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Ambient air measurements of volatile organic compounds (VOCs) and oxygenated volatile organic compounds (OVOCs) were conducted and characterised during a two-year grid study in the Pearl River Delta (PRD) region of southern China. The present grid study pioneered the systematic investigation of the nature and characteristics of complex VOC and OVOC sources at a regional scale. The largest contributing VOCs, accounting over 80% of the total VOCs mixing ratio, were toluene, ethane, ethyne, propane, ethene, butane, benzene, pentane, ethylbenzene, and xylenes. Sub-regional VOC spatial characteristics were identified, namely: i) relatively fresh pollutants, consistent with elevated vehicular and industrial activities, around the PRD estuary; and ii) a concentration gradient with higher mixing ratios of VOCs in the west as compared with the eastern part of PRD. Based on alkyl nitrate aging determination, a high hydroxyl radical (OH) concentration favoured fast hydrocarbon reactions and formation of locally produced ozone. The photochemical reactivity analysis showed aromatic hydrocarbons and alkenes together consisted of around 80% of the ozone formation potential (OFP) among the key VOCs. We also found that the OFP from OVOCs should not be neglected since their OFP contribution was more than one-third of that from VOCs alone. These findings support the choice of current air pollution control policy which focuses on vehicular sources but warrants further controls. Industrial emissions and VOCs emitted by solvents should be the next targets for ground-level ozone abatement. © 2012 Elsevier Ltd.

Single-cell atlases pose daunting computational challenges pertaining to the integration of spatial and temporal information and the visualization of trajectories across large atlases. We introduce StaVia, a computational framework that synergizes multi-faceted single-cell data with higher-order random walks that leverage the memory of cells' past states, fused with a cartographic Atlas View that offers intuitive graph visualization. This spatially aware cartography captures relationships between cell populations based on their spatial location as well as their gene expression and developmental stage. We demonstrate this using zebrafish gastrulation data, underscoring its potential to dissect complex biological landscapes in both spatial and temporal contexts., (© 2024. The Author(s).)

Background: The objective of this study is to examine the application of artificial intelligence (AI) algorithms in detecting oral potentially malignant disorders (OPMD) and oral cancerous lesions, and to evaluate the accuracy variations among different imaging tools employed in these diagnostic processes., Materials and Methods: A systematic search was conducted in four databases: Embase, Web of Science, PubMed, and Scopus. The inclusion criteria included studies using machine learning algorithms to provide diagnostic information on specific oral lesions, prospective or retrospective design, and inclusion of OPMD. Sensitivity and specificity analyses were also required. Forest plots were generated to display overall diagnostic odds ratio (DOR), sensitivity, specificity, negative predictive values, and summary receiver operating characteristic (SROC) curves. Meta-regression analysis was conducted to examine potential differences among different imaging tools., Results: The overall DOR for AI-based screening of OPMD and oral mucosal cancerous lesions from normal mucosa was 68.438 (95% CI= [39.484-118.623], I2 =86%). The area under the SROC curve was 0.938, indicating excellent diagnostic performance. AI-assisted screening showed a sensitivity of 89.9% (95% CI= [0.866-0.925]; I2 =81%), specificity of 89.2% (95% CI= [0.851-0.922], I2 =79%), and a high negative predictive value of 89.5% (95% CI= [0.851-0.927], I2 =96%). Meta-regression analysis revealed no significant difference among the three image tools. After generating a GOSH plot, the DOR was calculated to be 49.30, and the area under the SROC curve was 0.877. Additionally, sensitivity, specificity, and negative predictive value were 90.5% (95% CI [0.873-0.929], I2 =4%), 87.0% (95% CI [0.813-0.912], I2 =49%) and 90.1% (95% CI [0.860-0.931], I2 =57%), respectively. Subgroup analysis showed that clinical photography had the highest diagnostic accuracy., Conclusions: AI-based detection using clinical photography shows a high DOR and is easily accessible in the current era with billions of phone subscribers globally. This indicates that there is significant potential for AI to enhance the diagnostic capabilities of general practitioners to the level of specialists by utilizing clinical photographs, without the need for expensive specialized imaging equipment., (Copyright © 2024 The Author(s). Published by Wolters Kluwer Health, Inc.)

Motivation: Strain-level analysis of metagenomic data has garnered significant interest in recent years. Microbial single nucleotide polymorphisms (SNPs) are genomic variants that can reflect strain-level differences within a microbial species. The diversity and emergence of SNPs in microbial genomes may reveal evolutionary history and environmental adaptation in microbial populations. However, efficient discovery of shared polymorphic variants in a large collection metagenomic samples remains a computational challenge., Results: MetaQuad utilizes a density-based clustering technique to effectively distinguish between shared variants and non-polymorphic sites using shotgun metagenomic data. Empirical comparisons with other state-of-the-art methods show that MetaQuad significantly reduces the number of false positive SNPs without greatly affecting the true positive rate. We used MetaQuad to identify antibiotic-associated variants in patients who underwent Helicobacter pylori eradication therapy. MetaQuad detected 7591 variants across 529 antibiotic resistance genes. The nucleotide diversity of some genes is increased 6 weeks after antibiotic treatment, potentially indicating the role of these genes in specific antibiotic treatments., Availability and Implementation: MetaQuad is an open-source Python package available via https://github.com/holab-hku/MetaQuad., Competing Interests: None declared., (© The Author(s) 2024. Published by Oxford University Press.)

Understanding the emergence of human notochordal cells (NC) is essential for the development of regenerative approaches. We present a comprehensive investigation into the specification and generation of bona fide NC using a straightforward pluripotent stem cell (PSC)-based system benchmarked with human fetal notochord. By integrating in vitro and in vivo transcriptomic data at single-cell resolution, we establish an extended molecular signature and overcome the limitations associated with studying human notochordal lineage at early developmental stages. We show that TGF-β inhibition enhances the yield and homogeneity of notochordal lineage commitment in vitro . Furthermore, this study characterizes regulators of cell-fate decision and matrisome enriched in the notochordal niche. Importantly, we identify specific cell-surface markers opening avenues for differentiation refinement, NC purification, and functional studies. Altogether, this study provides a human notochord transcriptomic reference that will serve as a resource for notochord identification in human systems, diseased-tissues modeling, and facilitating future biomedical research., Competing Interests: The authors declare no competing interests., (© 2024 The Authors.)

The transformation of benign lesions to malignant tumours is a crucial aspect of understanding chondrosarcomas, which are malignant cartilage tumours that could develop from benign chondroid lesions. However, the process of malignant transformation for chondroid lesions remains poorly understood, and no reliable markers are available to aid clinical decision-making. To address this issue, we conducted a study analysing 11 primary cartilage tumours and controls using single-cell RNA sequencing. By creating a single-cell atlas, we were able to identify the role of endoplasmic reticulum (ER) stress in the malignant transformation of conventional central chondrosarcomas (CCCS). Our research revealed that lower levels of ER stress promote chondrosarcoma growth in a patient-derived xenograft mouse model, while intensive ER stress reduces primary chondrosarcoma cell viability. Furthermore, we discovered that the NF-κB pathway alleviates ER stress-induced apoptosis during chondrosarcoma progression. Our single-cell signatures and large public data support the use of key ER stress regulators, such as DNA Damage Inducible Transcript 3 (DDIT3; also known as CHOP), as malignant markers for overall patient survival. Ultimately, our study highlights the significant role that ER stress plays in the malignant transformation of cartilaginous tumours and provides a valuable resource for future diagnostic markers and therapeutic strategies., (© 2024. The Author(s).)

Single-cell clustered regularly interspaced short palindromic repeats-sequencing (scCRISPR-seq) is an emerging high-throughput CRISPR screening technology where the true cellular response to perturbation is coupled with infected proportion bias of guide RNAs (gRNAs) across different cell clusters. The mixing of these effects introduces noise into scCRISPR-seq data analysis and thus obstacles to relevant studies. We developed scDecouple to decouple true cellular response of perturbation from the influence of infected proportion bias. scDecouple first models the distribution of gene expression profiles in perturbed cells and then iteratively finds the maximum likelihood of cell cluster proportions as well as the cellular response for each gRNA. We demonstrated its performance in a series of simulation experiments. By applying scDecouple to real scCRISPR-seq data, we found that scDecouple enhances the identification of biologically perturbation-related genes. scDecouple can benefit scCRISPR-seq data analysis, especially in the case of heterogeneous samples or complex gRNA libraries., (© The Author(s) 2024. Published by Oxford University Press.)

The rapidly growing collection of public single-cell sequencing data has become a valuable resource for molecular, cellular, and microbial discovery. Previous studies mostly overlooked detecting pathogens in human single-cell sequencing data. Moreover, existing bioinformatics tools lack the scalability to deal with big public data. We introduce Vulture, a scalable cloud-based pipeline that performs microbial calling for single-cell RNA sequencing (scRNA-seq) data, enabling meta-analysis of host-microbial studies from the public domain. In our benchmarking experiments, Vulture is 66% to 88% faster than local tools (PathogenTrack and Venus) and 41% faster than the state-of-the-art cloud-based tool Cumulus, while achieving comparable microbial read identification. In terms of the cost on cloud computing systems, Vulture also shows a cost reduction of 83% ($12 vs. ${\$}$70). We applied Vulture to 2 coronavirus disease 2019, 3 hepatocellular carcinoma (HCC), and 2 gastric cancer human patient cohorts with public sequencing reads data from scRNA-seq experiments and discovered cell type-specific enrichment of severe acute respiratory syndrome coronavirus 2, hepatitis B virus (HBV), and Helicobacter pylori-positive cells, respectively. In the HCC analysis, all cohorts showed hepatocyte-only enrichment of HBV, with cell subtype-associated HBV enrichment based on inferred copy number variations. In summary, Vulture presents a scalable and economical framework to mine unknown host-microbial interactions from large-scale public scRNA-seq data. Vulture is available via an open-source license at https://github.com/holab-hku/Vulture., (© The Author(s) 2024. Published by Oxford University Press GigaScience.)

Background and Objectives: Detection and classification of heart murmur using mobile-phone-collected sound is an emerging approach to the scale-up screening of valvular heart disease at a population level. Nonetheless, the widespread adoption of artificial intelligence (AI) methods for this type of mobile health (mHealth) application requires highly accurate and lightweight AI models that can be deployed in consumer-grade mobile devices. This study presents a lightweight deep learning model and a self-supervised learning (SSL) method to utilise unlabelled data to improve the accuracy of valvular heart disease classification using phonocardiogram data., Methods: This study proposes a lightweight convolutional neural network (CNN) that consists of ten times fewer parameters than other deep learning models to classify phonocardiogram data. SSL is applied to harness a large collection of unlabelled data as pre-training to enhance the accuracy and robustness of the model and reduce the number of epochs required to converge. A mobile application prototype that encapsulates the model is developed to perform in-device inference and fine-turning., Results: The proposed lightweight model achieves an average accuracy of 98.65% in 10-fold cross-validation. When coupled with SSL using unlabelled data, the pre-trained model can reach an average accuracy higher than 99.4% in 10-fold cross-validation. Furthermore, SSL-trained models have a 4-20% improvement in classification accuracy over non-SSL-trained models when tested with perturbed or noisy data, suggesting that SSL improves robustness of the model. When deployed on common smartphones, in-device fine-tuning and inference of the model can be completed within 0.03-0.37 s, which is considerably faster than 0.22-5.7 s by a standard CNN model that have ten times the number of parameters. Our lightweight model also consumes only a third of the power compared to the larger standard model., Conclusion: This work presents a lightweight and accurate phonocardiogram classifier that supports near real-time performance on standard mobile devices., Competing Interests: Declaration of Competing Interest SM and JWKH are inventors of a filed patent application based on some aspects of the method described in this study. JWKH is the shareholder, director and chief executive officer of Vitome Limited., (Copyright © 2023 Elsevier B.V. All rights reserved.)

The sequence in the 5' untranslated regions (UTRs) is known to affect mRNA translation rates. However, the underlying regulatory grammar remains elusive. Here, we propose MTtrans, a multi-task translation rate predictor capable of learning common sequence patterns from datasets across various experimental techniques. The core premise is that common motifs are more likely to be genuinely involved in translation control. MTtrans outperforms existing methods in both accuracy and the ability to capture transferable motifs across species, highlighting its strength in identifying evolutionarily conserved sequence motifs. Our independent fluorescence-activated cell sorting coupled with deep sequencing (FACS-seq) experiment validates the impact of most motifs identified by MTtrans. Additionally, we introduce "GRU-rewiring," a technique to interpret the hidden states of the recurrent units. Gated recurrent unit (GRU)-rewiring allows us to identify regulatory element-enriched positions and examine the local effects of 5' UTR mutations. MTtrans is a powerful tool for deciphering the translation regulatory motifs., Competing Interests: Declaration of interests The authors declare no competing interests., (Copyright © 2023 Elsevier Inc. All rights reserved.)

Blood biomarkers hold potential for the early diagnosis of ischaemic stroke (IS). We aimed to evaluate the current weight of evidence and identify potential biomarkers and biological pathways for further investigation. We searched PubMed, EMBASE, the Cochrane Library and Web of Science, used R package meta4diag for diagnostic meta-analysis and applied Gene Ontology (GO) analysis to identify vital biological processes (BPs). Among 8544 studies, we included 182 articles with a total of 30,446 participants: 15675 IS, 2317 haemorrhagic stroke (HS), 1798 stroke mimics, 846 transient ischaemic attack and 9810 control subjects. There were 518 pooled biomarkers including 203 proteins, 114 genes, 108 metabolites and 88 transcripts. Our study generated two shortlists of biomarkers for future research: one with optimal diagnostic performance and another with low selection bias. Glial fibrillary acidic protein was eligible for diagnostic meta-analysis, with summary sensitivities and specificities for differentiating HS from IS between 3 h and 24 h after stroke onset ranging from 73% to 80% and 77% to 97%, respectively. GO analysis revealed the top five BPs associated with IS. This study provides a holistic view of early diagnostic biomarkers in IS. Two shortlists of biomarkers and five BPs warrant future investigation., Competing Interests: The authors declare no conflicts of interest.

Mammalian sirtuin 6 (SIRT6) regulates a spectrum of vital biological processes and has long been implicated in the progression of cancer. However, the mechanisms underlying the regulation of SIRT6 in tumorigenesis remain elusive. Here, we report that the tumor-suppressive function of SIRT6 in non-small cell lung cancer (NSCLC) is regulated by acetylation. Specifically, males absent on the first (MOF) acetylates SIRT6 at K128, K160, and K267, resulting in a decreased deacetylase activity of SIRT6 and attenuated SIRT6 tumor-suppressive function in NSCLC. Mechanistically, MOF-mediated SIRT6 acetylation hinders the interaction between SIRT6 and transcriptional factor FOXA2, which in turn leads to the transcriptional activation of ZEB2, thus promoting NSCLC progression. Collectively, these data indicate an acetylation-dependent mechanism that modulates SIRT6 tumor-suppressive function in NSCLC. Our findings suggest that the MOF-SIRT6-ZEB2 axis may represent a promising therapeutic target for the management of NSCLC., Competing Interests: Declaration of interests The authors declare no competing interests., (Copyright © 2023 The Authors. Published by Elsevier Inc. All rights reserved.)

Differential composition analysis - the identification of cell types that have statistically significant changes in abundance between multiple experimental conditions - is one of the most common tasks in single cell omic data analysis. However, it remains challenging to perform differential composition analysis in the presence of flexible experimental designs and uncertainty in cell type assignment. Here, we introduce a statistical model and an open source R package, DCATS, for differential composition analysis based on a beta-binomial regression framework that addresses these challenges. Our empirical evaluation shows that DCATS consistently maintains high sensitivity and specificity compared to state-of-the-art methods., (© 2023. The Author(s).)

Methods for cell clustering and gene expression from single-cell RNA sequencing (scRNA-seq) data are essential for biological interpretation of cell processes. Here, we present TRIAGE-Cluster which uses genome-wide epigenetic data from diverse bio-samples to identify genes demarcating cell diversity in scRNA-seq data. By integrating patterns of repressive chromatin deposited across diverse cell types with weighted density estimation, TRIAGE-Cluster determines cell type clusters in a 2D UMAP space. We then present TRIAGE-ParseR, a machine learning method which evaluates gene expression rank lists to define gene groups governing the identity and function of cell types. We demonstrate the utility of this two-step approach using atlases of in vivo and in vitro cell diversification and organogenesis. We also provide a web accessible dashboard for analysis and download of data and software. Collectively, genome-wide epigenetic repression provides a versatile strategy to define cell diversity and study gene regulation of scRNA-seq data., (© The Author(s) 2023. Published by Oxford University Press on behalf of Nucleic Acids Research.)

Background: Sex differences in the pathogenesis of hypertension exist. While gut microbiota (GM) has been associated with hypertension, it is unclear whether there are sex-linked differences in the association between GM and hypertension., Methods: We conducted a cross-sectional study to investigate the sex differences in associations between GM characterized by shotgun sequencing, GM-derived short-chain fatty acids, and 24-hour ambulatory blood pressure in 241 Hong Kong Chinese (113 men and 128 women; mean age, 54±6 years)., Results: The hypertensive group was associated with GM alterations; however, significant differences in β-diversity and GM composition in hypertensive versus normotensive groups were only observed in women and not in men under various statistical models adjusting for the following covariates: age, sex, body mass index, sodium intake estimated by spot urine analysis, blood glucose, triglycerides, low- and high-density lipoprotein cholesterol, smoking, menopause, and fatty liver status. Specifically, Ruminococcus gnavus , Clostridium bolteae , and Bacteroides ovatus were significantly more abundant in the hypertensive women, whereas Dorea formicigenerans was more abundant in the normotensive women. No bacterial species were found to be significantly associated with hypertension in men. Furthermore, total plasma short-chain fatty acids and propionic acid were independent predictors of systolic and diastolic blood pressure in women but not men., Conclusions: GM dysregulation was strongly associated with 24-hour ambulatory blood pressure in women but not men, which may be mediated through propionic acid. Our work suggests that sex differences may be an important consideration while assessing the role of GM in the development and treatment of hypertension., Competing Interests: Disclosures K.K. Lau received grants from Research Fund Secretariat of the Food and Health Bureau, Innovation and Technology Bureau, Research Grants Council, Amgen, Boehringer Ingelheim, Eisai, and Pfizer and consultation fees from Amgen, Boehringer Ingelheim, Daiichi Sankyo, and Sanofi, all outside the submitted work. S.Y. Leung has received research sponsorship from Pfizer, Merck, Servier, and Curegenix. S.K. Chung received a faculty research grant (FRG-22-024-FMD) and Dr Neher’s Biophysics Laboratory for Innovative Drug Discovery from the Macao Science and Technology Development Fund (FDCT project code: 001/2020/ALC), all outside of the submitted work. The other authors report no conflicts.

Mass spectrometry (MS)-based untargeted metabolomic and lipidomic approaches are being used increasingly in biomedical research. The adoption and integration of these data are critical to the overall multi-omic toolkit. Recently, a sample extraction method called Multi-ABLE has been developed, which enables concurrent generation of proteomic and untargeted metabolomic and lipidomic data from a small amount of tissue. The proteomics field has a well-established set of software for processing of acquired data; however, there is a lack of a unified, off-the-shelf, ready-to-use bioinformatics pipeline that can take advantage of and prepare concurrently generated metabolomic and lipidomic data for joint downstream analyses. Here we present an R pipeline called MultiABLER as a unified and simple upstream processing and analysis pipeline for both metabolomics and lipidomics datasets acquired using liquid chromatography-tandem mass spectrometry. The code is available via an open-source license at https://github.com/holab-hku/MultiABLER., Competing Interests: The authors declare that they have no competing interests., (© 2023 The Authors.)

Transient gut microbiota alterations have been reported after antibiotic therapy for Helicobacter pylori. However, alteration in the gut virome after H. pylori eradication remains uncertain. Here, we apply metagenomic sequencing to fecal samples of 44 H. pylori-infected patients at baseline, 6-week (N = 44), and 6-month (N = 33) after treatment. Following H. pylori eradication, we discover contraction of the gut virome diversity, separation of virome community with increased community difference, and shifting towards a higher proportion of core virus. While the gut microbiota is altered at 6-week and restored at 6-month, the virome community shows contraction till 6-month after the treatment with enhanced phage-bacteria interactions at 6-week. Multiple courses of antibiotic treatments further lead to lower virus community diversity when compared with treatment naive patients. Our results demonstrate that H. pylori eradication therapies not only result in transient alteration in gut microbiota but also significantly alter the previously less known gut virome community., (© 2023. The Author(s).)

Current single-cell visualisation techniques project high dimensional data into 'map' views to identify high-level structures such as cell clusters and trajectories. New tools are needed to allow the transversal through the high dimensionality of single-cell data to explore the single-cell local neighbourhood. StarmapVis is a convenient web application displaying an interactive downstream analysis of single-cell expression or spatial transcriptomic data. The concise user interface is powered by modern web browsers to explore the variety of viewing angles unavailable to 2D media. Interactive scatter plots display clustering information, while the trajectory and cross-comparison among different coordinates are displayed in connectivity networks. Automated animation of camera view is a unique feature of our tool. StarmapVis also offers a useful animated transition between two-dimensional spatial omic data to three-dimensional single cell coordinates. The usability of StarmapVis is demonstrated by four data sets, showcasing its practical usability. StarmapVis is available at: https://holab-hku.github.io/starmapVis., Competing Interests: The authors declare that they have no competing interests., (© 2023 The Authors.)

Background: Gut microbiota (GM) dysregulation, known as dysbiosis, has been proposed as a crucial driver of obesity associated with "Western" diet (WD) consumption. Gut dysbiosis is associated with increased gut permeability, inflammation, and insulin resistance. However, host metabolic pathways implicated in the pathophysiology of gut dysbiosis are still elusive. Exchange protein directly activated by cAMP (Epac) plays a critical role in cell-cell junction formation and insulin secretion. Here, we used homozygous Epac1-knockout (Epac1 -/- ), Epac2-knockout (Epac2 -/- ), and wild-type (WT) mice to investigate the role of Epac proteins in mediating gut dysbiosis, gut permeability, and inflammation after WD feeding., Results: The 16S rRNA gene sequencing of fecal DNA showed that the baseline GM of Epac2 -/- , but not Epac1 -/- , mice was represented by a significantly higher Firmicutes to Bacteroidetes ratio and significant alterations in several taxa compared to WT mice, suggesting that Epac2 -/- mice had gut dysbiosis under physiological conditions. However, an 8-week WD led to a similar gut microbiome imbalance in mice regardless of genotype. While Epac1 deficiency modestly exacerbated the WD-induced GM dysbiosis, the WD-fed Epac2 -/- mice had a more significant increase in gut permeability than corresponding WT mice. After WD feeding, Epac1 -/- , but not Epac2 -/- , mice had significantly higher mRNA levels of tumor necrosis factor-alpha (TNF-α) and F4/80 in the epididymal white adipose tissue (EWAT), increased circulating lipocalin-2 protein and more severe glucose intolerance, suggesting greater inflammation and insulin resistance in WD-fed Epac1 -/- mice than corresponding WT mice. Consistently, Epac1 protein expression was significantly reduced in the EWAT of WD-fed WT and Epac2 -/- mice., Conclusion: Despite significantly dysregulated baseline GM and a more pronounced increase in gut permeability upon WD feeding, WD-fed Epac2 -/- mice did not exhibit more severe inflammation and glucose intolerance than corresponding WT mice. These findings suggest that the role of gut dysbiosis in mediating WD-associated obesity may be context-dependent. On the contrary, we demonstrate that deficiency of host signaling protein, Epac1, drives inflammation and glucose intolerance which are the hallmarks of WD-induced obesity. Video abstract., (© 2022. The Author(s).)

The management of chronic wounds in the elderly such as pressure injury (also known as bedsore or pressure ulcer) is increasingly important in an ageing population. Accurate classification of the stage of pressure injury is important for wound care planning. Nonetheless, the expertise required for staging is often not available in a residential care home setting. Artificial-intelligence (AI)-based computer vision techniques have opened up opportunities to harness the inbuilt camera in modern smartphones to support pressure injury staging by nursing home carers. In this paper, we summarise the recent development of smartphone or tablet-based applications for wound assessment. Furthermore, we present a new smartphone application (app) to perform real-time detection and staging classification of pressure injury wounds using a deep learning-based object detection system, YOLOv4. Based on our validation set of 144 photos, our app obtained an overall prediction accuracy of 63.2%. The per-class prediction specificity is generally high (85.1%-100%), but have variable sensitivity: 73.3% (stage 1 vs. others), 37% (stage 2 vs. others), 76.7 (stage 3 vs. others), 70% (stage 4 vs. others), and 55.6% (unstageable vs. others). Using another independent test set, 8 out of 10 images were predicted correctly by the YOLOv4 model. When deployed in a real-life setting with two different ambient brightness levels with three different Android phone models, the prediction accuracy of the 10 test images ranges from 80 to 90%, which highlight the importance of evaluation of mobile health (mHealth) application in a simulated real-life setting. This study details the development and evaluation process and demonstrates the feasibility of applying such a real-time staging app in wound care management., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (© 2022 Lau, Yu, Yip, Luk, Wai, Sit, Wong and Ho.)

In recent years, the role of intrinsic biophysical features, especially cellular stiffness, in diverse cellular and disease processes is being increasingly recognized. New high throughput techniques for the quantification of cellular stiffness facilitate the study of their roles in health and diseases. In this review, we summarized recent discovery about how cellular stiffness is involved in cell stemness, tumorigenesis, and blood diseases. In addition, we review the molecular mechanisms underlying the gene regulation of cellular stiffness in health and disease progression. Finally, we discussed the current understanding on how the cytoskeleton structure and the regulation of these genes contribute to cellular stiffness, highlighting where the field of cellular stiffness is headed., Competing Interests: Competing interestsThe authors declare no competing interests., (© International Union for Pure and Applied Biophysics (IUPAB) and Springer-Verlag GmbH Germany, part of Springer Nature 2022, Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.)

Competing Interests: Conflict of interestThe authors declare no competing interests.

Shotgun metagenomics has enabled the discovery of antibiotic resistance genes (ARGs). Although there have been numerous studies benchmarking the bioinformatics methods for shotgun metagenomic data analysis, there has not yet been a study that systematically evaluates the performance of different experimental protocols on metagenomic species profiling and ARG detection. In this study, we generated 35 whole genome shotgun metagenomic sequencing data sets for five samples (three human stool and two microbial standard) using seven experimental protocols (KAPA or Flex kits at 50ng, 10ng, or 5ng input amounts; XT kit at 1ng input amount). Using this comprehensive resource, we evaluated the seven protocols in terms of robust detection of ARGs and microbial abundance estimation at various sequencing depths. We found that the data generated by the seven protocols are largely similar. The inter-protocol variability is significantly smaller than the variability between samples or sequencing depths. We found that a sequencing depth of more than 30M is suitable for human stool samples. A higher input amount (50ng) is generally favorable for the KAPA and Flex kits. This systematic benchmarking study sheds light on the impact of sequencing depth, experimental protocol, and DNA input amount on ARG detection in human stool samples.

The genome-editing Cas9 protein uses multiple amino-acid residues to bind the target DNA. Considering only the residues in proximity to the target DNA as potential sites to optimise Cas9's activity, the number of combinatorial variants to screen through is too massive for a wet-lab experiment. Here we generate and cross-validate ten in silico and experimental datasets of multi-domain combinatorial mutagenesis libraries for Cas9 engineering, and demonstrate that a machine learning-coupled engineering approach reduces the experimental screening burden by as high as 95% while enriching top-performing variants by ∼7.5-fold in comparison to the null model. Using this approach and followed by structure-guided engineering, we identify the N888R/A889Q variant conferring increased editing activity on the protospacer adjacent motif-relaxed KKH variant of Cas9 nuclease from Staphylococcus aureus (KKH-SaCas9) and its derived base editor in human cells. Our work validates a readily applicable workflow to enable resource-efficient high-throughput engineering of genome editor's activity., (© 2022. The Author(s).)

Background: Short-term antibiotics exposure is associated with alterations in microbiota and antibiotic resistance genes (ARGs) in the human gut. While antibiotics are critical in the successful eradication of Helicobacter pylori, the short-term and long-term impacts on the composition and quantity of antibiotics resistance genes after H. pylori eradication are unclear. This study used whole-genome shotgun metagenomic of stool samples to characterize the gut microbiota and ARGs, before and after H. pylori eradication therapy., Results: Forty-four H. pylori-infected patients were recruited, including 21 treatment naïve patients who received clarithromycin-based triple therapy (CLA group) and 23 patients who failed previous therapies, in which 10 received levofloxacin-based quadruple therapy (LEVO group) and 13 received other combinations (OTHER group). Stool samples were collected at baseline (before current treatment), 6 week and 6 month after eradication therapy. At baseline, there was only a slight difference among the three groups on ARGs and gut microbiota. After eradication therapy, there was a transient but significant increase in gut ARGs 6 week post-therapy, among which the LEVO group had the most significant ARGs alteration compared to other two groups. For treatment naïve patients, those with higher ErmF abundance were prone to fail CLA eradication and gain more ARGs after treatment. For gut microbiota, the bacteria richness decreased at 6 week and there was a significant difference in microbiota community among the three groups at 6 week., Conclusions: Our findings demonstrated the dynamic alterations in gut microbiota and ARGs induced by different eradication therapies, which could influence the choices of antibiotics in eradication therapy., (© 2021 John Wiley & Sons Ltd.)

Mitochondrial mutations are increasingly recognised as informative endogenous genetic markers that can be used to reconstruct cellular clonal structure using single-cell RNA or DNA sequencing data. However, identifying informative mtDNA variants in noisy and sparse single-cell sequencing data is still challenging with few computation methods available. Here we present an open source computational tool MQuad that accurately calls clonally informative mtDNA variants in a population of single cells, and an analysis suite for complete clonality inference, based on single cell RNA, DNA or ATAC sequencing data. Through a variety of simulated and experimental single cell sequencing data, we showed that MQuad can identify mitochondrial variants with both high sensitivity and specificity, outperforming existing methods by a large extent. Furthermore, we demonstrate its wide applicability in different single cell sequencing protocols, particularly in complementing single-nucleotide and copy-number variations to extract finer clonal resolution., (© 2022. The Author(s).)

Motivation: Scalable clustering algorithms are needed to analyze millions of cells in single cell RNA-seq (scRNA-seq) data., Results: Here, we present an open source python package called FlowGrid that can integrate into the Scanpy workflow to perform clustering on very large scRNA-seq datasets. FlowGrid implements a fast density-based clustering algorithm originally designed for flow cytometry data analysis. We introduce a new automated parameter tuning procedure, and show that FlowGrid can achieve comparable clustering accuracy as state-of-the-art clustering algorithms but at a substantially reduced run time for very large single cell RNA-seq datasets. For example, FlowGrid can complete a one-hour clustering task for one million cells in about five min., Availability and Implementation: https://github.com/holab-hku/FlowGrid., Supplementary Information: Supplementary data are available at Bioinformatics online., (© The Author(s) 2021. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

Microfluidic paper-based analytical devices (μPADs) have been widely explored for point-of-care testing due to their simplicity, low cost, and portability. μPADs with multiple-step reactions usually require precise flow control, especially flow-delay. This paper reports the numerical, mathematical, and experimental studies of flow delay through wax valves surrounded by PDMS walls on paper microfluidics. The predried surfactant in the sample zone diffuses into the liquid sample which can therefore flow through the wax valves. The delay time is automatically regulated by the diffusion of the surfactant after sample loading. The numerical study suggested that both the elevated contact angle and the reduced porosity and pore size in the wax printed region could effectively prevent water but allow liquids with lower contact angles ( e.g. , surfactant solutions) to flow through. The PDMS walls fabricated using a low-cost liquid dispenser effectively prevented the leakage of surfactant solutions. By controlling the quantity, diffusion distance, and type of the surfactant predried on the chip, the system successfully achieved a delay time ranging from 1.6 to 20 minutes. A mathematical model involving the above parameters was developed based on Fick's second law to predict the delay time. Finally, the flow-delay systems were applied in sequential mixing and distance-based detection of either glucose or alcohol. Linear ranges of 1-100 mg dL -1 and 1-40 mg dL -1 were achieved for glucose and alcohol, respectively. The lower limit detection (LOD) of glucose and alcohol was 1 mg dL -1 . The LOD of glucose was only 1/11 of that detected using μPADs without flow control, indicating the advantage of controlling fluid flow. The systematic findings in this study provide critical guidelines for the development and applications of wax valves in automatic flow delay for point-of-care testing.

Mitochondrial energy production and function rely on optimal concentrations of the essential redox-active lipid, coenzyme Q (CoQ). CoQ deficiency results in mitochondrial dysfunction associated with increased mitochondrial oxidative stress and a range of pathologies. What drives CoQ deficiency in many of these pathologies is unknown, just as there currently is no effective therapeutic strategy to overcome CoQ deficiency in humans. To date, large-scale studies aimed at systematically interrogating endogenous systems that control CoQ biosynthesis and their potential utility to treat disease have not been carried out. Therefore, we developed a quantitative high-throughput method to determine CoQ concentrations in yeast cells. Applying this method to the Yeast Deletion Collection as a genome-wide screen, 30 genes not known previously to regulate cellular concentrations of CoQ were discovered. In combination with untargeted lipidomics and metabolomics, phosphatidylethanolamine N-methyltransferase (PEMT) deficiency was confirmed as a positive regulator of CoQ synthesis, the first identified to date. Mechanistically, PEMT deficiency alters mitochondrial concentrations of one-carbon metabolites, characterized by an increase in the S-adenosylmethionine to S-adenosylhomocysteine (SAM-to-SAH) ratio that reflects mitochondrial methylation capacity, drives CoQ synthesis, and is associated with a decrease in mitochondrial oxidative stress. The newly described regulatory pathway appears evolutionary conserved, as ablation of PEMT using antisense oligonucleotides increases mitochondrial CoQ in mouse-derived adipocytes that translates to improved glucose utilization by these cells, and protection of mice from high-fat diet-induced insulin resistance. Our studies reveal a previously unrecognized relationship between two spatially distinct lipid pathways with potential implications for the treatment of CoQ deficiencies, mitochondrial oxidative stress/dysfunction, and associated diseases., (Copyright © 2021 The Authors. Published by Elsevier B.V. All rights reserved.)

Importance: Oral squamous cell carcinoma (SCC) is a lethal malignant neoplasm with a high rate of tumor metastasis and recurrence. Accurate diagnosis, prognosis prediction, and metastasis detection can improve patient outcomes. Deep learning for clinical image analysis can be used for diagnosis and prognosis in cancers, including oral SCC; its use in these areas can improve patient care and outcome., Observations: This review is a summary of the use of deep learning models for diagnosis, prognosis, and metastasis detection for oral SCC by analyzing information from pathological and radiographic images. Specifically, deep learning has been used to classify different cell types, to differentiate cancer cells from nonmalignant cells, and to identify oral SCC from other cancer types. It can also be used to predict survival, to differentiate between tumor grades, and to detect lymph node metastasis. In general, the performance of these deep learning models has an accuracy ranging from 77.89% to 97.51% and 76% to 94.2% with the use of pathological and radiographic images, respectively. The review also discusses the importance of using good-quality clinical images in sufficient quantity on model performance., Conclusions and Relevance: Applying pathological and radiographic images in deep learning models for diagnosis and prognosis of oral SCC has been explored, and most studies report results showing good classification accuracy. The successful use of deep learning in these areas has a high clinical translatability in the improvement of patient care.

Inferring cellular trajectories using a variety of omic data is a critical task in single-cell data science. However, accurate prediction of cell fates, and thereby biologically meaningful discovery, is challenged by the sheer size of single-cell data, the diversity of omic data types, and the complexity of their topologies. We present VIA, a scalable trajectory inference algorithm that overcomes these limitations by using lazy-teleporting random walks to accurately reconstruct complex cellular trajectories beyond tree-like pathways (e.g., cyclic or disconnected structures). We show that VIA robustly and efficiently unravels the fine-grained sub-trajectories in a 1.3-million-cell transcriptomic mouse atlas without losing the global connectivity at such a high cell count. We further apply VIA to discovering elusive lineages and less populous cell fates missed by other methods across a variety of data types, including single-cell proteomic, epigenomic, multi-omics datasets, and a new in-house single-cell morphological dataset., (© 2021. The Author(s).)

A group of circulating microRNAs (miRNAs) have been implicated in the pathogenesis of Parkinson's disease. However, a comprehensive study of the interactions between pathogenic miRNAs and their downstream Parkinson's disease (PD)-related target genes has not been performed. Here, we identified the miRNA expression profiles in the plasma and circulating exosomes of Parkinson's disease patients using next-generation RNA sequencing. Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) analyses showed that the miRNA target genes were enriched in axon guidance, neurotrophin signaling, cellular senescence, and the Transforming growth factor-β (TGF-β), mitogen-activated protein kinase (MAPK), phosphatidylinositol 3-kinase (PI3K)-protein kinase B (AKT) and mechanistic target of rapamycin (mTOR) signaling pathways. Furthermore, a group of aberrantly expressed miRNAs were selected and further validated in individual patient plasma, human neural stem cells (NSCs) and a rat model of PD. More importantly, the full scope of the regulatory network between these miRNAs and their PD-related gene targets in human neural stem cells was examined, and the findings revealed a similar but still varied downstream regulatory cascade involving many known PD-associated genes. Additionally, miR-23b-3p was identified as a novel direct regulator of alpha-synuclein, which is possibly the key component in PD. Our current study, for the first time, provides a glimpse into the regulatory network of pathogenic miRNAs and their PD-related gene targets in PD. Moreover, these PD-associated miRNAs may serve as biomarkers and novel therapeutic targets for PD., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2021 Cai, Chai, Xiong, Wei, Mao, Zhu, Li, Wei, Dai, Yang, Liu, Shu, Wang, Lu, Cai, Zheng, Mei, Zhou, Yang, Zhao, Shen, Ho, Chen and Xiong.)

Triaging and prioritising patients for RT-PCR test had been essential in the management of COVID-19 in resource-scarce countries. In this study, we applied machine learning (ML) to the task of detection of SARS-CoV-2 infection using basic laboratory markers. We performed the statistical analysis and trained an ML model on a retrospective cohort of 5148 patients from 24 hospitals in Hong Kong to classify COVID-19 and other aetiology of pneumonia. We validated the model on three temporal validation sets from different waves of infection in Hong Kong. For predicting SARS-CoV-2 infection, the ML model achieved high AUCs and specificity but low sensitivity in all three validation sets (AUC: 89.9-95.8%; Sensitivity: 55.5-77.8%; Specificity: 91.5-98.3%). When used in adjunction with radiologist interpretations of chest radiographs, the sensitivity was over 90% while keeping moderate specificity. Our study showed that machine learning model based on readily available laboratory markers could achieve high accuracy in predicting SARS-CoV-2 infection., (© 2021. The Author(s).)

The cell elasticity has attracted extensive research interests since it not only provides new insights into cell biology but also is an emerging mechanical marker for the diagnosis of some diseases. This paper reports the method for the precise measurement of mechanical properties of single cells deformed to a large extent using a novel microfluidic system integrated with a pressure feedback system and small particle separation unit. The particle separation system was employed to avoid the blockage of the cell deformation channel to enhance the measurement throughput. This system is of remarkable application potential in the precise evaluation of cell mechanical properties. In brief, this paper reports:•The manufacturing of the chip using standard soft lithography;•The methods to deform single cells in a microchannel and measure the relevant pressure drop using a pressure sensor connecting to the microfluidic chip;•Calculation of the mechanical properties including stiffness and fluidity of each cell based on a power-law rheology model describing the viscoelastic behaviors of cells;•Automatic and real-time measurement of the mechanical properties using video processing software., Competing Interests: The Authors confirm that there are no conflicts of interest., (© 2021 The Authors. Published by Elsevier B.V.)

Cancer growth is usually accompanied by metastasis which kills most cancer patients. Here we aim to study the effect of cisplatin at different doses on breast cancer growth and metastasis. Methods: We used cisplatin to treat breast cancer cells, then detected the migration of cells and the changes of epithelial-mesenchymal transition (EMT) markers by migration assay, Western blot, and immunofluorescent staining. Next, we analyzed the changes of RNA expression of genes by RNA-seq and confirmed the binding of activating transcription factor 3 (ATF3) to cytoskeleton related genes by ChIP-seq. Thereafter, we combined cisplatin and paclitaxel in a neoadjuvant setting to treat xenograft mouse models. Furthermore, we analyzed the association of disease prognosis with cytoskeletal genes and ATF3 by clinical data analysis. Results: When administered at a higher dose (6 mg/kg), cisplatin inhibits both cancer growth and metastasis, yet with strong side effects, whereas a lower dose (2 mg/kg) cisplatin blocks cancer metastasis without obvious killing effects. Cisplatin inhibits cancer metastasis through blocking early steps of EMT. It antagonizes transforming growth factor beta (TGFβ) signaling through suppressing transcription of many genes involved in cytoskeleton reorganization and filopodia formation which occur early in EMT and are responsible for cancer metastasis. Mechanistically, TGFβ and fibronectin-1 (FN1) constitute a positive reciprocal regulation loop that is critical for activating TGFβ/SMAD3 signaling, which is repressed by cisplatin induced expression of ATF3. Furthermore, neoadjuvant administration of cisplatin at 2 mg/kg in conjunction with paclitaxel inhibits cancer growth and blocks metastasis without causing obvious side effects by inhibiting colonization of cancer cells in the target organs. Conclusion: Thus, cisplatin prevents breast cancer metastasis through blocking early EMT, and the combination of cisplatin and paclitaxel represents a promising therapy for killing breast cancer and blocking tumor metastasis., Competing Interests: Competing Interests: The authors have declared that no competing interest exists., (© The author(s).)