Experiences from daily practice of upadacitinib treatment on atopic dermatitis with a focus on hand eczema: Results from the BioDay registry.

BACKGROUND: Real-world data on the effectiveness of upadacitinib on atopic dermatitis (AD), hand eczema (HE) and HE in the context of AD are limited. OBJECTIVES: To evaluate the effectiveness and safety of upadacitinib on AD and on HE in patients with AD. METHODS: This prospective observational cohort study includes clinical outcomes: Eczema Area and Severity Index (EASI), Investigator's Global Assessment (IGA), Hand Eczema Severity Index (HECSI), Photographic guide; and PROMs: average pruritus and pain Numeric Rating Scale (NRS) score of the past week, Patient-Oriented Eczema Measure (POEM), Patient-Oriented Eczema, Dermatology Life Quality Index (DLQI), Atopic Dermatitis Control Tool (ADCT), Patient Global Assessment of Disease (PGAD), Quality Of Life Hand Eczema Questionnaire (QOLHEQ) at baseline, Week 4, and Week 16 of upadacitinib-treated patients. Adverse events were monitored during each visit. RESULTS: Thirty-eight patients were included, of which 32 patients had HE. At Week 16, EASI-75 was achieved by 50.0%. Absolute cutoff score NRS-pruritus ≤4 was reached by 62.5%, POEM ≤7 by 37.5%, DLQI ≤5 by 59.4%, ADCT <7 by 68.8%, and PGAD rating of at least 'good' by 53.1%. HECSI-75 was achieved by 59.3% and (almost) clear on the Photographic guide by 74.1%. The minimally important change in QOLHEQ was achieved by 57.9%. Sub-analysis in patients with concomitant irritant contact dermatitis showed no differences. Safety analysis showed no new findings compared to clinical trials. CONCLUSIONS: Upadacitinib can be an effective treatment for patients with AD and concomitant HE in daily practice. Future studies should focus on the effectiveness of upadacitinib on chronic HE, especially on the different etiological subtypes of HE, including HE in non-atopic individuals.

A systematic study of critical miRNAs on cells proliferation and apoptosis by the shortest path.

BACKGROUND: MicroRNAs are a class of important small noncoding RNAs, which have been reported to be involved in the processes of tumorigenesis and development by targeting a few genes. Existing studies show that the imbalance between cell proliferation and apoptosis is closely related to the initiation and development of cancers. However, the impact of miRNAs on this imbalance has not been studied systematically. RESULTS: In this study, we first construct a cell fate miRNA-gene regulatory network. Then, we propose a systematical method for calculating the global impact of miRNAs on cell fate genes based on the shortest path. Results on breast cancer and liver cancer datasets show that most of the cell fate genes are perturbed by the differentially expressed miRNAs. Most of the top-identified miRNAs are verified in the Human MicroRNA Disease Database (HMDD) and are related to breast and liver cancers. Function analysis shows that the top 20 miRNAs regulate multiple cell fate related function modules and interact tightly based on their functional similarity. Furthermore, more than half of them can promote sensitivity or induce resistance to some anti-cancer drugs. Besides, survival analysis demonstrates that the top-ranked miRNAs are significantly related to the overall survival time in the breast and liver cancers group. CONCLUSION: In sum, this study can help to systematically study the important role of miRNAs on proliferation and apoptosis and thereby uncover the key miRNAs during the process of tumorigenesis. Furthermore, the results of this study will contribute to the development of clinical therapy based miRNAs for cancers.

Neural Indices of Vowel Discrimination in Monolingual and Bilingual Infants and Children.

OBJECTIVES: To examine maturation of neural discriminative responses to an English vowel contrast from infancy to 4 years of age and to determine how biological factors (age and sex) and an experiential factor (amount of Spanish versus English input) modulate neural discrimination of speech. DESIGN: Event-related potential (ERP) mismatch responses (MMRs) were used as indices of discrimination of the American English vowels [ε] versus [I] in infants and children between 3 months and 47 months of age. A total of 168 longitudinal and cross-sectional data sets were collected from 98 children (Bilingual Spanish-English: 47 male and 31 female sessions; Monolingual English: 48 male and 42 female sessions). Language exposure and other language measures were collected. ERP responses were examined in an early time window (160 to 360 msec, early MMR [eMMR]) and late time window (400 to 600 msec, late MMR). RESULTS: The eMMR became more negative with increasing age. Language experience and sex also influenced the amplitude of the eMMR. Specifically, bilingual children, especially bilingual females, showed more negative eMMR compared with monolingual children and with males. However, the subset of bilingual children with more exposure to English than Spanish compared with those with more exposure to Spanish than English (as reported by caretakers) showed similar amplitude of the eMMR to their monolingual peers. Age was the only factor that influenced the amplitude of the late MMR. More negative late MMR was observed in older children with no difference found between bilingual and monolingual groups. CONCLUSIONS: Consistent with previous studies, our findings revealed that biological factors (age and sex) and language experience modulated the amplitude of the eMMR in young children. The early negative MMR is likely to be the mismatch negativity found in older children and adults. In contrast, the late MMR amplitude was influenced only by age and may be equivalent to the Nc in infants and to the late negativity observed in some auditory passive oddball designs.

How does normalization impact RNA-seq disease diagnosis?

With the surge of next generation high-throughput technologies, RNA-seq data is playing an increasingly important role in disease diagnosis, in which normalization is assumed as an essential procedure to produce comparable samples. Recent studies have seen different normalization methods proposed to remove various technical biases in RNA sequencing. However, there are no previous studies evaluating the impacts of normalization on RNA-seq disease diagnosis. In this study, we investigate this problem by analyzing structured big data: RNA-seq data acquired from the TCGA portal for its popularity in RNA-seq disease diagnosis. We propose a novel normalization effect test algorithm, diagnostic index (d-index), and data entropy to analyze and evaluate the impacts of normalization on RNA-seq disease diagnosis by using state-of-the-art machine learning models. Furthermore, we present an original visualization analysis to compare the performance of normalized data versus raw data. We have found that normalized data yields generally an equivalent or even lower level diagnosis than its raw data. Moreover, some normalization approaches (e.g. RPKM) even bring negative effects in disease diagnosis. On the other hand, raw data seems to have the potential to decipher pathological status better or at least comparable than when the data is normalized. Our visualization analysis also shows that some normalization methods even bring 'outliers', which unavoidably decreases sample detectability in diagnosis. More importantly, our data entropy analysis shows that normalized data usually demonstrates equivalent or lower entropy values than raw data. Those data with high entropy values tend to achieve better diagnosis than those with low entropy values. In addition, we found that high-dimensional imbalance (HDI) data is unaffected by any normalization procedures in diagnosis, and fails almost all machine learning models by only recognizing majority types in spite of raw or normalized data. Our results suggest that normalized data may not demonstrate statistically significant advantages in disease diagnosis than its raw form. It further implies that normalization may not be an indispensable procedure in RNA-seq disease diagnosis or at least some normalization processes may not be. Instead, raw data may perform better for capturing more original transcriptome patterns in different pathological conditions.

Singular value thresholding two-stage matrix completion for drug sensitivity discovery.

Incomplete data presents significant challenges in drug sensitivity analysis, especially in critical areas like oncology, where precision is paramount. Our study introduces an innovative imputation method designed specifically for low-rank matrices, addressing the crucial challenge of data completion in anticancer drug sensitivity testing. Our method unfolds in two main stages: Initially, the singular value thresholding algorithm is employed for preliminary matrix completion, establishing a solid foundation for subsequent steps. Then, the matrix rows are segmented into distinct blocks based on hierarchical clustering of correlation coefficients, applying singular value thresholding to the largest block, which has been proved to possess the largest entropy. This is followed by a refined data restoration process, where the reconstructed largest block is integrated into the initial matrix completion to achieve the final matrix completion. Compared to other methods, our approach not only improves the accuracy of data restoration but also ensures the integrity and reliability of the imputed values, establishing it as a robust tool for future drug sensitivity analysis.

LESS: Label-efficient multi-scale learning for cytological whole slide image screening.

In computational pathology, multiple instance learning (MIL) is widely used to circumvent the computational impasse in giga-pixel whole slide image (WSI) analysis. It usually consists of two stages: patch-level feature extraction and slide-level aggregation. Recently, pretrained models or self-supervised learning have been used to extract patch features, but they suffer from low effectiveness or inefficiency due to overlooking the task-specific supervision provided by slide labels. Here we propose a weakly-supervised Label-Efficient WSI Screening method, dubbed LESS, for cytological WSI analysis with only slide-level labels, which can be effectively applied to small datasets. First, we suggest using variational positive-unlabeled (VPU) learning to uncover hidden labels of both benign and malignant patches. We provide appropriate supervision by using slide-level labels to improve the learning of patch-level features. Next, we take into account the sparse and random arrangement of cells in cytological WSIs. To address this, we propose a strategy to crop patches at multiple scales and utilize a cross-attention vision transformer (CrossViT) to combine information from different scales for WSI classification. The combination of our two steps achieves task-alignment, improving effectiveness and efficiency. We validate the proposed label-efficient method on a urine cytology WSI dataset encompassing 130 samples (13,000 patches) and a breast cytology dataset FNAC 2019 with 212 samples (21,200 patches). The experiment shows that the proposed LESS reaches 84.79%, 85.43%, 91.79% and 78.30% on the urine cytology WSI dataset, and 96.88%, 96.86%, 98.95%, 97.06% on the breast cytology high-resolution-image dataset in terms of accuracy, AUC, sensitivity and specificity. It outperforms state-of-the-art MIL methods on pathology WSIs and realizes automatic cytological WSI cancer screening.

Predicting post-discharge venous thromboembolism and bleeding among medical patients: External validation of a novel risk score utilizing ubiquitous biomarkers.

BACKGROUND: Post-hospitalization thromboprophylaxis can reduce venous thromboembolism (VTE) risk for non-surgical patients but may carry bleeding risks. We aimed to externally validate the Intermountain Risk Scores for hospital-associated venous thromboembolism (HA-VTE IMRS) and major bleeding (HA-MB IMRS) for VTE and bleeding outcomes. METHODS: Retrospective cohort study of adult patients discharged alive from medical services between 2015 and 2019. HA-VTE IMRS and HA-MB IMRS were calculated at the time of hospital discharge and dichotomized as high- or low-risk as described in the derivation manuscript. 90-day post-discharge VTE outcomes were assessed from diagnostic radiology reports, and bleeding outcomes were assessed using ICD-10 codes and blood bank transfusion records. RESULTS: Among 113,578 patients in the study, 66,340 patients (58.4 %) had a low-risk HA-VTE IMRS <7, versus 47,238 (41.6 %) high-risk ≥7. For bleed prediction, 71,576 patients (63 %) had a low-risk HA-MB IMRS <8, versus 42,002 (37 %) high-risk ≥8. VTE incidence was 1.1 % and 0.6 % while major bleeding incidence was 1.3 % and 0.1 % in high-risk versus low-risk cohorts, respectively. AUCs for VTE and bleed outcome discrimination were 0.59 and 0.78, respectively. Patients with a combined high-risk VTE score and low-risk bleeding score comprised 14.5 % of the population. CONCLUSION: In this external validation study, the HA-VTE IMRS had poor discrimination for VTE but the HA-MB IMRS had good discriminatory ability for major bleeding events. A sizable minority of patients were categorized as high VTE risk with low bleed risk, a population which may have an optimal risk-benefit profile for post-hospital thromboprophylaxis.

Estimate the incubation period of coronavirus 2019 (COVID-19).

COVID-19 is an infectious disease that presents unprecedented challenges to society. Accurately estimating the incubation period of the coronavirus is critical for effective prevention and control. However, the exact incubation period remains unclear, as COVID-19 symptoms can appear in as little as 2 days or as long as 14 days or more after exposure. Accurate estimation requires original chain-of-infection data, which may not be fully available from the original outbreak in Wuhan, China. In this study, we estimated the incubation period of COVID-19 by leveraging well-documented and epidemiologically informative chain-of-infection data collected from 10 regions outside the original Wuhan areas prior to February 10, 2020. We employed a proposed Monte Carlo simulation approach and nonparametric methods to estimate the incubation period of COVID-19. We also utilized manifold learning and related statistical analysis to uncover incubation relationships between different age and gender groups. Our findings revealed that the incubation period of COVID-19 did not follow general distributions such as lognormal, Weibull, or Gamma. Using proposed Monte Carlo simulations and nonparametric bootstrap methods, we estimated the mean and median incubation periods as 5.84 (95% CI, 5.42-6.25 days) and 5.01 days (95% CI 4.00-6.00 days), respectively. We also found that the incubation periods of groups with ages greater than or equal to 40 years and less than 40 years demonstrated a statistically significant difference. The former group had a longer incubation period and a larger variance than the latter, suggesting the need for different quarantine times or medical intervention strategies. Our machine-learning results further demonstrated that the two age groups were linearly separable, consistent with previous statistical analyses. Additionally, our results indicated that the incubation period difference between males and females was not statistically significant.

High-resolution in vivo monophasic gastric slow waves to quantify activation and recovery profiles.

BACKGROUND: Gastric bio-electrical slow waves are, in part, responsible for coordinating motility. Spatial dynamics about the recovery phase of slow wave recordings have not been thoroughly investigated due to the lack of suitable experimental techniques. METHODS: A high-resolution multi-channel suction electrode array was developed and applied in pigs to acquire monophasic gastric slow waves. Signal characteristics were verified against biphasic slow waves recorded by conventional surface contact electrode arrays. Monophasic slow wave events were categorized into two groups based on their morphological characteristics, after which their amplitudes, activation to recovery intervals, and gradients were quantified and compared. Coverage of activation and recovery maps for both electrode types were calculated and compared. KEY RESULTS: Monophasic slow waves had a more pronounced recovery phase with a higher gradient than biphasic slow waves (0.5 ± 0.1 vs. 0.3 ± 0.1 mV·s-1 ). Between the 2 groups of monophasic slow waves, there was a significant difference in amplitude (1.8 ± 0.5 vs. 1.1 ± 0.2 mV), activation time gradient (0.8 ± 0.2 vs. 0.3 ± 0.1 mV·s-1 ), and recovery time gradient (0.5 ± 0.1 vs. 0.3 ± 0.1 mV·s-1 ). For the suction and conventional contact electrode arrays, the recovery maps had reduced coverage compared to the activation maps (4 ± 6% and 43 ± 11%, respectively). CONCLUSIONS AND INFERENCES: A novel high-resolution multi-channel suction electrode array was developed and applied in vivo to record monophasic gastric slow waves. Slow wave recovery phase analysis could be performed more efficiently on monophasic signals compared with biphasic signals, due to the more identifiable recovery phases.

Quantification of Gastric Slow Wave Velocity Using Bipolar High-Resolution Recordings.

OBJECTIVE: Gastric bio-electrical slow waves are, in part, responsible for coordinating motility. High-resolution (HR) in vivo recordings can be used to capture the wavefront velocity of the propagating slow waves. A standard "marking-and-grouping" approach is typically employed along with manual review. Here, a bipolar velocity estimation (BVE) method was developed, which utilized local directional information to estimate the wavefront velocity in an efficient manner. METHODS: Unipolar in vivo HR recordings were used to construct bipolar recordings in different directions. Then, the local directionality of the slow wave was extracted by calculating time delay information. The accuracy of the method was verified using synthetic data and then validated with in vivo HR pig experimental recordings. RESULTS: Against ventilator noise amplitude of 0% - 70% of the average slow wave amplitude, the direction and speed error increased from 4.4° and 0.9 mm/s to 8.6° and 1.4 mm/s. For signals added with high-frequency noise with SNR of 60 dB - 12 dB, the error increased from 8.0° and 1.0 mm/s to 9.8° and 1.2 mm/s. With experimental data, the BVE algorithm resulted in 19.2 ±1.7° of direction error and 2.0 ± 0.2 mm/s of speed error, when compared to the standard "marking-and-grouping" method. CONCLUSION: Gastric slow wave wavefront velocities were estimated rapidly using the BVE algorithm with minimal errors. SIGNIFICANCE: The BVE algorithmenables the ability to estimate wavefront velocities in HR recordings in an efficient manner.

Deciphering landscape dynamics of cell fate decision via a Lyapunov method.

The embryonic stem cell (ESC) has the capacity to self-renew and maintain pluripotent, while continuously offering a source of various differentiated cell types. The fate decision process of remaining in the ground state or transiting to a differentiated state can be read out by the regulatory network of key transcription factors (TFs). However, its underlying mechanism remains to be fully elucidated. In this paper, we tackle this problem by proposing a novel cellular differentiation model for mouse embryonic stem cell (MESC) dynamics regulation: MESC-DRM. We employ nonlinear least-squares algorithm to infer model parameters by using benchmark datasets, construct a potential function by exploiting multivariate Gaussian distributions, and project the potential landscape into a 3D space to validate and replicate the stable cell states observed in experiments. The traditional cell landscape modeling techniques rely on the potential function visualization to decide the stable states of cells. But the visualization will be almost impossible when the dimensionality of the potential function is greater than 3. We handle the challenge by innovatively employing a Lyapunov method to resolve it through a more straightforward analytical approach. It also provides a more rigorous and robust way for accurate cell fate decision. The study not only validates the previous experimental results but also provides an insightful guide for cell fate decision besides inspiring future study on this topic.

A simple pre-disease state prediction method based on variations of gene vector features.

BACKGROUND: The progression of disease can be divided into three states: normal, pre-disease, and disease. Since a pre-disease state is the tipping point of disease deterioration, accurately predicting pre-disease state may help to prevent the progression of disease and develop feasible treatment in time. METHODS: In the perspective of gene regulatory network, the expression of a gene is regulated by its upstream genes, and then it also regulates that of its downstream genes. In this study, we define the expression value of these genes as a gene vector to depict its state in a specific sample. Then, we propose a novel pre-disease prediction method by such vector features. RESULTS: The results of an influenza virus infection dataset show that our method can successfully predict the pre-disease state. Furthermore, the pre-disease state related genes predicted by our methods are highly associated with each other and enriched in influenza virus infection related pathways. In addition, our method is more time efficient in calculation than previous works. The code of our method is accessed at https://github.com/ZhenshenBao/sPGVF.git.

A controlled nucleation and growth of Si nanowires by using a TiN diffusion barrier layer for lithium-ion batteries.

Uniform size of Si nanowires (NWs) is highly desirable to enhance the performance of Si NW-based lithium-ion batteries. To achieve a narrow size distribution of Si NWs, the formation of bulk-like Si structures such as islands and chunks needs to be inhibited during nucleation and growth of Si NWs. We developed a simple approach to control the nucleation of Si NWs via interfacial energy tuning between metal catalysts and substrates by introducing a conductive diffusion barrier. Owing to the high interfacial energy between Au and TiN, agglomeration of Au nanoparticle catalysts was restrained on a TiN layer which induced the formation of small Au nanoparticle catalysts on TiN-coated substrates. The resulting Au catalysts led to the nucleation and growth of Si NWs on the TiN layer with higher number density and direct integration of the Si NWs onto current collectors without the formation of bulk-like Si structures. The lithium-ion battery anodes based on Si NWs grown on TiN-coated current collectors showed improved specific gravimetric capacities (>30%) for various charging rates and enhanced capacity retention up to 500 cycles of charging-discharging.

Multi-resolution independent component analysis for high-performance tumor classification and biomarker discovery.

BACKGROUND: Although high-throughput microarray based molecular diagnostic technologies show a great promise in cancer diagnosis, it is still far from a clinical application due to its low and instable sensitivities and specificities in cancer molecular pattern recognition. In fact, high-dimensional and heterogeneous tumor profiles challenge current machine learning methodologies for its small number of samples and large or even huge number of variables (genes). This naturally calls for the use of an effective feature selection in microarray data classification. METHODS: We propose a novel feature selection method: multi-resolution independent component analysis (MICA) for large-scale gene expression data. This method overcomes the weak points of the widely used transform-based feature selection methods such as principal component analysis (PCA), independent component analysis (ICA), and nonnegative matrix factorization (NMF) by avoiding their global feature-selection mechanism. In addition to demonstrating the effectiveness of the multi-resolution independent component analysis in meaningful biomarker discovery, we present a multi-resolution independent component analysis based support vector machines (MICA-SVM) and linear discriminant analysis (MICA-LDA) to attain high-performance classifications in low-dimensional spaces. RESULTS: We have demonstrated the superiority and stability of our algorithms by performing comprehensive experimental comparisons with nine state-of-the-art algorithms on six high-dimensional heterogeneous profiles under cross validations. Our classification algorithms, especially, MICA-SVM, not only accomplish clinical or near-clinical level sensitivities and specificities, but also show strong performance stability over its peers in classification. Software that implements the major algorithm and data sets on which this paper focuses are freely available at https://sites.google.com/site/heyaumapbc2011/. CONCLUSIONS: This work suggests a new direction to accelerate microarray technologies into a clinical routine through building a high-performance classifier to attain clinical-level sensitivities and specificities by treating an input profile as a 'profile-biomarker'. The multi-resolution data analysis based redundant global feature suppressing and effective local feature extraction also have a positive impact on large scale 'omics' data mining.

ScGSLC: An unsupervised graph similarity learning framework for single-cell RNA-seq data clustering.

Accurate clustering of cells from single-cell RNA sequencing (scRNA-seq) data is an essential step for biological analysis such as putative cell type identification. However, scRNA-seq data has high dimension and high sparsity, which makes traditional clustering methods less effective to reflect the similarity between cells. Since genetic network fundamentally defines the functions of cell and deep learning shows strong advantages in network representation learning, we propose a novel scRNA-seq clustering framework ScGSLC based on graph similarity learning. ScGSLC effectively integrates scRNA-seq data and protein-protein interaction network to a graph. Then graph convolution network is employed by ScGSLC to embedding graph and clustering the cells by the calculated similarity between graphs. Unsupervised clustering results of nine public data sets demonstrate that ScGSLC shows better performance than the state-of-the-art methods.

Improving preprocedure antithrombotic management: Implementation and sustainment of a best practice alert and pharmacist referral process.

BACKGROUND: Electronic medical record-based interventions such as best practice alerts, or reminders, have been proposed to improve evidence-based medication prescribing. Formal implementation evaluation including long-term sustainment are not commonly reported. Preprocedural medication management is often a complex issue for patients taking antithrombotic medications. METHODS: We implemented a best practice alert (BPA) that recommended referral to an anticoagulation clinic before outpatient elective gastrointestinal (GI) endoscopies. Eligible patients were taking an oral anticoagulant (warfarin or direct oral anticoagulant [DOAC]) and/or antiplatelet medications. Patients referred to the anticoagulation clinic were compared to those managed by the ordering provider. Outcomes assessed included guideline-adherent drug management before endoscopy, documentation of a medication management plan, guideline-adherent rates of bridging for high-risk patients taking warfarin, and evaluation for sustained use of BPA. RESULTS: Eighty percent of patients (553/691) were referred to the anticoagulation clinic during the initial 13-month study period. Most referrals came from gastroenterologists (397/553; 71.8%) followed by primary care providers (127/554; 22.9%). Patients referred had improved rates of guideline-adherent medication management compared to those who were not referred (97.4% vs 91.0%; P = .001). Documentation of medication plan was significantly higher in the referred group (99.1% vs 59.4%; P ≤ .001). There were no differences in rates of appropriate bridging for patients taking warfarin. Implementation of the BPA also resulted in sustained, consistent use over an additional 18 months following the initial study period. CONCLUSION: Implementation of a BPA before elective outpatient GI endoscopies was associated with improved rates of guideline-adherent medication management and documented management plan, while streamlining preprocedural medication management.