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OBJECTIVES: Although the incidence of isolated gastric varices type 1 (IGV1) bleeding is low, the condition is highly dangerous and associated with high mortality, making its treatment challenging. We aimed to compare the safety and efficacy of endoscopic clipping combined with cyanoacrylate injection (EC-CYA) vs. transjugular intrahepatic portosystemic shunt (TIPS) in treating IGV1. METHODS: In a single-center, randomized controlled trial, patients with IGV1 bleeding were randomly assigned to the EC-CYA group or TIPS group. The primary end-points were gastric variceal rebleeding rates and technical success. Secondary end-points included cumulative nonbleeding rates, mortality, and complications. RESULTS: A total of 156 patients between January 2019 and April 2023 were selected and randomly assigned to the EC-CYA group (n = 76) and TIPS group (n = 80). The technical success rate was 100% for both groups. The rebleeding rates were 14.5% in the EC-CYA group and 8.8% in the TIPS group, showing no significant difference (P = 0.263). Kaplan-Meier analysis revealed that the cumulative nonbleeding rates at 6, 12, 24, and 36 months for the two groups lacked statistical significance (P = 0.344). Similarly, cumulative survival rates at 12, 24, and 36 months for the two groups were not statistically significant (P = 0.916). The bleeding rates from other causes were 13.2% and 6.3% for the respective groups, showing no significant difference (P = 0.144). No instances of ectopic embolism were observed in either group. The incidence of hepatic encephalopathy (HE) in the TIPS group was statistically higher than that in the EC-CYA group (P = 0.001). CONCLUSION: Both groups are effective in controlling IGV1 bleeding. Notably, EC-CYA did not result in ectopic embolism, and the incidence of HE was lower than that observed with TIPS.

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BACKGROUND: Insomnia disorder (ID) is one of the most common sleep problems, usually accompanied by anxiety and depression symptoms. Functional magnetic resonance imaging (fMRI) study suggests that both poor sleep quality and negative emotion are linked to the dysregulation of brain network related to emotion processing in ID patients. Acupuncture therapy has been proven effective in improving sleep quality and mood of ID patients, but the involved neurobiological mechanism remains unclear. We aimed to investigate the modulation effect of acupuncture on resting-state functional connectivity (rsFC) of the emotional network (EN) in patients experiencing insomnia. METHODS: A total of 30 healthy controls (HCs) and 60 ID patients were enrolled in this study. Sixty ID patients were randomly assigned to real and sham acupuncture groups and attended resting-state fMRI scans before and after 4 weeks of acupuncture treatment. HCs completed an MRI/fMRI scan at baseline. The rsFC values within EN were calculated, and Hamilton Anxiety Scale (HAMA), Hamilton Depression Scale (HAMD), Pittsburgh Sleep Quality Index (PSQI), Hyperarousal Scale (HAS), and actigraphy data were collected for clinical efficacy evaluation. RESULTS: Resting-state FC analysis showed abnormalities in rsFC centered on the thalamus and dorsolateral prefrontal cortex within EN of ID patients compared to HCs. After real acupuncture treatment, rsFC of the anterior cingulate cortex, hippocampus, and amygdala were increased compared with the sham acupuncture group (p < 0.05, FDR corrected). In real acupuncture group, the rsFC value was decreased between left amygdala and left thalamus after 4 weeks of treatment compared with baseline. A trend of correlation was found that the increased rsFC value between the right amygdala and left hippocampus was positively correlated with the decreased HAMA scores across all ID patients, and the decreased left amygdala rsFC value with the left thalamus was negatively correlated with the increased sleep efficiency in the real acupuncture group. CONCLUSION: Our findings showed that real acupuncture could produce a positive effect on modulating rsFC within network related to emotion processing in ID patients, which may illustrate the central mechanism underlying acupuncture for insomnia in improving sleep quality and emotion regulation. TRIAL REGISTRATION: http://www.chictr.org.cn ., ChiCTR1800015282, 20/03/2018.

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Background: Hypertensive heart disease (HHD) is a major global public health issue resulting from hypertension-induced end-organ damage. The aim of this study was to examine the global impact, risk factors, and age-period-cohort (APC) model of HHD from 1990 to 2019. Methods: Data from the 2019 Global Burden of Disease were used to assess age-adjusted HHD prevalence, disability-adjusted life years (DALYs), mortality rates, and contributions of HHD risk factors with 95% uncertainty intervals (UIs). APC models were used to analyze global age, period, and cohort mortality trends for HHD. Results: In 2019, 18.6 million prevalent HHD cases led to 1.16 million fatalities and 21.51 million DALYs. Age-adjusted rates were 233.8 (95%UI = 170.5-312.9) per 100,000 individuals for prevalence, 15.2 (11.2-16.7) for mortality, and 268.2 (204.6-298.1) for DALYs. Regionally, the Cook Islands (703.1), Jordan (561.6), and Kuwait (514.9) had the highest age-standardized incidence of HHD in 2019. There were significant increases in HHD prevalence in Andean Latin America (16.7%), western sub-Saharan Africa (5.6%), and eastern sub-Saharan Africa (4.6%). Mortality rate varied widely among countries. Risk factors like elevated systolic blood pressure and high body mass index significant influenced DALY rates, especially in females. The APC model revealed an association between mortality rates and age, with a decreasing mortality risk over time and improved survival rates for a later birth cohort. Conclusions: Despite the reduction in prevalence, HHD remains a significant public health issue, particularly in nations with low sociodemographic indices. To alleviate the impact of HHD, prevention efforts should concentrate on the management of hypertension, weight loss, and lifestyle improvement.

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INTRODUCTION: Alzheimer's disease (AD) is often misclassified in electronic health records (EHRs) when relying solely on diagnosis codes. This study aimed to develop a more accurate, computable phenotype (CP) for identifying AD patients using structured and unstructured EHR data. METHODS: We used EHRs from the University of Florida Health (UFHealth) system and created rule-based CPs iteratively through manual chart reviews. The CPs were then validated using data from the University of Texas Health Science Center at Houston (UTHealth) and the University of Minnesota (UMN). RESULTS: Our best-performing CP was "patient has at least 2 AD diagnoses and AD-related keywords in AD encounters," with an F1-score of 0.817 at UF, 0.961 at UTHealth, and 0.623 at UMN, respectively. DISCUSSION: We developed and validated rule-based CPs for AD identification with good performance, which will be crucial for studies that aim to use real-world data like EHRs. Highlights: Developed a computable phenotype (CP) to identify Alzheimer's disease (AD) patients using EHR data.Utilized both structured and unstructured EHR data to enhance CP accuracy.Achieved a high F1-score of 0.817 at UFHealth, and 0.961 and 0.623 at UTHealth and UMN.Validated the CP across different demographics, ensuring robustness and fairness.

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Hutchinson-Gilford Progeria syndrome (HGPS) is a lethal premature aging disorder caused by a de novo heterozygous mutation that leads to the accumulation of a splicing isoform of Lamin A termed progerin. Progerin expression deregulates the organization of the nuclear lamina and the epigenetic landscape. Progerin has also been observed to accumulate at low levels during normal aging in cardiovascular cells of adults that do not carry genetic mutations linked with HGPS. Therefore, the molecular mechanisms that lead to vascular dysfunction in HGPS may also play a role in vascular aging-associated diseases, such as myocardial infarction and stroke. Here, we show that HGPS patient-derived vascular smooth muscle cells (VSMCs) recapitulate HGPS molecular hallmarks. Transcriptional profiling revealed cardiovascular disease remodeling and reactive oxidative stress response activation in HGPS VSMCs. Proteomic analyses identified abnormal acetylation programs in HGPS VSMC replication fork complexes, resulting in reduced H4K16 acetylation. Analysis of acetylation kinetics revealed both upregulation of K16 deacetylation and downregulation of K16 acetylation. This correlates with abnormal accumulation of error-prone nonhomologous end joining (NHEJ) repair proteins on newly replicated chromatin. The knockdown of the histone acetyltransferase MOF recapitulates preferential engagement of NHEJ repair activity in control VSMCs. Additionally, we find that primary donor-derived coronary artery vascular smooth muscle cells from aged individuals show similar defects to HGPS VSMCs, including loss of H4K16 acetylation. Altogether, we provide insight into the molecular mechanisms underlying vascular complications associated with HGPS patients and normative aging.

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More than ever, scientific progress in cancer research hinges on our ability to combine datasets and extract meaningful interpretations to better understand diseases and ultimately inform the development of better treatments and diagnostic tools. To enable the successful sharing and use of big data, the NCI developed the Cancer Research Data Commons (CRDC), providing access to a large, comprehensive, and expanding collection of cancer data. The CRDC is a cloud-based data science infrastructure that eliminates the need for researchers to download and store large-scale datasets by allowing them to perform analysis where data reside. Over the past 10 years, the CRDC has made significant progress in providing access to data and tools along with training and outreach to support the cancer research community. In this review, we provide an overview of the history and the impact of the CRDC to date, lessons learned, and future plans to further promote data sharing, accessibility, interoperability, and reuse. See related articles by Brady et al., p. 1384, Wang et al., p. 1388, and Pot et al., p. 1396.

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INTRODUCTION: Alzheimer's Disease (AD) are often misclassified in electronic health records (EHRs) when relying solely on diagnostic codes. This study aims to develop a more accurate, computable phenotype (CP) for identifying AD patients by using both structured and unstructured EHR data. METHODS: We used EHRs from the University of Florida Health (UF Health) system and created rule-based CPs iteratively through manual chart reviews. The CPs were then validated using data from the University of Texas Health Science Center at Houston (UT Health) and the University of Minnesota (UMN). RESULTS: Our best-performing CP is " patient has at least 2 AD diagnoses and AD-related keywords " with an F1-score of 0.817 at UF, and 0.961 and 0.623 at UT Health and UMN, respectively. DISCUSSION: We developed and validated rule-based CPs for AD identification with good performance, crucial for studies that aim to use real-world data like EHRs.

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Liquid crystals are a vital component of modern photonics, and recent studies have demonstrated the exceptional sensing properties of stimuli-responsive cholesteric liquid crystals. However, existing cholesteric liquid crystal-based sensors often rely on the naked eye perceptibility of structural color or the measurement of wavelength changes by spectrometric tools, which limits their practical applications. Therefore, developing a platform that produces recognizable sensing signals is critical. In this study, we present a visual sensing platform based on geometric phase encoding of stimuli-responsive cholesteric liquid crystal polymers that generates real-time visual patterns, rather than frequency changes. To demonstrate this platform's effectiveness, we used a humidity-responsive cholesteric liquid crystal polymer film encoded with a q-plate pattern, which revealed that humidity causes a shape change in the vortex beam reflected from the encoded cholesteric liquid crystal polymers. Moreover, we developed a prototype platform towards remote humidity monitoring benefiting from the high directionality and long-range transmission properties of laser beams carrying orbital angular momentum. Our approach provides a novel sensing platform for cholesteric liquid crystals-based sensors that offers promising practical applications. The ability to generate recognizable sensing signals through visual patterns offers a new level of practicality in the sensing field with stimuli-responsive cholesteric liquid crystals. This platform might have significant implications for a broad readership and will be of interest to researchers working in the field of photonics and sensing technology.

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The benefits and harms of lung cancer screening (LCS) for patients in the real-world clinical setting have been argued. Recently, discriminative prediction modeling of lung cancer with stratified risk factors has been developed to investigate the real-world effectiveness of LCS from observational data. However, most of these studies were conducted at the population level that only measured the difference in the average outcome between groups. In this study, we built counterfactual prediction models for lung cancer risk and mortality and examined for individual patients whether LCS as a hypothetical intervention reduces lung cancer risk and subsequent mortality. We investigated traditional and deep learning (DL)-based causal methods that provide individualized treatment effect (ITE) at the patient level and evaluated them with a cohort from the OneFlorida+ Clinical Research Consortium. We further discussed and demonstrated that the ITE estimation model can be used to personalize clinical decision support for a broader population.

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BACKGROUND: Prognostic models of hospital-induced delirium, that include potential predisposing and precipitating factors, may be used to identify vulnerable patients and inform the implementation of tailored preventive interventions. It is recommended that, in prediction model development studies, candidate predictors are selected on the basis of existing knowledge, including knowledge from clinical practice. The purpose of this article is to describe the process of identifying and operationalizing candidate predictors of hospital-induced delirium for application in a prediction model development study using a practice-based approach. METHODS: This study is part of a larger, retrospective cohort study that is developing prognostic models of hospital-induced delirium for medical-surgical older adult patients using structured data from administrative and electronic health records. First, we conducted a review of the literature to identify clinical concepts that had been used as candidate predictors in prognostic model development-and-validation studies of hospital-induced delirium. Then, we consulted a multidisciplinary task force of nine members who independently judged whether each clinical concept was associated with hospital-induced delirium. Finally, we mapped the clinical concepts to the administrative and electronic health records and operationalized our candidate predictors. RESULTS: In the review of 34 studies, we identified 504 unique clinical concepts. Two-thirds of the clinical concepts (337/504) were used as candidate predictors only once. The most common clinical concepts included age (31/34), sex (29/34), and alcohol use (22/34). 96% of the clinical concepts (484/504) were judged to be associated with the development of hospital-induced delirium by at least two members of the task force. All of the task force members agreed that 47 or 9% of the 504 clinical concepts were associated with hospital-induced delirium. CONCLUSIONS: Heterogeneity among candidate predictors of hospital-induced delirium in the literature suggests a still evolving list of factors that contribute to the development of this complex phenomenon. We demonstrated a practice-based approach to variable selection for our model development study of hospital-induced delirium. Expert judgement of variables enabled us to categorize the variables based on the amount of agreement among the experts and plan for the development of different models, including an expert-model and data-driven model.

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OBJECTIVE: To observe the effects of Tiaoshen (regulating the spirit) acupuncture on cognitive function and sleep quality in patients with primary insomnia (PI). METHODS: Sixty patients with PI were randomly divided into an observation group (30 cases, 2 cases dropped off) and a control group (30 cases, 2 cases dropped off, 1 case was excluded). The patients in the observation group were treated with acupuncture at Baihui (GV 20), Shenting (GV 24), Sishencong (EX-HN 1), and bilateral Benshen (GB 13), Shenmen (HT 7), Neiguan (PC 6), Sanyinjiao (SP 6). The patients in the control group were treated with shallow needling at non-effective points. Each treatment was provided for 30 min, once every other day, 3 treatments per week for 4 weeks. The Montreal cognitive assessment (MoCA), digit span test (DST), trail making test (TMT)-A, Pittsburgh sleep quality index (PSQI), and fatigue scale-14 (FS-14) were used to assess cognitive function and sleep quality before and after treatment, as well as in follow-up of 4-week after treatment completion. Correlation analysis was conducted between the differences in PSQI scores and differences in MoCA scores before and after treatment in the observation group. RESULTS: Compared with before treatment, the total score, visuospatial and executive function score and delayed memory score of MoCA as well as DST backward score were increased (P<0.01), while TMT-A time, PSQI and FS-14 scores were significantly reduced (P<0.01) after treatment and in follow-up in the observation group. Compared with before treatment, the PSQI score in the control group was reduced (P<0.01, P<0.05). After treatment and in follow-up, the observation group had significantly higher total score, visuospatial and executive function score, delayed memory score of MoCA, and DST backward score compared to the control group (P<0.05, P<0.01). In the observation group, the TMT-A time was significantly shorter than that in the control group (P<0.05, P<0.01), and the PSQI and FS-14 scores were significantly lower than those in the control group (P<0.01). In the observation group, there was a negative correlation between the difference in PSQI scores (post-treatment minus pre-treatment) and the difference in MoCA scores (post-treatment minus pre-treatment) (r=-0.481, P<0.01). A similar negative correlation was found between the difference in PSQI scores (follow-up minus pre-treatment) and the difference in MoCA scores (follow-up minus pre-treatment) (r=-0.282, P<0.05). CONCLUSION: Tiaoshen acupuncture could improve cognitive function, enhance sleep quality, and alleviate daytime fatigue in patients with PI. The improvement in cognitive function in patients with PI is correlated with the improvement in sleep quality.

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The cholinergic neurons in the nucleus basalis of Meynert (NBM) are a key structure in cognition, the dysfunction of which is associated with various neurological disorders, especially dementias. However, the whole-brain neural connectivity to cholinergic neurons in the NBM remains to be further and comprehensively researched. Using virus-based, specific, retrograde, and anterograde tracing, we illustrated the monosynaptic inputs and axon projections of NBM cholinergic neurons in choline acetyltransferase (ChAT)-Cre transgenic mice. Our results showed that NBM cholinergic neurons received mainly inputs from the caudate putamen and the posterior limb of the anterior commissure in the subcortex. Moreover, the majority of cholinergic terminals from the NBM were observed in the cortex mantle, including the motor cortex, sensory cortex, and visual cortex. Interestingly, although NBM cholinergic neurons received input projections from the caudate putamen, interstitial nucleus of the posterior limb of the anterior commissure, and central amygdaloid nucleus, NBM cholinergic neurons sparsely sent axon projection to innervate these areas. Furthermore, primary motor cortex, secondary motor cortex, and primary somatosensory cortex received abundant inputs from the NBM but sent few outputs to the NBM. Taken together, our results reveal the detailed and specific connectivity of cholinergic neurons of the NBM and provide a neuroanatomic foundation for further studies to explore the important physiological functions of NBM cholinergic neurons.

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Background: Insomnia disorder (ID) seriously affects the quality of people's daily life, and acupuncture is an effective therapy for it. As an essential component of the upward activation system, the locus coeruleus (LC) plays a crucial role in sleep-wake regulation, its aberrant functional connectivity (FC) is found to be involved in ID. The purpose of this study was to explore the modulation effect of acupuncture on the resting state FC of LC in ID patients. Methods: 60 ID patients were recruited and randomly assigned to real acupuncture (RA) or sham acupuncture (SA) treatment. Resting-state functional magnetic resonance imaging (fMRI) data were collected before and after the treatment. With LC as the region of interest, the FC method was adopted to examine acupuncture-related modulation of intrinsic connectivity in ID patients. The Pittsburgh Sleep Quality Index (PSQI), Hyperarousal Scale (HAS), and actigraphy were used to assess sleep quality and cortical hyperarousal states. Associations between clinical outcomes and FC features were calculated using Pearson's correlation analysis. Results: The improvement in sleep quality and hyperarousal in the RA group was greater than that in the SA group. After treatment, the FC between the LC and left inferior frontal gyrus (IFG) decreased in the RA group. The FC between the LC and left insula and supramarginal gyrus (SMG) was higher in the RA group. The change of LC FC values with the SMG was negatively associated with the change in PSQI scores. Conclusion: Acupuncture can modulate FC between the LC and IFG, insular gyrus, and SMG. This may imply the potential mechanism of acupuncture treatment for insomnia.

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PURPOSE: Immunotherapy (IO) plus tyrosine kinase inhibitor (TKI) has become the first-line treatment for advanced renal cell carcinoma, despite the lack of prognostic biomarkers. Cyclin-dependent kinase 5 (CDK5) affects the tumor microenvironment, which may influence the efficacy of TKI+IO. MATERIALS AND METHODS: Two cohorts from our center (Zhongshan Metastatic Renal Cell Carcinoma [ZS-MRCC] cohort, Zhongshan High-risk Localized Renal Cell Carcinoma [ZS-HRRCC] cohort) and one cohort from a clinical trial (JAVELIN-101) were enrolled. The expression of CDK5 of each sample was determined by RNA sequencing. Immune infiltration and T cell function were evaluated by flow cytometry and immunohistochemistry. Response and progression-free survival (PFS) were set as primary endpoints. RESULTS: Patients of low CDK5 expression showed higher objective response rate (60.0% vs. 23.3%) and longer PFS in both cohorts (ZS-MRCC cohort, p=0.014; JAVELIN-101 cohort, p=0.040). CDK5 expression was enhanced in non-responders (p < 0.05). In the ZS-HRRCC cohort, CDK5 was associated with decreased tumor-infiltrating CD8+ T cells, which was proved by immunohistochemistry (p < 0.05) and flow cytometry (Spearman's ρ=-0.49, p < 0.001). In the high CDK5 subgroup, CD8+ T cells revealed a dysfunction phenotype with decreased granzyme B, and more regulatory T cells were identified. A predictive score was further constructed by random forest, involving CDK5 and T cell exhaustion features. The RFscore was also validated in both cohorts. By utilizing the model, more patients might be distinguished from the overall cohort. Additionally, only in the low RFscore did TKI+IO outperform TKI monotherapy. CONCLUSION: High-CDK5 expression was associated with immunosuppression and TKI+IO resistance. RFscore based on CDK5 may be utilized as a biomarker to determine the optimal treatment strategy.

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BACKGROUND: Prior studies on the association between asthma and cancer show inconsistent results. This study aimed to generate additional evidence on the association between asthma and cancer, both overall, and by cancer type, in the United States. METHOD: We conducted a retrospective cohort study using 2012-2020 electronic health records and claims data in the OneFlorida+ clinical research network. Our study population included a cohort of adult patients with asthma (n = 90,021) and a matching cohort of adult patients without asthma (n = 270,063). We built Cox proportional hazards models to examine the association between asthma diagnosis and subsequent cancer risk. RESULTS: Our results showed that asthma patients were more likely to develop cancer compared to patients without asthma in multivariable analysis (hazard ratio [HR] = 1.36, 99% confidence interval [CI] = 1.29-1.44). Elevated cancer risk was observed in asthma patients without (HR = 1.60; 99% CI: 1.50-1.71) or with (HR = 1.11; 99% CI: 1.03-1.21) inhaled steroid use. However, in analyses of specific cancer types, cancer risk was elevated for nine of 13 cancers in asthma patients without inhaled steroid use but only for two of 13 cancers in asthma patients with inhaled steroid use, suggesting a protective effect of inhaled steroid use on cancer. CONCLUSION: This is the first study to report a positive association between asthma and overall cancer risk in the US population. More in-depth studies using real-word data are needed to further explore the causal mechanisms of asthma on cancer risk.

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BACKGROUND: Cognitive tests and biomarkers are the key information to assess the severity and track the progression of Alzheimer's' disease (AD) and AD-related dementias (AD/ADRD), yet, both are often only documented in clinical narratives of patients' electronic health records (EHRs). In this work, we aim to (1) assess the documentation of cognitive tests and biomarkers in EHRs that can be used as real-world endpoints, and (2) identify, extract, and harmonize the different commonly used cognitive tests from clinical narratives using natural language processing (NLP) methods into categorical AD/ADRD severity. METHODS: We developed a rule-based NLP pipeline to extract the cognitive tests and biomarkers from clinical narratives in AD/ADRD patients' EHRs. We aggregated the extracted results to the patient level and harmonized the cognitive test scores into severity categories using cutoffs determined based on both relevant literature and domain knowledge of AD/ADRD clinicians. RESULTS: We identified an AD/ADRD cohort of 48,912 patients from the University of Florida (UF) Health system and identified 7 measurements (6 cognitive tests and 1 biomarker) that are frequently documented in our data. Our NLP pipeline achieved an overall F1-score of 0.9059 across the 7 measurements. Among the 6 cognitive tests, we were able to harmonize 4 cognitive test scores into severity categories, and the population characteristics of patients with different severity were described. We also identified several factors related to the availability of their documentation in EHRs. CONCLUSION: This study demonstrates that our NLP pipelines can extract cognitive tests and biomarkers of AD/ADRD accurately for downstream studies. Although, the documentation of cognitive tests and biomarkers in EHRs appears to be low, RWD is still an important resource for AD/ADRD research. Nevertheless, providing standardized approach to document cognitive tests and biomarkers in EHRS are also warranted.

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Immunotherapy plus tyrosine kinase inhibitor (IO-TKI) has become the standard first-line therapy for advanced renal cell carcinoma (RCC). However, the modest response rate of IO-TKI therapy and the absence of biomarkers limited the selection of treatment strategies for RCC patients. There were three cohorts enrolled: two from our facility (ZS-MRCC and ZS-HRRCC) and one from a clinical study (JAVELIN-101). By RNA sequencing, the expression of ADAM9 in each sample was measured. By flow cytometry and immunohistochemistry, immune infiltration and T cell function were examined. Primary outcomes were established as treatment response and progression-free survival (PFS). Patients with low-ADAM9 expression had a higher objective response rate (56.5% vs 13.6%, P = 0.01) and longer PFS in both cohorts. In the ZS-HRRCC cohort, the expression of ADAM9 was associated with increased tumor-infiltrating T cells, which was proved by immunohistochemistry (P < 0.05) and flow cytometry (Spearman's ρ = 0.42, P < 0.001). In the high-ADAM9 group, CD8+ and CD4+ T cells revealed an exhausted phenotype with decreased GZMB (Spearman's ρ = - 0.31, P = 0.05, and Spearman's ρ = - 0.49, P < 0.001, respectively), and fewer Macrophages were identified. A predictive RFscore was further constructed by random forest approach, involving ADAM9 and immunologic genes. Only in the subgroup with the lower RFscore did IO-TKI outperform TKI monotherapy. High-ADAM9 expression was associated with immunosuppression and IO-TKI resistance. Expression of ADAM9 was also associated with the exhaustion and dysfunction of T cells. ADAM9-based RFscore has the potential to be used as a biomarker to distinguish the optimal patient treatment methods between IO-TKI and TKI monotherapy.

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Alzheimer's disease (AD) and AD-related dementias (AD/ADRD) are a group of progressive neurodegenerative diseases. The progression of AD can be conceptualized as a continuum in which patients progress from normal cognition to preclinical AD (i.e., no symptoms but biological changes in the brain) to mild cognitive impairment (MCI) due to AD (i.e., mild symptoms but not interfere with daily activities), followed by increasing severity of dementia due to AD. Early detection and prediction models for the transition of MCI to AD/ADRD are needed, and efforts have been made to build predictions of MCI conversion to AD/ADRD. However, most existing studies developing such prediction models did not consider the competing risks of death, which may result in biased risk estimates. In this study, we aim to develop a prediction model for AD/ADRD among patients with MCI considering the competing risks of death using a semi-competing risk approach.

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The use of phase change materials (PCMs) in concrete is a double-edged sword that improves the thermal inertia but degrades the mechanical properties of concrete. It has been an essential but unsolved issue to enhance the thermal capacity of PCMs while non-decreasing their mechanical strength. To this end, this work designs a novel 3D printing phase change aggregate to prepare concrete with prominent thermal capacity and ductility. The work investigated the effects of 3D printing phase change aggregate on the compressive strength and splitting tensile strength of concrete. The compressive strength of phase change aggregate concrete is 21.18 MPa, but the ductility of concrete improves. The splitting tensile strength was 1.45 MPa. The peak strain is 11.69 × 10-3, nearly 13 times that of basalt aggregate concrete. Moreover, using 3D printing phase change aggregate reduced concrete's early peak hydration temperature by 7.1%. The thermal insulation capacity of the experiment cube model with phase change concrete has been improved. The results show that the novel 3D printing change aggregate concrete has good mechanical properties and latent heat storage, providing a guideline for applying PCMs in building materials.