.
Results: A core dataset consisting of 81 data elements with 281 response options was defined, including information about, for example, demography, medical history, symptoms, therapy, medications or laboratory values of COVID-19 patients. Data elements and response options were mapped to SNOMED CT, LOINC, UCUM, ICD-10-GM and ATC, and FHIR profiles for interoperable data exchange were defined.
Conclusion: GECCO provides a compact, interoperable dataset that can help to make COVID-19 research data more comparable across studies and institutions. The dataset will be further refined in the future by adding domain-specific extension modules for more specialized use cases.
When a visual stimulus is repeated, average neuronal responses typically decrease, yet they might maintain or even increase their impact through increased synchronization. Previous work has found that many repetitions of a grating lead to increasing gamma-band synchronization. Here we show in awake macaque area V1 that both, repetition-related reductions in firing rate and increases in gamma are specific to the repeated stimulus. These effects showed some persistence on the timescale of minutes. Further, gamma increases were specific to the presented stimulus location. Importantly, repetition effects on gamma and on firing rates generalized to natural images. These findings suggest that gamma-band synchronization subserves the adaptive processing of repeated stimulus encounters, both for generating efficient stimulus responses and possibly for memory formation.