Recent advancements in artificial intelligence for breast cancer: Image augmentation, segmentation, diagnosis, and prognosis approaches.

Breast cancer is a significant global health burden, with increasing morbidity and mortality worldwide. Early screening and accurate diagnosis are crucial for improving prognosis. Radiographic imaging modalities such as digital mammography (DM), digital breast tomosynthesis (DBT), magnetic resonance imaging (MRI), ultrasound (US), and nuclear medicine techniques, are commonly used for breast cancer assessment. And histopathology (HP) serves as the gold standard for confirming malignancy. Artificial intelligence (AI) technologies show great potential for quantitative representation of medical images to effectively assist in segmentation, diagnosis, and prognosis of breast cancer. In this review, we overview the recent advancements of AI technologies for breast cancer, including 1) improving image quality by data augmentation, 2) fast detection and segmentation of breast lesions and diagnosis of malignancy, 3) biological characterization of the cancer such as staging and subtyping by AI-based classification technologies, 4) prediction of clinical outcomes such as metastasis, treatment response, and survival by integrating multi-omics data. Then, we then summarize large-scale databases available to help train robust, generalizable, and reproducible deep learning models. Furthermore, we conclude the challenges faced by AI in real-world applications, including data curating, model interpretability, and practice regulations. Besides, we expect that clinical implementation of AI will provide important guidance for the patient-tailored management.

Electroacupuncture attenuates myocardial ischemia-reperfusion injury by inhibiting microglial engulfment of dendritic spines.

A major side effect of reperfusion therapy following myocardial infarction is myocardial ischemia-reperfusion injury (MIRI). Electroacupuncture preconditioning (EA-pre) has a long history in the treatment of cardiovascular diseases. Here, we demonstrate how EA-pre attenuates MIRI by affecting the phagocytosis of neuronal dendritic spines of microglia of the fastigial nucleus (FNmicroglia). We observed that EA-pre increased activity in FNGABA and then improved myocardial injury by inhibiting abnormal activities of glutaminergic neurons of the FN (FNGlu) during MIRI. Interestingly, we observed changes in the quantity and shape of FN microglia in mice treated with EA-pre and a decrease in the phagocytosis of FNGABA neuronal dendritic spines by microglia. Furthermore, the effects of improving MIRI were reversed when EA-pre mice were chemically activated by intra-FN lipopolysaccharide injection. Overall, our results provide new insight indicating that EA-pre regulates microglial engulfment capacity, thus promoting the improvement of cardiac sympathetic nervous disorder during MIRI.

Proteome-Wide Association Studies for Blood Lipids and Comparison with Transcriptome-Wide Association Studies.

Blood lipid traits are treatable and heritable risk factors for heart disease, a leading cause of mortality worldwide. Although genome-wide association studies (GWAS) have discovered hundreds of variants associated with lipids in humans, most of the causal mechanisms of lipids remain unknown. To better understand the biological processes underlying lipid metabolism, we investigated the associations of plasma protein levels with total cholesterol (TC), triglycerides (TG), high-density lipoprotein cholesterol (HDL), and low-density lipoprotein cholesterol (LDL) in blood. We trained protein prediction models based on samples in the Multi-Ethnic Study of Atherosclerosis (MESA) and applied them to conduct proteome-wide association studies (PWAS) for lipids using the Global Lipids Genetics Consortium (GLGC) data. Of the 749 proteins tested, 42 were significantly associated with at least one lipid trait. Furthermore, we performed transcriptome-wide association studies (TWAS) for lipids using 9,714 gene expression prediction models trained on samples from peripheral blood mononuclear cells (PBMCs) in MESA and 49 tissues in the Genotype-Tissue Expression (GTEx) project. We found that although PWAS and TWAS can show different directions of associations in an individual gene, 40 out of 49 tissues showed a positive correlation between PWAS and TWAS signed p-values across all the genes, which suggests a high-level consistency between proteome-lipid associations and transcriptome-lipid associations.

Detection, Clinical Application, and Manipulation of RNA Modifications.

ConspectusWith increasing research interest, more than 170 types of chemical modifications of RNA have been characterized. The epigenetic modifications of RNA do not alter the primary sequence of RNA but modulate the gene activity. Increasing numbers of regulatory functions of these RNA modifications, particularly in controlling mRNA fate and gene expression, are being discovered. To gain a deeper understanding of the biological significance and clinical prospects of RNA modifications, the development of innovative labeling and detection methodologies is of great importance. Owing to the dynamic features of RNA modifications and the fact that only a portion of genes are modified, detection methods should accurately reveal the precise distribution and modification level of RNA modifications. In general, detection methodologies identify specific RNA modifications in two ways: (1) enriching modification-containing RNAs; and (2) altering the Watson-Crick base pairing pattern to produce truncation or mutation signatures. Additionally, it is important to develop flexible and accurate manipulation tools that enable the installation or removal of RNA modifications at specific positions to investigate the biological functions of a single site. With the development of detection and manipulation methods, the scientific understanding of the biological functions of RNA modifications has increased, paving the way for applications of RNA modifications in disease diagnosis and treatments.In this Account, we provide a brief summary of recent efforts to develop methodologies for detecting RNA modifications. Through the evolution of these detection techniques, our team has uncovered the potential biological roles of RNA modifications in diseases such as diabetic cardiovascular complications, viral infections, and hematologic malignancies. We mainly summarize the recently developed strategies for manipulating RNA modifications. The advent of these programmable editing tools allows for the precise installation or removal of RNA modifications at specific positions. As a result, the biological functions of RNA modifications at these specific loci could be identified, further advancing our knowledge in this field.With this Account, we anticipate providing chemical and biological researchers with comprehensive strategies to discover the underlying mechanisms of RNA modification-mediated biological processes. Although the field of RNA modifications has undergone rapid progress in recent years, our understanding of most of these RNA modifications remains incomplete. We hope to inspire efforts to expand the toolbox for investigating RNA modifications and promote translational research on epigenetics in clinical diagnosis and treatment.

The Chinese thermal comfort dataset.

Heating and cooling in buildings accounts for over 20% of total energy consumption in China. Therefore, it is essential to understand the thermal requirements of building occupants when establishing building energy codes that would save energy while maintaining occupants' thermal comfort. This paper introduces the Chinese thermal comfort dataset, established by seven participating institutions under the leadership of Xi'an University of Architecture and Technology. The dataset comprises 41,977 sets of data collected from 49 cities across five climate zones in China over the past two decades. The raw data underwent careful quality control procedure, including systematic organization, to ensure its reliability. Each dataset contains environmental parameters, occupants' subjective responses, building information, and personal information. The dataset has been instrumental in the development of indoor thermal environment evaluation standards and energy codes in China. It can also have broader applications, such as contributing to the international thermal comfort dataset, modeling thermal comfort and adaptive behaviors, investigating regional differences in indoor thermal conditions, and examining occupants' thermal comfort responses.

Antibody-Free Fluorine-Assisted Metabolic Sequencing of RNA N4-Acetylcytidine.

N4-Acetylcytidine (ac4C) has been found to affect a variety of cellular and biological processes. For a mechanistic understanding of the roles of ac4C in biology and disease, we present an antibody-free, fluorine-assisted metabolic sequencing method to detect RNA ac4C, called "FAM-seq". We successfully applied FAM-seq to profile ac4C landscapes in human 293T, HeLa, and MDA cell lines in parallel with the reported acRIP-seq method. By comparison with the classic ac4C antibody sequencing method, we found that FAM-seq is a convenient and reliable method for transcriptome-wide mapping of ac4C. Because this method holds promise for detecting nascent RNA ac4C modifications, we further investigated the role of ac4C in regulating chemotherapy drug resistance in chronic myeloid leukemia. The results indicated that drug development or combination therapy could be enhanced by appreciating the key role of ac4C modification in cancer therapy.

Major Adverse Cardiovascular Events and Prognosis in Patients With Coronary Slow Flow.

This study aimed to investigate its clinical implications, risk factors, prognosis, and overall long-term outcomes. Demographic profiles, various clinical characteristics, and clinical outcomes were compared between 614 patients with coronary slow flow (CSF) and 428 patients with normal coronary artery. The incidence of CSF was found to be 2.65%. Significant differences were observed between patients with CSF and control subjects in terms of sex, chest tightness, hyperlipidemia, smoking history, alcohol consumption, age, height, weight, body mass index, diastolic blood pressure, heart rate, and body surface area (P < 0.05). CSF (hazard ratio: 1.531; 95% confidence interval: 1.064-2.202; p = 0.022) proved to be independent prognostic predictors of major adverse cardiovascular events (MACEs). Kaplan-Meier survival evaluations for MACEs presented a worser outcome for patients with CSF. Patients with CSF are at high risk for cardiovascular events and experience generally poor clinical outcomes.

Carbapenem is not always the best choice in the treatment of septic shock.

BACKGROUND: Septic shock is a global public health burden. In addition to the improvement of the level of individual care, the improvement of the overall hospital quality control management is also an essential key aspect of the Surviving Sepsis Campaign (SSC). Using of antibiotics is a cornerstone in the treatment of septic shock, so we conducted this study to investigate the influence of antibiotics and pathogenic bacteria on the mortality of septic shock at the level of overall hospital in China. METHODS: This was an observational database study in 2021 enrolled the data of 787 hospitals from 31 provinces/municipalities/autonomous regions of Mainland China collected in a survey from January 1, 2021 to December 31, 2021. RESULTS: The proportion of ICU patients with septic shock was 3.55%, while the patient mortality of septic shock was 23.08%. While carbapenem was the most preferred antibiotic medication used in 459 of the 782 hospitals, the preference for carbapenem did not show significant effect on the patient mortality in the treatment of septic shock (p-value 0.59). Compared with patients with fermenting bacteria as the most common pathogenic bacteria causing septic shock, patients with non-fermenting bacteria had a higher mortality (p-value 0.01). CONCLUSIONS: Whether using carbapenem as the preferred antibiotic or not, did not show effect on the patient mortality of septic shock. Compared with patients with fermenting bacteria as the most common pathogenic bacteria, patients of septic shock with non-fermenting bacteria had a higher mortality.

Protein Identification and Quantification Using Porous Silicon Arrays, Optical Measurements, and Machine Learning.

We report a versatile platform based on an array of porous silicon (PSi) thin films that can identify analytes based on their physical and chemical properties without the use of specific capture agents. The ability of this system to reproducibly classify, quantify, and discriminate three proteins separately is demonstrated by probing the reflectance of PSi array elements with a unique combination of pore size and buffer pH, and by analyzing the optical signals using machine learning. Protein identification and discrimination are reported over a concentration range of two orders of magnitude. This work represents a significant first step towards a low-cost, simple, versatile, and robust sensor platform that is able to detect biomolecules without the added expense and limitations of using capture agents.

Benefits of Combining Sonchus brachyotus DC. Extracts and Synbiotics in Alleviating Non-Alcoholic Fatty Liver Disease.

Non-alcoholic fatty liver disease, commonly abbreviated to NAFLD, is a pervasive ailment within the digestive system, exhibiting a rising prevalence, and impacting individuals at increasingly younger ages. Those afflicted by NAFLD face a heightened vulnerability to the onset of profound liver fibrosis, cardiovascular complications, and malignancies. Currently, NAFLD poses a significant threat to human health, and there is no approved therapeutic treatment for it. Recent studies have shown that synbiotics, which regulate intestinal microecology, can positively impact glucolipid metabolism, and improve NAFLD-related indicators. Sonchus brachyotus DC., a Chinese herb, exhibits hepatoprotective and potent antioxidant properties, suggesting its potential therapeutic use in NAFLD. Our preclinical animal model investigation suggests that the synergy between Sonchus brachyotus DC. extracts and synbiotics is significantly more effective in preventing and treating NAFLD, compared to the isolated use of either component. As a result, this combination holds the potential to introduce a fresh and encouraging therapeutic approach to addressing NAFLD.

Metabolomics Analysis Provides Novel Insights into the Difference in Meat Quality between Different Pig Breeds.

The Chuanzang black (CB) pig is a new crossbred between Chinese local breeds and modern breeds. Here, we investigated the growth performance, plasma indexes, carcass traits, and meat quality characteristics of conventional DLY (Duroc × Landrace × Yorkshire) crossbreed and CB pigs. The LC-MS/MS-based metabolomics of pork from DLY and CB pigs, as well as the relationship between the changes in the metabolic spectrum and meat quality, were analyzed. In this study, CB pigs presented lower final body weight, average daily gain, carcass weight, and eye muscle area than DLY pigs (p ˂ 0.05). Conversely, the ratio of feed to gain, marbling score, and meat color score of longissimus dorsi (LD) were higher in CB than DLY pigs (p ˂ 0.05). Moreover, psoas major (PM) showed a higher meat color score and a lower cooking loss in CB than DLY pigs (p ˂ 0.05). Interestingly, CB pigs showed lower myofiber diameter and area but higher myofiber density than DLY pigs (p ˂ 0.05). Furthermore, the mRNA expression levels of MyHC I, PPARδ, MEF2C, NFATC1, and AMPKα1 were higher in CB than DLY pigs (p ˂ 0.05). Importantly, a total of 753 metabolites were detected in the two tissues (e.g., psoas major and longissimus dorsi) of CB and DLY pigs, of which the difference in metabolite profiles in psoas major between crossbreeds was greater than that in longissimus dorsi. Specifically, palmitic acid, stearic acid, L-aspartic acid, corticosterone, and tetrahydrocorticosterone were the most relevant metabolites of psoas major meat quality, and tetrahydrocorticosterone, L-Palmitoylcarnitine, arachidic acid, erucic acid, and 13Z,16Z-docosadienoic acid in longissimus dorsi meat were positively correlated with meat quality. The most significantly enriched KEGG pathways in psoas major and longissimus dorsi pork were galactose metabolism and purine metabolism, respectively. These results not only indicated improved meat quality in CB pigs as compared to DLY pigs but may also assist in rational target selection for nutritional intervention or genetic breeding in the swine industry.

Measuring the worldwide spread of COVID-19 using a comprehensive modeling method.

BACKGROUND: With the global spread of COVID-19, detecting high-risk countries/regions timely and dynamically is essential; therefore, we sought to develop automatic, quantitative and scalable analysis methods to observe and estimate COVID-19 spread worldwide and further generate reliable and timely decision-making support for public health management using a comprehensive modeling method based on multiple mathematical models. METHODS: We collected global COVID-19 epidemic data reported from January 23 to September 30, 2020, to observe and estimate its possible spread trends. Countries were divided into three outbreak levels: high, middle, and low. Trends analysis was performed by calculating the growth rate, and then country grouping was implemented using group-based trajectory modeling on the three levels. Individual countries from each group were also chosen to further disclose the outbreak situations using two predicting models: the logistic growth model and the SEIR model. RESULTS: All 187 observed countries' trajectory subgroups were identified using two grouping strategies: with and without population consideration. By measuring epidemic trends and predicting the epidemic size and peak of individual countries, our study found that the logistic growth model generally estimated a smaller epidemic size than the SEIR model. According to SEIR modeling, confirmed cases in each country would take an average of 9-12 months to reach the outbreak peak from the day the first case occurred. Additionally, the average number of cases at the peak time will reach approximately 10-20% of the countries' populations, and the countries with high trends and a high predicted size must pay special attention and implement public health interventions in a timely manner. CONCLUSIONS: We demonstrated comprehensive observations and predictions of the COVID-19 outbreak in 187 countries using a comprehensive modeling method. The methods proposed in this study can measure COVID-19 development from multiple perspectives and are generalizable to other epidemic diseases. Furthermore, the methods also provide reliable and timely decision-making support for public health management.

mtPGS: Leverage multiple correlated traits for accurate polygenic score construction.

Accurate polygenic scores (PGSs) facilitate the genetic prediction of complex traits and aid in the development of personalized medicine. Here, we develop a statistical method called multi-trait assisted PGS (mtPGS), which can construct accurate PGSs for a target trait of interest by leveraging multiple traits relevant to the target trait. Specifically, mtPGS borrows SNP effect size similarity information between the target trait and its relevant traits to improve the effect size estimation on the target trait, thus achieving accurate PGSs. In the process, mtPGS flexibly models the shared genetic architecture between the target and the relevant traits to achieve robust performance, while explicitly accounting for the environmental covariance among them to accommodate different study designs with various sample overlap patterns. In addition, mtPGS uses only summary statistics as input and relies on a deterministic algorithm with several algebraic techniques for scalable computation. We evaluate the performance of mtPGS through comprehensive simulations and applications to 25 traits in the UK Biobank, where in the real data mtPGS achieves an average of 0.90%-52.91% accuracy gain compared to the state-of-the-art PGS methods. Overall, mtPGS represents an accurate, fast, and robust solution for PGS construction in biobank-scale datasets.

Association of Device-Measured Physical Activity With Cardiovascular Outcomes in Individuals With Hypertension.

BACKGROUND: There is insufficient evidence to show that the guidelines' recommendations of physical activity (PA) are associated with long-term benefits in individuals with hypertension. METHODS: This prospective cohort study included UK Biobank participants with hypertension. Time spent on vigorous-intensity PA (VPA), moderate-intensity PA (MPA), and light-intensity PA (LPA) measured by wrist-worn accelerometer were extracted. The primary outcomes were major adverse cardiovascular events (including cardiovascular death, stroke, and myocardial infarction) and all-cause mortality. The secondary outcomes were cardiovascular death, myocardial infarction, and stroke, respectively. The relationships of PA with outcomes were analyzed using Cox regression models. RESULTS: This study included 49 060 eligible participants with a median follow-up of 7.0 years. MPA was inversely associated with risks of major adverse cardiovascular events and all-cause mortality. Modest amounts of light-intensity PA or VPA were likely to be more beneficial than higher amounts of either in all-cause death or cardiovascular outcomes. Compared with the least active group, 150 to 300 min/wk of MPA or more was significantly associated with decreased risk of all-cause death (by 34%-54%) and major adverse cardiovascular events (by 23%-41%), but 75 to 150 min/wk of VPA or more was associated with few further benefits, even weakening the cardiovascular benefits. CONCLUSIONS: MPA had an inverse dose-response association with the risk of all-cause mortality and cardiovascular outcomes in individuals with hypertension. Modest amounts of light-intensity PA or VPA are also beneficial, but higher amounts may be not. MPA may be the optimal PA intensity for individuals with hypertension. Further researches are required to determine whether high levels of VPA should be recommended.

Digital image analysis allows objective stratification of patients with silent PIT1-lineage pituitary neuroendocrine tumors.

Studies describing the clinical presentation and prognosis of patients with silent PIT1 (pituitary specific transcription factor)-lineage pituitary neuroendocrine tumors (PitNETs) are rare. We identified patients with positive PIT1 tumor staining but without evidence of hormone hypersecretion at a tertiary center. Clusters were obtained according to cell morphology and immunostaining from each patient's digitally segmented whole slide image. We compared the clinical presentations, radiological features, and prognoses of the different clusters. We identified 146 patients (68 male, 42.9 ± 14.1 years old) with silent PIT1-lineage PitNETs. Morphology clustering suggested that tumors with large nuclei and apparent eccentricity were associated with a higher proportion of aggressiveness and a higher hazard of recurrence [hazard ratio (HR): 2.64, (95% CI, 1.06-6.55), p = 0.037]. Immunohistochemical clustering suggested that tumors with thyroid stimulating hormone (TSH) staining or all negative PIT1-lineage hormones were associated with a higher proportion of aggressiveness and a higher risk of recurrence [HR: 12.4, (95% CI, 1.60-93.5), p = 0.015]. We obtained three-tier risk profiles by combining morphological and immunohistochemical clustering. Patients with the high-risk profile presented the highest recurrence rate compared with those in the medium-risk and low-risk profiles [HR: 3.54, (95% CI, 1.40-8.93), p = 0.002]. In conclusion, digital image analysis based on cell morphology and immunohistochemical staining allows objective stratification of patients with silent PIT1-lineage tumors. Typical morphological characteristics of high-risk tumors are large tumor nuclei and high eccentricity, and typical immunostaining characteristics are TSH staining or negative staining for all PIT1-lineage hormones.

Comparative genomics and DNA methylation analysis of Pseudomonas aeruginosa clinical isolate PA3 by single-molecule real-time sequencing reveals new targets for antimicrobials.

Introduction: Pseudomonas aeruginosa (P.aeruginosa) is an important opportunistic pathogen with broad environmental adaptability and complex drug resistance. Single-molecule real-time (SMRT) sequencing technique has longer read-length sequences, more accuracy, and the ability to identify epigenetic DNA alterations. Methods: This study applied SMRT technology to sequence a clinical strain P. aeruginosa PA3 to obtain its genome sequence and methylation modification information. Genomic, comparative, pan-genomic, and epigenetic analyses of PA3 were conducted. Results: General genome annotations of PA3 were discovered, as well as information about virulence factors, regulatory proteins (RPs), secreted proteins, type II toxin-antitoxin (TA) pairs, and genomic islands. A genome-wide comparison revealed that PA3 was comparable to other P. aeruginosa strains in terms of identity, but varied in areas of horizontal gene transfer (HGT). Phylogenetic analysis showed that PA3 was closely related to P. aeruginosa 60503 and P. aeruginosa 8380. P. aeruginosa's pan-genome consists of a core genome of roughly 4,300 genes and an accessory genome of at least 5,500 genes. The results of the epigenetic analysis identified one main methylation sites, N6-methyladenosine (m6A) and 1 motif (CATNNNNNNNTCCT/AGGANNNNNNNATG). 16 meaningful methylated sites were picked. Among these, purH, phaZ, and lexA are of great significance playing an important role in the drug resistance and biological environment adaptability of PA3, and the targeting of these genes may benefit further antibacterial studies. Disucssion: This study provided a detailed visualization and DNA methylation information of the PA3 genome and set a foundation for subsequent research into the molecular mechanism of DNA methyltransferase-controlled P. aeruginosa pathogenicity.

Retrospective pharmacogenetic study of psoriasis highlights the role of KLK7 in TNF signaling.

BACKGROUND: Multiple treatment options are available for the management of psoriasis, but clinical response varies among individual patients and no biomarkers are available to facilitate treatment selection for improved patient outcomes. OBJECTIVES: We aim to utilize retrospective data to conduct a pharmacogenetic study. This design can bypass the obstacle of patient follow-up in prospective study and increase the sample size. Successful implementation of the study can nominate new candidates and their target genes to explore the potential genetic pathways associated with drug response in the treatment of psoriasis. METHODS: We conducted a retrospective pharmacogenetic study using self-evaluated treatment response from 1,942 genotyped psoriatic patients. We examined 6,502,658 genetic markers to model their associations with responses from six treatment options using linear regression, adjusting for cohort variables and demographic features. We further utilized an integrative approach incorporating epigenomic, transcriptomic, and a longitudinal clinical cohort to provide biological implications for the top signals associated with drug response. RESULTS: Two novel markers were revealed to be associated with treatment response: rs1991820 (p = 1.30×10-6) for anti-TNF biologics; and rs62264137 (p = 2.94×10-6) for methotrexate, which also associated with cutaneous mRNA expression levels of two known psoriasis-related genes KLK7 (p = 1.0×10-12) and CD200 (p = 5.4×10-6). We demonstrate that KLK7 expression is increased in the psoriatic epidermis as shown by immunohistochemistry as well as single-cell RNA-sequencing, and we highlight its responsiveness to anti-TNF treatment. By inhibiting the expression of KLK7, we further illustrate keratinocytes have decrease in pro-inflammatory responses to TNF. CONCLUSIONS: Our study implicates the genetic regulation of cytokine responses in predicting clinical drug response and supports the association between pharmacogenetic loci and anti-TNF response, as shown here for KLK7.

Temporal and spatial heterogeneity of indoor and outdoor temperatures and their relationship with thermal sensation from a global perspective.

People spend most of their time indoors. However, indoor temperature and individual thermal exposure are generally not considered in epidemiological studies of temperature and health. Based on the American Society of Heating, Refrigerating, and Air-Conditioning Engineers (ASHRAE) RP-884 Database, the ASHRAE Global Thermal Comfort Database II and the Chinese Thermal Comfort Database, this study first explored the relationship between outdoor temperature, indoor temperature and thermal sensation from a global perspective. Moreover, the potential influence of spatiotemporal heterogeneity on health studies was explored. A breakpoint was found at approximately 11.5 °C in the segmented regression of indoor and outdoor temperature, and the slope of the curve was greater when outdoor temperature was above the breakpoint (n = 67,896). Based on multi-group propensity score matching (PSM) and generalizedadditivemodels (GAM), spatiotemporal heterogeneity was found in the relationship between indoor and outdoor temperatures after adjusting for building type and year. Furthermore, the relationship between indoor temperature and thermal sensation was influenced by the outdoor temperature. This study highlights the importance of considering indoor temperature or individual thermal exposure in temperature-related health studies.

DTR-Net: Dual-space 3D Tooth Model Reconstruction from Panoramic X-ray Images.

In digital dentistry, cone-beam computed tomography (CBCT) can provide complete 3D tooth models, yet suffers from a long concern of requiring excessive radiation dose and higher expense. Therefore, 3D tooth model reconstruction from 2D panoramic X-ray image is more cost-effective, and has attracted great interest in clinical applications. In this paper, we propose a novel dual-space framework, namely DTR-Net, to reconstruct 3D tooth model from 2D panoramic X-ray images in both image and geometric spaces. Specifically, in the image space, we apply a 2D-to-3D generative model to recover intensities of CBCT image, guided by a task-oriented tooth segmentation network in a collaborative training manner. Meanwhile, in the geometric space, we benefit from an implicit function network in the continuous space, learning using points to capture complicated tooth shapes with geometric properties. Experimental results demonstrate that our proposed DTR-Net achieves state-of-the-art performance both quantitatively and qualitatively in 3D tooth model reconstruction, indicating its potential application in dental practice.

HOXD8 suppresses renal cell carcinoma growth by upregulating SHMT1 expression.

Amplification of amino acids synthesis is reported to promote tumorigenesis. The serine/glycine biosynthesis pathway is a reversible conversion of serine and glycine catalyzed by cytoplasmic serine hydroxymethyltransferase (SHMT)1 and mitochondrial SHMT2; however, the role of SHTM1 in renal cell carcinoma (RCC) is still unclear. We found that low SHMT1 expression is correlated with poor survival of RCC patients. The in vitro study showed that overexpression of SHMT1 suppressed RCC proliferation and migration. In the mouse tumor model, SHMT1 significantly retarded RCC tumor growth. Furthermore, by gene network analysis, we found several SHMT1-related genes, among which homeobox D8 (HOXD8) was identified as the SHMT1 regulator. Knockdown of HOXD8 decreased SHMT1 expression, resulting in faster RCC growth, and rescued the SHMT1 overexpression-induced cell migration defects. Additionally, ChIP assay found the binding site of HOXD8 to SHMT1 promoter was at the -456~-254 bp region. Taken together, SHMT1 functions as a tumor suppressor in RCC. The transcription factor HOXD8 can promote SHMT1 expression and suppress RCC cell proliferation and migration, which provides new mechanisms of SHMT1 in RCC tumor growth and might be used as a potential therapeutic target candidate for clinical treatment.