Liver-specific mitochondrial amidoxime-reducing component 1 (Mtarc1) knockdown protects the liver from diet-induced MASH in multiple mouse models.

BACKGROUND: Human genetic studies have identified several mitochondrial amidoxime-reducing component 1 (MTARC1) variants as protective against metabolic dysfunction-associated steatotic liver disease. The MTARC1 variants are associated with decreased plasma lipids and liver enzymes and reduced liver-related mortality. However, the role of mARC1 in fatty liver disease is still unclear. METHODS: Given that mARC1 is mainly expressed in hepatocytes, we developed an N-acetylgalactosamine-conjugated mouse Mtarc1 siRNA, applying it in multiple in vivo models to investigate the role of mARC1 using multiomic techniques. RESULTS: In ob/ob mice, knockdown of Mtarc1 in mouse hepatocytes resulted in decreased serum liver enzymes, LDL-cholesterol, and liver triglycerides. Reduction of mARC1 also reduced liver weight, improved lipid profiles, and attenuated liver pathological changes in 2 diet-induced metabolic dysfunction-associated steatohepatitis mouse models. A comprehensive analysis of mARC1-deficient liver from a metabolic dysfunction-associated steatohepatitis mouse model by metabolomics, proteomics, and lipidomics showed that Mtarc1 knockdown partially restored metabolites and lipids altered by diet. CONCLUSIONS: Taken together, reducing mARC1 expression in hepatocytes protects against metabolic dysfunction-associated steatohepatitis in multiple murine models, suggesting a potential therapeutic approach for this chronic liver disease.

Weakened APC/C activity at mitotic exit drives cancer vulnerability to KIF18A inhibition.

The efficacy of current antimitotic cancer drugs is limited by toxicity in highly proliferative healthy tissues. A cancer-specific dependency on the microtubule motor protein KIF18A therefore makes it an attractive therapeutic target. Not all cancers require KIF18A, however, and the determinants underlying this distinction remain unclear. Here, we show that KIF18A inhibition drives a modest and widespread increase in spindle assembly checkpoint (SAC) signaling from kinetochores which can result in lethal mitotic delays. Whether cells arrest in mitosis depends on the robustness of the metaphase-to-anaphase transition, and cells predisposed with weak basal anaphase-promoting complex/cyclosome (APC/C) activity and/or persistent SAC signaling through metaphase are uniquely sensitive to KIF18A inhibition. KIF18A-dependent cancer cells exhibit hallmarks of this SAC:APC/C imbalance, including a long metaphase-to-anaphase transition, and slow mitosis overall. Together, our data reveal vulnerabilities in the cell division apparatus of cancer cells that can be exploited for therapeutic benefit.

Hepatitis C virus infects and perturbs liver stem cells.

Hepatitis C virus (HCV) is the leading cause of death from liver disease. How HCV infection causes lasting liver damage and increases cancer risk remains unclear. Here, we identify bipotent liver stem cells as novel targets for HCV infection, and their erroneous differentiation as the potential cause of impaired liver regeneration and cancer development. We show 3D organoids generated from liver stem cells from actively HCV-infected individuals carry replicating virus and maintain low-grade infection over months. Organoids can be infected with a primary HCV isolate. Virus-inclusive single-cell RNA sequencing uncovered transcriptional reprogramming in HCV+ cells supporting hepatocytic differentiation, cancer stem cell development, and viral replication while stem cell proliferation and interferon signaling are disrupted. Our data add a new pathogenesis mechanism-infection of liver stem cells-to the biology of HCV infection that may explain progressive liver damage and enhanced cancer risk through an altered stem cell state.ImportanceThe hepatitis C virus (HCV) causes liver disease, affecting millions. Even though we have effective antivirals that cure HCV, they cannot stop terminal liver disease. We used an adult stem cell-derived liver organoid system to understand how HCV infection leads to the progression of terminal liver disease. Here, we show that HCV maintains low-grade infections in liver organoids for the first time. HCV infection in liver organoids leads to transcriptional reprogramming causing cancer cell development and altered immune response. Our finding shows how HCV infection in liver organoids mimics HCV infection and patient pathogenesis. These results reveal that HCV infection in liver organoids contributes to liver disease progression.

Novel Procedure for Automatic Registration between Cone-Beam Computed Tomography and Intraoral Scan Data Supported with 3D Segmentation.

In contemporary practice, intraoral scans and cone-beam computed tomography (CBCT) are widely adopted techniques for tooth localization and the acquisition of comprehensive three-dimensional models. Despite their utility, each dataset presents inherent merits and limitations, prompting the pursuit of an amalgamated solution for optimization. Thus, this research introduces a novel 3D registration approach aimed at harmonizing these distinct datasets to offer a holistic perspective. In the pre-processing phase, a retrained Mask-RCNN is deployed on both sagittal and panoramic projections to partition upper and lower teeth from the encompassing CBCT raw data. Simultaneously, a chromatic classification model is proposed for segregating gingival tissue from tooth structures in intraoral scan data. Subsequently, the segregated datasets are aligned based on dental crowns, employing the robust RANSAC and ICP algorithms. To assess the proposed methodology's efficacy, the Euclidean distance between corresponding points is statistically evaluated. Additionally, dental experts, including two orthodontists and an experienced general dentist, evaluate the clinical potential by measuring distances between landmarks on tooth surfaces. The computed error in corresponding point distances between intraoral scan data and CBCT data in the automatically registered datasets utilizing the proposed technique is quantified at 0.234 ± 0.019 mm, which is significantly below the 0.3 mm CBCT voxel size. Moreover, the average measurement discrepancy among expert-identified landmarks ranges from 0.368 to 1.079 mm, underscoring the promise of the proposed method.

Anesthetic Management for a Child With a Newly Identified Mitochondrial Disease SLC25A46 Mutation: A Case Report.

SLC25A46 mutation is a newly recognized mitochondrial mutation causing neurological and muscular abnormalities. We describe a first-ever report of the anesthetic management of a seven-year-old boy with an SLC25A46 mutation during a major orthopedic procedure. The patient was nonverbal and presented with cerebral visual impairment, torticollis, and lower extremity contractures. Because of his new diagnosis of mitochondrial disease and history of delayed awakening after anesthesia, we performed general anesthesia with sevoflurane, a low-dose ketamine infusion, and small doses of fentanyl while avoiding propofol and maintaining normoglycemia and normothermia. No postoperative complications were noted during the recovery period.

Enhancement of Hip X-ray with Convolutional Autoencoder for Increasing Prediction Accuracy of Bone Mineral Density.

It is very important to keep track of decreases in the bone mineral density (BMD) of elderly people since it can be correlated with the risk of incidence of major osteoporotic fractures leading to fatal injuries. Even though dual-energy X-ray absorptiometry (DXA) is the one of the most precise measuring techniques used to quantify BMD, most patients have restricted access to this machine due to high cost of DXA equipment, which is also rarely distributed to local clinics. Meanwhile, the conventional X-rays, which are commonly used for visualizing conditions and injuries due to their low cost, combine the absorption of both soft and bone tissues, consequently limiting its ability to measure BMD. Therefore, we have proposed a specialized automated smart system to quantitatively predict BMD based on a conventional X-ray image only by reducing the soft tissue effect supported by the implementation of a convolutional autoencoder, which is trained using proposed synthesized data to generate grayscale values of bone tissue alone. From the enhanced image, multiple features are calculated from the hip X-ray to predict the BMD values. The performance of the proposed method has been validated through comparison with the DXA value, which shows high consistency with correlation coefficient of 0.81 and mean absolute error of 0.069 g/cm2.

Machine Learning-Based Measurement of Regional and Global Spinal Parameters Using the Concept of Incidence Angle of Inflection Points.

This study delves into the application of convolutional neural networks (CNNs) in evaluating spinal sagittal alignment, introducing the innovative concept of incidence angles of inflection points (IAIPs) as intuitive parameters to capture the interplay between pelvic and spinal alignment. Pioneering the fusion of IAIPs with machine learning for sagittal alignment analysis, this research scrutinized whole-spine lateral radiographs from hundreds of patients who visited a single institution, utilizing high-quality images for parameter assessments. Noteworthy findings revealed robust success rates for certain parameters, including pelvic and C2 incidence angles, but comparatively lower rates for sacral slope and L1 incidence. The proposed CNN-based machine learning method demonstrated remarkable efficiency, achieving an impressive 80 percent detection rate for various spinal angles, such as lumbar lordosis and thoracic kyphosis, with a precise error threshold of 3.5°. Further bolstering the study's credibility, measurements derived from the novel formula closely aligned with those directly extracted from the CNN model. In conclusion, this research underscores the utility of the CNN-based deep learning algorithm in delivering precise measurements of spinal sagittal parameters, and highlights the potential for integrating machine learning with the IAIP concept for comprehensive data accumulation in the domain of sagittal spinal alignment analysis, thus advancing our understanding of spinal health.

Proteomic and genetic analyses of influenza A viruses identify pan-viral host targets.

Influenza A Virus (IAV) is a recurring respiratory virus with limited availability of antiviral therapies. Understanding host proteins essential for IAV infection can identify targets for alternative host-directed therapies (HDTs). Using affinity purification-mass spectrometry and global phosphoproteomic and protein abundance analyses using three IAV strains (pH1N1, H3N2, H5N1) in three human cell types (A549, NHBE, THP-1), we map 332 IAV-human protein-protein interactions and identify 13 IAV-modulated kinases. Whole exome sequencing of patients who experienced severe influenza reveals several genes, including scaffold protein AHNAK, with predicted loss-of-function variants that are also identified in our proteomic analyses. Of our identified host factors, 54 significantly alter IAV infection upon siRNA knockdown, and two factors, AHNAK and coatomer subunit COPB1, are also essential for productive infection by SARS-CoV-2. Finally, 16 compounds targeting our identified host factors suppress IAV replication, with two targeting CDK2 and FLT3 showing pan-antiviral activity across influenza and coronavirus families. This study provides a comprehensive network model of IAV infection in human cells, identifying functional host targets for pan-viral HDT.

Nuclear accumulation of host transcripts during Zika Virus Infection.

Zika virus (ZIKV) infects fetal neural progenitor cells (NPCs) causing severe neurodevelopmental disorders in utero. Multiple pathways involved in normal brain development are dysfunctional in infected NPCs but how ZIKV centrally reprograms these pathways remains unknown. Here we show that ZIKV infection disrupts subcellular partitioning of host transcripts critical for neurodevelopment in NPCs and functionally link this process to the up-frameshift protein 1 (UPF1). UPF1 is an RNA-binding protein known to regulate decay of cellular and viral RNAs and is less expressed in ZIKV-infected cells. Using infrared crosslinking immunoprecipitation and RNA sequencing (irCLIP-Seq), we show that a subset of mRNAs loses UPF1 binding in ZIKV-infected NPCs, consistent with UPF1's diminished expression. UPF1 target transcripts, however, are not altered in abundance but in subcellular localization, with mRNAs accumulating in the nucleus of infected or UPF1 knockdown cells. This leads to diminished protein expression of FREM2, a protein required for maintenance of NPC identity. Our results newly link UPF1 to the regulation of mRNA transport in NPCs, a process perturbed during ZIKV infection.

Automated analysis of three-dimensional CBCT images taken in natural head position that combines facial profile processing and multiple deep-learning models.

BACKGROUND AND OBJECTIVES: Analyzing three-dimensional cone beam computed tomography (CBCT) images has become an indispensable procedure for diagnosis and treatment planning of orthodontic patients. Artificial intelligence, especially deep-learning techniques for analyzing image data, shows great potential for medical and dental image analysis and diagnosis. To explore the feasibility of automating measurement of 13 geometric parameters from three-dimensional cone beam computed tomography images taken in natural head position (NHP), this study proposed a smart system that combined a facial profile analysis algorithm with deep-learning models. MATERIALS AND METHODS: Using multiple views extracted from the cone beam computed tomography data of 170 cases as a dataset, our proposed method automatically calculated 13 dental parameters by partitioning, detecting regions of interest, and extracting the facial profile. Subsequently, Mask-RCNN, a trained decentralized convolutional neural network was applied to detect 23 landmarks. All the techniques were integrated into a software application with a graphical user interface designed for user convenience. To demonstrate the system's ability to replace human experts, 30 CBCT data were selected for validation. Two orthodontists and one advanced general dentist located required landmarks by using a commercial dental program. The differences between manual and developed methods were calculated and reported as the errors. RESULTS: The intraclass correlation coefficients (ICCs) and 95% confidence interval (95% CI) for intra-observer reliability were 0.98 (0.97-0.99) for observer 1; 0.95 (0.93-0.97) for observer 2; 0.98 (0.97-0.99) for observer 3 after measuring 13 parameters two times at two weeks interval. The combined ICC for intra-observer reliability was 0.97. The ICCs and 95% CI for inter-observer reliability were 0.94 (0.91-0.97). The mean absolute value of deviation was around 1 mm for the length parameters, and smaller than 2° for angle parameters. Furthermore, ANOVA test demonstrated the consistency between the measurements of the proposed method and those of human experts statistically (Fdis=2.68, ɑ=0.05). CONCLUSIONS: The proposed system demonstrated the high consistency with the manual measurements of human experts and its applicability. This method aimed to help human experts save time and efforts for analyzing three-dimensional CBCT images of orthodontic patients.

Identification of potential crucial genes and key pathways shared in Inflammatory Bowel Disease and cervical cancer by machine learning and integrated bioinformatics.

BACKGROUND: Recently, Inflammatory Bowel Disease (IBD) has been proven as a risk factor for the increasing incidence of cervical cancer (CC) development. In this study, we identify these potential hub genes and their significant pathways that commonly interact between IBD and CC and these pathological mechanisms. To this end, we use bioinformatics and systems biology approaches to analyze the miRNA-mRNA, TFs-mRNA regulatory network. METHODS AND FINDINGS: The reanalysis dataset from Gene Expression Omnibus (GEO) and the cancer genome atlas (TCGA) found these common differentially expressed genes (DEGs) between IBD and CC, clustered via weighted gene co-expression network analysis, and the vital modules significantly related to cervical cancer were identified. These hub genes of the key module were identified and explored in biological mechanism pathway analysis. Organelle fission, nuclear envelope, protein serine/threonine kinase activity, and the Human T-cell leukemia virus 1 infection pathway were the major enriched pathways for the common DEGs. Due to the high connectivity, the common DEGs with protein-protein interaction (PPI) network disclosed hub proteins (CDK1, MAD2L1, and CCNB1). This study also showed the classification algorithms of ten hub genes (MAD2L1, CCNB2, CDK1, CCNA2, BUB1B, KIF11, TTK, BUB1, CCNB1, ASPM) with accuracy >0.90 suggesting the novel biomarker potential of the hub genes. The microRNAs (miRNA), and transcription factors (TFs) mRNA regulatory network, five transcription factors, and twelve miRNAs are strongly linked to three hub genes. Gene drug interaction analysis found seven drugs compound that interacts with the hub gene. CONCLUSIONS: In the current study, our procedure has hypothesized the comprehensive understanding of disease mechanisms vital for both CC and IBD that may mediate their interaction. Our results suggest the further investigation of the molecules for the treatment of IBD and CC.

The Role of miR-29s in Human Cancers-An Update.

MicroRNAs (miRNAs) are small non-coding RNAs that directly bind to the 3' untranslated region (3'-UTR) of the target mRNAs to inhibit their expression. The miRNA-29s (miR-29s) are suggested to be either tumor suppressors or oncogenic miRNAs that are strongly dysregulated in various types of cancer. Their dysregulation alters the expression of their target genes, thereby exerting influence on different cellular pathways including cell proliferation, apoptosis, migration, and invasion, thereby contributing to carcinogenesis. In the present review, we aimed to provide an overview of the current knowledge on the miR-29s biological network and its functions in cancer, as well as its current and potential applications as a diagnostic and prognostic biomarker and/or a therapeutic target in major types of human cancer.

Risperidone Induced DNA Methylation Changes in Dopamine Receptor and Stathmin Genes in Mice Exposed to Social Defeat Stress.

Objective: Understanding complex epigenetic mechanisms is necessary to fully elucidate the effects of antipsychotic drug. This study investigated DNA methylation and mRNA expression levels of dopamine D2 and D1 receptor (Drd2 and Drd1, respectively), nuclear receptor subfamily 3, group C, member 1 (Nr3c1) and stathmin 1 (Stmn1) in brain regions of mice exposed to social defeat stress (SDS) and effects of risperidone on altered methylation and mRNA expression levels induced by SDS. Methods: Following SDS for 10 days, risperidone (0.2 mg/kg) or vehicle was administered to adult mice for 7 days. Brain tissues from the prefrontal cortex (PFC), hippocampus (HIP) and amygdala (AMY) were processed to measure methylation and mRNA levels of Drd2, Drd1, Nr3c1 and Stmn1 using pyrosequencing and real time-polymerase chain reaction. Results: We found altered methylation status of Nr3c1 and Stmn1 in the HIP and AMY of mice exposed to SDS. These changes were reversed by risperidone treatment. In addition, different methylation patterns of Drd2 and Drd1 in the PFC and AMY between defeated and control mice were identified with risperidone treatment. Conclusion: These findings suggest that risperidone can cause epigenetic changes in Drd2, Drd1, Nr3c1 and Stmn1 in defeated mice. These changes could be epigenetic mechanisms underlying antipsychotic efficacy.

Immune-related biomarkers shared by inflammatory bowel disease and liver cancer.

It has been indicated that there is an association between inflammatory bowel disease (IBD) and hepatocellular carcinoma (HCC). However, the molecular mechanism underlying the risk of developing HCC among patients with IBD is not well understood. The current study aimed to identify shared genes and potential pathways and regulators between IBD and HCC using a system biology approach. By performing the different gene expression analyses, we identified 871 common differentially expressed genes (DEGs) between IBD and HCC. Of these, 112 genes overlapped with immune genes were subjected to subsequent bioinformatics analyses. The results revealed four hub genes (CXCL2, MMP9, SPP1 and SRC) and several other key regulators including six transcription factors (FOXC1, FOXL1, GATA2, YY1, ZNF354C and TP53) and five microRNAs (miR-124-3p, miR-34a-5p, miR-1-3p, miR-7-5p and miR-99b-5p) for these disease networks. Protein-drug interaction analysis discovered the interaction of the hub genes with 46 SRC-related and 11 MMP9- related drugs that may have a therapeutic effect on IBD and HCC. In conclusion, this study sheds light on the potential connecting mechanisms of HCC and IBD.

A functional map of HIV-host interactions in primary human T cells.

Human Immunodeficiency Virus (HIV) relies on host molecular machinery for replication. Systematic attempts to genetically or biochemically define these host factors have yielded hundreds of candidates, but few have been functionally validated in primary cells. Here, we target 426 genes previously implicated in the HIV lifecycle through protein interaction studies for CRISPR-Cas9-mediated knock-out in primary human CD4+ T cells in order to systematically assess their functional roles in HIV replication. We achieve efficient knockout (>50% of alleles) in 364 of the targeted genes and identify 86 candidate host factors that alter HIV infection. 47 of these factors validate by multiplex gene editing in independent donors, including 23 factors with restrictive activity. Both gene editing efficiencies and HIV-1 phenotypes are highly concordant among independent donors. Importantly, over half of these factors have not been previously described to play a functional role in HIV replication, providing numerous novel avenues for understanding HIV biology. These data further suggest that host-pathogen protein-protein interaction datasets offer an enriched source of candidates for functional host factor discovery and provide an improved understanding of the mechanics of HIV replication in primary T cells.

Intelligent Evaluation of Global Spinal Alignment by a Decentralized Convolutional Neural Network.

Degenerative changes of the spine can cause spinal misalignment, with part of the spine arching beyond normal limits or moving in an incorrect direction, potentially resulting in back pain and significantly limiting a person's mobility. The most important parameters related to spinal misalignment include pelvic incidence, pelvic tilt, lumbar lordosis, thoracic kyphosis, and cervical lordosis. As a general rule, alignment of the spine for diagnosis and surgical treatment is estimated based on geometrical parameters measured manually by experienced doctors. However, these measurements consume the time and effort of experts to perform repetitive tasks that could be automated, especially with the powerful support of current artificial intelligence techniques. This paper focuses on creation of a decentralized convolutional neural network to precisely measure 12 spinal alignment parameters. Specifically, this method is based on detecting regions of interest with its dimensions that decrease by three orders of magnitude to focus on the necessary region to provide the output as key points. Using these key points, parameters representing spinal alignment are calculated. The quality of the method's performance, which is the consistency of the measurement results with manual measurement, is validated by 30 test cases and shows 10 of 12 parameters with a correlation coefficient > 0.8, with pelvic tilt having the smallest absolute deviation of 1.156°.

Impact of Social Defeat Stress on DNA Methylation in Drd2, Nr3c1, and Stmn1 in Wild-type and Stmn1 Knock-out Mice.

OBJECTIVE: Epigenetic profiles can be modified by stress. Dopamine receptor D2 (Drd2), glucocorticoid receptor gene (Nr3c1) and Stathmin 1 (Stmn1) genes are all implicated in adaptation to stress. The aim of study is to investigate impact of social defeat on DNA methylation in Drd2, Nr3c1, and Stmn1 in wild-type (WT) and Stmn1 knock-out (KO) mice. METHODS: The WT and Stmn1 KO mice were subjected to chronic social defeat. Brain tissues of the prefrontal cortex (PFC), amygdala (AMY) and hippocampus (HIP) were obtained. We measured DNA methylation levels of the Drd2, Nr3c1, and Stmn1 genes in the PFC, AMY, and HIP using pyrosequencing. RESULTS: In WT mice, social defeat stress did not induce any changes in Drd2 methylation, whereas significant hypermethylation occurred in Nr3c1 and Stmn1 in the susceptible and unsusceptible groups, respectively, compared to the control group. The methylation responses in the Stmn1 KO mice differed from those seen in the WT mice, such that hypermethylation was evident in all three genes in the susceptible and unsusceptible groups compared to control group. Comparison of the Stmn1 KO and WT mice revealed the same pattern of hypermethylation for all three genes. CONCLUSION: Social defeat stress induced different epigenetic modifications in three genes among control, unsusceptible, and susceptible groups of WT and Stmn1 KO mice. In particular, hypermethylation of Nr3c1 in the HIP of the susceptible group, and of Stmn1 in the AMY of the unsusceptible group in WT mice, could serve as epigenetic biomarkers of stress susceptibility and stress resilience, respectively.

Longitudinal symptom network structure in first-episode psychosis: a possible marker for remission.

BACKGROUND: Network approach has been applied to a wide variety of psychiatric disorders. The aim of the present study was to identify network structures of remitters and non-remitters in patients with first-episode psychosis (FEP) at baseline and the 6-month follow-up. METHODS: Participants (n = 252) from the Korean Early Psychosis Study (KEPS) were enrolled. They were classified as remitters or non-remitters using Andreasen's criteria. We estimated network structure with 10 symptoms (three symptoms from the Positive and Negative Syndrome Scale, one depressive symptom, and six symptoms related to schema and rumination) as nodes using a Gaussian graphical model. Global and local network metrics were compared within and between the networks over time. RESULTS: Global network metrics did not differ between the remitters and non-remitters at baseline or 6 months. However, the network structure and nodal strengths associated with positive-self and positive-others scores changed significantly in the remitters over time. Unique central symptoms for remitters and non-remitters were cognitive brooding and negative-self, respectively. The correlation stability coefficients for nodal strength were within the acceptable range. CONCLUSION: Our findings indicate that network structure and some nodal strengths were more flexible in remitters. Negative-self could be an important target for therapeutic intervention.

Pedestrian Crossing Decisions in Virtual Environments: Behavioral Validity in CAVEs and Head-Mounted Displays.

OBJECTIVE: To contribute to the validation of virtual reality (VR) as a tool for analyzing pedestrian behavior, we compared two types of high-fidelity pedestrian simulators to a test track. BACKGROUND: While VR has become a popular tool in pedestrian research, it is uncertain to what extent simulator studies evoke the same behavior as nonvirtual environments. METHOD: An identical experimental procedure was replicated in a CAVE automatic virtual environment (CAVE), a head-mounted display (HMD), and on a test track. In each group, 30 participants were instructed to step forward whenever they felt the gap between two approaching vehicles was adequate for crossing. RESULTS: Our analyses revealed distinct effects for the three environments. Overall acceptance was highest on the test track. In both simulators, crossings were initiated later, but a relationship between gap size and crossing initiation was apparent only in the CAVE. In contrast to the test track, vehicle speed significantly affected acceptance rates and safety margins in both simulators. CONCLUSION: For a common decision task, the results obtained in virtual environments deviate from those in a nonvirtual test bed. The consistency of differences indicates that restrictions apply when predicting real-world behavior based on VR studies. In particular, the higher susceptibility to speed effects warrants further investigation, since it implies that differences in perceptual processing alter experimental outcomes. APPLICATION: Our observations should inform the conclusions drawn from future research in pedestrian simulators, for example by accounting for a higher sensitivity to speed variations and a greater uncertainty associated with crossing decisions.

Harshening stem cell research and precision medicine: The states of human pluripotent cells stem cell repository diversity, and racial and sex differences in transcriptomes.

In vitro investigation on human development, disease modeling, and drug discovery has been empowered by human induced pluripotent stem cell (hiPSC) technologies that form the foundation of precision medicine. Race and sex genetic backgrounds have become a major focus of many diseases modeling and drug response evaluation in the pharmaceutical industry. Here, we gathered data from major stem cell repositories to analyze the diversity with respect to ethnicity, sex, and disease types; and we also analyzed public datasets to unravel transcriptomics differences between samples of different ethnicities and sexes. We found a lack of diversity despite the large sample size of human induced pluripotent stem cells. In the ethnic comparison, the White group made up the majority of the banked hiPSCs. Similarly, for the organ/disease type and sex comparisons, the neural and male hiPSCs accounted for the majority of currently available hiPSCs. Bulk RNA-seq and single-cell transcriptomic analysis coupled with Machine Learning and Network Analysis revealed panels of gene features differently expressed in healthy hiPSCs and human induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs) of different races and sexes. The data highlights the current ethnic and sex inequality in stem cell research and demonstrates the molecular biological diversity of hiPSCs and cardiomyocytes from different races and genders. We postulate that future efforts in stem cell biology, regenerative and precision medicine should be guided towards an inclusive, diverse repository reflecting the prevalence of diseases across racial and ethnic groups and the sexes, important for both common and rare disease modeling, drug screening, and cell therapeutics.