Enhancing m6A modification of lncRNA through METTL3 and RBM15 to promote malignant progression in bladder cancer.

Objective: Bladder cancer is one of the most prominent malignancies affecting the urinary tract, characterized by a poor prognosis. Our previous research has underscored the pivotal role of m6A methylation in the progression of bladder cancer. Nevertheless, the precise relationship between N6-methyladenosine (m6A) regulation of long non-coding RNA (lncRNA) and bladder cancer remains elusive. Methods: This study harnessed sequencing data and clinical records from 408 bladder cancer patients in the TCGA database. Employing R software, we conducted bioinformatics analysis to establish an m6A-lncRNA co-expression network. Analyzing the differences between high and low-risk groups, particularly at the immunological level, and subsequently investigating the primary regulatory factors of these lncRNA, validating the findings through experiments, and exploring their specific cellular functions. Results: We identified 50 m6A-related lncRNA with prognostic significance through univariate Cox regression analysis. In parallel, we employed a LASSO-Cox regression model to pinpoint 11 lncRNA and calculate risk scores for bladder cancer patients. Based on the median risk score, patients were categorized into low-risk and high-risk groups. The high-risk cohort exhibited notably lower survival rates than their low-risk counterparts. Further analysis pointed to RBM15 and METTL3 as potential master regulators of these m6A-lncRNA. Experimental findings also shed light on the upregulated expression of METTlL3 and RBM15 in bladder cancer, where they contributed to the malignant progression of tumors. The experimental findings demonstrated a significant upregulation of METTL3 and RBM15 in bladder cancer specimens, implicating their contributory role in the oncogenic progression. Knockdown of METTL3 and RBM15 resulted in a marked attenuation of tumor cell proliferation, invasion, and migration, which was concomitant with a downregulation in the cellular m6A methylation status. Moreover, these results revealed that RBM15 and METTL3 function in a synergistic capacity, positing their involvement in cancer promotion via the upregulation of m6A modifications in long non-coding RNAs. Additionally, this study successfully developed an N-methyl-N-nitrosourea (MNU)-induced rat model of in situ bladder carcinoma, confirming the elevated expression of RBM15 and METTL3, which paralleled the overexpression of m6A-related- lncRNAs observed in bladder cancer cell lines. This congruence underscores the potential utility of these molecular markers in in vivo models that mirror human malignancies. Conclusion: This study not only offers novel molecular targets,but also enriches the research on m6A modification in bladder cancer, thereby facilitating its clinical translation.

Air pollution and diabetes mellitus: Association and validation in a desert area in China.

BACKGROUND: Despite the growing evidence pointing to the detrimental effects of air pollution on diabetes mellitus (DM), the relationship remains poorly explored, especially in desert-adjacent areas characterized by high aridity and pollution. METHODS: We conducted a cross-sectional study with health examination data from over 2.9 million adults in two regions situated in the southern part of the Taklamakan Desert, China. We assessed three-year average concentrations (2018-2020) of particulate matter (PM1, PM2.5, and PM10), carbon monoxide (CO), nitrogen dioxide (NO2), and sulfur dioxide (SO2) through a space-time extra-trees model. After adjusting for various covariates, we employed generalized linear mixed models to evaluate the association between exposure to air pollutants and DM. RESULTS: The odds ratios for DM associated with a 10 µg/m3 increase in PM1, PM2.5, PM10, CO, and NO2 were 1.898 (95% CI: 1.741, 2.070), 1.07 (95% CI: 1.053, 1.086), 1.013 (95% CI: 1.008, 1.018), 1.009 (95% CI: 1.007, 1.011), and 1.337 (95% CI: 1.234, 1.449), respectively. Notably, men, individuals aged ≥50 years, those with lower educational attainment, nonsmokers, and those not engaging in physical exercise displayed more susceptible to the adverse effects of air pollution. Multiple sensitivity analyses confirmed the stability of these findings. CONCLUSIONS: Our study provides robust evidence of a correlation between prolonged exposure to air pollution and the prevalence of DM among individuals living in the desert-adjacent areas. This research contributes to the expanding knowledge on the relationship between air pollution exposure and DM prevalence in desert-adjacent areas.

Exposure to ambient air pollution and metabolic dysfunction-associated fatty liver disease: Findings from over 2.7 million adults in Northwestern China.

Ambient air pollutants exposures may lead to aggravated Metabolic Dysfunction-Associated Fatty Liver Disease (MAFLD). However, there is still a scarcity of empirical studies that have rigorously estimated this association, especially in regions where air pollution is severe. To fill in the literature gap, we conducted a cross-sectional study involving 2711,207 adults living in five regions of southern Xinjiang Uyghur Autonomous Region in 2021. Using a Space-Time Extra-Trees model, we assessed the four-year (2017-2020) average concentrations of particulate matter with aerodynamic diameter ≤1 µm (PM1), particulate matter with aerodynamic diameter ≤2.5 µm (PM2.5), particulate matter with aerodynamic diameter ≤10 µm (PM10), ozone (O3), sulfur dioxide (SO2), and carbon monoxide (CO), and then assigned these values to the participants. Generalized linear mixed models were employed to examine the relationships between air pollutants and the prevalence of MAFLD, with adjustment for multiple confounding factors. The odds ratios and 95% confidence intervals of MAFLD were 2.002 (1.826-2.195), 1.133 (1.108-1.157), 1.034 (1.027-1.040), 1.077 (1.023-1.134), 2.703 (2.322-3.146) and 1.033 (1.029-1.036) per 10 µg/m3 increase in the 4-year average PM1, PM2.5, PM10, O3, SO2 and CO exposures, respectively. The robustness of the findings was confirmed by a series of sensitivities. In summary, long-term exposure to ambient air pollutants was associated with increased odds of MAFLD, particularly in males and individuals with unhealthy lifestyles.

m6A modification mediates SLC3A2/SLC7A5 translation in 3-methylcholanthrene-induced uroepithelial transformation.

3-Methylcholanthracene (3-MC) is one of the most carcinogenic polycyclic aromatic hydrocarbons (PAHs). Long-term exposure to PAHs has been thought of as an important factor in urothelial tumorigenesis. N6-methyladenosine (m6A) exists widely in eukaryotic organisms and regulates the expression level of specific genes by regulating mRNA stability, translation efficiency, and nuclear export efficiency. Currently, the potential molecular mechanisms that regulate m6A modification for 3-MC carcinogenesis remain unclear. Here, we profiled mRNA, m6A, translation and protein level using "-omics" methodologies, including transcriptomes, m6A profile, translatomes, and proteomics in 3-MC-transformed urothelial cells and control cells. The key molecules SLC3A2/SLC7A5 were screened and identified in 3-MC-induced uroepithelial transformation. Moreover, SLC7A5/SLC3A2 promoted uroepithelial cells malignant phenotype in vitro and in vivo. Mechanically, METTL3 and ALKBH5 mediated m6A modification of SLC3A2/SLC7A5 mRNA in 3-MC-induced uroepithelial transformation by upregulating the translation of SLC3A2/SLC7A5. Furthermore, programmable m6A modification of SLC3A2/SLC7A5 mRNA affected the expression of its proteins. Taken together, our results revealed that the m6A modification-mediated SLC3A2/SLC7A5 translation promoted 3-MC-induced uroepithelial transformation, suggesting that targeting m6A modification of SLC3A2/SLC7A5 may be a potential therapeutic strategy for bladder cancer related to PAHs.

Severer air pollution, poorer cognitive function: Findings from 176,345 elders in Northwestern China.

BACKGROUND: Limited evidence exists regarding the link between air pollution exposure and cognitive function in developing countries, particularly in areas with abundant natural sources of particulate matter. OBJECTIVES: To investigate this association in a large representative sample of the elderly in northwestern China. METHODS: We performed a cross-sectional study among 176,345 participants aged 60-100 years in northwestern China in 2020. A satellite-based spatiotemporal model was applied to assess three-year annual averages of particulate matter with an aerodynamic diameter ≤ 2.5 µm (PM2.5), ≤ 10 µm (PM10), sulfur dioxide (SO2), nitrogen dioxide (NO2), carbon monoxide (CO), and ozone (O3) at residential address. Poor cognitive function was assessed using the Mini-Mental State Examination (MMSE). Generalized linear mixed models were used to assess associations. RESULTS: Compared with participants with the lowest quartiles of PM2.5, PM10, and O3 levels, those with the second, third, and highest quartiles of air pollutants consistently showed increased odds of poor cognitive function and decreased MMSE scores. The odds ratios of poor cognitive function associated with a 10 µg/m3 increment in PM2.5, PM10, and O3 were 1.26 (95 % confidence interval [CI]: 1.17, 1.36), 1.06 (95 %CI: 1.04, 1.08), and 2.76 (95 %CI: 2.11, 3.62), respectively. Subgroup analyses suggested stronger associations between air pollution exposures and poor cognitive function among participants who were younger, were non-Uyghur and were physically active. CONCLUSION: Long-term exposures to PM2.5, PM10 and O3 were associated with poor cognitive function in elders. Our results suggest that reducing air pollution may alleviate the burden of poor cognitive function in the elderly.

Targeted m6A demethylation of ITGA6 mRNA by a multisite dCasRx-m6A editor inhibits bladder cancer development.

INTRODUCTION: N6-methyladenosine (m6A) modification contributes to the pathogenesis and development of various cancers, including bladder cancer (BCa). In particular, integrin α6 (ITGA6) promotes BCa progression by cooperatively regulating multisite m6A modification. However, the therapeutic effect of targeting ITGA6 multisite m6A modifications in BCa remains unknown. OBJECTIVES: We aim to develop a multisite dCasRx- m6A editor for assessing the effects of the multisite dCasRx-m6A editor targeted m6A demethylation of ITGA6 mRNA in BC growth and progression. METHODS: The multisite dCasRx- m6A editor was generated by cloning. m6A-methylated RNA immunoprecipitation (meRIP), luciferase reporter, a single-base T3 ligase-based qPCR-amplification, Polysome profiling and meRIP-seq experiments were performed to determine the targeting specificity of the multisite dCasRx-m6A editor. We performed cell phenotype analysis and used in vivo mouse xenograft models to assess the effects of the multisite dCasRx-m6A editor in BC growth and progression. RESULTS: We designed a targeted ITGA6 multi-locus guide (g)RNA and established a bidirectional deactivated RfxCas13d (dCasRx)-based m6A-editing platform, comprising a nucleus-localized dCasRx fused with the catalytic domains of methyltransferase-like 3 (METTL3-CD) or α-ketoglutarate-dependent dioxygenase alkB homolog 5 (ALKBH5-CD), to simultaneously manipulate the methylation of ITGA6 mRNA at four m6A sites. The results confirmed the dCasRx-m6A editor modified m6A at multiple sites in ITGA6 mRNA, with low off-target effects. Moreover, targeted m6A demethylation of ITGA6 mRNA by the multisite dCasRx-m6A editor significantly reduced BCa cell proliferation and migration in vitro and in vivo. Furthermore, the dCasRx-ALKBH5-CD and ITGA6 multi-site gRNA delivered to 5-week-old BALB/cJNju-Foxn1nu/Nju nude mice via adeno-associated viral vectors significantly inhibited BCa cell growth. CONCLUSION: Our study proposes a novel therapeutic tool for the treatment of BC by applying the multisite dCasRx-m6A editor while highlighting its potential efficacy for treating other diseases associated with abnormal m6A modifications.

Exposure to ambient air pollution and chronic bronchitis: Findings from over 6.6 million adults in northwestern China.

BACKGROUND: Ambient air pollution increases the risk of respiratory mortality and morbidity, but evidence concerning effects of air pollution on chronic bronchitis (CB) is scarce. This study aimed to evaluate the associations of a set of air pollutants with the burden of CB, and to explore potential modifiers on the associations. METHODS: In 2020, a total of 6,556,440 adults living in the Northwestern region of China were recruited. The Space-Time Extra-Trees model was employed to assess the annual average concentrations of six air pollutants for the three years (2017-2019) before 2020 , and subsequently allocated to the participants based on the latitude and longitude of their home addresses. We investigated the associations between the levels of various air pollutants and the odds of CB using generalized linear mixed models, and conducted multiple sensitivity analyses and subgroup analyses. RESULTS: The odds of CB displays an approximately linear association with particulate matter with aerodynamic diameter ≤2.5 µm (PM2.5), particulate matter with aerodynamic diameter ≤10 µm (PM10), while it shows a non-linear relationship with gaseous pollutants. In the adjusted model, the odds ratios and 95% confidence intervals for CB per 10 µg/m3 increase in PM2.5, PM10, and sulfur dioxide (SO2) were 1.297 (1.262-1.332), 1.072 (1.064-1.080), and 2.587 (2.186-3.063), respectively. Furthermore, several additional sensitivity analyses demonstrated the stability of these associations. Subgroup analyses found that the aforementioned associations were greater among participants aged below 50 years old and those who smoked and had no leisure time exercise. CONCLUSION: Long-term exposure to ambient air pollutants may increase the odds of CB, especially among younger people and those with unhealthy lifestyles.

Investigation of the Relationship between Permanent Deformation and Dynamic Modulus Performance for Bearing-Layer Asphalt Mixture.

Of major concern is the lack of correlation between the material design and structural function of asphalt pavement in China. The objective of this paper is to identify the layer in asphalt pavement where permanent deformation occurs most seriously and to propose a control index for that layer's asphalt mixture. The permanent deformation of each layer was determined through the utilization of thickness measurements obtained from field cores. The results indicate that the reduction in thickness is more significant in the driving lane than in the ridge band and shoulder. This phenomenon can be attributed to the intensified densification and shearing deformation that arise from the combined impacts of recurrent axle loads and high temperatures. Compared to surface and base layers, the bearing layer is the primary area of concern for permanent deformation in asphalt pavement. Therefore, it is imperative to incorporate the ability of bearing-layer asphalt mixture to withstand permanent deformation as a crucial design parameter. The dynamic modulus of the bearing-layer asphalt mixture is significantly influenced by the type of asphalt, gradation, and asphalt content, compared to other design parameters. Based on the relationship established between dynamic modulus and dynamic stability, with creep rate as the intermediate term, a control standard was proposed to evaluate the permanent deformation of the bearing-layer asphalt mixture. This study can provide reasonable and effective guidance for prolonging pavement life and improving pavement performance.

FGSI: distant supervision for relation extraction method based on fine-grained semantic information.

Relation extraction is one of the important steps in building a knowledge graph. Its main objective is to extract semantic relationships from identified entity pairs in sentences, playing a crucial role in semantic understanding and knowledge graph construction. Remote supervised relation extraction aligns knowledge bases with natural language texts and generates labeled data, which alleviates the burden of manually annotating datasets. However, the labeled corpus obtained from remote supervision contains a large amount of noisy data, which greatly affects the training of relation extraction models. In this paper, we propose the hypothesis that key semantic information within the sentence plays a crucial role in entity relation extraction in the task of remote supervised relation extraction. Based on this hypothesis, we divide the sentence into three segments by splitting it according to the positions of entities, starting from within the sentence. Then, using intra-sentence attention mechanisms, we identify fine-grained semantic features within the sentence to reduce the interference of irrelevant noise information. We also improved the intra-bag attention mechanism by setting a threshold gate to filter out low-relevant noisy sentences, minimizing the impact of noise on the relation extraction model, and making full use of available positive semantic information. Experimental results show that the proposed relation extraction model in this paper achieves improvements in precision-recall curve, P@N value, and AUC value compared to existing methods, demonstrating the effectiveness of this model.

Age-related decline of online visuomotor adaptation: a combined effect of deteriorations of motor anticipation and execution.

The literature has established that the capability of visuomotor adaptation decreases with aging. However, the underlying mechanisms of this decline are yet to be fully understood. The current study addressed this issue by examining how aging affected visuomotor adaptation in a continuous manual tracking task with delayed visual feedback. To distinguish separate contributions of the declined capability of motor anticipation and deterioration of motor execution to this age-related decline, we recorded and analyzed participants' manual tracking performances and their eye movements during tracking. Twenty-nine older people and twenty-three young adults (control group) participated in this experiment. The results showed that the age-related decline of visuomotor adaptation was strongly linked to degraded performance in predictive pursuit eye movement, indicating that declined capability motor anticipation with aging had critical influences on the age-related decline of visuomotor adaptation. Additionally, deterioration of motor execution, measured by random error after controlling for the lag between target and cursor, was found to have an independent contribution to the decline of visuomotor adaptation. Taking these findings together, we see a picture that the age-related decline of visuomotor adaptation is a joint effect of the declined capability of motor anticipation and the deterioration of motor execution with aging.

Machine learning for predicting diabetes risk in western China adults.

OBJECTIVE: Diabetes mellitus is a global epidemic disease. Long-time exposure of patients to hyperglycemia can lead to various type of chronic tissue damage. Early diagnosis of and screening for diabetes are crucial to population health. METHODS: We collected the national physical examination data in Xinjiang, China, in 2020 (a total of more than 4 million people). Three types of physical examination indices were analyzed: questionnaire, routine physical examination and laboratory values. Integrated learning, deep learning and logistic regression methods were used to establish a risk model for type-2 diabetes mellitus. In addition, to improve the convenience and flexibility of the model, a diabetes risk score card was established based on logistic regression to assess the risk of the population. RESULTS: An XGBoost-based risk prediction model outperformed the other five risk assessment algorithms. The AUC of the model was 0.9122. Based on the feature importance ranking map, we found that hypertension, fasting blood glucose, age, coronary heart disease, ethnicity, parental diabetes mellitus, triglycerides, waist circumference, total cholesterol, and body mass index were the most important features of the risk prediction model for type-2 diabetes. CONCLUSIONS: This study established a diabetes risk assessment model based on multiple ethnicities, a large sample and many indices, and classified the diabetes risk of the population, thus providing a new forecast tool for the screening of patients and providing information on diabetes prevention for healthy populations.

TROP2 translation mediated by dual m6A/m7G RNA modifications promotes bladder cancer development.

RNA modifications, including adenine methylation (m6A) of mRNA and guanine methylation (m7G) of tRNA, are crucial for the biological function of RNA. However, the mechanism underlying the translation of specific genes synergistically mediated by dual m6A/m7G RNA modifications in bladder cancer (BCa) remains unclear. We demonstrated that m6A methyltransferase METTL3-mediated programmable m6A modification of oncogene trophoblast cell surface protein 2 (TROP2) mRNA promoted its translation during malignant transformation of bladder epithelial cells. m7G methyltransferase METTL1 enhanced TROP2 translation by mediating m7G modification of certain tRNAs. TROP2 protein inhibition decreased the proliferation and invasion of BCa cells in vitro and in vivo. Moreover, synergistical knockout of METTL3/METTL1 inhibited BCa cell proliferation, migration, and invasion; however, TROP2 overexpression partially abrogated its effect. Furthermore, TROP2 expression was significantly positively correlated with the expression levels of METTL3 and METTL1 in BCa patients. Overall, our results revealed that METTL3/METTL1-mediated dual m6A/m7G RNA modifications enhanced TROP2 translation and promoted BCa development, indicating a novel RNA epigenetic mechanism in BCa.

N6-methyladenosine mediates Nrf2 protein expression involved in PM2.5-induced pulmonary fibrosis.

It has been reported that particulate matter with an aerodynamic diameter of <2.5 µm (PM2.5) could induce epithelial-mesenchymal transition (EMT)- and extracellular matrix (ECM)-related pulmonary fibrosis (PF). The transcription factor Nrf2 alleviated PM2.5-induced PF by antagonizing oxidative stress. The N6-methyladenosine (m6A) modification plays a significant role in the stress response. However, the effect of m6A modification on the mechanisms of Nrf2-mediated defense against PM2.5-induced PF remained unknown. Here, we explored the role and the underlying molecular mechanisms of m6A methylation of Nrf2 mRNA in PM2.5-induced PF. We established filtered air (FA), unfiltered air (UA), and concentrated PM2.5 air (CA) group mice model and 0, 50, and 100 µg/mL PM2.5-treated 16HBE cell models. The extent of lung fibrosis in mice and fibrosis indicators were detected by histopathological analysis, immunohistochemical staining and western blotting. The molecular mechanism of m6A-modified Nrf2 was demonstrated by m6A-methylated RNA immunoprecipitation (MeRIP), RNA immunoprecipitation (RIP), qRT-PCR and T3 ligase-based PCR. Our data showed that PM2.5 exposure for 16 weeks could induce pulmonary fibrosis and activate Nrf2 signaling pathway. m6A methyltransferase METTL3 was upregulated after PM2.5 treatment in vivo and in vitro. Moreover, METTL3 mediated m6A modification of Nrf2 mRNA and promoted Nrf2 translation in mice and 16HBE cells after PM2.5 exposure. Mechanistically, three m6A-modified sites (1317, 1376 and 935; numbered relative to the first nucleotide of 3'UTR) of Nrf2 mRNA were identified in PM2.5-treatment 16HBE cells. Furthermore, the m6A binding proteins YTHDF1/IGF2BP1 promoted Nrf2 translation by binding to m6A residues of Nrf2 mRNA. Our results revealed the mechanism of m6A mediated Nrf2 signaling pathway against oxidative stress, which affected the development of PM2.5-induced PF.

Development and Validation of a Chinese Resilience Scale for Young Children.

(1) Background: Resilience research began in the child population as a validity scale to describe children's psychological wellbeing and ability to cope with negative events, and to some extent, to predict recovery and adaptation when they experience adversity again. In view of the important developmental implications of resilience in young children and the lack of a Chinese children's resilience scale, this study developed a resilience scale for young Chinese children based on a systematic review of existing international resilience scales and the characteristics of the Chinese cultural background. (2) Methods: The scale was developed by referring to existing scales, expert interviews, item collation and item finalization, developing original items, then deleting and determining items through item analysis, and finally, comparing with existing scales to obtain the internal and external validity of this scale. (3) Results: The results showed that the scale has good measurement properties, internal consistency reliability, and internal and external validity. (4) Conclusions: Through the development and validation of the Resilience Scale for young children in China, the scale can be used to measure the resilience of young children in China.

Design and development of a disease-specific clinical database system to increase the availability of hospital data in China.

Purpose: In order to meet restrictions and difficulties in the development of hospital medical informatization and clinical databases in China, in this study, a disease-specific clinical database system (DSCDS) was designed and built. It provides support for the full utilization of real world medical big data in clinical research and medical services for specific diseases. Methods: The development of DSCDS involved (1) requirements analysis on precision medicine, medical big data, and clinical research; (2) design schematics and basic architecture; (3) standard datasets of specific diseases consisting of common data elements (CDEs); (4) collection and aggregation of specific disease data scattered in various medical business systems of the hospital; (5) governance and quality improvement of specific disease data; (6) data storage and computing; and (7) design of data application modules. Results: A DSCDS for liver cirrhosis was created in the gastrointestinal department of a 3A grade hospital in China and had more than nine data application modules. Based on this DSCDS, a series of clinical studies are being carried out, such as retrospective or prospective cohorts, prognostic studies using multimodal data, and follow-up studies. Conclusion: The development of the DSCDS for liver cirrhosis in this paper provides experience and reference for the design and development of DSCDSs for other specific diseases in China; it can even expand to the development of DSCDSs in other countries if they have the demand for DSCDS and the same or better medical informatization foundation. DSCDS has more accurate, standard, comprehensive, multimodal and usable data of specific diseases than the general clinical database system and clinical data repository (CDR) and provides a credible data foundation for medical research, clinical decision-making and improving the medical service quality of specific diseases. Supplementary Information: The online version contains supplementary material available at 10.1007/s13755-023-00211-4.

Latent profiles of psychological status among populations cumulatively exposed to a flood and the recurrence of the COVID-19 pandemic in China.

Henan Province in Central China was hit by unprecedented, rain-triggered floods in July 2021 and experienced a recurrence of the COVID-19 pandemic. The current study aims to identify the latent profiles of psychological status and acceptance of change among Henan residents who have been cumulatively exposed to these floods and the COVID-19 pandemic. A total of 977 participants were recruited. Latent profile analysis (LPA) was used to explore underlying patterns of psychological status (i.e., perceived risk of the COVID-19 pandemic, post-traumatic stress symptoms, anxiety and rumination) and acceptance of change. The predictors were evaluated with multinomial logistic regression. LPA identified four patterns of psychological status and acceptance of change: high distress/high acceptance (5.1%), moderate distress/moderate acceptance (20.1%), mild distress/mild acceptance (45.5%), and resilience (29.3%). The additive impact of the floods and COVID-19 pandemic and negative emotion during the floods were the risk factors, while flood coping efficacy, trust, and a closer psychological distance change were the protective factors. The present study therefore provides novel evidence on psychological status after both a natural disaster and a major public health event. The cumulative effects of the floods and the COVID-19 pandemic may have heightened the risk of post-disaster maladaptation. A complex relationship between psychological outcomes and acceptance of change was also found. The findings of this study thus provide a foundation for both disaster management and psychological assistance for particular groups.

Cross-domain few-shot learning based on pseudo-Siamese neural network.

Cross-domain few-shot learning is one of the research highlights in machine learning. The difficulty lies in the accuracy drop of cross-domain network learning on a single domain due to the differences between the domains. To alleviate the problem, according to the idea of contour cognition and the process of human recognition, we propose a few-shot learning method based on pseudo-Siamese convolution neural network. The original image and the sketch map are respectively sent to the branch network in the pre-training and meta-learning process. While maintaining the original image features, the contour features are separately extracted as branch for training at the same time to improve the accuracy and generalization of learning. We conduct cross-domain few-shot learning experiments and good results have been achieved using mini-ImageNet as source domain, EuroSAT and ChestX as the target domains. Also, the results are qualitatively analyzed using a heatmap to verify the feasibility of our method.

Psychometric Evaluation of the Cooper-Norcross Inventory of Preferences-Therapist Version.

The Cooper-Norcross Inventory of Preferences (C-NIP) is a commonly used and psychometrically validated measure of client preferences in therapy. However, the C-NIP version for therapists (C-NIP-T) has not yet been validated. This study aimed to develop a Chinese version of the C-NIP-T and test its factor structure, reliability, and concurrent validity. A national sample of 1,054 Chinese mental health professionals completed the C-NIP-T and provided relevant demographic information. Confirmatory factor analysis (CFA) and exploratory structural equation modeling (ESEM) were used to examine the factor structure of the C-NIP-T. ESEM provided stronger evidence than CFA for the hypothesized four-factor model. Internal consistency coefficients (Cronbach's α) of the four subscales ranged between .60 and .76. Full or partial scalar invariance was established across therapists' therapeutic orientations, gender, personal therapy, and clinical experience. There were significant differences in subscale scores among therapists who identified as cognitive/cognitive-behavioral, psychoanalytic/psychodynamic, and humanistic/client-centered, supporting the concurrent validity of the C-NIP-T. The C-NIP-T is a psychometrically sound measure that can be used to assess therapists' preferences about therapy.

Development and validation of prediction models for hypertension risks: A cross-sectional study based on 4,287,407 participants.

Objective: To develop an optimal screening model to identify the individuals with a high risk of hypertension in China by comparing tree-based machine learning models, such as classification and regression tree, random forest, adaboost with a decision tree, extreme gradient boosting decision tree, and other machine learning models like an artificial neural network, naive Bayes, and traditional logistic regression models. Methods: A total of 4,287,407 adults participating in the national physical examination were included in the study. Features were selected using the least absolute shrinkage and selection operator regression. The Borderline synthetic minority over-sampling technique was used for data balance. Non-laboratory and semi-laboratory analyses were carried out in combination with the selected features. The tree-based machine learning models, other machine learning models, and traditional logistic regression models were constructed to identify individuals with hypertension, respectively. Top features selected using the best algorithm and the corresponding variable importance score were visualized. Results: A total of 24 variables were finally included for analyses after the least absolute shrinkage and selection operator regression model. The sample size of hypertensive patients in the training set was expanded from 689,025 to 2,312,160 using the borderline synthetic minority over-sampling technique algorithm. The extreme gradient boosting decision tree algorithm showed the best results (area under the receiver operating characteristic curve of non-laboratory: 0.893 and area under the receiver operating characteristic curve of semi-laboratory: 0.894). This study found that age, systolic blood pressure, waist circumference, diastolic blood pressure, albumin, drinking frequency, electrocardiogram, ethnicity (uyghur, hui, and other), body mass index, sex (female), exercise frequency, diabetes mellitus, and total bilirubin are important factors reflecting hypertension. Besides, some algorithms included in the semi-laboratory analyses showed less improvement in the predictive performance compared to the non-laboratory analyses. Conclusion: Using multiple methods, a more significant prediction model can be built, which discovers risk factors and provides new insights into the prediction and prevention of hypertension.

Targeting 4-1BB for tumor immunotherapy from bench to bedside.

Immune dysfunction has been proposed as a factor that may contribute to disease progression. Emerging evidence suggests that immunotherapy aims to abolish cancer progression by modulating the balance of the tumor microenvironment. 4-1BB (also known as CD137 and TNFRS9), a member of tumor necrosis factor receptor superfamily, has been validated as an extremely attractive and promising target for immunotherapy due to the upregulated expression in the tumor environment and its involvement in tumor progression. More importantly, 4-1BB-based immunotherapy approaches have manifested powerful antitumor effects in clinical trials targeting 4-1BB alone or in combination with other immune checkpoints. In this review, we will summarize the structure and expression of 4-1BB and its ligand, discuss the role of 4-1BB in the microenvironment and tumor progression, and update the development of drugs targeting 4-1BB. The purpose of the review is to furnish a comprehensive overview of the potential of 4-1BB as an immunotherapeutic target and to discuss recent advances and prospects for 4-1BB in cancer therapy.