A diamond-bearing core-mantle boundary on Mercury.

Abundant carbon was identified on Mercury by MESSENGER, which is interpreted as the remnant of a primordial graphite flotation crust, suggesting that the magma ocean and core were saturated in carbon. We re-evaluate carbon speciation in Mercury's interior in light of the high pressure-temperature experiments, thermodynamic models and the most recent geophysical models of the internal structure of the planet. Although a sulfur-free melt would have been in the stability field of graphite, sulfur dissolution in the melt under the unique reduced conditions depressed the sulfur-rich liquidus to temperatures spanning the graphite-diamond transition. Here we show it is possible, though statistically unlikely, that diamond was stable in the magma ocean. However, the formation of a solid inner core caused diamond to crystallize from the cooling molten core and formation of a diamond layer becoming thicker with time.

Prediction of Prostate Cancer Risk Stratification Based on A Nonlinear Transformation Stacking Learning Strategy.

PURPOSE: Prostate cancer (PCa) is an epithelial malignancy that originates in the prostate gland and is generally categorized into low, intermediate, and high-risk groups. The primary diagnostic indicator for PCa is the measurement of serum prostate-specific antigen (PSA) values. However, reliance on PSA levels can result in false positives, leading to unnecessary biopsies and an increased risk of invasive injuries. Therefore, it is imperative to develop an efficient and accurate method for PCa risk stratification. Many recent studies on PCa risk stratification based on clinical data have employed a binary classification, distinguishing between low to intermediate and high risk. In this paper, we propose a novel machine learning (ML) approach utilizing a stacking learning strategy for predicting the tripartite risk stratification of PCa. METHODS: Clinical records, featuring attributes selected using the lasso method, were utilized with 5 ML classifiers. The outputs of these classifiers underwent transformation by various nonlinear transformers and were then concatenated with the lasso-selected features, resulting in a set of new features. A stacking learning strategy, integrating different ML classifiers, was developed based on these new features. RESULTS: Our proposed approach demonstrated superior performance, achieving an accuracy of 0.83 and an area under the receiver operating characteristic curve value of 0.88 in a dataset comprising 197 PCa patients with 42 clinical characteristics. CONCLUSION: This study aimed to improve clinicians' ability to rapidly assess PCa risk stratification while reducing the burden on patients. This was achieved by using artificial intelligence-related technologies as an auxiliary method for diagnosing PCa.

A preliminary report on the exploration of salivary bacterial diversity by the multiplex SNaPshot assay.

Salivary bacterial community composition is associated with the host's internal and environmental factors, which have potential applications in forensic practice. The 16S rRNA gene sequencing is the most commonly used strategy for detecting salivary bacterial diversity; however, its platforms are not compatible with capillary electrophoresis (CE) platforms commonly used for forensic applications. Therefore, we attempted to detect the salivary bacterial diversity using a single nucleotide polymorphism (SNP) assay. Salivary bacterial diversity varies among diverse geographic locations, making it a potential supplementary biomarker for forensic geographic sourcing. To evaluate the performance of the multiplex SNaPshot assay, saliva samples from three geographic locations in China were analyzed using the multiplex SNaPshot assay and 16S rRNA gene sequencing. We screened SNPs from two high-relative-abundance salivary genera (Streptococcus and Veillonella) to construct a multiplex SNaPshot system that can be used on the CE platform. The stability and sensitivity of the multiplex SNaPshot system were also tested. A random forest classification model was used to classify samples from different regions to explore the ability of salivary bacteria to discriminate between geographic sources. Six bacterial SNPs were screened and a multiplex SNaPshot system was constructed. The stability results showed that the typing of salivary stains that were placed indoors for different days was not affected in this study. Two-thirds of mocked salivary stain samples showed more than 90% of typing results obtained for salivary stain samples with an input of 0.1 µl saliva. The results of principal coordinate analysis based on salivary bacterial diversity showed significant differences between samples from the three different geographic locations. The accuracy of the random forest classification was 66.67% based on the multiplex SNaPshot assay and 83.33% based on the 16S rRNA gene sequencing. In conclusion, this is the first attempt to detect salivary bacterial diversity using a multiplex SNaPshot bacterial SNP assay. The geographic difference in human salivary bacterial community composition was significant, as revealed by the multiplex SNaPshot assay; however, its performance in discriminating geographic sources was lower than that of 16S rRNA gene sequencing. This strategy based on bacterial SNP loci may favor the detection of human bacterial diversity in common forensic laboratories but requires further exploration in larger sample sizes and more bacterial SNP loci.

Multimodality deep learning radiomics nomogram for preoperative prediction of malignancy of breast cancer: a multicenter study.

Background. Breast cancer is the most prevalent cancer diagnosed in women worldwide. Accurately and efficiently stratifying the risk is an essential step in achieving precision medicine prior to treatment. This study aimed to construct and validate a nomogram based on radiomics and deep learning for preoperative prediction of the malignancy of breast cancer (MBC).Methods. The clinical and ultrasound imaging data, including brightness mode (B-mode) and color Doppler flow imaging, of 611 breast cancer patients from multiple hospitals in China were retrospectively analyzed. Patients were divided into one primary cohort (PC), one validation cohort (VC) and two test cohorts (TC1 and TC2). A multimodality deep learning radiomics nomogram (DLRN) was constructed for predicting the MBC. The performance of the proposed DLRN was comprehensively assessed and compared with three unimodal models via the calibration curve, the area under the curve (AUC) of receiver operating characteristics and the decision curve analysis.Results. The DLRN discriminated well between the MBC in all cohorts [overall AUC (95% confidence interval): 0.983 (0.973-0.993), 0.972 (0.952-0.993), 0.897 (0.823-0.971), and 0.993 (0.977-1.000) on the PC, VC, test cohorts1 (TC1) and test cohorts2 TC2 respectively]. In addition, the DLRN performed significantly better than three unimodal models and had good clinical utility.Conclusion. The DLRN demonstrates good discriminatory ability in the preoperative prediction of MBC, can better reveal the potential associations between clinical characteristics, ultrasound imaging features and disease pathology, and can facilitate the development of computer-aided diagnosis systems for breast cancer patients. Our code is available publicly in the repository athttps://github.com/wupeiyan/MDLRN.

ULS4US: universal lesion segmentation framework for 2D ultrasound images.

Objective. Deep learning (DL) methods have been widely utilized in ultrasound (US) image segmentation tasks. However, current DL segmentation methods for US images are typically developed only for lesion segmentation of specific organs; e.g. breast or thyroid US. So far, there is currently no general-purpose lesion segmentation framework for US images that can be implemented across various organs in computer aided diagnosis scenarios. Considering that most lesion locations in US images have abnormal ultrasonic echo intensities or patterns that may be visually distinct from surrounding normal tissues or organs, it is thus possible to develop a universal lesion segmentation framework for US images (named as ULS4US), focusing on effectively identifying and segmenting lesions of various sizes in different organs.Approach. The proposed ULS4US framework comprises three components: (1) a multiple-in multi-out (MIMO) UNet that incorporates multiscale features extracted from the US image and lesion, (2) a novel two-stage lesion-aware learning algorithm that recursively locates and segments the lesions in a reinforced manner, and (3) a lesion-adaptive loss function for the MIMO-UNet that integrates two weighted components and one self-supervised component designed for intra- and inter-branches of network outputs, respectively.Main Results. Compared to six state-of-the-art segmentation models, ULS4US has achieved superior performance (accuracy of 0.956, DSC of 0.836, HD of 7.849, and mIoU of 0.731) in a unified dataset consisting of two public and three private US image datasets, which include over 2200 images of three specific types of organs. Comparative experiments on both individual and unified datasets suggest that ULS4US is likely scalable with additional data.Significance. The study demonstrates the potential of DL-based universal lesion segmentation approaches in clinical US, which would substantially reduce clinician workload and enhance diagnostic accuracy.

Predictors of Thyroid Gland Invasion in Total Laryngectomy for Advanced Laryngeal Squamous Cell Carcinoma.

Objectives: Thyroidectomy for advanced laryngeal squamous cell carcinoma (LSCC) is controversial. This study aimed to identify predictors of thyroid gland invasion in patients with LSCC and management of the thyroid gland during total laryngectomy. Patients and Methods: Clinical data and pathological characteristics of 113 patients, who underwent laryngectomy with thyroidectomy for advanced LSCC in Guangdong Provincial People's Hospital between 2009 and 2019, were retrospectively analyzed. The incidence and predictors of thyroid gland invasion were analyzed, and a new predictor was proposed using a parallel test. Results: Of 113 patients, 25.7% exhibited thyroid invasion. A new predictor that combined the lower third of thyroid cartilage invasion and thyroid gland invasion on computed tomography/magnetic resonance imaging (CT/MRI) was associated with pathological thyroid gland invasion (P = 0.001; sensitivity, 88.2%; negative predictive value, 95%). Conclusion: Thyroidectomy may be required during total laryngectomy in those with invasion of the lower third of thyroid cartilage and/or thyroid gland invasion revealed on CT/MRI instead of being performed routinely.

Intermittent fasting enhances hippocampal NPY expression to promote neurogenesis after traumatic brain injury.

OBJECTIVES: Interventions for preventing cognitive dysfunction after traumatic brain injury (TBI) are limited. Given that adult hippocampal neurogenesis after brain injury contributes to cognitive recovery, and hippocampal neurogenesis is potentially affected by nutritional factors, the aim of this study was to examine whether fasting could promote hippocampal neurogenesis and thus ameliorate the cognitive defects after TBI. METHODS: The present study used 8- to 10-wk-old C57 BL/6 N mice weighing 23 g, half males and half females. The mice were randomly assigned to each group, with 10 to 18 mice per group. All mice were housed in an approved animal facility with a 12-h light/dark cycle. In the metabolic study (food intake, body weight, blood glucose, triacylglycerol, total cholesterol, and ß-hydroxybutyric acid ), 54 mice (male:female = 1:1) were randomized to the ad libitum (AL) group (n = 18) and the intermittent fasting (IF) group (n = 36). In the neurogenesis study, 45 mice (male:female = 1:1) were randomized to AL (n  = 18), IF (n  = 9), IF + scramble (n  = 9), and the IF + neuropeptide Y (NPY)_siRNA (n  = 9) groups. In the Morris water maze test, 48 mice (male:female = 1:1) were randomized to AL (n  = 12), IF (n  = 12), IF + scramble (n  = 12), and the IF + NPY_siRNA (n  = 12) groups. RESULTS: We showed that a 1-mo-long IF regimen enhanced the proliferation of neural stem cells in the subgranular zone of the hippocampus 3 d after TBI, in addition to improving the cognitive performance in the Morris water maze test. Furthermore, an increase in the hippocampal NPY expression was detected in the IF group after the injury, compared with the mice fed AL, and local knockdown of NPY in vivo attenuated the effects of IF on TBI. CONCLUSIONS: These findings suggest that IF promotes hippocampal neurogenesis after TBI by a mechanism that involves enhancement of NPY expression, to alleviate cognitive dysfunction caused by injury.

Thyroid gland invasion in total laryngectomy: A systematic review and meta-analysis.

BACKGROUND: Although guidelines indicate that thyroidectomy should be performed routinely during total laryngectomy in patients with advanced laryngeal cancer, its clinical indications remain controversial. Some researchers believe that thyroid invasion is uncommon and that thyroid preservation should be considered in most cases. OBJECTIVE: This study aimed to identify the incidence and predictors of thyroid invasion in patients with laryngeal cancer to facilitate decision-making regarding whether to perform thyroidectomy during total laryngectomy. MATERIALS AND METHODS: The author conducted a systematic review and meta-analysis of all published articles retrieved from a search of the MEDLINE (1982-2020) and EMBASE (1971-2020) databases. The published studies of advanced laryngeal cancer with total laryngectomy and partial or total thyroidectomy for laryngeal cancer were selected. The incidence and predictors of thyroid invasion were analyzed. RESULTS: We analyzed 25 studies (2177 cases), of which 176 people (8.08%) had thyroid invasion. Subglottic tumors (odds ratio [OR], 3.74; 95% CI, 1.75-7.99), T4 stage tumors (OR, 2.39; 95% CI, 1.20-4.75), subglottic extension (OR, 3.85; 95% CI,2.09-7.11), and thyroid cartilage invasion (OR, 3.98; 95% CI, 1.47-10.75) are risk factors for thyroid invasion, and no statistically significant difference was noted between recurrent tumor and thyroid invasion. CONCLUSION: The risk of thyroid invasion was significantly higher when advanced laryngeal cancer involved subglottic tumors, T4 stage tumors, subglottic extension, and thyroid cartilage invasion. The overall incidence of thyroid gland invasion was low; therefore, thyroidectomy may be performed for cases deemed risky rather than as a routine measure of total laryngectomy. RESEARCH REGISTRY UIN: reviewregistry1226.

Optogenetics for Understanding and Treating Brain Injury: Advances in the Field and Future Prospects.

Optogenetics is emerging as an ideal method for controlling cellular activity. It overcomes some notable shortcomings of conventional methods in the elucidation of neural circuits, promotion of neuroregeneration, prevention of cell death and treatment of neurological disorders, although it is not without its own limitations. In this review, we narratively review the latest research on the improvement and existing challenges of optogenetics, with a particular focus on the field of brain injury, aiming at advancing optogenetics in the study of brain injury and collating the issues that remain. Finally, we review the most current examples of research, applying photostimulation in clinical treatment, and we explore the future prospects of these technologies.

Migration, transformation and nitrate source in the Lihu Underground River based on dual stable isotopes of δ15N-NO3- and δ18O-NO3.

Nitrate (NO3-) pollution is a common phenomenon in karst underground rivers, which are important water sources in karst landscapes. For drinking water safety and environmental protection, it is crucial to accurately identify NO3- sources and their migration and transformation processes in the Lihu Underground River. In this study, water samples of the Lihu Underground River in Guangxi were collected in May 2014, October 2014, January 2015, and July 2015, and water chemical and dual isotopic (δ15N-NO3- and δ18O-NO3-) approaches were used to evaluate the NO3- characteristics and sources in the Lihu Underground River. The concentration of NO3- in the Lihu Underground River ranged from 1.16 to 19.78 mg·L-1, with an average of 9.30 mg·L-1, which is more than 37% of the WHO standard (10 mg·L-1). The concentrations of NO3- in the wet season (May 2014 and July 2015) were slightly lower than those in the dry season (from October 2014 to January 2015) at most sampling sites due to dilution effects. The migration and transformation processes of NO3- were analyzed by comparing the measured and calculated concentrations of NO3- in the Lihu Underground River. In the dry season (from October 2014 to January 2015), the variation in NO3- concentration upstream and midstream of the Lihu Underground River was affected by exogenous input and nitrification. From midstream to the outlet of Xiaolongdong, it is affected by self-purification factors, including physical processes, chemical processes, and biological processes. In the wet season (May 2014 and July 2015), the dilution and mixing effects were the main factors controlling the variation in NO3- concentration in the Lihu Underground River. The contribution rates of potential NO3- sources (incl. atmospheric precipitation (AP), NO3- fertilizer (NF), NH4+ in fertilizer and rainfall (NFA), soil organic nitrogen (SON), and manure and sewage (M&S)) were quantitatively evaluated by using the IsoSource model. The results showed that in May 2014, the main sources of NO3- were M&S and NF, with contribution rates of 46% and 41%, respectively. In October 2014, NO3- sources were M&S with a contribution rate of 47%, followed by NFA with a contribution rate of 31%. In January 2015, NO3- sources in groundwater were M&S, with a contribution rate of 53%, followed by NFA (34%). In July 2015, the main NO3- sources were M&S and NF, whose contribution rates were 54% and 39%, respectively.

Monitoring of persistent organic pollutants in seawater of the Pearl River Estuary with rapid on-site active SPME sampling technique.

An on-site active solid-phase microextraction (SPME) sampling technique coupled with gas chromatography-mass spectrometry (GC-MS) for sampling and monitoring 16 polycyclic aromatic hydrocarbons (PAHs) and 8 organochlorine pesticides (OCPs) in seawater was developed. Laboratory experiments demonstrated that the sampling-rate calibration method was practical and could be used for the quantification of on-site sampling. The proposed method was employed for field tests which covered large amounts of water samples in the Pearl River Estuary in rainy and dry seasons. The on-site SPME sampling method can avoid the contamination of sample, the losses of analytes during sample transportation, as well as the usage of solvent and time-consuming sample preparation process. Results indicated that the technique with the designed device can address the requirement of modern environment water analysis. In addition, the sources, bioaccumulation and potential risk to human of the PAHs and OCPs in seawater of the Pearl River Estuary were discussed.