Non-Hermitian Strongly Interacting Dirac Fermions.

Exotic quantum phases and phase transition in the strongly interacting Dirac systems have attracted tremendous interests. On the other hand, non-Hermitian physics, usually associated with dissipation arising from the coupling to environment, emerges as a frontier of modern physics in recent years. In this Letter, we investigate the interplay between non-Hermitian physics and strong correlation in Dirac-fermion systems. We generalize the projector quantum Monte-Carlo (PQMC) algorithm to the non-Hermitian interacting fermionic systems. Employing PQMC simulation, we decipher the ground-state phase diagram of the honeycomb Hubbard model with spin resolved non-Hermitian asymmetric hopping processes. The antiferromagnetic (AFM) ordering induced by Hubbard interaction is enhanced by the non-Hermitian asymmetric hopping. Combining PQMC simulation and renormalization group analysis, we reveal that the quantum phase transition between Dirac semi-metal and AFM phases belongs to Hermitian chiral XY universality class, implying that a Hermitian Gross-Neveu transition is emergent at the quantum critical point although the model is non-Hermitian.

Narazaciclib, a novel multi-kinase inhibitor with potent activity against CSF1R, FLT3 and CDK6, shows strong anti-AML activity in defined preclinical models.

CSF1R is a receptor tyrosine kinase responsible for the growth/survival/polarization of macrophages and overexpressed in some AML patients. We hypothesized that a novel multi-kinase inhibitor (TKi), narazaciclib (HX301/ON123300), with high potency against CSF1R (IC50 ~ 0.285 nM), would have anti-AML effects. We tested this by confirming HX301's high potency against CSF1R (IC50 ~ 0.285 nM), as well as other kinases, e.g. FLT3 (IC50 of ~ 19.77 nM) and CDK6 (0.53 nM). An in vitro proliferation assay showed that narazaciclib has a high growth inhibitory effect in cell cultures where CSF1R or mutant FLT3-ITD variants that may be proliferation drivers, including primary macrophages (IC50 of 72.5 nM) and a subset of AML lines (IC50 < 1.5 µM). In vivo pharmacology modeling of narazaciclib using five AML xenografts resulted in: inhibition of MV4-11 (FLT3-ITD) subcutaneous tumor growth and complete suppression of AM7577-PDX (FLT3-ITD/CSF1Rmed) systemic growth, likely due to the suppression of FLT3-ITD activity; complete suppression of AM8096-PDX (CSF1Rhi/wild-type FLT3) growth, likely due to the inhibition of CSF1R ("a putative driver"); and nonresponse of both AM5512-PDX and AM7407-PDX (wild-type FLT3/CSF1Rlo). Significant leukemia load reductions in bone marrow, where disease originated, were also achieved in both responders (AM7577/AM8096), implicating that HX301 might be a potentially more effective therapy than those only affecting peripheral leukemic cells. Altogether, narazaciclib can potentially be a candidate treatment for a subset of AML with CSF1Rhi and/or mutant FLT3-ITD variants, particularly second generation FLT3 inhibitor resistant variants.

ATG-101 is a tetravalent PD-L1×4-1BB bispecific antibody that stimulates anti-tumor immunity through PD-L1 blockade and PD-L1-directed 4-1BB activation.

Immune checkpoint inhibitors (ICI) have transformed cancer treatment. However, only a minority of patients achieve a profound response. Many patients are innately resistant while others acquire resistance to ICIs. Furthermore, hepatotoxicity and suboptimal efficacy have hampered the clinical development of agonists of 4-1BB, a promising immune stimulating target. To effectively target 4-1BB and treat diseases resistant to ICIs, we engineered ATG-101, a tetravalent "2+2" PD-L1×4-1BB bispecific antibody. ATG-101 bound PD-L1 and 4-1BB concurrently, with a greater affinity for PD-L1, and potently activated 4-1BB+ T cells when crosslinked with PD-L1+ cells. ATG-101 activated exhausted T cells upon PD-L1 binding, indicating a possible role in reversing T-cell dysfunction. ATG-101 displayed potent antitumor activity in numerous in vivo tumor models, including those resistant or refractory to ICIs. ATG-101 greatly increased the proliferation of CD8+ T cells, the infiltration of effector memory T cells, and the ratio of CD8+ T/Treg cells in the tumor microenvironment (TME), rendering an immunologically "cold" tumor "hot". Comprehensive characterization of the TME after ATG-101 treatment using single-cell RNA-sequencing further revealed an altered immune landscape that reflected increased antitumor immunity. ATG-101 was well-tolerated and did not induce hepatotoxicity in non-human primates. According to computational semi-mechanistic pharmacology modeling, 4-1BB/ATG-101/PD-L1 trimer formation and PD-L1 receptor occupancy were both maximized at around 2 mg/kg of ATG-101, providing guidance regarding the optimal biological dose for clinical trials. In summary, by localizing to PD-L1-rich microenvironments and activating 4-1BB+ immune cells in a PD-L1 crosslinking-dependent manner, ATG-101 safely inhibits growth of ICI resistant and refractory tumors.

Association between the cumulative triglyceride-glucose index and the recurrence of atrial fibrillation after radiofrequency catheter ablation.

BACKGROUND: Triglyceride-glucose (TyG) index values are a new surrogate marker for insulin resistance. This study aimed to explore the relationship between cumulative TyG index values and atrial fibrillation (AF) recurrence after radiofrequency catheter ablation (RFCA). METHODS: A total of 576 patients with AF who underwent RFCA at the Second Affiliated Hospital of Xi'an Jiaotong University were included in this study. The participants were grouped based on cumulative TyG index values tertiles within 3 months after ablation. Cox regression and restricted cubic spline analyses were used to determine the relationship between cumulative TyG index values and AF recurrence. The predictive value of all risk factors was assessed by receiver operating curve analysis. RESULTS: There were 375 patients completed the study (age: 63.23 ± 10.73 years, 64.27% male). The risk of AF recurrence increased with increasing cumulative TyG index values tertiles. After adjusting for potential confounders, patients in the medium cumulative TyG index group [hazard ratio (HR) = 4.949, 95% CI: 1.778-13.778, P = 0.002] and the high cumulative TyG index group (HR = 8.716, 95% CI: 3.371-22.536, P < 0.001) had a higher risk of AF recurrence than those in the low cumulative TyG index group. The restricted cubic spline regression model also showed an increased risk of AF recurrence with increasing cumulative TyG index values. When considering cumulative TyG index values, left atrial diameter, and lactate dehydrogenase levels as a comprehensive factor, the model could effectively predict AF recurrence after RFCA [area under the curve (AUC) = 0.847, 95% CI: 0.797-0.897, P < 0.001]. CONCLUSIONS: Cumulative TyG index values were a risk factor for AF recurrence after RFCA. Monitoring longitudinal TyG index values may assist with optimized for risk stratification and outcome prediction for AF recurrence.

Construction and validation of artificial intelligence pathomics models for predicting pathological staging in colorectal cancer: Using multimodal data and clinical variables.

OBJECTIVE: This retrospective observational study aims to develop and validate artificial intelligence (AI) pathomics models based on pathological Hematoxylin-Eosin (HE) slides and pathological immunohistochemistry (Ki67) slides for predicting the pathological staging of colorectal cancer. The goal is to enable AI-assisted accurate pathological staging, supporting healthcare professionals in making efficient and precise staging assessments. METHODS: This study included a total of 267 colorectal cancer patients (training cohort: n = 213; testing cohort: n = 54). Logistic regression algorithms were used to construct the models. The HE image features were used to build the HE model, the Ki67 image features were used for the Ki67 model, and the combined model included features from both the HE and Ki67 images, as well as tumor markers (CEA, CA724, CA125, and CA242). The predictive results of the HE model, Ki67 model, and tumor markers were visualized through a nomogram. The models were evaluated using ROC curve analysis, and their clinical value was estimated using decision curve analysis (DCA). RESULTS: A total of 260 deep learning features were extracted from HE or Ki67 images. The AUC for the HE model and Ki67 model in the training cohort was 0.885 and 0.890, and in the testing cohort, it was 0.703 and 0.767, respectively. The combined model and nomogram in the training cohort had AUC values of 0.907 and 0.926, and in the testing cohort, they had AUC values of 0.814 and 0.817. In clinical DCA, the net benefit of the Ki67 model was superior to the HE model. The combined model and nomogram showed significantly higher net benefits compared to the individual HE model or Ki67 model. CONCLUSION: The combined model and nomogram, which integrate pathomics multi-modal data and clinical-pathological variables, demonstrated superior performance in distinguishing between Stage I-II and Stage III colorectal cancer. This provides valuable support for clinical decision-making and may improve treatment strategies and patient prognosis. Furthermore, the use of immunohistochemistry (Ki67) slides for pathomics modeling outperformed HE slide, offering new insights for future pathomics research.

Development and validation of a Radiopathomics model based on CT scans and whole slide images for discriminating between Stage I-II and Stage III gastric cancer.

OBJECTIVE: This study aimed to develop and validate an artificial intelligence radiopathological model using preoperative CT scans and postoperative hematoxylin and eosin (HE) stained slides to predict the pathological staging of gastric cancer (stage I-II and stage III). METHODS: This study included a total of 202 gastric cancer patients with confirmed pathological staging (training cohort: n = 141; validation cohort: n = 61). Pathological histological features were extracted from HE slides, and pathological models were constructed using logistic regression (LR), support vector machine (SVM), and NaiveBayes. The optimal pathological model was selected through receiver operating characteristic (ROC) curve analysis. Machine learnin algorithms were employed to construct radiomic models and radiopathological models using the optimal pathological model. Model performance was evaluated using ROC curve analysis, and clinical utility was estimated using decision curve analysis (DCA). RESULTS: A total of 311 pathological histological features were extracted from the HE images, including 101 Term Frequency-Inverse Document Frequency (TF-IDF) features and 210 deep learning features. A pathological model was constructed using 19 selected pathological features through dimension reduction, with the SVM model demonstrating superior predictive performance (AUC, training cohort: 0.949; validation cohort: 0.777). Radiomic features were constructed using 6 selected features from 1834 radiomic features extracted from CT scans via SVM machine algorithm. Simultaneously, a radiopathomics model was built using 17 non-zero coefficient features obtained through dimension reduction from a total of 2145 features (combining both radiomics and pathomics features). The best discriminative ability was observed in the SVM_radiopathomics model (AUC, training cohort: 0.953; validation cohort: 0.851), and clinical decision curve analysis (DCA) demonstrated excellent clinical utility. CONCLUSION: The radiopathomics model, combining pathological and radiomic features, exhibited superior performance in distinguishing between stage I-II and stage III gastric cancer. This study is based on the prediction of pathological staging using pathological tissue slides from surgical specimens after gastric cancer curative surgery and preoperative CT images, highlighting the feasibility of conducting research on pathological staging using pathological slides and CT images.

Skeletal Editing of Benzene Motif: Photopromoted Transannulation for Synthesis of DNA-Encoded Seven-Membered Rings.

In this report, we present a photopromoted, metal-free transannulation of phenyl azides for the synthesis of DNA-encoded seven-membered rings. The transformation is efficiently achieved through a skeletal editing strategy targeting the benzene motif coupled with a Reversible Adsorption to Solid Support (RASS) strategy. A variety of valuable DNA-encoded seven-membered ring compounds, including DNA-encoded 3H-azepines, azepinones, and unnatural amino acids, are now accessible. Crucially, this DNA-compatible protocol can also be applied for the introduction of complex molecules, as exemplified by Lorcaserin and Betahistine. The selective conversion of readily available phenyl rings into high-value seven-membered rings offers a promising avenue for the construction of diversified and drug-like DNA-encoded library.

Bridging and Bonding Social Capital by Analyzing the Demographics, User Activities, and Social Network Dynamics of Sexual Assault Centers on Twitter: Mixed Methods Study.

BACKGROUND: Social media platforms have gained popularity as communication tools for organizations to engage with clients and the public, disseminate information, and raise awareness about social issues. From a social capital perspective, relationship building is seen as an investment, involving a complex interplay of tangible and intangible resources. Social media-based social capital signifies the diverse social networks that organizations can foster through their engagement on social media platforms. Literature underscores the great significance of further investigation into the scope and nature of social media use, particularly within sectors dedicated to service delivery, such as sexual assault organizations. OBJECTIVE: This study aims to fill a research gap by investigating the use of Twitter by sexual assault support agencies in Canada. It seeks to understand the demographics, user activities, and social network structure within these organizations on Twitter, focusing on building social capital. The research questions explore the demographic profile, geographic distribution, and Twitter activity of these organizations as well as the social network dynamics of bridging and bonding social capital. METHODS: This study used purposive sampling to investigate sexual assault centers in Canada with active Twitter accounts, resulting in the identification of 124 centers. The Twitter handles were collected, yielding 113 unique handles, and their corresponding Twitter IDs were obtained and validated. A total of 294,350 tweets were collected from these centers, covering >93.54% of their Twitter activity. Preprocessing was conducted to prepare the data, and descriptive analysis was used to determine the center demographics and age. Furthermore, geolocation mapping was performed to visualize the center locations. Social network analysis was used to explore the intricate relationships within the network of sexual assault center Twitter accounts, using various metrics to assess the network structure and connectivity dynamics. RESULTS: The results highlight the substantial presence of sexual assault organizations on Twitter, particularly in provinces such as Ontario, British Columbia, and Quebec, underscoring the importance of tailored engagement strategies considering regional disparities. The analysis of Twitter account creation years shows a peak in 2012, followed by a decline in new account creations in subsequent years. The monthly tweet activity shows November as the most active month, whereas July had the lowest activity. The study also reveals variations in Twitter activity, account creation patterns, and social network dynamics, identifying influential social queens and marginalized entities within the network. CONCLUSIONS: This study presents a comprehensive landscape of the demographics and activities of sexual assault centers in Canada on Twitter. This study suggests that future research should explore the long-term consequences of social media use and examine stakeholder perceptions, providing valuable insights to improve communication practices within the nonprofit human services sector and further the missions of these organizations.

Diabetes mellitus and Alzheimer's disease: Vacuolar adenosine triphosphatase as a potential link.

Globally, the incidence of diabetes mellitus (DM) and Alzheimer's disease (AD) is increasing year by year, causing a huge economic and social burden, and their pathogenesis and aetiology have been proven to have a certain correlation. In recent years, more and more studies have shown that vacuolar adenosine triphosphatases (v-ATPases) in eukaryotes, which are biomolecules regulating lysosomal acidification and glycolipid metabolism, play a key role in DM and AD. This article describes the role of v-ATPase in DM and AD, including its role in glycolysis, insulin secretion and insulin resistance (IR), as well as its relationship with lysosomal acidification, autophagy and ß-amyloid (Aß). In DM, v-ATPase is involved in the regulation of glucose metabolism and IR. v-ATPase is closely related to glycolysis. On the one hand, v-ATPase affects the rate of glycolysis by affecting the secretion of insulin and changing the activities of key glycolytic enzymes hexokinase (HK) and phosphofructokinase 1 (PFK-1). On the other hand, glucose is the main regulator of this enzyme, and the assembly and activity of v-ATPase depend on glucose, and glucose depletion will lead to its decomposition and inactivation. In addition, v-ATPase can also regulate free fatty acids, thereby improving IR. In AD, v-ATPase can not only improve the abnormal brain energy metabolism by affecting lysosomal acidification and autophagy but also change the deposition of Aß by affecting the production and degradation of Aß. Therefore, v-ATPase may be the bridge between DM and AD.

Construction of a Metal-Silica Interface for Semihydrogenation of Alkynes.

Fabricating optimum surface structures represents an attractive approach for synthesizing supported catalysts with high activity and specific selectivity. New active sites could be flexibly constructed via the strong metal-support interaction under the redox condition. Herein, we demonstrated the formation of a new Rh-Si surface on a silica-modified carbon nanotube supported Rh catalyst under the high-temperature reduction condition as well as a thin amorphous silica coating layer and weak chemisorption toward the CO molecule. The electronic interactions between Rh and Si, along with the particular structure, guarantee desirable catalytic performance for the semihydrogenation of phenylacetylene under mild conditions. This facile approach might be extensively used in constructing new active sites with robust activity and specific selectivity in diverse heterogeneous catalysis systems.

Spindle apparatus coiled-coil protein 1 (SPDL1) serves as a novel prognostic biomarker in triple-negative breast cancer.

BACKGROUND: Triple-negative breast cancer (TNBC) has a poor prognosis, an ineffective diagnosis, and a high degree of aggressiveness. Therefore, novel therapeutic targets for TNBC urgently need to be identified. METHODS: Through a series of bioinformatics analyses, including analysis of differential gene expression, protein-protein interaction (PPI) network, univariate cox regression, immune infiltration, pathway enrichment, etc, as well as auxiliary immunohistochemistry (IHC) and protein quantitativae analysis, to explore prognostic marker for TNBC. RESULTS: In TNBC tissues, we found that SPDL1 (CCDC99) was considerably overexpressed at both the mRNA and protein levels compared to that in normal and non-TNBC tissues. Additionally, we found that SPDL1-high expression was strongly linked to poor prognosis in TNBC patients. Excessive SPDL1 expression was positively correlated with tumor growth and strongly linked to the cell cycle, DNA replication, and the p53 signaling pathway. In addition, CIBERSORT analysis revealed that SPDL1 can affect the tumor immune microenvironment (TME) in TNBC, encourage the development of TNBC and act as a potential prognostic biomarker for TNBC. Patients with SPDL1-high expression were more sensitive to AZD8055. Notably, we discovered that SPDL1 is highly expressed in the majority of malignancies and may have an impact on the pancancer prognosis. CONCLUSIONS: SPDL1 can serve as a novel prognostic marker for TNBC and pancancer patients.

Supportive Interventions of Chinese Police in Domestic Violence: Do Officer Knowledge and Training Matter?

Policing domestic violence (DV) poses significant challenges in China due to cultural, legal, and organizational complexities. Policing DV in China favors mediation over assertive interventions, complicating law enforcement's role. While previous research has focused on coercive interventions by Chinese police, there is limited information on non-coercive, supportive approaches. This study investigates the relationship between police officers' knowledge and training regarding the Anti-DV law and their willingness to provide supportive services to DV victims in China. It also considers various individual and organizational factors. The data used in this study are derived from the Policing DV in China project, with a sample of 1,353 respondents who had experience dealing with DV cases within the past 3 years. The study focuses on three dependent variables representing supportive approaches to DV cases: Referral, Counseling, and Protection orders. Independent variables include officers' knowledge of the Anti-DV law and agency training. Control variables include the use of body-worn cameras (BWC) and attitudes toward Violence Tolerance, Male Dominance, and Gender Equality. Additionally, demographic variables, working environment, length of service, and police rank are considered. The analytical approach involves a three-step strategy, incorporating descriptive, bivariate analyses, and regression analyses. The results are interpreted using odds ratios and average marginal effects, and statistical software such as SPSS by IBM and R by Open-Source Model is utilized for data analysis. Key findings indicate that more than half of the officers referred intimate partner violence survivors to shelters and assisted victims in filing protection orders. Counseling practices varied across provinces and between male and female officers. Agency training and the use of BWC were positively associated with non-coercive and supportive approaches, while knowledge of the DV Act, male dominance score, and gender equality score did not predict the use of such approaches. Demographic characteristics, including police rank, length of service, and province of employment, influenced the utilization of non-coercive and supportive approaches. This study examines the challenges faced by Chinese police officers when responding to DV cases and their willingness to provide supportive interventions. The study highlights the complexities surrounding the initiation of protection orders due to officers' legal knowledge and discretion. The study emphasizes the importance of police support in addressing DV in China and the role of agency training in promoting non-coercive responses. It highlights regional variations in police support and underscores the need for addressing disparities in service provision across different provinces.

A study for quality evaluation of Lysimachiae herba from different origins based on fingerprint-activity relationship modeling and multi-component content determination.

ETHNOPHARMACOLOGICAL RELEVANCE: Lysimachiae Herba (LH), called Jinqiancao in Chinese, is a commonly used traditional Chinese medicine in clinical practice. Doctors in the Qing Dynasty recorded that it tastes bitter, sour, and slightly cold, and it belongs to the liver, gallbladder, kidney, and bladder meridians. It has the effects of removing dampness and jaundice, eliminating gallstones, and reducing blood stasis. Because of its potent pharmacological effects, it is extensively utilized in the treatment of hepatobiliary and urinary system stones, jaundice, hepatitis, and cholecystitis. Although LH is included in "Sichuan authentic Chinese herbal medicine records", the quality of it from different origins still lacks reliable evaluation methods, which is difficult to reflect the high quality of LH from Sichuan. AIMS OF THE STUDY: This study aimed to establish a fingerprint-activity relationship model between the fingerprint of LH and its protective effect on cholestatic liver injury, and to evaluate the quality of LH from Sichuan and Guizhou by multivariate statistical analysis. MATERIALS AND METHODS: 20 batches of LH samples were collected from Sichuan and Guizhou. Characteristic fingerprints of samples were established by UHPLC-Triple TOF-MS/MS and the chemical pattern recognition analysis was carried out by HCA. Then, a rat model of cholestatic liver injury was established by intragastric administration of ANIT. Combined with the common peak information of fingerprint and pharmacodynamic index results, GCA and BCA were used to screen the efficacy markers. Finally, based on UHPLC-QTRAP-MS/MS, the content of efficacy markers was simultaneously determined, and the overall quality of LH from two origins was evaluated by PCA and TOPSIS. RESULTS: In the fingerprint of 20 batches of LH, 15 common peaks were identified in the negative ion mode, and the similarity was between 0.887 and 0.981. Pharmacological results showed that, compared with the control group, the content of AST, ALT, ALP, TBA, TBIL, and MDA in serum increased, and the content of GSH and SOD activity decreased after 48 h of ANIT administration. In addition, compared to the model group, different doses of LH from the two origins could decrease the serum levels of AST, ALT, ALP, TBA, TBIL, and MDA, raise the levels of GSH and SOD activity, reduce the infiltration range of inflammatory cells, and improve the cholestatic liver injury in rats. Among them, the pharmacodynamic indices of the SCHD group were significantly better. GCA and BCA showed that a total of 7 constituents related to the efficacy were screened, which were proanthocyanidin B1, ferulic acid, hyperoside, astragalin, nicotiflorin, afzelin, and kaempferol. Besides, the content of 7 active constituents in samples from Sichuan was higher than that from Guizhou, indicating that the quality of samples from Sichuan may be better, consistent with the result of the pharmacological experiment. CONCLUSION: The quality and efficacy of LH from different origins were stable, and all of them had protective effects on cholestatic liver injury in rats. The method established in this study is accurate and reliable, and it can be used to comprehensively evaluate the internal quality of LH.

Emergence of Competing Orders and Possible Quantum Spin Liquid in SU(N) Fermions.

In the past few decades, tremendous efforts have been made toward understanding the exotic physics emerging from competition between various ordering tendencies in strongly correlated systems. Employing state-of-the-art quantum Monte Carlo simulation, we investigate an interacting SU(N) fermionic model with varying interaction strength and value of N, and we unveil the ground-state phase diagram of the model exhibiting a plethora of exotic phases. For small values of N-namely, N=2, 3-the ground state is an antiferromagnetic (AFM) phase, whereas in the large-N limit, a staggered valence bond solid (VBS) order is dominant. For intermediate values of N such as N=4, 5, remarkably, our study reveals that distinct VBS orders appear in the weak and strong coupling regimes. More fantastically, the competition between staggered and columnar VBS ordering tendencies gives rise to a Mott insulating phase without spontaneous symmetry breaking (SSB), existing in a large interacting parameter regime, which is consistent with a gapped quantum spin liquid. Our study not only provides a platform to investigate the fundamental physics of quantum many-body systems-it also offers a novel route toward searching for exotic states of matter such as quantum spin liquid in realistic quantum materials.

VARIDT 3.0: the phenotypic and regulatory variability of drug transporter.

The phenotypic and regulatory variability of drug transporter (DT) are vital for the understanding of drug responses, drug-drug interactions, multidrug resistances, and so on. The ADME property of a drug is collectively determined by multiple types of variability, such as: microbiota influence (MBI), transcriptional regulation (TSR), epigenetics regulation (EGR), exogenous modulation (EGM) and post-translational modification (PTM). However, no database has yet been available to comprehensively describe these valuable variabilities of DTs. In this study, a major update of VARIDT was therefore conducted, which gave 2072 MBIs, 10 610 TSRs, 46 748 EGRs, 12 209 EGMs and 10 255 PTMs. These variability data were closely related to the transportation of 585 approved and 301 clinical trial drugs for treating 572 diseases. Moreover, the majority of the DTs in this database were found with multiple variabilities, which allowed a collective consideration in determining the ADME properties of a drug. All in all, VARIDT 3.0 is expected to be a popular data repository that could become an essential complement to existing pharmaceutical databases, and is freely accessible without any login requirement at: https://idrblab.org/varidt/.

Self-harm and interpersonal violence due to high temperature from the global burden of disease study 2019: A 30-year assessment.

BACKGROUND: The impact of global warming on health due to climate change is increasingly studied, but the global burden of self-harm and interpersonal violence attributable to high temperature is still limited. This study aimed to systematically assess the burden of self-harm and interpersonal violence attributable to high temperature globally or by region and climate zone from 1990 to 2019. METHODS: We obtained the global, regional, and national deaths, disability-adjusted life years (DALYs), age-standardized mortality rates (ASMR), and age-standardized disability-adjusted life year rates (ASDR) of self-harm and interpersonal violence due to high temperature from 1990 to 2019 through the Global Burden of Disease Study (GBD) 2019. The burden of self-harm and interpersonal violence due to high temperature was estimated by age, sex, climate zone, the socio-demographic index (SDI), and the healthcare access and quality index (HAQ). Average annual percentage changes (AAPCs) in ASMR and ASDR were calculated for 1990-2019 using the Joinpoint model. RESULTS: From 1990 to 2019, the global deaths and DALYs related to self-harm and interpersonal violence due to high temperature increased from 20,002 (95% UI, 9243 to 41,928) and 1,107,216 (95% UI, 512,062 to 2,319,477) to 26,459 (95% UI, 13,574 to 47,265) and 1,382,487 (95% UI, 722,060 to 2,474,441), respectively. However, the ASMR and ASDR showed varying degrees of decreasing trends, with decreases of 13.36% and 12.66%, respectively. The ASMR was high and declining in low and low-middle SDI regions, particularly in tropical and subtropical regions. In addition, SDI and HAQ index were negatively correlated with ASMR in 204 countries and regions. CONCLUSIONS: The global burden of self-harm and interpersonal violence attributed to high temperature has decreased over the past 30 years, but the number of deaths and DALYs continues to rise. Climate change continues to make heat stress a significant risk factor for self-harm and interpersonal violence worldwide.

A comprehensive radiopathological nomogram for the prediction of pathological staging in gastric cancer using CT-derived and WSI-based features.

OBJECTIVE: This study aims to develop and validate an innovative radiopathomics model that combines radiomics and pathomics features to effectively differentiate between stages I-II and stage III gastric cancer (pathological staging). METHODS: Our study included 200 patients with well-defined stages of gastric cancer divided into a training cohort (n = 140) and a test cohort (n = 60). Radiomics features were extracted from contrast-enhanced CT images using PyRadiomics, while pathomics features were obtained from whole slide images of pathological specimens through a fine-tuned deep learning model (ResNet-18). After rigorous feature dimensionality reduction and selection, we constructed radiomics models (SVM_rad, LR_rad, and MLP_rad) and pathomics models (SVM_path, LR_path, and MLP_path) utilizing support vector machine (SVM), logistic regression (LR), and multilayer perceptron (MLP) algorithms. The optimal radiomics and pathomics models were chosen based on comprehensive evaluation criteria such as ROC curves, Hosmer‒Lemeshow tests, and calibration curve tests. Feature patterns extracted from the best-performing radiomics model (MLP_rad) and pathomics model (SVM_rad) were integrated to create a powerful radiopathomics nomogram. RESULTS: From a pool of 1834 radiomics features extracted from CT images, 14 were selected to construct radiomics models. Among these, the MLP_rad model exhibited the most robust predictive performance (AUC, training cohort: 0.843; test cohort: 0.797). Likewise, 10 pathomics features were chosen from 512 extracted from whole slide images to build pathomics models, with the SVM_path model demonstrating the highest predictive efficiency (AUC, training cohort: 0.937; test cohort: 0.792). The combined radiopathomics nomogram model exhibited optimal discriminative ability (AUC, training cohort: 0.951; test cohort: 0.837), as confirmed by decision curve analysis (DCA), which indicated superior clinical effectiveness. CONCLUSION: This study presents a cutting-edge radiopathomics nomogram model designed to predict pathological staging in gastric cancer, distinguishing between stages I-II and stage III. Our research leverages preoperative CT images and histopathological slides to forecast gastric cancer staging accurately, potentially facilitating the estimation of staging before radical gastric cancer surgery in the future.

Genomic data and ecological niche modeling reveal an unusually slow rate of molecular evolution in the Cretaceous Eupteleaceae.

Living fossils are evidence of long-term sustained ecological success. However, whether living fossils have little molecular changes remains poorly known, particularly in plants. Here, we have introduced a novel method that integrates phylogenomic, comparative genomic, and ecological niche modeling analyses to investigate the rate of molecular evolution of Eupteleaceae, a Cretaceous relict angiosperm family endemic to East Asia. We assembled a high-quality chromosome-level nuclear genome, and the chloroplast and mitochondrial genomes of a member of Eupteleaceae (Euptelea pleiosperma). Our results show that Eupteleaceae is most basal in Ranunculales, the earliest-diverging order in eudicots, and shares an ancient whole-genome duplication event with the other Ranunculales. We document that Eupteleaceae has the slowest rate of molecular changes in the observed angiosperms. The unusually low rate of molecular evolution of Eupteleaceae across all three independent inherited genomes and genes within each of the three genomes is in association with its conserved genome architecture, ancestral woody habit, and conserved niche requirements. Our findings reveal the evolution and adaptation of living fossil plants through large-scale environmental change and also provide new insights into early eudicot diversification.