Superconductivity and Charge-Density-Wave-Like Transition in Th2Cu4As5.

We report the synthesis, crystal structure, and physical properties of a novel ternary compound, Th2Cu4As5. The material crystallizes in a tetragonal structure with lattice parameters a = 4.0639(3) Å and c = 24.8221(17) Å. Its structure can be described as an alternating stacking of fluorite-type Th2As2 layers with antifluorite-type double-layered Cu4As3 slabs. The measurement of electrical resistivity, magnetic susceptibility, and specific heat reveals that Th2Cu4As5 undergoes bulk superconducting transition at 4.2 K. Additionally, all these physical quantities exhibit anomalies at 48 K, accompanied by a sign change in the Hall coefficient, suggesting a charge-density-wave-like (CDW) phase transition. Drawing from both experimental data and band calculations, we propose that the superconducting and CDW-like phase transitions are, respectively, associated with the Cu4As3 slabs and the As plane in the Th2As2 layers.

One-Step Hydrothermal Synthesis of NVO Cathodes with Varied Lattice NH4 + Content: Effect on Structural Evolution and Electrochemical Performance.

Aqueous zinc ion batteries have been extensively researched due to their distinctive advantages such as low cost and high safety. Vanadium oxides are important cathode materials, however, poor cycle life caused by vanadium dissolution limits their application. Recent studies show that the lattice NH4 + in vanadium oxides can act as a pillar to enhance structural stability and play a crucial role in improving its cycling stability. Nevertheless, there is still a lack of research on the effect of the lattice NH4 + content on structural evolution and electrochemical performance. Herein, we synthesize vanadium oxides with different contents of lattice NH4 + by a one-step hydrothermal reaction. The vanadium oxides with lattice NH4 + exhibit high initial capacity, as well as good cycling stability and rate performance compared to bare vanadium oxide. Combined with electrochemical analyses, ex-situ structural characterizations, and in-situ X-ray diffraction tests, we reveal that the lattice NH4 + content plays a positive role in vanadium oxides' structural stability and cation diffusion kinetics. This work presents a direction for designing high-performance vanadium cathodes for aqueous zinc ion batteries.

THSD1 Suppresses Autophagy-Mediated Focal Adhesion Turnover by Modulating the FAK-Beclin 1 Pathway.

Focal adhesions (FAs) play a crucial role in cell spreading and adhesion, and their autophagic degradation is an emerging area of interest. This study investigates the role of Thrombospondin Type 1 Domain-Containing Protein 1 (THSD1) in regulating autophagy and FA stability in brain endothelial cells, shedding light on its potential implications for cerebrovascular diseases. Our research reveals a physical interaction between THSD1 and FAs. Depletion of THSD1 significantly reduces FA numbers, impairing cell spreading and adhesion. The loss of THSD1 also induces autophagy independently of changes in mTOR and AMPK activation, implying that THSD1 primarily governs FA dynamics rather than serving as a global regulator of nutrient and energy status. Mechanistically, THSD1 negatively regulates Beclin 1, a central autophagy regulator, at FAs through interactions with focal adhesion kinase (FAK). THSD1 inactivation diminishes FAK activity and relieves its inhibitory phosphorylation on Beclin 1. This, in turn, promotes the complex formation between Beclin 1 and ATG14, a critical event for the activation of the autophagy cascade. In summary, our findings identify THSD1 as a novel regulator of autophagy that degrades FAs in brain endothelial cells. This underscores the distinctive nature of THSD1-mediated, cargo-directed autophagy and its potential relevance to vascular diseases due to the loss of endothelial FAs. Investigating the underlying mechanisms of THSD1-mediated pathways holds promise for discovering novel therapeutic targets in vascular diseases.

FDX1 enhances endometriosis cell cuproptosis via G6PD-mediated redox homeostasis.

Cuproptosis is a new form of programmed cell death, which is associated with the mitochondrial TCA (tricarboxylic acid) cycle. But the functions of cuproptosis in endometriosis progression are still unknown. Here, we find that cuproptosis suppresses the growth of endometriosis cells and the growth of ectopic endometrial tissues in a mouse model. FDX1 as a key regulator in cuproptosis pathway could promote cuproptosis in endometriosis cells. Interestingly, FDX1 interacts with G6PD, and reduces its protein stability, which predominantly affects the cellular redox-regulating systems. Then, the reduced G6PD activity enhances cuproptosis via down-regulating NADPH and GSH levels. Collectively, our study demonstrates that FDX1 mediates cuproptosis in endometriosis via G6PD pathway, resulting in repression of endometriosis cell proliferation and metastasis.

Development and validation of a blood biomarker score for predicting mortality risk in the general population.

BACKGROUND: Blood biomarkers for multiple pathways, such as inflammatory response, lipid metabolism, and hormonal regulation, have been suggested to influence the risk of mortality. However, few studies have systematically evaluated the combined predictive ability of blood biomarkers for mortality risk. METHODS: We included 267,239 participants from the UK Biobank who had measurements of 28 blood biomarkers and were free of cardiovascular disease (CVD) and cancer at baseline (2006-2010). We developed sex-specific blood biomarker scores for predicting all-cause mortality risk in a training set of 247,503 participants from England and Wales, and validated the results in 19,736 participants from Scotland. Cox and LASSO regression analyses were performed to identify independent predictors for men and women separately. Discrimination and calibration were evaluated by C-index and calibration plots, respectively. We also assessed mediating effects of the biomarkers on the association between traditional risk factors (current smoking, obesity, physical inactivity, hypertension, diabetes) and mortality. RESULTS: A total of 13 independent predictive biomarkers for men and 17 for women were identified and included in the score development. Compared to the lowest tertile of the score, the highest tertile showed a hazard ratio of 5.36 (95% confidence interval [CI] 5.04-5.71) in men and 4.23 (95% CI 3.87-4.62) in women for all-cause mortality. In the validation set, the score yielded a C-index of 0.73 (95% CI 0.72-0.75) in men and 0.70 (95% CI 0.68-0.73) in women for all-cause mortality; it was also predictive of CVD (C-index of 0.76 in men and 0.79 in women) and cancer (C-index of 0.70 in men and 0.67 in women) mortality. Moreover, the association between traditional risk factors and all-cause mortality was largely mediated by cystatin C, C-reactive protein, 25-hydroxyvitamin D, and hemoglobin A1c. CONCLUSIONS: We established sex-specific blood biomarker scores for predicting all-cause and cause-specific mortality in the general population, which hold the potential to identify high-risk individuals and improve targeted prevention of premature death.

Applying a Deep-Learning-Based Keypoint Detection in Analyzing Surface Nanostructures.

Scanning tunneling microscopy (STM) imaging has been routinely applied in studying surface nanostructures owing to its capability of acquiring high-resolution molecule-level images of surface nanostructures. However, the image analysis still heavily relies on manual analysis, which is often laborious and lacks uniform criteria. Recently, machine learning has emerged as a powerful tool in material science research for the automatic analysis and processing of image data. In this paper, we propose a method for analyzing molecular STM images using computer vision techniques. We develop a lightweight deep learning framework based on the YOLO algorithm by labeling molecules with its keypoints. Our framework achieves high efficiency while maintaining accuracy, enabling the recognitions of molecules and further statistical analysis. In addition, the usefulness of this model is exemplified by exploring the length of polyphenylene chains fabricated from on-surface synthesis. We foresee that computer vision methods will be frequently used in analyzing image data in the field of surface chemistry.

Digital workflow of labial guides for alveolar ridge reduction during implant-supported full-arch rehabilitation.

This article presents a technique for fabricating two-piece labial guides using resin and cobalt-chromium for guided alveoloplasty, implant placement, and immediate full-arch implant-supported fixed complete denture placement. This technique reduces tissue damage and overcomes the negative effect of the anatomical position of the palatal neuropore and mobility of the tissue flap on the positioning and stability of the guide. The use of labial retention metal guides with improved mechanical properties of cobalt-chromium and fixation plugs allows the unilateral placement of the guide. Thus, minimally invasive implant surgery can be performed owing to the absence of large palatal flap elevation and obstruction. Such a design provides better stability of the guides and clear visual access during surgery.

COMPARISONS BETWEEN DIGITAL-GUIDED AND NONDIGITAL PROTOCOL IN IMPLANT PLANNING, PLACEMENT, AND RESTORATIONS: A SYSTEMATIC REVIEW AND META-ANALYSIS OF RANDOMIZED CONTROLLED TRIALS.

OBJECTIVES: The present study aimed to systematically review the current randomized clinical trials (RCTs) with respect to computer-aided design/computer-aided manufactured (CAD/CAM) techniques in the process of implant planning, placement, and rehabilitation. MATERIALS AND METHODS: Four independent reviewers conducted an electronic and manual literature search using several databases, including the National Library of Medicine (MEDLINE-PubMed), Cochrane Central Register of Controlled Trials (CENTRAL), and EMBASE. Articles were included if they were RCTs involving the interventions regarding the computer-guided impression, placement, and manufacturing process. The outcomes of interest include clinical and patient-reported outcomes and time efficiency. A meta-analysis was conducted to evaluate the time efficiency, pain severity, accuracy of implant placement, and postsurgery marginal bone level. RESULTS: A total of 39 and 25 articles were included in the qualitative and quantitative analysis, respectively. The results of the meta-analysis showed that significantly less time was spent performing the digital impression procedure than the conventional impression (P = .002). In addition, the average adjustment time of the final prosthesis was significantly less than the nondigital fabricated prosthesis (P = .0005). Computer-guided groups reported significantly lower painkiller consumption compared to control groups (P = .03). CONCLUSIONS: Digital impressions and CAD/CAM procedures are time-saving and provide stable and predictable outcomes. Moreover, computer-guided surgery can effectuate an accurate implant placement and less postsurgery discomfort.

Untargeted plasma metabolomics for risk prediction of hepatocellular carcinoma: A prospective study in two Chinese cohorts.

Characterization of metabolic perturbation prior to hepatocellular carcinoma (HCC) may deepen the understanding of causal pathways and identify novel biomarkers for early prevention. We conducted two 1:1 matched nested case-control studies (108 and 55 pairs) to examine the association of plasma metabolome (profiled using LC-MS) with the risk of HCC based on two prospective cohorts in China. Differential metabolites were identified by paired t tests and orthogonal partial least-squares discriminant analysis (OPLS-DA). Weighted gene coexpression network analysis (WGCNA) was performed to classify metabolites into modules for identifying biological pathways involved in hepatocarcinogenesis. We assessed the risk predictivity of metabolites using multivariable logistic regression models. Among 612 named metabolites, 44 differential metabolites were identified between cases and controls, including 12 androgenic/progestin steroid hormones, 8 bile acids, 10 amino acids, 6 phospholipids, and 8 others. These metabolites were associated with HCC in the multivariable logistic regression analyses, with odds ratios ranging from 0.19 (95% confidence interval [CI]: 0.11-0.35) to 5.09 (95% CI: 2.73-9.50). WGCNA including 612 metabolites showed 8 significant modules related to HCC risk, including those representing metabolic pathways of androgen and progestin, primary and secondary bile acids, and amino acids. A combination of 18 metabolites of independent effects showed the potential to predict HCC risk, with an AUC of 0.87 (95% CI: 0.82-0.92) and 0.86 (95% CI: 0.80-0.93) in the training and validation sets, respectively. In conclusion, we identified a panel of plasma metabolites that could be implicated in hepatocellular carcinogenesis and have the potential to predict HCC risk.

Plasma metabolomic profiles for colorectal cancer precursors in women.

How metabolome changes influence the early process of colorectal cancer (CRC) development remains unknown. We conducted a 1:2 matched nested case-control study to examine the associations of pre-diagnostic plasma metabolome (profiled using LC-MS) with risk of CRC precursors, including conventional adenomas (n = 586 vs. 1141) and serrated polyps (n = 509 vs. 993), in the Nurses' Health Study (NHS) and NHSII. Conditional logistic regression was used to estimate odds ratios (OR) and 95% confidence intervals (CI). We used the permutation-based Westfall and Young approach to account for multiple testing. Subgroup analyses were performed for advanced conventional adenomas (defined as at least one adenoma of ≥ 10 mm or with high-grade dysplasia, or tubulovillous or villous histology) and high-risk serrated polyps that were located in the proximal colon or with size of ≥ 10 mm. After multiple testing correction, among 207 metabolites, higher levels of C36:3 phosphatidylcholine (PC) plasmalogen were associated with lower risk of conventional adenomas, with the OR (95% CI) comparing the 90th to the 10th percentile of 0.62 (0.48-0.81); C54:8 triglyceride (TAG) was associated with higher risk of serrated polyps (OR = 1.79, 95% CI: 1.31-2.43), and phenylacetylglutamine (PAG) was associated with lower risk (OR = 0.57, 95% CI:0.43-0.77). PAG was also inversely associated with advanced adenomas (OR = 0.57, 95% CI: 0.36-0.89) and high-risk serrated polyps (OR = 0.54, 95% CI: 0.32-0.89), although the multiple testing-corrected p value was > 0.05. Our findings suggest potential roles of lipid metabolism and phenylacetylglutamine, a microbial metabolite, in the early stage of colorectal carcinogenesis, particularly for the serrated pathway.

On-surface synthesis of ethers through dehydrative coupling of hydroxymethyl substituents.

On-surface synthesis has been a subject of intensive research during the last decade. Various chemical reactions have been developed on surfaces to prepare compounds and carbon nanostructures, most of which are centered on the carbon-carbon bond formation. Despite the vast progress so far, the diversity of functional groups in organic chemistry has been far less explored in on-surface synthesis. Herein, we study the surface-assisted synthesis of ethers through the homocoupling of hydroxymethyl substituents on Ag(111). By using two hydroxymethyl substituent functionalized molecular precursors with different symmetries, we have achieved the formation of ether chains and rings. High-resolution scanning tunneling microscopy complemented with density functional theory calculations are used to support our findings and offer mechanistic insights into the reaction. This work expands the toolbox of on-surface reactions for the bottom-up fabrication of more sophisticated functional nanostructures.

Accuracy of Reduction Depths of Tooth Preparation for Porcelain Laminate Veneers Assisted by Different Tooth Preparation Guides: An In Vitro Study.

PURPOSE: To evaluate the accuracy (trueness and precision) of reduction depths of guided veneer preparation assisted by four tooth preparation guides. MATERIALS AND METHODS: Fifty resin artificial teeth were randomly divided into five groups (n = 10): a freehand group (F), silicone guide group (S), thermoplastic guide group (T), 3D printed uniform guide group (D), and 3D printed auto-stop guide group (A). A preparation for a window veneer on the maxillary right central incisor was performed by two surgeons who used tooth preparation guides for assistance. The maxillary right central incisors were scanned before and after the preparation. The reduction depths were measured in the cervical, middle, and incisal thirds of the prepared surface, and depth maps were created using Geomagic Control X software. The accuracy of the reduction depths at each third was evaluated using both trueness and precision values. The trueness of the reduction depths was determined by calculating the mean absolute differences (MADs) compared to the planning depth, and precision was determined by the standard deviation (SD). The collected data were statistically analyzed using one-way ANOVA and the least significant difference test (α = 0.05). RESULTS: The MAD ± SD values of the reduction depths in the cervical-third region in groups F, S, T, D, and A were 0.19 ± 0.04, 0.12 ± 0.03, 0.09 ± 0.02, 0.07 ± 0.02, and 0.05 ± 0.01 mm, respectively. In the middle-third region, the MAD ± SD values of groups F-A were 0.19 ± 0.05, 0.13 ± 0.02, 0.09 ± 0.01, 0.06 ± 0.01, and 0.05 ± 0.01 mm. In the incisal-third region, the MAD ± SD values were 0.27 ± 0.05, 0.16 ± 0.04, 0.11 ± 0.03, 0.07 ± 0.01, and 0.05 ± 0.01 mm, respectively. Significant differences in trueness and precision values were found across different groups (F = 45.378, p = 0.000), where group F showed higher MADs than the other 4 groups (p = 0.000), and the highest MADs were detected in group S among the 4 groups in all regions (p = 0.000). Group T showed significantly higher MADs than groups D (P = 0.008) and A (p = 0.001), except in the cervical-third region, where no significant difference was observed between groups T and D (p = 0.077). There was no significant difference between groups D and A (p = 0.148). The deviation map showed significant differences among groups (F = 15.963, p = 0.000), group T presented less deviation than group F (p = 0.000) and group S (p = 0.027) and showed more deviation than group A (p = 0.007). CONCLUSION: Tooth preparation guides provided more accuracy for veneer preparation than freehand preparation. Among the 4 guides, the 3D printed auto-stop guide presented the lowest absolute difference (0.05 mm) and the silicone guide showed the highest absolute difference of preparation (0.12-0.16 mm).

Heteroatom-Edged [4]Triangulene: Facile Synthesis and Two-Dimensional On-Surface Self-Assemblies.

Triangulenes have attracted enormous interest in organic chemistry and materials science, but suffer from their high instability towards oxygen. Embedding heteroatoms into triangulenes provides a new class of ambient stable materials for various applications. However, [3]heterotriangulenes have dominated the chemistry of heteroatom-doped triangulenes, while their higher homologues have been rarely explored. In this work, we synthesize a new [4]heterotriangulene with three oxygen-boron-oxygen (OBO) segments incorporated into the zigzag edges. The planar geometry of the OBO-doped [4]triangulene is demonstrated by single-crystal X-ray diffraction. Self-assembly on metal surfaces reveals substrate-dependent nanostructures, leading to different long-range ordered 2D patterns on Ag and Cu substrates with negligible defects.

A Deep-Learning Framework for the Automated Recognition of Molecules in Scanning-Probe-Microscopy Images.

Computer vision as a subcategory of deep learning tackles complex vision tasks by dealing with data of images. Molecular images with exceptionally high resolution have been achieved thanks to the development of techniques like scanning probe microscopy (SPM). However, extracting useful information from SPM image data requires careful analysis which heavily relies on human supervision. In this work, we develop a deep learning framework using an advanced computer vision algorithm, Mask R-CNN, to address the challenge of molecule detection, classification and instance segmentation in binary molecular nanostructures. We employ the framework to determine two triangular-shaped molecules of similar STM appearance. Our framework could accurately differentiate two molecules and label their positions. We foresee that the application of computer vision in SPM images will become an indispensable part in the field, accelerating data mining and the discovery of new materials.

Dopamine D1 or D2 receptor-expressing neurons in the central nervous system.

Dopamine signals mainly through D1 receptors (D1Rs) and D2 receptors (D2Rs); D1R-expressing or D2R-expressing neurons contribute to distinct reward and addictive behaviors. Traditionally, transgenic mice expressing green fluorescent protein (GFP) under D1R or D2R promoters are used for fluorescent verification in electrophysiology studies, whereas Cre mice are employed for behavioral research. However, it is unknown whether the same neuronal populations are targeted in GFP and Cre mice. Additionally, while D1Rs and D2Rs are known to be expressed in different striatal neurons, their expression patterns outside the striatum remain unclear. The present study addressed these two questions by using several transgenic mouse lines expressing fluorescent proteins (GFP or tdTomato) or Cre under the control of D1R or D2R promoters. We found a high degree of overlap between GFP-positive and Cre-positive neurons in the striatum and hippocampus. Additionally, we discovered that D1Rs and D2Rs were highly segregated in the orbitofrontal cortex, prefrontal cortex, dorsal and ventral hippocampus, and amygdala: ~4-34 percent of neurons co-expressed these receptors. Importantly, slice electrophysiological studies demonstrated that D1R-positive and D1R-negative hippocampal neurons were functionally distinct in a mouse line generated by crossing Drd1a-Cre mice with a Cre reporter Ai14 line. Lastly, we discovered that chronic alcohol intake differentially altered D1R-positive and D2R-positive neuron excitability in the ventral CA1. These data suggest that GFP and Cre mice target the same populations of striatal neurons, D1R-expressing or D2R-expressing neurons are highly segregated outside the striatum, and these neurons in the ventral hippocampal may exert distinct roles in alcohol addiction.

Analytical performance of Envisia: a genomic classifier for usual interstitial pneumonia.

BACKGROUND: Clinical guidelines specify that diagnosis of interstitial pulmonary fibrosis (IPF) requires identification of usual interstitial pneumonia (UIP) pattern. While UIP can be identified by high resolution CT of the chest, the results are often inconclusive, making surgical lung biopsy necessary to reach a definitive diagnosis (Raghu et al., Am J Respir Crit Care Med 183(6):788-824, 2011). The Envisia genomic classifier differentiates UIP from non-UIP pathology in transbronchial biopsies (TBB), potentially allowing patients to avoid an invasive procedure (Brown et al., Am J Respir Crit Care Med 195:A6792, 2017). To ensure patient safety and efficacy, a laboratory developed test (LDT) must meet strict regulatory requirements for accuracy, reproducibility and robustness. The analytical characteristics of the Envisia test are assessed and reported here. METHODS: The Envisia test utilizes total RNA extracted from TBB samples to perform Next Generation RNA Sequencing. The gene count data from 190 genes are then input to the Envisia genomic classifier, a machine learning algorithm, to output either a UIP or non-UIP classification result. We characterized the stability of RNA in TBBs during collection and shipment, and evaluated input RNA mass and proportions on the limit of detection of UIP. We evaluated potentially interfering substances such as blood and genomic DNA. Intra-run, inter-run, and inter-laboratory reproducibility of test results were also characterized. RESULTS: RNA content within TBBs preserved in RNAprotect is stable for up to 14 days with no detectable change in RNA quality. The Envisia test is tolerant to variation in RNA input (5 to 30 ng), with no impact on classifier results. The Envisia test can tolerate dilution of non-UIP and UIP classification signals at the RNA level by up to 60% and 20%, respectively. Analytical specificity studies utilizing UIP and non-UIP samples mixed with genomic DNA (up to 30% relative input) demonstrated no impact to classifier results. The Envisia test tolerates up to 22% of blood contamination, well beyond the level observed in TBBs. The test is reproducible from RNA extraction through to Envisia test result (standard deviation of 0.20 for Envisia classification scores on > 7-unit scale). CONCLUSIONS: The Envisia test demonstrates the robust analytical performance required of an LDT. Envisia can be used to inform the diagnoses of patients with suspected IPF.

The effect of two different renal denervation strategies on blood pressure in resistant hypertension: Comparison of full-length versus proximal renal artery ablation.

BACKGROUND: Renal denervation (RDN) is used to manage blood pressure (BP) in patients with resistant hypertension (rHT), but effectiveness is still a concern, and key arterial portion for successful RDN is not clear. OBJECTIVE: The aim of this study was to investigate the efficacy and safety of proximal versus full-length renal artery ablation in patients with resistant hypertension (rHT). METHODS: Forty-seven patients with rHT were randomly assigned to receive full-length ablation (n = 23) or proximal ablation (n = 24) of the renal arteries. All lesions were treated with radiofrequency energy via a saline-irrigated catheter. Office BP was measured during 12 months of follow-up and ambulatory BP at baseline and 6 months (n = 15 in each group). RESULTS: Compared with full-length ablation, proximal ablation reduced the number of ablation points in both the right (6.1 ± 0.7 vs. 3.3 ± 0.6, P < 0.001) and the left renal arteries (6.2 ± 0.7 vs. 3.3 ± 0.8, P < 0.001), with significantly shorter RF delivery time (P < 0.001), but higher RF power (P = 0.011). Baseline office BPs was 179.4 ± 13.7/102.8 ± 9.4 mm Hg in the full-length group and 181.9 ± 12.8/103.5 ± 8.9 mm Hg in the proximal group (P > 0.5). Similar office BPs was reduced by -39.4 ± 11.5/-20.9 ± 7.1 mm Hg at 6 months and -38.2 ± 10.3/-21.5 ± 5.8 mm Hg at 12 months in the full-length group (P < 0.001), -42.0 ± 11.6/-21.4 ± 7.9 mm Hg at 6 months and -40.9 ± 10.3/-22.1 ± 5.6 mm Hg at 12 months in the proximal group (P < 0.001), and progressive BP reductions were observed over the 6 months (P < 0.001) in both groups. The drop in ambulatory 24-hr SBP and DBP were significantly less than the drop in office BP (P < 0.001). No renovascular or other adverse complications were observed. CONCLUSIONS: The results indicate that proximal RDN has a similar efficacy and safety profile compared with full-length RDN, and propose the proximal artery as the key portion for RDN. © 2016 Wiley Periodicals, Inc.

Evaluating tooth segmentation accuracy and time efficiency in CBCT images using artificial intelligence: A systematic review and Meta-analysis.

OBJECTIVES: This systematic review and meta-analysis aimed to assess the current performance of artificial intelligence (AI)-based methods for tooth segmentation in three-dimensional cone-beam computed tomography (CBCT) images, with a focus on their accuracy and efficiency compared to those of manual segmentation techniques. DATA: The data analyzed in this review consisted of a wide range of research studies utilizing AI algorithms for tooth segmentation in CBCT images. Meta-analysis was performed, focusing on the evaluation of the segmentation results using the dice similarity coefficient (DSC). SOURCES: PubMed, Embase, Scopus, Web of Science, and IEEE Explore were comprehensively searched to identify relevant studies. The initial search yielded 5642 entries, and subsequent screening and selection processes led to the inclusion of 35 studies in the systematic review. Among the various segmentation methods employed, convolutional neural networks, particularly the U-net model, are the most commonly utilized. The pooled effect of the DSC score for tooth segmentation was 0.95 (95 %CI 0.94 to 0.96). Furthermore, seven papers provided insights into the time required for segmentation, which ranged from 1.5 s to 3.4 min when utilizing AI techniques. CONCLUSIONS: AI models demonstrated favorable accuracy in automatically segmenting teeth from CBCT images while reducing the time required for the process. Nevertheless, correction methods for metal artifacts and tooth structure segmentation using different imaging modalities should be addressed in future studies. CLINICAL SIGNIFICANCE: AI algorithms have great potential for precise tooth measurements, orthodontic treatment planning, dental implant placement, and other dental procedures that require accurate tooth delineation. These advances have contributed to improved clinical outcomes and patient care in dental practice.

Charting epimutation dynamics in human hematopoietic differentiation.

DNA methylation plays a critical role in hematopoietic differentiation. Epimutation is a stochastic variation in DNA methylation that induces epigenetic heterogeneity. However, the effects of epimutations on normal hematopoiesis and hematopoietic diseases remain unclear. In this study, we developed a Julia package called EpiMut that enabled rapid and accurate quantification of epimutations. EpiMut was used to evaluate and provide an epimutation landscape in steady-state hematopoietic differentiation involving 13 types of blood cells ranging from hematopoietic stem/progenitor cells to mature cells. We showed that substantial genomic regions exhibited epigenetic variations rather than significant differences in DNA methylation levels between the myeloid and lymphoid lineages. Stepwise dynamics of epimutations were observed during the differentiation of each lineage. Importantly, we found that epimutation significantly enriched signals associated with lineage differentiation. Furthermore, epimutations in hematopoietic stem cells (HSCs) derived from various sources and acute myeloid leukemia were related to the function of HSCs and malignant cell disorders. Taken together, our study comprehensively documented an epimutation map and uncovered its important roles in human hematopoiesis, thereby offering insights into hematopoietic regulation.

Associations of serum cystatin C concentrations with total mortality and mortality of 12 site-specific cancers.

Purpose: Cystatin C (CysC), beyond its biomarker role of renal function, has been implicated in various physical and pathological activities. However, the impact of serum CysC on cancer mortality in a general population remains unknown. We aimed to examine the associations of serum CysC concentrations with total mortality and mortality of 12 site-specific cancers. Methods: We included 241,008 participants of the UK Biobank cohort with CysC measurements who had normal creatinine-based estimated glomerular filtration rates and were free of cancer and renal diseases at baseline (2006-2010). Death information was obtained from the National Health Service death records through 28 February 2021. Multivariable Cox proportional hazards models were used to compute hazard ratios (HR) per one standard deviation increase in log-transformed CysC concentrations and 95% confidence intervals (95% CI) for mortality. Results: Over a median follow-up of 12.1 (interquartile range, 11.3-12.8) years, 5,744 cancer deaths occurred. We observed a positive association between serum CysC concentrations and total cancer mortality (HR = 1.16, 95% CI: 1.12-1.20). Specifically, participants with higher serum CysC concentrations had increased mortality due to lung cancer (HR = 1.12, 95% CI: 1.05-1.20), blood cancer (HR = 1.29, 95% CI: 1.16-1.44), brain cancer (HR = 1.19, 95% CI: 1.04-1.36), esophageal cancer (HR = 1.20, 95% CI: 1.05-1.37), breast cancer (HR = 1.18, 95% CI: 1.03-1.36), and liver cancer (HR = 1.49, 95% CI: 1.31-1.69). Conclusion: Our findings indicate that higher CysC concentrations are associated with increased mortality due to lung, blood, brain, esophageal, breast, and liver cancers. Future studies are necessary to clarify underlying mechanisms.