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Abstract Background Determining the impact of somatic mutations requires understanding the functional relationship of genes acquiring mutations; however, it is largely unknown how mutations in functionally related genes influence each other. Methods We employed non-synonymous-to-synonymous or dNdS ratios to evaluate the evolutionary dependency (ED) of gene pairs, assuming a mutation in one gene of a gene pair can affect the evolutionary fitness of mutations in its partner genes as mutation context. We employed PanCancer- and tumor type-specific mutational profiles to infer the ED of gene pairs and evaluated their biological relevance with respect to gene dependency and drug sensitivity. Results We propose that dNdS ratios of gene pairs and their derived cdNS (context-dependent dNdS) scores as measure of ED distinguishing gene pairs either as synergistic (SYN) or antagonistic (ANT). Mutation contexts can induce substantial changes in the evolutionary fitness of mutations in the paired genes, e.g., IDH1 and IDH2 mutation contexts lead to substantial increase and decrease of dNdS ratios of ATRX indels and IDH1 missense mutations corresponding to SYN and ANT relationship with positive and negative cdNS scores, respectively. The impact of gene silencing or knock-outs on cell viability (genetic dependencies) often depends on ED, suggesting that ED can guide the selection of candidates for synthetic lethality such as TCF7L2-KRAS mutations. Using cell line-based drug sensitivity data, the effects of targeted agents on cell lines are often associated with mutations of genes exhibiting ED with the target genes, informing drug sensitizing or resistant mutations for targeted inhibitors, e.g., PRSS1 and CTCF mutations as resistant mutations to EGFR and BRAF inhibitors for lung adenocarcinomas and melanomas, respectively. Conclusions We propose that the ED of gene pairs evaluated by dNdS ratios can advance our understanding of the functional relationship of genes with potential biological and clinical implications.

Abstract In this paper, we systematically investigate the general dark matter-electron interactions within the framework of effective field theories (EFTs). We consider both the non-relativistic (NR) EFT and the relativistic EFT descriptions of the interactions with the spin of dark matter (DM) up to one, i.e., the scalar (ϕ), fermion (χ), and vector (X) DM scenarios. We first collect the leading-order NR EFT operators describing the DM-electron interactions, and construct especially the NR operators for the vector DM case. Next, we consider all possible leading-order relativistic EFT operators including those with a photon field and perform the NR reduction to match them onto the NR EFT. Then we rederive the DM-bound-electron scattering rate within the NR EFT framework and find that the matrix element squared, which is the key input that encodes the DM and atomic information, can be compactly decomposed into three terms. Each term is a product of a DM response function (a 0,1,2), which is essentially a factor of Wilson coefficients squared, and its corresponding generalized atomic response function W ~ 0 , 1 , 2 $$ \left({\overset{\sim }{W}}_{0,1,2}\right) $$ . Lastly, we employ the electron recoil data from the DM direct detection experiments (including XENON10, XENON1T, and PandaX-4T) to constrain all the non-relativistic and relativistic operators in all three DM scenarios. We set strong bounds on the DM-electron interactions in the sub-GeV region. Particularly, we find that the latest PandaX-4T S2-only data provide stringent constraints on dark matter with a mass greater than approximately 20 MeV, surpassing those from the previous XENON10 and XENON1T experiments.

Abstract Lithium batteries with solid-state electrolytes are an appealing alternative to state-of-the-art non-aqueous lithium-ion batteries with liquid electrolytes because of safety and energy aspects. However, engineering development at the cell level for lithium batteries with solid-state electrolytes is limited. Here, to advance this aspect and produce high-energy lithium cells, we introduce a cell design based on advanced parametrization of microstructural and architectural parameters of electrode and electrolyte components. To validate the cell design proposed, we assemble and test (applying a stack pressure of 3.74 MPa at 45 °C) 10-layer and 4-layer solid-state lithium pouch cells with a solid polymer electrolyte, resulting in an initial specific energy of 280 Wh kg−1 (corresponding to an energy density of 600 Wh L−1) and 310 Wh kg−1 (corresponding to an energy density of 650 Wh L−1) respectively.

Abstract Topological materials confined in 1D can transform computing technologies, such as 1D topological semimetals for nanoscale interconnects and 1D topological superconductors for fault-tolerant quantum computing. As such, understanding crystallization of 1D-confined topological materials is critical. Here, we demonstrate 1D template-assisted nanowire synthesis where we observe diameter-dependent phase selectivity for tungsten phosphides. A phase bifurcation occurs to produce tungsten monophosphide and tungsten diphosphide at the cross-over nanowire diameter regime of 35–70 nm. Four-dimensional scanning transmission electron microscopy is used to identify the two phases and to map crystallographic orientations of grains at a few nm resolution. The 1D-confined phase selectivity is attributed to the minimization of the total surface energy, which depends on the nanowire diameter and chemical potentials of precursors. Theoretical calculations are carried out to construct the diameter-dependent phase diagram, which agrees with experimental observations. Our findings suggest a crystallization route to stabilize topological materials confined in 1D.

Abstract Background More than 50% of people who die by suicide have not been in contact with formal mental health services. The rate of people who fly ‘under the radar’ of mental health services is higher among men than women, indicating a need to improve engagement strategies targeted towards men who experience suicidal thoughts and/or behaviours. In Australia, a range of mental health support services exist, designed specifically for men, yet, a substantial proportion of men do not use these services. The aim of this study is to evaluate whether a brief online video-based messaging intervention is an effective approach for encouraging men with suicidal thoughts and/or behaviours to engage with existing support services. Methods Informed by a literature review, surveys, and consultation with men with a lived experience of suicidal thoughts and/or behaviours, we designed five video-based messages that will be used in this five-arm randomised controlled trial. A total of 380 (76 per arm) men aged 18 years or older with suicidal thoughts who are not currently accessing formal mental health services will be recruited online and randomly assigned to watch one of the five web-based video messages. After viewing the video, men will be presented with information about four existing Australian support services, along with links to these services. The primary outcome will be help-seeking, operationalised as a click on any one of the four support service links, immediately after viewing the video. Secondary outcomes include immediate self-reported help-seeking intentions in addition to self-reported use of the support services during a 1-week follow-up period. We will also use the Discrete Choice Experiment methodology to determine what aspects of support services (e.g. low cost, short appointment wait times) are most valued by this group of men. Discussion This study is the first to evaluate the effectiveness of a brief web-based video messaging intervention for promoting engagement with existing support services among men with suicidal thoughts who are not currently receiving formal help. If found to be effective, this would represent a scalable, cost-effective approach to promote help-seeking for this at-risk population. Limitations and strengths of this study design are discussed.

Abstract Background Noonan syndrome (NS) is a rare genetic disease, and patients who suffer from it exhibit a facial morphology that is characterized by a high forehead, hypertelorism, ptosis, inner epicanthal folds, down-slanting palpebral fissures, a highly arched palate, a round nasal tip, and posteriorly rotated ears. Facial analysis technology has recently been applied to identify many genetic syndromes (GSs). However, few studies have investigated the identification of NS based on the facial features of the subjects. Objectives This study develops advanced models to enhance the accuracy of diagnosis of NS. Methods A total of 1,892 people were enrolled in this study, including 233 patients with NS, 863 patients with other GSs, and 796 healthy children. We took one to 10 frontal photos of each subject to build a dataset, and then applied the multi-task convolutional neural network (MTCNN) for data pre-processing to generate standardized outputs with five crucial facial landmarks. The ImageNet dataset was used to pre-train the network so that it could capture generalizable features and minimize data wastage. We subsequently constructed seven models for facial identification based on the VGG16, VGG19, VGG16-BN, VGG19-BN, ResNet50, MobileNet-V2, and squeeze-and-excitation network (SENet) architectures. The identification performance of seven models was evaluated and compared with that of six physicians. Results All models exhibited a high accuracy, precision, and specificity in recognizing NS patients. The VGG19-BN model delivered the best overall performance, with an accuracy of 93.76%, precision of 91.40%, specificity of 98.73%, and F1 score of 78.34%. The VGG16-BN model achieved the highest AUC value of 0.9787, while all models based on VGG architectures were superior to the others on the whole. The highest scores of six physicians in terms of accuracy, precision, specificity, and the F1 score were 74.00%, 75.00%, 88.33%, and 61.76%, respectively. The performance of each model of facial recognition was superior to that of the best physician on all metrics. Conclusion Models of computer-assisted facial recognition can improve the rate of diagnosis of NS. The models based on VGG19-BN and VGG16-BN can play an important role in diagnosing NS in clinical practice.

Background: Lifestyle changes, such as those related to the COVID-19 pandemic, including alterations in physical activity and dietary habits, are known to affect pregnancy outcomes. In particular, suboptimal intrauterine conditions during pregnancy are known to influence not only fetal growth but also growth during infancy. However, research on the impact of the environmental changes caused by the COVID-19 pandemic on the growth of infants and children during their early years is lacking. To address this issue, this study evaluated the effect of the COVID-19 pandemic on obesity in infants. Methods: This retrospective cohort study used the data collected from the Korea National Health Insurance (KNHI) claims database. The data of 1985,678 women who delivered infants between 2015 and 2021 were collected. Women who delivered during the pandemic and those who delivered during the pre-pandemic period were matched in a 1:1 frequency-matched pair procedure for factors such as age, hypertension, diabetes mellitus, preeclampsia, gestational diabetes mellitus, mode of delivery, gestational age at delivery, offspring sex, and birth weight. Finally, 197,580 women were enrolled. The weight and head circumference of infants (4–6 months of age) of the COVID-19 pandemic group were compared with those of the pre-pandemic group. Results: The COVID-19 pandemic group infants exhibited significantly higher weight and prevalence of obesity at 4–6 months of age compared to infants in the pre-pandemic group. After adjustment for covariates, pandemic group infants had a higher risk of obesity (odds ratio: 1.54, 95% confidence interval: 1.51–1.57) compared to the pre-pandemic group infants. Conclusion: The COVID-19 pandemic has had a notable impact on the weight of infants aged 4–6 months. This suggests that pandemic conditions may influence the growth of newborns, underscoring the importance of monitoring and assessing trends in the growth of infants born during such crises.

Abstract Background The molecular mechanism of fetal cystic hygroma (CH) is still unclear, and no study has previously reported the transcriptome changes of single cells in CH. In this study, single-cell transcriptome sequencing (scRNA-seq) was used to investigate the characteristics of cell subsets in the lesion tissues of CH patients. Methods Lymphoid tissue collected from CH patients and control donors for scRNA-seq analysis. Differentially expressed gene enrichment in major cell subpopulations as well as cell-cell communication were analyzed. At the same time, the expression and interactions of important VEGF signaling pathway molecules were analyzed, and potential transcription factors that could bind to KDR (VEGFR2) were predicted. Results The results of scRNA-seq showed that fibroblasts accounted for the largest proportion in the lymphatic lesions of CH patients. There was a significant increase in the proportion of lymphatic endothelial cell subsets between the cases and controls. The VEGF signaling pathway is enriched in lymphatic endothelial cells and participates in the regulation of cell-cell communication between lymphatic endothelial cells and other cells. The key regulatory gene KDR in the VEGF signaling pathway is highly expressed in CH patients and interacts with other differentially expressed EDN1, TAGLN, and CLDN5 Finally, we found that STAT1 could bind to the KDR promoter region, which may play an important role in promoting KDR up-regulation. Conclusion Our comprehensive delineation of the cellular composition in tumor tissues of CH patients using single-cell RNA-sequencing identified the enrichment of lymphatic endothelial cells in CH and highlighted the activation of the VEGF signaling pathway in lymphoid endothelial cells as a potential modulator. Simple summary The molecular and cellular pathogenesis of fetal cystic hygroma (CH) remains largely unknown. This study examined the distribution and gene expression signature of each cell subpopulation and the possible role of VEGF signaling in lymphatic endothelial cells in regulating the progression of CH by single-cell transcriptome sequencing. The enrichment of lymphatic endothelial cells in CH and the activation of the VEGF signaling pathway in lymphatic endothelial cells provide some clues to the pathogenesis of CH from the perspective of cell subpopulations.

Background/Aims Metabolic dysfunction-associated steatotic liver disease (MASLD) is characterized by fat accumulation in the liver. MASLD encompasses both steatosis and MASH. Since MASH can lead to cirrhosis and liver cancer, steatosis and MASH must be distinguished during patient treatment. Here, we investigate the genomes, epigenomes, and transcriptomes of MASLD patients to identify signature gene set for more accurate tracking of MASLD progression. Methods Biopsy-tissue and blood samples from patients with 134 MASLD, comprising 60 steatosis and 74 MASH patients were performed omics analysis. SVM learning algorithm were used to calculate most predictive features. Linear regression was applied to find signature gene set that distinguish the stage of MASLD and to validate their application into independent cohort of MASLD. Results After performing WGS, WES, WGBS, and total RNA-seq on 134 biopsy samples from confirmed MASLD patients, we provided 1,955 MASLD-associated features, out of 3,176 somatic variant callings, 58 DMRs, and 1,393 DEGs that track MASLD progression. Then, we used a SVM learning algorithm to analyze the data and select the most predictive features. Using linear regression, we identified a signature gene set capable of differentiating the various stages of MASLD and verified it in different independent cohorts of MASLD and a liver cancer cohort. Conclusions We identified a signature gene set (i.e., CAPG, HYAL3, WIPI1, TREM2, SPP1, and RNASE6) with strong potential as a panel of diagnostic genes of MASLD-associated disease.

Purpose: 17q12 copy number variants (CNVs) have variable presentations and incomplete penetrance, challenging prenatal counseling and management. This study aims to investigate the intrauterine phenotype. Methods: We included 48 fetuses diagnosed with 17q12 microdeletion or microduplication by chromosomal microarray analysis. Results: For 17q12 deletion, renal anomalies were found in 35 fetuses (35/37, 94.6 %), with hyperechogenic kidneys (HEK, 28/37, 75.7 %) and multicystic dysplastic kidneys (17/37, 45.9 %) being the most common findings. Duodenal obstruction (DO) was most frequently combined in 17q12 duplication fetuses. In addition, cardiac abnormalities were the first reported prenatal phenotype in 17q12 duplication fetuses. Conclusion: Our study shows that HEK and DO are the most predominant presentations of 17q12 deletion and duplication, respectively, and cardiac structural abnormalities may be associated with the latter. Although 17q12 CNVs have incomplete penetrance and variable expressivity and may be mainly involved in neurodevelopmental disorders, their short-term prognosis appears positive.

IntroductionUniversity students are particularly susceptible to developing high levels of stress, which occur when environmental demands outweigh an individual’s ability to cope. The growing advent of mental health smartphone apps has led to a surge in use by university students seeking ways to help them cope with stress. Use of these apps has afforded researchers the unique ability to collect extensive amounts of passive sensing data including GPS and step detection. Despite this, little is known about the relationship between passive sensing data and stress. Further, there are no established methodologies or tools to predict stress from passive sensing data in this group.MethodsIn this study, we establish a clear machine learning-based methodological pipeline for processing passive sensing data and extracting features that may be relevant in the context of mental health.ResultsWe then use this methodology to determine the relationship between passive sensing data and stress in university students.DiscussionIn doing so, we offer the first proof-of-principle data for the utility of our methodological pipeline and highlight that passive sensing data can indeed digitally phenotype stress in university students.Clinical trial registrationAustralia New Zealand Clinical Trials Registry (ANZCTR), identifier ACTRN12621001223820.

Garnet-type Li7La3Zr2O12 (LLZO) Li-ion solid electrolytes are promising candidates for safe, next-generation solid-state batteries. In this study, we synthesize Ga-doped LLZO (Ga–LLZO) electrolytes using a microwave-assisted solvothermal method followed by low-temperature heat treatment. The nanostructured precursor (

Digital phenotyping is a promising method for advancing scalable detection and prediction methods in mental health research and practice. However, little is known about how digital phenotyping data are used to make inferences about youth mental health. We conducted a scoping review of 35 studies to better understand how passive sensing (e.g., Global Positioning System, microphone etc) and electronic usage data (e.g., social media use, device activity etc) collected via smartphones are used in detecting and predicting depression and/or anxiety in young people between 12 and 25 years-of-age. GPS and/or Wifi association logs and accelerometers were the most used sensors, although a wide variety of low-level features were extracted and computed (e.g., transition frequency, time spent in specific locations, uniformity of movement). Mobility and sociability patterns were explored in more studies compared to other behaviours such as sleep, phone use, and circadian movement. Studies used machine learning, statistical regression, and correlation analyses to examine relationships between variables. Results were mixed, but machine learning indicated that models using feature combinations (e.g., mobility, sociability, and sleep features) were better able to predict and detect symptoms of youth anxiety and/or depression when compared to models using single features (e.g., transition frequency). There was inconsistent reporting of age, gender, attrition, and phone characteristics (e.g., operating system, models), and all studies were assessed to have moderate to high risk of bias. To increase translation potential for clinical practice, we recommend the development of a standardised reporting framework to improve transparency and replicability of methodology.

Key Clinical Message In previous reports, hypothyroidism, hypopituitrism, and hypogonadism were common endocrine causes of SCFE, but this is the first time that congenital adrenal hyperplasia has been observed. As such, patients who have undergone long‐term endocrine treatment for congenital adrenal hyperplasia could potentially be subjected to a higher risk for SCFE.

Abstract Background Lung cancer (LC) is an important comorbidity of interstitial lung disease (ILD) and has a poor prognosis. The clinical characteristics and outcome of each ILD subtype in LC patients have not been sufficiently investigated. Therefore, this study aimed to evaluate the difference between idiopathic pulmonary fibrosis (IPF) and non-IPF ILD as well as prognostic factors in patients with ILD-LC. Methods The medical records of 163 patients diagnosed with ILD-LC at Asan Medical Center from January 2018 to May 2023 were retrospectively reviewed. Baseline characteristics and clinical outcomes were compared between the IPF-LC and non-IPF ILD-LC groups, and prognostic factors were analyzed using the Cox proportional-hazard model. Results The median follow-up period was 11 months after the cancer diagnosis. No statistically significant differences were observed in clinical characteristics and mortality rates (median survival: 26 vs. 20 months, p = 0.530) between the groups. The independent prognostic factors in patients with ILD-LC were higher level of Krebs von den Lungen-6 (≥ 1000 U/mL, hazard ratio [HR] 1.970, 95% confidence interval [CI] 1.026-3.783, p = 0.025) and advanced clinical stage of LC (compared with stage I, HR 3.876 for stage II, p = 0.025, HR 5.092 for stage III, p = 0.002, and HR 5.626 for stage IV, p = 0.002). In terms of treatment, surgery was the significant factor for survival (HR 0.235; 95% CI 0.106-0.520; p

Dual immunoglobulin domain-containing cell adhesion molecule (DICAM) is a type I transmembrane protein that presents in various cells including renal tubular cells. This study evaluated the expression and protective role of DICAM in renal tubular cell injury. HK-2 cells were incubated and treated with lipopolysaccharide (LPS, 30 μg/mL) or hydrogen peroxide (H2O2, 100 μM) for 24 h. To investigate the effect of the gene silencing of DICAM, small interfering RNA of DICAM was used. Additionally, to explain its role in cellular response to injury, DICAM was overexpressed using an adenoviral vector. DICAM protein expression levels significantly increased following treatment with LPS or H2O2 in HK-2 cells. In response to oxidative stress, DICAM showed an earlier increase (2–4 h following treatment) than neutrophil gelatinase-associated lipocalin (NGAL) (24 h following treatment). DICAM gene silencing increased the protein expression of inflammation-related markers, including IL-1β, TNF-α, NOX4, integrin β1, and integrin β3, in H2O2-induced HK-2 cell injury. Likewise, in the LPS-induced HK-2 cell injury, DICAM knockdown led to a decrease in occludin levels and an increase in integrin β3, IL-1β, and IL-6 levels. Furthermore, DICAM overexpression followed by LPS-induced HK-2 cell injury resulted in an increase in occludin levels and a decrease in integrin β1, integrin β3, TNF-α, IL-1β, and IL-6 levels, suggesting an alleviating effect on inflammatory responses. DICAM was elevated in the early stage of regular tubular cell injury and may protect against renal tubular injury through its anti-inflammatory properties. DICAM has a potential as an early diagnostic marker and therapeutic target for renal cell injury.