What predicts response to sertraline for people with depression in primary care? a secondary data analysis of moderators in the PANDA trial.

PURPOSE: Antidepressants are a first-line treatment for depression, yet many patients do not respond. There is a need to understand which patients have greater treatment response but there is little research on patient characteristics that moderate the effectiveness of antidepressants. This study examined potential moderators of response to antidepressant treatment. METHODS: The PANDA trial investigated the clinical effectiveness of sertraline (n = 326) compared with placebo (n = 329) in primary care patients with depressive symptoms. We investigated 11 potential moderators of treatment effect (age, employment, suicidal ideation, marital status, financial difficulty, education, social support, family history of depression, life events, health and past antidepressant use). Using multiple linear regression, we investigated the appropriate interaction term for each of these potential moderators with treatment as allocated. RESULTS: Family history of depression was the only variable with weak evidence of effect modification (p-value for interaction = 0.048), such that those with no family history of depression may have greater benefit from antidepressant treatment. We found no evidence of effect modification (p-value for interactions≥0.29) by any of the other ten variables. CONCLUSION: Evidence for treatment moderators was extremely limited, supporting an approach of continuing discuss antidepressant treatment with all patients presenting with moderate to severe depressive symptoms.

The future of rapid and automated single-cell data analysis using reference mapping.

As the number of single-cell datasets continues to grow rapidly, workflows that map new data to well-curated reference atlases offer enormous promise for the biological community. In this perspective, we discuss key computational challenges and opportunities for single-cell reference-mapping algorithms. We discuss how mapping algorithms will enable the integration of diverse datasets across disease states, molecular modalities, genetic perturbations, and diverse species and will eventually replace manual and laborious unsupervised clustering pipelines.

Characterizing human postprandial metabolic response using multiway data analysis.

INTRODUCTION: Analysis of time-resolved postprandial metabolomics data can improve our understanding of the human metabolism by revealing similarities and differences in postprandial responses of individuals. Traditional data analysis methods often rely on data summaries or univariate approaches focusing on one metabolite at a time. OBJECTIVES: Our goal is to provide a comprehensive picture in terms of the changes in the human metabolism in response to a meal challenge test, by revealing static and dynamic markers of phenotypes, i.e., subject stratifications, related clusters of metabolites, and their temporal profiles. METHODS: We analyze Nuclear Magnetic Resonance (NMR) spectroscopy measurements of plasma samples collected during a meal challenge test from 299 individuals from the COPSAC2000 cohort using a Nightingale NMR panel at the fasting and postprandial states (15, 30, 60, 90, 120, 150, 240 min). We investigate the postprandial dynamics of the metabolism as reflected in the dynamic behaviour of the measured metabolites. The data is arranged as a three-way array: subjects by metabolites by time. We analyze the fasting state data to reveal static patterns of subject group differences using principal component analysis (PCA), and fasting state-corrected postprandial data using the CANDECOMP/PARAFAC (CP) tensor factorization to reveal dynamic markers of group differences. RESULTS: Our analysis reveals dynamic markers consisting of certain metabolite groups and their temporal profiles showing differences among males according to their body mass index (BMI) in response to the meal challenge. We also show that certain lipoproteins relate to the group difference differently in the fasting vs. dynamic state. Furthermore, while similar dynamic patterns are observed in males and females, the BMI-related group difference is observed only in males in the dynamic state. CONCLUSION: The CP model is an effective approach to analyze time-resolved postprandial metabolomics data, and provides a compact but a comprehensive summary of the postprandial data revealing replicable and interpretable dynamic markers crucial to advance our understanding of changes in the metabolism in response to a meal challenge.

Mitigating the affective and cognitive consequences of social exclusion: an integrative data analysis of seven social disconnection interventions.

BACKGROUND: Being socially excluded has detrimental effects, with prolonged exclusion linked to loneliness and social isolation. Social disconnection interventions that do not require direct support actions (e.g., "how can I help?") offer promise in mitigating the affective and cognitive consequences of social exclusion. We examine how various social disconnection interventions involving friends and unknown peers might mitigate social exclusion by buffering (intervening before) and by promoting recovery (intervening after). METHODS: We present an integrative data analysis (IDA) of five studies (N = 664) that systematically exposed participants to exclusion (vs. inclusion) social dynamics. Using a well-validated paradigm, participants had a virtual interaction with two other people. Unbeknownst to participants, the other people's behavior was programmed to either behave inclusively toward the participant or for one to behave exclusively. Critically, our social disconnection interventions experimentally manipulated whether a friend was present (vs. an unknown peer vs. being alone), the nature of interpersonal engagement (having a face-to-face conversation vs. a reminder of an upcoming interaction vs. mere presence), and the timing of the intervention in relation to the social dynamic (before vs. during vs. after). We then assessed participants' in-the-moment affective and cognitive responses, which included mood, feelings of belonging, sense of control, and social comfort. RESULTS: Experiencing exclusion (vs. inclusion) led to negative affective and cognitive consequences. However, engaging in a face-to-face conversation with a friend before the exclusion lessened its impact (p < .001). Moreover, a face-to-face conversation with a friend after exclusion, and even a reminder of an upcoming interaction with a friend, sped-up recovery (ps < .001). There was less conclusive evidence that a face-to-face conversation with an unknown peer, or that the mere presence of a friend or unknown peer, conferred protective benefits. CONCLUSIONS: The findings provide support for the effectiveness of social disconnection interventions that involve actual (i.e., face-to-face) or symbolic (i.e., reminders) interactions with friends. These interventions target momentary vulnerabilities that arise from social exclusion by addressing negative affect and cognitions before or after they emerge. As such, they offer a promising approach to primary prevention prior to the onset of loneliness and social isolation.

Blockchain en salud: transformando la seguridad y la gestión de datos clínicos / Blockchain in health: Transforming security and clinical data management

Introducción Los avances tecnológicos continúan transformando la sociedad, incluyendo el sector de la salud. La naturaleza descentralizada y verificable de la tecnología blockchain presenta un gran potencial para abordar desafíos actuales en la gestión de datos sanitarios. Discusión Este artículo indaga sobre cómo la adopción generalizada de blockchain se enfrenta a importantes desafíos y barreras que deben abordarse, como la falta de regulación, la complejidad técnica, la salvaguarda de la privacidad y los costos tanto económicos como tecnológicos. La colaboración entre profesionales médicos, tecnólogos y legisladores es esencial para establecer un marco normativo sólido y una capacitación adecuada. Conclusión La tecnología blockchain tiene potencial de revolucionar la gestión de datos en el sector de la salud, mejorando la calidad de la atención médica, empoderando a los usuarios y fomentando la compartición segura de datos. Es necesario un cambio cultural y regulatorio, junto a más evidencia, para concluir sus ventajas frente a las alternativas tecnológicas existentes. (AU)

Introduction Technological advances continue to transform society, including the health sector. The decentralized and verifiable nature of blockchain technology presents great potential for addressing current challenges in healthcare data management. Discussion This article reports on how the generalized adoption of blockchain faces important challenges and barriers that must be addressed, such as the lack of regulation, technical complexity, safeguarding privacy, and economic and technological costs. Collaboration between medical professionals, technologists and legislators is essential to establish a solid regulatory framework and adequate training. Conclusion Blockchain technology has the potential to revolutionize data management in the healthcare sector, improving the quality of medical care, empowering users, and promoting the secure sharing of data, but an important cultural change is needed, along with more evidence, to reveal its advantages in front of the existing technological alternative. (AU)

Traffic trajectory data analysis technology based on HMM model map matching algorithm.

The rapid growth of traffic trajectory data and the development of positioning technology have driven the demand for its analysis. However, in the current application scenarios, there are some problems such as the deviation between positioning data and real roads and low accuracy of existing trajectory data traffic prediction models. Therefore, a map matching algorithm based on hidden Markov models is proposed in this study. The algorithm starts from the global route, selects K nearest candidate paths, and identifies candidate points through the candidate paths. It uses changes in speed, angle, and other information to generate a state transition matrix that match trajectory points to the actual route. When processing trajectory data in the experiment, K = 5 is selected as the optimal value, the algorithm takes 51 ms and the accuracy is 95.3%. The algorithm performed well in a variety of road conditions, especially in parallel and mixed road sections, with an accuracy rate of more than 96%. Although the time loss of this algorithm is slightly increased compared with the traditional algorithm, its accuracy is stable. Under different road conditions, the accuracy of the algorithm is 98.3%, 97.5%, 94.8% and 96%, respectively. The accuracy of the algorithm based on traditional hidden Markov models is 95.9%, 95.7%, 95.4% and 94.6%, respectively. It can be seen that the accuracy of the algorithm designed has higher precision. The experiment proves that the map matching algorithms based on hidden Markov models is superior to other algorithms in terms of matching accuracy. This study makes the processing of traffic trajectory data more accurate.

[Clinical data analysis of 117 patients with ruptured abdominal aortic aneurysm].

Objective: To examine the perioperative clinical features and prognosis of patients with ruptured abdominal aortic aneurysms (rAAA) who received surgical repair. Methods: The clinical data of rAAA patients who underwent surgical repair and were admitted to the Surgical Intensive Care Unit of Beijing Anzhen Hospital, Capital Medical University from August 2005 to November 2020 were retrospectively analyzed, including the general clinical features, surgical mode, intraoperative conditions, postoperative complications, and fatality rate. Results: There were 117 patients with rAAA, with a median age of 68 (62,77) years, including 93 men (79.5%) and 24 women (20.5%). The main clinical manifestation was abdominal pain (n=115, 98.3%). Among them, 65 (55.6%) patients underwent endovascular aneurysm repair (EVAR), while 52 (44.4%) underwent open surgical repair (OSR). The common postoperative complications include acute gastrointestinal dysfunction (n=116, 99.1%), shock (n=89, 76.1%), acute respiratory distress syndrome (n=85, 72.6%), pancreatic injury (n=56, 47.9%), coagulation dysfunction (n=55, 47.0%), disseminated intravascular coagulation (n=46, 39.3%), acute kidney injury (n=39, 33.3%), infection/sepsis (n=28, 23.9%), gastrointestinal bleeding (n=17, 14.5%), and abdominal compartment syndrome (n=12, 10.3%). The overall postoperative in-hospital fatality rate was 10.3% (12/117). Preoperative use of vasopressors and inotropes, retroperitoneal hematoma, and postoperative abdominal compartment syndrome, gastrointestinal hemorrhage, acute kidney injury, and diffuse intravascular coagulation significantly increased the fatality rate [5/11, 6/24, 5/16, 6/12, 6/17, 23.1%(9/39), 19.6%(9/46), respectively]. Conclusion: The postoperative mortality of rAAA patients is still high in the era of EVAR, especially in patients with preoperative existence of shock and retroperitoneal hematoma, and with postoperative abdominal compartment syndrome, coagulation dysfunction, and acute kidney injury. It is necessary to strengthen perioperative monitoring and management of these patients to reduce the fatality rate.

Propensity score matching as an effective strategy for biomarker cohort design and omics data analysis.

BACKGROUND: Analysis of omics data that contain multidimensional biological and clinical information can be complex and make it difficult to deduce significance of specific biomarker factors. METHODS: We explored the utility of propensity score matching (PSM), a statistical technique for minimizing confounding factors and simplifying the examination of specific factors. We tested two datasets generated from cohorts of colorectal cancer (CRC) patients, one comprised of immunohistochemical analysis of 12 protein markers in 544 CRC tissues and another consisting of RNA-seq profiles of 163 CRC cases. We examined the efficiency of PSM by comparing pre- and post-PSM analytical results. RESULTS: Unlike conventional analysis which typically compares randomized cohorts of cancer and normal tissues, PSM enabled direct comparison between patient characteristics uncovering new prognostic biomarkers. By creating optimally matched groups to minimize confounding effects, our study demonstrates that PSM enables robust extraction of significant biomarkers while requiring fewer cancer cases and smaller overall patient cohorts. CONCLUSION: PSM may emerge as an efficient and cost-effective strategy for multiomic data analysis and clinical trial design for biomarker discovery.

mbDecoda: a debiased approach to compositional data analysis for microbiome surveys.

Potentially pathogenic or probiotic microbes can be identified by comparing their abundance levels between healthy and diseased populations, or more broadly, by linking microbiome composition with clinical phenotypes or environmental factors. However, in microbiome studies, feature tables provide relative rather than absolute abundance of each feature in each sample, as the microbial loads of the samples and the ratios of sequencing depth to microbial load are both unknown and subject to considerable variation. Moreover, microbiome abundance data are count-valued, often over-dispersed and contain a substantial proportion of zeros. To carry out differential abundance analysis while addressing these challenges, we introduce mbDecoda, a model-based approach for debiased analysis of sparse compositions of microbiomes. mbDecoda employs a zero-inflated negative binomial model, linking mean abundance to the variable of interest through a log link function, and it accommodates the adjustment for confounding factors. To efficiently obtain maximum likelihood estimates of model parameters, an Expectation Maximization algorithm is developed. A minimum coverage interval approach is then proposed to rectify compositional bias, enabling accurate and reliable absolute abundance analysis. Through extensive simulation studies and analysis of real-world microbiome datasets, we demonstrate that mbDecoda compares favorably with state-of-the-art methods in terms of effectiveness, robustness and reproducibility.

Exploring patterns in mental health treatment and interests of single adults in the United States: a secondary data analysis.

Objective: The objective of this study is to examine mental health treatment utilization and interest among the large and growing demographic of single adults in the United States, who face unique societal stressors and pressures that may contribute to their heightened need for mental healthcare. Method: We analyzed data from 3,453 single adults, focusing on those with possible mental health treatment needs by excluding those with positive self-assessments. We assessed prevalence and sociodemographic correlates of mental health treatment, including psychotherapy and psychiatric medication use, and interest in attending psychotherapy among participants who had never attended. Results: 26% were in mental health treatment; 17% were attending psychotherapy, 16% were taking psychiatric medications, and 7% were doing both. Further, 64% had never attended psychotherapy, of which 35% expressed interest in future attendance. There were differences in current psychotherapy attendance and psychiatric medication use by gender and sexual orientation, with women and gay/lesbian individuals more likely to engage in both forms of mental health treatment. Additionally, interest in future psychotherapy among those who had never attended varied significantly by age, gender, and race. Younger individuals, women, and Black/African-American participants showed higher likelihoods of interest in psychotherapy. Conclusion: Our research highlights a critical gap in mental health treatment utilization among single adults who may be experiencing a need for those services. Despite a seemingly higher likelihood of engagement in mental health treatment compared to the general population, only a minority of single adults in our sample were utilizing mental health treatment. This underutilization and the observed demographic disparities in mental health treatment underscore the need for targeted outreach, personalized treatment plans, enhanced provider training, and policy advocacy to ensure equitable access to mental healthcare for single adults across sociodemographic backgrounds.

Optimizing Human-Robot Teaming Performance through Q-Learning-Based Task Load Adjustment and Physiological Data Analysis.

The transition to Industry 4.0 and 5.0 underscores the need for integrating humans into manufacturing processes, shifting the focus towards customization and personalization rather than traditional mass production. However, human performance during task execution may vary. To ensure high human-robot teaming (HRT) performance, it is crucial to predict performance without negatively affecting task execution. Therefore, to predict performance indirectly, significant factors affecting human performance, such as engagement and task load (i.e., amount of cognitive, physical, and/or sensory resources required to perform a particular task), must be considered. Hence, we propose a framework to predict and maximize the HRT performance. For the prediction of task performance during the development phase, our methodology employs features extracted from physiological data as inputs. The labels for these predictions-categorized as accurate performance or inaccurate performance due to high/low task load-are meticulously crafted using a combination of the NASA TLX questionnaire, records of human performance in quality control tasks, and the application of Q-Learning to derive task-specific weights for the task load indices. This structured approach enables the deployment of our model to exclusively rely on physiological data for predicting performance, thereby achieving an accuracy rate of 95.45% in forecasting HRT performance. To maintain optimized HRT performance, this study further introduces a method of dynamically adjusting the robot's speed in the case of low performance. This strategic adjustment is designed to effectively balance the task load, thereby enhancing the efficiency of human-robot collaboration.

Phenotypic analysis of ataxia in spinocerebellar ataxia type 6 mice using DeepLabCut.

This study emphasizes the benefits of open-source software such as DeepLabCut (DLC) and R to automate, customize and enhance data analysis of motor behavior. We recorded 2 different spinocerebellar ataxia type 6 mouse models while performing the classic beamwalk test, tracked multiple body parts using the markerless pose-estimation software DLC and analyzed the tracked data using self-written scripts in the programming language R. The beamwalk analysis script (BAS) counts and classifies minor and major hindpaw slips with an 83% accuracy compared to manual scoring. Nose, belly and tail positions relative to the beam, as well as the angle at the tail base relative to the nose and tail tip were determined to characterize motor deficits in greater detail. Our results found distinct ataxic abnormalities such as an increase in major left hindpaw slips and a lower belly and tail position in both SCA6 ataxic mouse models compared to control mice at 18 months of age. Furthermore, a more detailed analysis of various body parts relative to the beam revealed an overall lower body position in the SCA684Q compared to the CT-longQ27PC mouse line at 18 months of age, indicating a more severe ataxic deficit in the SCA684Q group.

Split Pool Ligation-based Single-cell Transcriptome sequencing (SPLiT-seq) data processing pipeline comparison.

BACKGROUND: Single-cell sequencing techniques are revolutionizing every field of biology by providing the ability to measure the abundance of biological molecules at a single-cell resolution. Although single-cell sequencing approaches have been developed for several molecular modalities, single-cell transcriptome sequencing is the most prevalent and widely applied technique. SPLiT-seq (split-pool ligation-based transcriptome sequencing) is one of these single-cell transcriptome techniques that applies a unique combinatorial-barcoding approach by splitting and pooling cells into multi-well plates containing barcodes. This unique approach required the development of dedicated computational tools to preprocess the data and extract the count matrices. Here we compare eight bioinformatic pipelines (alevin-fry splitp, LR-splitpipe, SCSit, splitpipe, splitpipeline, SPLiTseq-demultiplex, STARsolo and zUMI) that have been developed to process SPLiT-seq data. We provide an overview of the tools, their computational performance, functionality and impact on downstream processing of the single-cell data, which vary greatly depending on the tool used. RESULTS: We show that STARsolo, splitpipe and alevin-fry splitp can all handle large amount of data within reasonable time. In contrast, the other five pipelines are slow when handling large datasets. When using smaller dataset, cell barcode results are similar with the exception of SPLiTseq-demultiplex and splitpipeline. LR-splitpipe that is originally designed for processing long-read sequencing data is the slowest of all pipelines. Alevin-fry produced different down-stream results that are difficult to interpret. STARsolo functions nearly identical to splitpipe and produce results that are highly similar to each other. However, STARsolo lacks the function to collapse random hexamer reads for which some additional coding is required. CONCLUSION: Our comprehensive comparative analysis aids users in selecting the most suitable analysis tool for efficient SPLiT-seq data processing, while also detailing the specific prerequisites for each of these pipelines. From the available pipelines, we recommend splitpipe or STARSolo for SPLiT-seq data analysis.

Is the surge in cesarean section rates during the COVID-19 pandemic truly substantiated?

BACKGROUND: Cesarean section (C-section) rates, deemed a critical health indicator, have experienced a historical increase. The advent of the COVID-19 pandemic significantly impacted healthcare patterns including delays or lack of follow-up in treatment and an increased number of patients with acute problems in hospitals. This study aimed to explore whether the observed surge is a genuine consequence of pandemic-related factors. METHODS: This study employs an Interrupted Time Series (ITS) design to analyze monthly C-section rates from March 2018 to January 2023 in Kurdistan province, Iran. Segmented regression modeling is utilized for robust data analysis. RESULTS: The C-section rate did not show a significant change immediately after the onset of COVID-19. However, the monthly trend increased significantly during the post-pandemic period (p < 0.05). Among primigravid women, a significant monthly increase was observed before February 2020 (p < 0.05). No significant change was observed in the level or trend of C-section rates among primigravid women after the onset of COVID-19. CONCLUSION: This study underscores the significant and enduring impact of the COVID-19 pandemic in further increasing the C-section rates over the long term, the observed variations in C-section rates among primigravid women indicate that the COVID-19 pandemic had no statistically significant impact.

Stakeholder opinions on perceived sub-standard emergency obstetric and newborn care in Ghana.

BACKGROUND: Sub-Saharan Africa is unlikely to achieve sustainable development goal (SDG) 3 on maternal and neonatal health due to perceived sub-standard maternal and newborn care in the region. This paper sought to explore the opinions of stakeholders on intricacies dictating sub-standard emergency obstetric and newborn care (EmONC) in health facilities in Northern Ghana. METHODS: Drawing from a qualitative study design, data were obtained from six focus group discussions (FGDs) among 42 health care providers and 27 in-depth interviews with management members, clients and care takers duly guided by the principle of data saturation. Participants were purposively selected from basic and comprehensive level facilities. Data analysis followed Braun and Clarke's qualitative thematic analysis procedure. RESULTS: Four themes and 13 sub-themes emerged as root drivers to sub-standard care. Specfically, the findings highlight centralisation of EmONC, inadequate funding, insufficient experiential training, delay in recruitment of newly trained essential staff and provider disinterest in profession. CONCLUSION: Setbacks in the training and recruitment systems in Ghana, inadequate investment in rural health coupled with extent of health provider inherent disposition to practice may be partly responsible for sub-standard obstetric care in the study area. Interventions targeting the afore-mentioned areas may reduce events of sub-standard care.

What Is Quality Food? The Opinion of Key of the Brazilian Food System.

The aim of the study was to explore the concept of quality food in the opinion of key informants of the food system. This qualitative research included 208 key informants related to the food supply for Brazilian public food services. The participants were grouped into three groups according to their participation in the food system: 1. Food production; 2. Management and marketing; 3. Meal's production process. Key informants answered the following question: "In your opinion, what is quality food?". The answers were analysed through qualitative content analysis. The data analysis resulted in 52 codes grouped into eight categories, expressing the opinion of the study participants about what quality food is: nutritional, sustainable, sensory, hygienic-sanitary, care, regulatory, dependability and symbolic. Nutritional and sustainable dimensions were predominantly considered. The activities carried out in the food system seem to influence the opinion on food quality. The groups linked to food production put more emphasis on the sustainable dimension, followed by the nutritional dimension, while the groups linked to management and marketing and to the meals production process put more emphasis on the nutritional and sensory dimensions. These differences may indicate a difficulty in the transition towards a more sustainable and healthy food system.

Exploring the pathogenesis of chronic atrophic gastritis with atherosclerosis via microarray data analysis.

Although several studies have reported a link between chronic atrophic gastritis (CAG) and atherosclerosis, the underlying mechanisms have not been elucidated. The present study aimed to investigate the molecular mechanisms common to both diseases from a bioinformatics perspective. Gene expression profiles were obtained from the Gene Expression Omnibus database. Data on atherosclerosis and CAG were downloaded from the GSE28829 and GSE60662 datasets, respectively. We identified the differentially expressed genes co-expressed in CAG and atherosclerosis before subsequent analyses. We constructed and identified the hub genes and performed functional annotation. Finally, the transcription factor (TF)-target genes regulatory network was constructed. In addition, we validated core genes and certain TFs. We identified 116 common differentially expressed genes after analyzing the 2 datasets (GSE60662 and GSE28829). Functional analysis highlighted the significant contribution of immune responses and the positive regulation of tumor necrosis factor production and T cells. In addition, phagosomes, leukocyte transendothelial migration, and cell adhesion molecules strongly correlated with both diseases. Furthermore, 16 essential hub genes were selected with cytoHubba, including PTPRC, TYROBP, ITGB2, LCP2, ITGAM, FCGR3A, CSF1R, IRF8, C1QB, TLR2, IL10RA, ITGAX, CYBB, LAPTM5, CD53, CCL4, and LY86. Finally, we searched for key gene-related TFs, especially SPI1. Our findings reveal a shared pathogenesis between CAG and atherosclerosis. Such joint pathways and hub genes provide new insights for further studies.

scLENS: data-driven signal detection for unbiased scRNA-seq data analysis.

High dimensionality and noise have limited the new biological insights that can be discovered in scRNA-seq data. While dimensionality reduction tools have been developed to extract biological signals from the data, they often require manual determination of signal dimension, introducing user bias. Furthermore, a common data preprocessing method, log normalization, can unintentionally distort signals in the data. Here, we develop scLENS, a dimensionality reduction tool that circumvents the long-standing issues of signal distortion and manual input. Specifically, we identify the primary cause of signal distortion during log normalization and effectively address it by uniformizing cell vector lengths with L2 normalization. Furthermore, we utilize random matrix theory-based noise filtering and a signal robustness test to enable data-driven determination of the threshold for signal dimensions. Our method outperforms 11 widely used dimensionality reduction tools and performs particularly well for challenging scRNA-seq datasets with high sparsity and variability. To facilitate the use of scLENS, we provide a user-friendly package that automates accurate signal detection of scRNA-seq data without manual time-consuming tuning.