Track Disease Progression Research Articles

Constructing tongue coating recognition model using deep transfer learning to assist syndrome diagnosis and its potential in noninvasive ethnopharmacological evaluation

Ethnopharmacological relevanceTongue coating has been used as an effective signature of health in traditional Chinese medicine (TCM). The level of greasy coating closely relates to the strength of dampness or pathogenic qi in TCM theory. Previous empirical studies and our systematic review have shown the relation between greasy coating and various diseases, including gastroenteropathy, coronary heart disease, and coronavirus disease 2019 (COVID-19). However, the objective and intelligent greasy coating and related diseases recognition methods are still lacking. The construction of the artificial intelligent tongue recognition models may provide important syndrome diagnosis and efficacy evaluation methods, and contribute to the understanding of ethnopharmacological mechanisms based on TCM theory. Aim of the studyThe present study aimed to develop an artificial intelligent model for greasy tongue coating recognition and explore its application in COVID-19. Materials and methodsHerein, we developed greasy tongue coating recognition networks (GreasyCoatNet) using convolutional neural network technique and a relatively large (N = 1486) set of tongue images from standard devices. Tests were performed using both cross-validation procedures and a new dataset (N = 50) captured by common cameras. Besides, the accuracy and time efficiency comparisons between the GreasyCoatNet and doctors were also conducted. Finally, the model was transferred to recognize the greasy coating level of COVID-19. ResultsThe overall accuracy in 3-level greasy coating classification with cross-validation was 88.8% and accuracy on new dataset was 82.0%, indicating that GreasyCoatNet can obtain robust greasy coating estimates from diverse datasets. In addition, we conducted user study to confirm that our GreasyCoatNet outperforms TCM practitioners, yet only consuming roughly 1% of doctors’ examination time. Critically, we demonstrated that GreasyCoatNet, along with transfer learning, can construct more proper classifier of COVID-19, compared to directly training classifier on patient versus control datasets. We, therefore, derived a disease-specific deep learning network by finetuning the generic GreasyCoatNet. ConclusionsOur framework may provide an important research paradigm for differentiating tongue characteristics, diagnosing TCM syndrome, tracking disease progression, and evaluating intervention efficacy, exhibiting its unique potential in clinical applications.

Brief smell identification test performance in RBD, DLB and AD

AbstractBackgroundImpaired olfaction has been associated with neurodegenerative diseases and may be a useful tool to aid in diagnosis or track disease progression. We sought to compare olfaction in participants with the clinical diagnoses of idiopathic REM sleep behavior disorder (RBD), dementia with Lewy bodies (DLB), or Alzheimer’s disease dementia (AD).MethodParticipants from the Mayo Clinic Alzheimer’s Disease Research Center database who had a diagnosis of RBD (n=27, 56% male), DLB (n=51, 84% male), or AD (n=83, 47% male), and were given the Brief Smell Identification Test (B‐SIT) were included in this analysis. The B‐SIT score was calculated as the sum of correct responses (12 possible). Scores were categorized as reflecting anosmia (<6), hyposmia (6‐10), and normal olfactory function (>10). All participants underwent clinical and neuropsychological evaluations.ResultGlobal cognition was lower in AD and DLB than RBD (MMSE 21.5 ±4.9 in AD, 23.3 ±4.3 in DLB, 28.8 ± 1.2 in RBD; p <0.001). The mean B‐SIT total score was significantly lower for DLB (4.5 ± 3.1) compared to RBD (7.5 ± 3.0) or AD (7.0 ± 2.7) (p<0.001) using an ANOVA F‐test. Anosmia was present in 76% of DLB, which compared to 32% in RBD, and 32% in AD (p=0.9). Hyposmia was present in 18% of DLB, 47% of RBD, and 52% of AD (p=ns). Of 22 patients with normal olfaction, X% were DLB, X% RBD, and X% AD (p=X). An item analysis illustrated that participants with DLB were less likely to identify Menthol, Clove, Strawberry, Smoke, and Soap than those with RBD and AD. On serial examination, 12 RBD (60%) patients’ B‐SIT score decreased by at least one point at their most recent visit compared to their baseline visit.ConclusionThese findings suggest that impaired olfactory function is frequent in RBD, DLB and AD, with olfaction being worst in DLB in this cohort. Olfaction was declining in most of those with RBD who underwent serial evaluations. Further analyses are needed to determine if any scents are more sensitive to phenotype, progression, and degree of cognitive impairment. Supported by: NIH grants AG016574, NS100620, AG056639, AG015866

Utility of extracellular vesicles and their MicroRNA cargo as biomarkers for Alzheimer's disease

AbstractBackgroundAlzheimer’s disease (AD) is the most common cause of dementia. Developing effective therapies is dependent on the availability of biomarkers that accurately diagnosis AD and track disease progression. MiRNAs, which can be sorted into extracellular vesicles (EVs), reflect the disease state of many neurological disorders and have the potential to serve as biomarkers.MethodCerebrospinal fluid (CSF) from AD, Parkinson’s disease (PD), and control (n=10/group) participants was analyzed by vesicle flow cytometry for size, concentration, and subtypes of EVs. In matching CSF and isolated CSF‐EVs we measured the expression of 34 miRNAs (previously validated candidate AD biomarkers) and assessed magnitude of fold change and classification potential for AD.ResultThe EVs in CSF from AD and PD were significantly larger than control CSF and there were fewer microvesicles. CSF‐EVs had 25 detectable miRNAs out of 34 assayed; while 30 were detectable in total CSF. For the detectable miRNAs in CSF‐EVs, 52% had at least a 1.5 fold change in expression in AD, relative to control. In total CSF, 30% of the assayed miRNAs had a 1.5 fold change in AD versus control. Classification performance for AD from controls was improved for CSF‐EV miRNAs, compared to total CSF. Four of the CSF‐EV miRNAs also had an AUC > 0.7 for classifying AD from PD.ConclusionEV biogenesis in neurodegenerative diseases is disrupted compared to neurologically normal controls, which leads to an altered EV profile in CSF. Further, the miRNA expression profile of CSF‐EVs improves the performance of candidate AD biomarkers.

Comparing the clinical utility and diagnostic performance of cerebrospinal fluid P‐tau181, P‐tau217 and P‐tau231 assays

AbstractBackgroundSeveral studies indicate that increases in cerebrospinal fluid (CSF) phosphorylated tau (P‐tau) in Alzheimer’s disease (AD) occur in response to very early amyloid‐β (Aβ) pathology and precede widespread tau aggregation. Thus far, however, there are no studies comparing different P‐tau isoforms—i.e., tau phosphorylated at threonine‐181 (P‐tau181), 217 (P‐tau217) and 213 (P‐tau231) levels—in relation to Aβ and Tau PET across the symptomatic stages of AD, nor data directly comparing their diagnostic performance for separating AD dementia from non‐AD neurodegenerative disorders and for identifying abnormal Aβ and Tau PET status. We herein aimed to address these questions using cross‐sectional data from the Swedish BioFINDER‐2 study.MethodWe included cross‐sectional data for 629 participants (cognitively unimpaired, amyloid‐β (Aβ)‐positive mild cognitive impairment, Alzheimer’s disease (AD) dementia and non‐AD disorders). In addition to comparing P‐tau181 and P‐tau217 measured using assays with the same capture antibodies from Eli Lilly (P‐tau181Lilly and P‐tau217Lilly; Meso Scale Discovery platform) with P‐tau231 from ADx NeuroSciences (P‐tau231ADx; sandwich ELISA), we also compared P‐tau181Lilly with P‐tau181 measurements from two commonly used assays (Innotest [P‐tau181Innotest; sandwich ELISA] and Elecsys [P‐tau181Elecsys; fully automated electrochemiluminescence immunoassay]). Aβ and Tau PET were performed using [18F]flutemetamol and [18F]RO948.The discriminative performance of CSF P‐tau measures was assessed using the area under the receiver operating characteristic curve (AUC).ResultThough all P‐tau variants increased across the AD continuum, P‐tau217Lilly showed the greatest dynamic range (13‐fold‐increase vs 1.9‐5.4‐fold‐increase for other P‐tau variants for AD dementia vs non‐AD) (Figure 1). P‐tau217Lillyshowed stronger correlations with Aβ‐ and tau‐PET (P<0.0001) (Figure 2). P‐tau217Lilly exhibited higher accuracy than other P‐tau variants for separating AD dementia from non‐AD (AUC, 0.991vs 0.906‐0.982, P<0.0001) and for identifying Aβ‐PET positivity (AUC, 0.951 vs 0.816‐0.924, P<0.0001) and tau‐PET positivity (AUC, 0.957 vs 0.836‐0.938, P<0.0001) (Figure 3). Finally, P‐tau181Lilly generally performed better than the other P‐tau181 assays, (e.g., AD dementia vs non‐AD, AUC, 0.976 vs 0.923, P<0.0001).ConclusionCSF P‐tau217Lilly seem to be more useful than other included P‐tau assays in the diagnostic work‐up of AD and for tracking disease progression. Varied results across P‐tau181 assays also highlights the importance of anti‐tau antibodies for biomarker performance.

Using amyloid PET as a biomarker to detect progression of early Alzheimer’s disease

AbstractBackgroundAlzheimer’s disease (AD), the most common form of dementia, is a complex polygenic disease with genetic, cellular, pathologic, and clinical heterogeneity. Recently, significant attempts have been made for identifying AD biomarkers for reliably tracking disease progression in its early asymptomatic stages. To this end, amyloid imaging technique has provided a breakthrough in deciphering the pathophysiology and accumulation of amyloid beta (Aβ) and hyperphosphorylated tau deposits in the brain. As such, our lab and multiple other research groups have used biomarker levels such as CSF Aβ tau, TREM2, and amyloid positivity for genetic association studies and identified novel genes and loci associated to AD. In a recent study, Raghavan et al., analyzed 4,314 participants and identified a novel locus for amyloidosis within RBFOX1 gene, however, this finding has not been replicated yet.MethodIn order to investigate the underlying genetic basis for brain amyloidosis in AD, we aim at systematically analyzing the largest collection of amyloid imaging (N=6,243), data across multiple ethnicities from multicenter cohorts (ADRC, A4, DIAN, ADNI, and ADNIDOD) as a quantitative trait to identify the functional variants and genes driving the association of AD.ResultResults and key findings will be presented at the AAIC meeting.ConclusionWe will leverage these datasets to generate prediction models for amyloid positivity and Mandelian Randomization (MR) analyses. Furthermore, the identification of AD‐specific molecular signatures and pathways will enable the characterization of appropriate therapeutic targets for the prevention and/or treatment of AD.

Complement and NfL associations with brain structure and functional connectivity alterations in presymptomatic and symptomatic GRN mutation carriers

AbstractBackgroundProgranulin (GRN) mutations cause autosomal dominant frontotemporal lobar degeneration. Clinical trials for GRN‐targeted therapies are underway, yet reliable biomarkers to predict onset and track disease progression remain elusive. Task‐free functional MRI (tf‐fMRI) connectivity may be more sensitive than structural measures for detecting presymptomatic changes (Dopper, 2014; Premi, 2016). Presymptomatic GRN feature tf‐fMRI hyperconnectivity that is more pronounced in older carriers presumably closer to symptom onset, suggesting hyperconnectivity may be a harbinger of disease (Lee, 2019). To aid the interpretation of hyperconnectivity, we explored relationships of structural and tf‐fMRI measures with candidate fluid biomarkers for GRN: CSF complement proteins C3b and C1q, which drive neurodegeneration in GRN ‐/‐ mice (Lui, 2016) and plasma neurofilament light chain (NfL), a marker of axonal injury.MethodWe studied 20 symptomatic GRN (Sx), 39 presymptomatic GRN (preSx), and 67 healthy‐controls (HC). Cross‐sectional CSF C3b, C1q, and plasma NfL concentrations were compared between groups. Correlations controlling for age assessed relationships between fluid biomarkers and symptom severity (CDR®+NACC‐FTLD box score). Voxelwise multiple regression models examined interactions between fluid biomarkers and gene status on grey matter probability maps (GM) and voxelwise tf‐fMRI whole‐brain weighted degree (WBD) maps.ResultComplement concentrations were similar between Sx, preSx, and HC. Across all GRN, C3b concentrations increased with symptom severity (r=0.34, p=0.04). In Sx, but not preSx, higher C3b was associated with increased WBD in medial‐frontal regions, left insula, and thalamus compared to HC. Sx had higher NfL concentrations compared to preSx and HC. Across all GRN and in Sx, higher NfL was associated with greater symptom severity (r=0.60, p<0.001; r=0.72, p<0.001). In Sx, higher NfL was associated with lower GM in precuneus and bilateral frontoinsular cortices, but not WBD, compared to HC. In preSx, higher NfL was associated with increased WBD in thalamus and right temporal regions, but not GM, compared to HC.ConclusionC3b and NfL concentrations correlated with symptom severity. C3b concentrations were associated with tf‐fMRI connectivity alterations in Sx while NfL concentrations were associated with connectivity alterations in preSx. Longitudinal studies are needed to determine temporal relationships between altered connectivity, atrophy, complement, NfL rise, and symptom onset.

Decreased purine metabolite levels in cerebrospinal fluid of transgenic cerebral amyloid angiopathy rats

AbstractBackgroundCerebral amyloid angiopathy (CAA) is a major cause of intracerebral hemorrhages and contributes to cognitive decline. The currently applied modified Boston criteria fall short in detecting early‐stage disease and biomarkers to track disease progression are lacking. In the CAA Fluid Biomarker Evaluation (CAFE) study we aim to identify novel CAA‐specific biomarkers. We hypothesize that levels of metabolites in cerebrospinal fluid (CSF) are altered due to amyloid‐β deposits.MethodHigh‐resolution liquid chromatography quadrupole time‐of‐flight mass spectrometry was used to analyze metabolite levels in an untargeted fashion (Coene et al., 2018; Peters et al., 2020) in CSF derived from small‐vessel CAA transgenic (rTg‐DI) and wild‐type (WT) rats. Candidate features identified in a discovery study (n=8 WT; n=10 rTg; 6 months old) were validated in larger groups of rats including various ages (n=30 WT; n=29 rTg; 3‐6‐12 months old). Features were annotated based on accurate mass, specific retention time and semi‐quantitative intensity by comparison with reference standard compounds and online database matching.ResultOf the 48 significantly different features discovered in the pilot, 27 could be validated in the second study. Several features corresponded with reference compounds of metabolites involved in the purine metabolism pathway, which included guanosine, guanine, inosine, xanthine and hypoxanthine. All these levels were significantly decreased in all groups of the rTg rats compared to WT, some progressing with older age (Figure 1). An additional n=605 features were identified with significantly different levels between the various groups. Molecular formulas for other possible interesting features could be computed, whose metabolite identities are currently being confirmed by using standard compounds.ConclusionMetabolomics analyses of the CSF of transgenic rats shows promise in discovering novel potential biomarkers for CAA. Using a robust animal model allows us to study biomarkers along the trajectory of disease progression. Our results suggest a global down‐regulation of the purine pathway (Figure 2), a finding previously observed in early‐stage Alzheimer’s disease (Alonso‐Andrés et al., 2017).

Patient attitudes towards remote memory clinic assessment.

Due to demand on UK memory clinic services, most patients have limited consultant interaction before diagnosis/discharge. Technology offers an opportunity for remote assessment, from telephone/video-based consultations to fully digitised cognitive assessments with potential to track disease progression. Whilst many acute services utilise remote assessment, there are perceived barriers in memory clinic populations. However, COVID-19 and related national restrictions may have altered patients' attitudes towards and experience with remote assessment tools. We aimed to investigate attitudes including confidence and perceived challenges towards remote assessment as well as access and experience with technology amongst Oxfordshire memory clinic patients. Between June and September 2020, all patients awaiting initial memory clinic assessment were asked to participate in a standardised semi-quantitative survey as part of an Oxford Health NHS Foundation Trust service evaluation. Designed with service-user input, questions aimed to capture availability, experience and confidence using technology and patients' comfort with assessment, diagnosis and future care discussions being conducted remotely, as well as any concerns or comments. Amongst 73 respondents (average age=79.1 years), access to technology was high; 82% reported telephone access and 58% to a laptop, tablet, smartphone or combination of the three. 17% reported previous use of web-based video conferencing tools, and although confidence using these tools was 7%, this increased with written instruction or relative assistance. Similarly, whilst under half of the respondents felt comfortable with assessments, diagnosis or future care discussions occurring remotely, this increased to approximately two thirds with relative presence (67%, 69% and 66%, respectively). Qualitative analysis of patient's comments regarding remote assessment also revealed concerns over wait times/urgent need for assessment. However, 62% preferred to wait for an in-person visit, rather than an immediate remote appointment. This survey demonstrates availability of technology in this population but a disparity in willingness to engage in remote assessment. Consequently, there is a need to diverge from one-size-fits-all models to a tiered approach that helps facilitate individual choice based on the availability/confidence with technology and level of relative support. The Oxford Brain Health Centre, an integrated clinical-research service, provides an opportunity to research this tiered approach in clinical practice.

Advances in the diagnosis, evaluation, and management of leptomeningeal disease.

Leptomeningeal metastasis (LM) is a devastating complication of cancer with variable clinical presentation and limited benefit from existing treatment options. In this review, we discuss advances in LM diagnostics and therapeutics with the potential to reverse this grim course. Emerging cerebrospinal fluid circulating tumor cell and cell-free tumor DNA analysis technologies will improve diagnosis of LM, while providing crucial genetic information, capturing tumor heterogeneity, and quantifying disease burden. Circulating tumor cells and cell-free tumor DNA have utility as biomarkers to track disease progression and treatment response. Treatment options for LM include ventriculoperitoneal shunting for symptomatic relief, radiation therapy including whole-brain radiation and focal radiation for bulky leptomeningeal involvement, and systemic and intrathecal medical therapies, including targeted and immunotherapies based on tumor mutational profiling. While existing treatments for LM have limited efficacy, recent advances in liquid biopsy together with increasing availability of targeted treatments will lead to rational multimodal individualized treatments and improved patient outcomes.

Plasma proteome profiling using tandem mass tag labeling technology reveals potential biomarkers for Parkinson's disease: a preliminary study.

Parkinson's disease (PD) is the second most frequently occurring progressive neurodegenerative disorder. Biomarkers are useful indicators for tracking disease progression, early diagnosis, and intervention of disease progression. We aimed to develop plasma biomarker panel which maybe aid to predict the onset and progression of PD. Tandem mass tag (TMT) mass spectrometry was applied using an Orbitrap Lumos mass spectrometer to analyze plasma protein expression in patients diagnosed with PD and healthy controls. In total, 555 proteins were quantified. Using a cut-off of p < 0.05 and a fold change of >1.2 for the variation in expression, 25 proteins were differentially expressed between the PD and control groups. Sixteen proteins were upregulated and nine were downregulated. Several proteins, including Chitinase-3-like protein 1 (CHI3L1) and thymosin beta-4 (TMSB4X) were implicated in PD pathogenesis. The data from the TMT-based proteomic profiling of plasma samples in PD may help advance the understanding of the molecular mechanisms of PD and identify potential novel biomarkers of PD for further characterization.

P‐tau235: a novel biomarker for staging preclinical Alzheimer’s disease

Alzheimer’s disease (AD) is characterised by a long preclinical phase. Although phosphorylated tau (p‐tau) species such as p‐tau217 and p‐tau231 provide accurate detection of early pathological changes, other biomarkers capable of staging disease progression during preclinical AD are still needed. Combining exploratory and targeted mass spectrometry methods in neuropathologically confirmed brain tissue, we observed that p‐tau235 is a prominent feature of AD pathology. In addition, p‐tau235 seemed to be preceded by p‐tau231, in what appeared to be a sequential phosphorylation event. To exploit its biomarker potential in cerebrospinal fluid (CSF), we developed and validated a new p‐tau235 Simoa assay. Using three clinical cohorts, we demonstrated that (i) CSF p‐235 increases early in AD continuum, and (ii) changes in CSF p‐tau235 and p‐tau231 levels during preclinical AD are consistent with the sequential phosphorylation evidence in AD brain. In conclusion, CSF p‐tau235 appears to be not only a highly specific biomarker of AD but also a promising staging biomarker for the preclinical phase. Thus, it could prove useful tracking disease progression and help enriching clinical trial recruitment.

Estimating Respiratory Rate From Breath Audio Obtained Through Wearable Microphones.

Respiratory rate (RR) is a clinical metric used to assess overall health and physical fitness. An individual's RR can change from their baseline due to chronic illness symptoms (e.g., asthma, congestive heart failure), acute illness (e.g., breathlessness due to infection), and over the course of the day due to physical exhaustion during heightened exertion. Remote estimation of RR can offer a cost-effective method to track disease progression and cardio-respiratory fitness over time. This work investigates a model-driven approach to estimate RR from short audio segments obtained after physical exertion in healthy adults. Data was collected from 21 individuals using microphone-enabled, near-field headphones before, during, and after strenuous exercise. RR was manually annotated by counting perceived inhalations and exhalations. A multi-task Long-Short Term Memory (LSTM) network with convolutional layers was implemented to process mel-filterbank energies, estimate RR in varying background noise conditions, and predict heavy breathing, indicated by an RR of more than 25 breaths per minute. The multi-task model performs both classification and regression tasks and leverages a mixture of loss functions. It was observed that RR can be estimated with a concordance correlation coefficient (CCC) of 0.76 and a mean squared error (MSE) of 0.2, demonstrating that audio can be a viable signal for approximating RR.Clinical relevance-The subject technology facilitates the use of accessible, aesthetically acceptable wearable headphones to provide a technologically efficient and cost-effective method to estimate respiratory rate and track cardio-respiratory fitness over time.

A genetic link between risk for Alzheimer's disease and severe COVID-19 outcomes via the OAS1 gene.

Recently, we reported oligoadenylate synthetase 1 (OAS1) contributed to the risk of Alzheimer’s disease, by its enrichment in transcriptional networks expressed by microglia. However, the function of OAS1 within microglia was not known.Using genotyping from 1313 individuals with sporadic Alzheimer’s disease and 1234 control individuals, we confirm the OAS1 variant, rs1131454, is associated with increased risk for Alzheimer’s disease. The same OAS1 locus has been recently associated with severe coronavirus disease 2019 (COVID-19) outcomes, linking risk for both diseases. The single nucleotide polymorphisms rs1131454(A) and rs4766676(T) are associated with Alzheimer’s disease, and rs10735079(A) and rs6489867(T) are associated with severe COVID-19, where the risk alleles are linked with decreased OAS1 expression. Analysing single-cell RNA-sequencing data of myeloid cells from Alzheimer’s disease and COVID-19 patients, we identify co-expression networks containing interferon (IFN)-responsive genes, including OAS1, which are significantly upregulated with age and both diseases. In human induced pluripotent stem cell-derived microglia with lowered OAS1 expression, we show exaggerated production of TNF-α with IFN-γ stimulation, indicating OAS1 is required to limit the pro-inflammatory response of myeloid cells.Collectively, our data support a link between genetic risk for Alzheimer’s disease and susceptibility to critical illness with COVID-19 centred on OAS1, a finding with potential implications for future treatments of Alzheimer’s disease and COVID-19, and development of biomarkers to track disease progression.

Revealing the Timeline of Structural MRI Changes in Premanifest to Manifest Huntington Disease.

Background and ObjectivesLongitudinal measurements of brain atrophy using structural MRI (sMRI) can provide powerful markers for tracking disease progression in neurodegenerative diseases. In this study, we use a disease progression model to learn individual-level disease times and hence reveal a new timeline of sMRI changes in Huntington disease (HD).MethodsWe use data from the 2 largest cohort imaging studies in HD—284 participants from TRACK-HD (100 control, 104 premanifest, and 80 manifest) and 159 participants from PREDICT-HD (36 control and 128 premanifest)—to train and test the model. We longitudinally register T1-weighted sMRI scans from 3 consecutive time points to reduce intraindividual variability and calculate regional brain volumes using an automated segmentation tool with rigorous manual quality control.ResultsOur model reveals, for the first time, the relative magnitude and timescale of subcortical and cortical atrophy changes in HD. We find that the largest (∼20% average change in magnitude) and earliest (∼2 years before average abnormality) changes occur in the subcortex (pallidum, putamen, and caudate), followed by a cascade of changes across other subcortical and cortical regions over a period of ∼11 years. We also show that sMRI, when combined with our disease progression model, provides improved prediction of onset over the current best method (root mean square error = 4.5 years and maximum error = 7.9 years vs root mean square error = 6.6 years and maximum error = 18.2 years).DiscussionOur findings support the use of disease progression modeling to reveal new information from sMRI, which can potentially inform imaging marker selection for clinical trials.

A longitudinal sampling study of transcriptomic and epigenetic profiles in patients with thrombocytopenia syndrome

Severe fever with thrombocytopenia syndrome (SFTS) is a novel tick-borne infectious disease caused by a new type of SFTS virus (SFTSV). Here, a longitudinal sampling study is conducted to explore the differences in transcript levels after SFTSV infection, and to characterize the transcriptomic and epigenetic profiles of hospitalized patients. The results reveal significant changes in the mRNA expression of certain genes from onset to recovery. Moreover, m6A-seq reveals that certain genes related with immune regulation may be regulated by m6A. Besides the routine tests such as platelet counts, serum ALT and AST levels testing, distinct changes in myocardial enzymes, coagulation function, and inflammation are well correlated with the clinical data and sequencing data, suggesting that clinical practitioners should monitor the above indicators to track disease progression and guide personalized treatment. In this study, the transcript changes and RNA modification may lend a fresh perspective to our understanding of the SFTSV and play a significant role in the discovery of drugs for effective treatment of this disease.

CombiFlow: combinatorial AML-specific plasma membrane expression profiles allow longitudinal tracking of clones

AbstractAcute myeloid leukemia (AML) often presents as an oligoclonal disease whereby multiple genetically distinct subclones can coexist within patients. Differences in signaling and drug sensitivity of such subclones complicate treatment and warrant tools to identify them and track disease progression. We previously identified >50 AML-specific plasma membrane (PM) proteins, and 7 of these (CD82, CD97, FLT3, IL1RAP, TIM3, CD25, and CD123) were implemented in routine diagnostics in patients with AML (n = 256) and myelodysplastic syndrome (n = 33). We developed a pipeline termed CombiFlow in which expression data of multiple PM markers is merged, allowing a principal component–based analysis to identify distinctive marker expression profiles and to generate single-cell t-distributed stochastic neighbor embedding landscapes to longitudinally track clonal evolution. Positivity for one or more of the markers after 2 courses of intensive chemotherapy predicted a shorter relapse-free survival, supporting a role for these markers in measurable residual disease (MRD) detection. CombiFlow also allowed the tracking of clonal evolution in paired diagnosis and relapse samples. Extending the panel to 36 AML-specific markers further refined the CombiFlow pipeline. In conclusion, CombiFlow provides a valuable tool in the diagnosis, MRD detection, clonal tracking, and understanding of clonal heterogeneity in AML.

Proposed Mobility Assessments with Simultaneous Full-Body Inertial Measurement Units and Optical Motion Capture in Healthy Adults and Neurological Patients for Future Validation Studies: Study Protocol.

Healthy adults and neurological patients show unique mobility patterns over the course of their lifespan and disease. Quantifying these mobility patterns could support diagnosing, tracking disease progression and measuring response to treatment. This quantification can be done with wearable technology, such as inertial measurement units (IMUs). Before IMUs can be used to quantify mobility, algorithms need to be developed and validated with age and disease-specific datasets. This study proposes a protocol for a dataset that can be used to develop and validate IMU-based mobility algorithms for healthy adults (18–60 years), healthy older adults (>60 years), and patients with Parkinson’s disease, multiple sclerosis, a symptomatic stroke and chronic low back pain. All participants will be measured simultaneously with IMUs and a 3D optical motion capture system while performing standardized mobility tasks and non-standardized activities of daily living. Specific clinical scales and questionnaires will be collected. This study aims at building the largest dataset for the development and validation of IMU-based mobility algorithms for healthy adults and neurological patients. It is anticipated to provide this dataset for further research use and collaboration, with the ultimate goal to bring IMU-based mobility algorithms as quickly as possible into clinical trials and clinical routine.

Changes in LASSI-L performance over time among older adults with amnestic MCI and amyloid positivity: A preliminary study

There is a pressing need to develop measures that are sensitive to the earliest subtle cognitive changes of Alzheimer's disease (AD) to improve early detection and track disease progression. The Loewenstein-Acevedo Scales of Semantic Interference (LASSI-L) has been shown to successfully discriminate between cognitively unimpaired (CU) older adults and those with amnestic mild cognitive impairment (MCI) and to correlate with total and regional brain amyloid load. The present study investigated how the LASSI-L scores change over time among three distinct diagnostic groups. Eighty-six community-dwelling older adults underwent a baseline evaluation including: a clinical interview, a neuropsychological evaluation, Magnetic Resonance Imaging (MRI), and amyloid Positron Emission Tomography (PET). A follow up evaluation was conducted 12 months later. Initial mean values were calculated using one-way ANOVAs and chi-square analyses. Post-hoc comparisons were conducted using Tukey's Honestly Significant Difference (HSD). A 3 × 2 repeated measures analysis was utilized to examine differences in LASSI-L performance over time. The MCI amyloid positive group demonstrated a significantly greater decline in LASSI-L performance than the MCI amyloid negative and CU groups respectively. The scales that best differentiated the three groups included the Cued A2, which taps into maximum learning capacity, and Cued B2, which assesses the failure to recover from proactive semantic interference. Our findings further support the LASSI-L's discriminative validity.

Digital endpoints for self-administered home-based functional assessment in pediatric Friedreich's ataxia.

BackgroundFriedreich’s ataxia is an inherited, progressive, neurodegenerative disease that typically begins in childhood. Disease severity is commonly assessed with rating scales, such as the modified Friedreich’s Ataxia Rating Scale, which are usually administered in the clinic by a neurology specialist.ObjectiveThis study evaluated the utility of home‐based, self‐administered digital endpoints in children with Friedreich’s ataxia and unaffected controls and their relationship to standard clinical rating scales.MethodsIn a cross‐sectional study with 25 participants (13 with Friedreich’s ataxia and 12 unaffected controls, aged 6–15 years), home‐based digital endpoints that reflect activities of daily living were recorded over 1 week. Domains analyzed were hand motor function with a digitized drawing, automated analysis of speech with a recorded oral diadochokinesis test, and gait and balance with wearable sensors.ResultsHand‐drawing and speech tests were easy to conduct and generated high‐quality data. The sensor‐based gait and balance tests suffered from technical limitations in this study setup. Several parameters discriminated between groups or correlated strongly with modified Friedreich’s Ataxia Rating Scale total score and activities of daily living total score in the Friedreich’s ataxia group. Hand‐drawing parameters also strongly correlated with standard 9‐hole peg test scores.InterpretationDeploying digital endpoints in home settings is feasible in this population, results in meaningful and robust data collection, and may allow for frequent sampling over longer periods of time to track disease progression. Care must be taken when training participants, and investigators should consider the complexity of the tasks and equipment used.

Near-Infrared Optical Spectroscopy In Vivo Distinguishes Subjects with Alzheimer’s Disease from Age-Matched Controls

Background:Medical imaging methods such as PET and MRI aid clinical assessment of Alzheimer’s disease (AD). Less expensive, less technically demanding, and more widely deployable technologies are needed to expand objective screening for diagnosis, treatment, and research. We previously reported brain tissue near-infrared optical spectroscopy (NIR) in vitro indicating the potential to meet this need.Objective:To determine whether completely non-invasive, clinical, NIR in vivo can distinguish AD patients from age-matched controls and to show the potential of NIR as a clinical screen and monitor of therapeutic efficacy.Methods:NIR spectra were acquired in vivo. Three groups were studied: autopsy-confirmed AD, control and mild cognitive impairment (MCI). A feature selection approach using the first derivative of the intensity normalized spectra was used to discover spectral regions that best distinguished “AD-alone” (i.e., without other significant neuropathology) from controls. The approach was then applied to other autopsy-confirmed AD cases and to clinically diagnosed MCI cases.Results:Two regions about 860 and 895 nm completely separate AD patients from controls and differentiate MCI subjects according to the degree of impairment. The 895 nm feature is more important in separating MCI subjects from controls (ratio-of-weights: 1.3); the 860 nm feature is more important for distinguishing MCI from AD (ratio-of-weights: 8.2).Conclusion:These results form a proof of the concept that near-infrared spectroscopy can detect and classify diseased and normal human brain in vivo. A clinical trial is needed to determine whether the two features can track disease progression and monitor potential therapeutic interventions.