Endoscopic clipping combined with cyanoacrylate injection vs. transjugular intrahepatic portosystemic shunt in the treatment of isolated gastric variceal bleeding: Randomized controlled trial.

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.

Whole-brain neural connectivity to cholinergic neurons in the nucleus basalis of Meynert.

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.

Unfavorable immunotherapy plus tyrosine kinase inhibition outcome of metastatic renal cell carcinoma after radical nephrectomy with increased ADAM9 expression.

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.

Application of a practice-based approach in variable selection for a prediction model development study of hospital-induced delirium.

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.

Longitudinal trajectories of HIV care engagement since diagnosis among persons with HIV in the Florida Ryan White program.

HIV care engagement is a dynamic process. We employed group-based trajectory modeling to examine longitudinal patterns in care engagement among people who were newly diagnosed with HIV and enrolled in the Ryan White program in Florida (n = 9,755) between 2010 and 2015. Five trajectories were identified (47.9% "in care" with 1-2 care visit(s) per 6 months, 18.0% "frequent care" with 3 or more care visits per 6 months, 11.0% "re-engage", 11.0% "gradual drop out", 12.6% "early dropout") based on the number of care attendances (including outpatient/case management visits, viral load or CD4 test) for each six-month during the first five years since diagnosis. Relative to "in care", people in the "frequent care" trajectory were more likely to be Hispanic/Latino and older at HIV diagnosis, whereas people in the three suboptimal care retention trajectories were more likely to be younger. Area deprivation index, rurality, and county health rankings were also strongly associated with care trajectories. Individual- and community-level factors associated to the three suboptimal care retention trajectories, if confirmed to be causative and actionable, could be prioritized to improve HIV care engagement.

Distributed Quasi-Poisson regression algorithm for modeling multi-site count outcomes in distributed data networks.

BACKGROUND: Observational studies incorporating real-world data from multiple institutions facilitate study of rare outcomes or exposures and improve generalizability of results. Due to privacy concerns surrounding patient-level data sharing across institutions, methods for performing regression analyses distributively are desirable. Meta-analysis of institution-specific estimates is commonly used, but has been shown to produce biased estimates in certain settings. While distributed regression methods are increasingly available, methods for analyzing count outcomes are currently limited. Count data in practice are commonly subject to overdispersion, exhibiting greater variability than expected under a given statistical model. OBJECTIVE: We propose a novel computational method, a one-shot distributed algorithm for quasi-Poisson regression (ODAP), to distributively model count outcomes while accounting for overdispersion. METHODS: ODAP incorporates a surrogate likelihood approach to perform distributed quasi-Poisson regression without requiring patient-level data sharing, only requiring sharing of aggregate data from each participating institution. ODAP requires at most three rounds of non-iterative communication among institutions to generate coefficient estimates and corresponding standard errors. In simulations, we evaluate ODAP under several data scenarios possible in multi-site analyses, comparing ODAP and meta-analysis estimates in terms of error relative to pooled regression estimates, considered the gold standard. In a proof-of-concept real-world data analysis, we similarly compare ODAP and meta-analysis in terms of relative error to pooled estimatation using data from the OneFlorida Clinical Research Consortium, modeling length of stay in COVID-19 patients as a function of various patient characteristics. In a second proof-of-concept analysis, using the same outcome and covariates, we incorporate data from the UnitedHealth Group Clinical Discovery Database together with the OneFlorida data in a distributed analysis to compare estimates produced by ODAP and meta-analysis. RESULTS: In simulations, ODAP exhibited negligible error relative to pooled regression estimates across all settings explored. Meta-analysis estimates, while largely unbiased, were increasingly variable as heterogeneity in the outcome increased across institutions. When baseline expected count was 0.2, relative error for meta-analysis was above 5% in 25% of iterations (250/1000), while the largest relative error for ODAP in any iteration was 3.59%. In our proof-of-concept analysis using only OneFlorida data, ODAP estimates were closer to pooled regression estimates than those produced by meta-analysis for all 15 covariates. In our distributed analysis incorporating data from both OneFlorida and the UnitedHealth Group Clinical Discovery Database, ODAP and meta-analysis estimates were largely similar, while some differences in estimates (as large as 13.8%) could be indicative of bias in meta-analytic estimates. CONCLUSIONS: ODAP performs privacy-preserving, communication-efficient distributed quasi-Poisson regression to analyze count outcomes using data stored within multiple institutions. Our method produces estimates nearly matching pooled regression estimates and sometimes more accurate than meta-analysis estimates, most notably in settings with relatively low counts and high outcome heterogeneity across institutions.

Prodromal clinical, demographic, and socio-ecological correlates of asthma in adults: a 10-year statewide big data multi-domain analysis.

Objectives: To identify prodromal correlates of asthma as compared to chronic obstructive pulmonary disease and allied-conditions (COPDAC) using a multi domain analysis of socio-ecological, clinical, and demographic domains.Methods: This is a retrospective case-risk-control study using data from Florida's statewide Healthcare Cost and Utilization Project (HCUP). Patients were grouped into three groups: asthma, COPDAC (without asthma), and neither asthma nor COPDAC. To identify socio-ecological, clinical, demographic, and clinical predictors of asthma and COPDAC, we used univariate analysis, feature ranking by bootstrapped information gain ratio, multivariable logistic regression with LogitBoost selection, decision trees, and random forests.Results: A total of 141,729 patients met inclusion criteria, of whom 56,052 were diagnosed with asthma, 85,677 with COPDAC, and 84,737 with neither asthma nor COPDAC. The multi-domain approach proved superior in distinguishing asthma versus COPDAC and non-asthma/non-COPDAC controls (area under the curve (AUROC) 84%). The best domain to distinguish asthma from COPDAC without controls was prior clinical diagnoses (AUROC 82%). Ranking variables from all the domains found the most important predictors for the asthma versus COPDAC and controls were primarily socio-ecological variables, while for asthma versus COPDAC without controls, demographic and clinical variables such as age, CCI, and prior clinical diagnoses, scored better.Conclusions: In this large statewide study using a machine learning approach, we found that a multi-domain approach with demographics, clinical, and socio-ecological variables best predicted an asthma diagnosis. Future work should focus on integrating machine learning-generated predictive models into clinical practice to improve early detection of those common respiratory diseases.

Design and methodology challenges of environment-wide association studies: A systematic review.

Environment-wide association studies (EWAS) are an untargeted, agnostic, and hypothesis-generating approach to exploring environmental factors associated with health outcomes, akin to genome-wide association studies (GWAS). While design, methodology, and replicability standards for GWAS are established, EWAS pose many challenges. We systematically reviewed published literature on EWAS to categorize scope, impact, types of analytical approaches, and open challenges in designs and methodologies. The Web of Science and PubMed databases were searched through multiple queries to identify EWAS articles between January 2010 and December 2018, and a systematic review was conducted following the Preferred Reporting Item for Systematic Reviews and Meta-Analyses (PRISMA) reporting standard. Twenty-three articles met our inclusion criteria and were included. For each study, we categorized the data sources, the definitions of study outcomes, the sets of environmental variables, and the data engineering/analytical approaches, e.g. neighborhood definition, variable standardization, handling of multiple hypothesis testing, model selection, and validation. We identified limited exploitation of data sources, high heterogeneity in analytical approaches, and lack of replication. Despite of the promising utility of EWAS, further development of EWAS will require improved data sources, standardization of study designs, and rigorous testing of methodologies.

Risk of health morbidity for the uninsured: 10-year evidence from a large hospital center in Boston, Massachusetts.

OBJECTIVE: To investigate the independent contribution of insurance status toward the risk of diagnosis of specific clinical comorbidities for individuals admitted to intensive care unit (ICU). DESIGN: Retrospective analysis of secondary database. SETTING: Ten years of public de-identified ICU electronic medical records from a large hospital in USA. PARTICIPANTS: Patients (18-65 years old) who had private insurance or no insurance were extracted from the database. MAIN OUTCOME MEASURES: Independent association of insurance status (uninsured vs. privately insured) with the risk of diagnosis of specific clinical comorbidities. RESULTS: Among 14 268 (from 11 753 patients) admissions to ICU between 2001 and 2012, 96% of them were covered by private insurance. Patients with private insurance had higher proportion of females, married, White race, longer ICU stay and more procedures during stay, and fewer deaths. A lower CCI was observed in uninsured patients. At multivariable analysis, uninsured patients had higher odds of death and of admissions for accidental falls, substance or alcohol abuse. CONCLUSIONS: Patients with no insurance coverage were at higher risk of death and of admission for physical and substance-related injury. We did not observe a higher risk for acute life-threatening diseases such as myocardial infarction or kidney failure. The lower CCI observed in the uninsured may be explained by under diagnosis or voluntary withdrawal from coverage in the pre-Affordable Care Act era. Replication of findings is warranted in other populations, among those with government-subsidized insurance and in the procedure/prescription domains.

Development of a personalized diagnostic model for kidney stone disease tailored to acute care by integrating large clinical, demographics and laboratory data: the diagnostic acute care algorithm - kidney stones (DACA-KS).

BACKGROUND: Kidney stone (KS) disease has high, increasing prevalence in the United States and poses a massive economic burden. Diagnostics algorithms of KS only use a few variables with a limited sensitivity and specificity. In this study, we tested a big data approach to infer and validate a 'multi-domain' personalized diagnostic acute care algorithm for KS (DACA-KS), merging demographic, vital signs, clinical, and laboratory information. METHODS: We utilized a large, single-center database of patients admitted to acute care units in a large tertiary care hospital. Patients diagnosed with KS were compared to groups of patients with acute abdominal/flank/groin pain, genitourinary diseases, and other conditions. We analyzed multiple information domains (several thousands of variables) using a collection of statistical and machine learning models with feature selectors. We compared sensitivity, specificity and area under the receiver operating characteristic (AUROC) of our approach with the STONE score, using cross-validation. RESULTS: Thirty eight thousand five hundred and ninety-seven distinct adult patients were admitted to critical care between 2001 and 2012, of which 217 were diagnosed with KS, and 7446 with acute pain (non-KS). The multi-domain approach using logistic regression yielded an AUROC of 0.86 and a sensitivity/specificity of 0.81/0.82 in cross-validation. Increase in performance was obtained by fitting a super-learner, at the price of lower interpretability. We discussed in detail comorbidity and lab marker variables independently associated with KS (e.g. blood chloride, candidiasis, sleep disorders). CONCLUSIONS: Although external validation is warranted, DACA-KS could be integrated into electronic health systems; the algorithm has the potential used as an effective tool to help nurses and healthcare personnel during triage or clinicians making a diagnosis, streamlining patients' management in acute care.

Integrative genomics positions MKRN1 as a novel ribonucleoprotein within the embryonic stem cell gene regulatory network.

In embryonic stem cells (ESCs), gene regulatory networks (GRNs) coordinate gene expression to maintain ESC identity; however, the complete repertoire of factors regulating the ESC state is not fully understood. Our previous temporal microarray analysis of ESC commitment identified the E3 ubiquitin ligase protein Makorin-1 (MKRN1) as a potential novel component of the ESC GRN. Here, using multilayered systems-level analyses, we compiled a MKRN1-centered interactome in undifferentiated ESCs at the proteomic and ribonomic level. Proteomic analyses in undifferentiated ESCs revealed that MKRN1 associates with RNA-binding proteins, and ensuing RIP-chip analysis determined that MKRN1 associates with mRNAs encoding functionally related proteins including proteins that function during cellular stress. Subsequent biological validation identified MKRN1 as a novel stress granule-resident protein, although MKRN1 is not required for stress granule formation, or survival of unstressed ESCs. Thus, our unbiased systems-level analyses support a role for the E3 ligase MKRN1 as a ribonucleoprotein within the ESC GRN.

Nonclassical Phosphomolybdates with Different Degrees of Reduction: Syntheses and Structural and Photo/Electrocatalytic Properties.

Four nonclassical phosphomolybdates, formulated as (H2pytty)8[{Mn(H2pytty)(H2O)3}{Sr⊂P6Mo6(V) Mo12(VI)O73}]2·16H2O (1), [{Mn(H3pytty)(H2O)3}2{Sr⊂P6Mo4(V)Mo14(VI)O73}]·18H2O (2), (H3pytp) (H2pytty)2[{Fe(H2O)4}{Sr⊂P6Mo3(V)Mo15(VI)O73}]·5H2O (3), and (H2pytty)2[{Cd(H2O)4}{Cd(H2O)3 (H3pytty)}{Sr⊂P6Mo5(V)Mo13(VI)O73}]·9H2O (4) (pytty = 3-(pyrazin-2-yl)-5-(1H-1,2,4-triazol-3-yl)-1,2,4-triazolyl; pytp = 4'-(4″-pyridyl)-2,4':6',4″-terpyridine) were hydrothermally synthesized and fully characterized. The penta- and hexa-reduced basket clusters represent the highest reduced level of basket-based polyoxometalate so far. In addition, transition metal complexes as bridge units were introduced to basket system for the first time to induce rare two-dimensional inorganic-organic hybrid layer. The results indicate that reduced degrees of the basket clusters of compounds 1-4 can be tuned by altering the molar ratio of organic ligand pytty and Na2MoO4. Compounds 1-4 exhibit bifunctional electrocatalytic behaviors for oxidation of dopamine and reduction of H2O2. Electrocatalytic mechanism, chronoamperometric experiments and electrocatalytic stability are studied in detail. In addition, the compounds show highly efficient catalytic ability for the degradation of typical dyes under UV irradiation.

pH and ligand dependent assembly of Well-Dawson arsenomolybdate capped architectures.

Five Well-Dawson-type arsenomolybdates, formulated as [Cu(2,2'-bpy)2][{Cu(2,2'-bpy)}3{As2(V)Mo2(V)Mo16(VI)O62}]·4H2O (1), [H2(4,4'-bpy)]2.5[As(III)(As2(V)Mo2(V)Mo16(VI)O62)]·5H2O (2), (pyr)(imi)(Himi)3[As2(III)(As2(V)Mo3(V)Mo15(VI)O62)]·3H2O (3), [As3(III)(As2(V)Mo3(V)Mo15(VI)O62)]·4H2O (4), and (H2btp)3[As2(V)Mo18(VI)O62]·6H2O (5) (bpy = bipyridine, pyr = pyrazine, imi = imidazole, btp = 1,5-bis(triazol)pentane), have been hydrothermally synthesized and structurally characterized by the elemental analysis, TG, IR, UV-vis-NIR, XPS, XRD, and single-crystal X-ray diffraction. The structural analysis indicates that compounds 1-4 contain rare reduced Dawson {As2Mo18O62} (abbreviated as {As2Mo18}) anions as parent cluster unit, which are capped by a certain number of Cu(II) or As(III) species on different coordination positions via altering pH values and organic ligand of the reaction system. Compounds 1 and 2 are asymmetric tricopper and monoarsenate(III) capped assemble by three {Cu(bpy)}(2+) and a {AsO3} fragments, respectively. Compounds 3 and 4 are symmetric biarsenate(III) and triarsenate(III) capped cluster by four and six half occupancy {AsO3} units, respectively. Compound 5 is uncapped {As2Mo18} structures. Compounds 1-4 represent infrequent Dawson arsenomolybdate capped architectures, especially 2-4, as arsenate(III) capped Dawson-type assemblies are observed for the first time. Compounds 1-5 display good electrocatalytic activity on reduction of nitrite. Compounds 1, 2, 3, and 5 exhibit fluorescent properties in the solid state at room temperature. In addition, magnetic properties of 1-4 have been investigated in detail.

Impact of hypertensive heart disease, risk factors, and age-period-cohort models across 204 nations and regions from 1990 to 2019: a global perspective from the 2019 global burden of disease study.

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.

Developing the Meaning-Centered Program for Chinese Americans with Advanced Cancer: Applying Cultural Adaptation Frameworks.

Asian Americans have surpassed Hispanics as the fastest-growing racial/ethnic group in the United States and Chinese Americans are the largest Asian American subgroup. Cancer is the leading cause of death among Chinese Americans while heart disease remains the leading cause of death in the U.S. overall. Foreign-born immigrants are more likely to be diagnosed with advanced stage cancers than their native-born counterparts. Patients with advanced cancer have specific psychosocial needs, such as end-of-life concerns and existential distress. Meaning-Centered Psychotherapy (MCP), which enhances a sense of meaning in life to increase QOL, is among the most promising psychosocial treatments for advanced cancer patients, having demonstrated efficacy in several randomized controlled trials. Our preliminary qualitative work suggested a meaning-centered intervention was acceptable but required adaptation to ensure ecological validity among Chinese cancer patients. This paper presents the cultural and linguistic adaptation of Meaning-Centered Program for Chinese Americans with advanced cancer (MCP-Ch), which was informed by the Ecological Validity Model (EVM) and Psychotherapy Adaptation and Modification Framework (PAMF) for cultural adaptation of evidence-based interventions. Implementation considerations, which will be assessed in the next phase of the project, are also discussed. MCP-Ch is used as a case example to illustrate how to adapt culturally syntonic and sustainable evidence-based psychosocial interventions for racial/ethnic minority cancer populations.

Research trends and hotspots of acupuncture therapy for obesity from 2004 to 2023: A bibliometric analysis.

BACKGROUND: Acupuncture has been increasingly used in obese people, yet no bibliometric analysis of acupuncture studies for obesity exists. This bibliometric study analyzed research trends, cooperation, and knowledge dissemination on acupuncture for obesity over the past 20 years to inform future directions. OBJECTIVES: To investigate the characteristics, hotspots, and frontiers of global scientific output in acupuncture research for obesity over the past two decades. METHODS: We retrieved publications on acupuncture for obesity published from 2004 to 2023 from the Web of Science Core Collection. Using Citespace, VOSviewer, and Bibliometrix, we analyzed and visualized the publications, countries, institutions, authors, journals, and keywords from various angles. RESULTS: A total of 357 articles were retrieved. The results showed that the number of annual publications on acupuncture for obesity has increased over time. China had the most publications (217 articles, 60.87 %), with Chengdu University of Traditional Chinese Medicine (24 articles, 6.72 %) as the most prolific institution. Ergene Neyhan, Huang Wei, and Zhou Zhongyu were the most productive authors (9 articles), and the article published by Cho SH in 2009 was the most cited article (60 citations). The most published journal was Evidence-based Complementary and Alternative Medicine, while the Journal of American Journal of Chinese Medicine was the most frequently cited. Recently, "electroacupuncture" "insulin resistance" and "systematic review" appeared frequently. The research hotspots and trends of acupuncture for obesity mainly focus on interventions, hotspot diseases, and mechanism exploration. According to keyword analysis, randomized controlled trials of catgut embedding and mechanistic studies of gut flora are the trends for future research. CONCLUSION: Although China is the leading producer, multinational co-operation still needs to be strengthened. Currently, the main interventions for obesity were acupuncture and electroacupuncture, and the hot diseases were obesity combined with polycystic ovary syndrome and metabolic syndrome. In addition, randomised controlled trials of catgut embedding and mechanistic studies of gut flora may be potential directions for future research in the field of acupuncture for obesity.

Real-World Effectiveness of Lung Cancer Screening Using Deep Learning-Based Counterfactual Prediction.

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.

A biodegradable lipid nanoparticle delivers a Cas9 ribonucleoprotein for efficient and safe in situ genome editing in melanoma.

The development of melanoma is closely related to Braf gene, which is a suitable target for CRISPR/Cas9 based gene therapy. CRISPR/Cas9-sgRNA ribonucleoprotein complexes (RNPs) stand out as the safest format compared to plasmid and mRNA delivery. Similarly, lipid nanoparticles (LNPs) emerge as a safer alternative to viral vectors for delivering the CRISPR/Cas9-sgRNA gene editing system. Herein, we have designed multifunctional cationic LNPs specifically tailored for the efficient delivery of Cas9 RNPs targeting the mouse Braf gene through transdermal delivery, aiming to treat mouse melanoma. LNPs are given a positive charge by the addition of a newly synthesized polymer, deoxycholic acid modified polyethyleneimine (PEI-DOCA). Positive charge enables LNPs to be delivered in vivo by binding to negatively charged cell membranes and proteins, thereby facilitating efficient skin penetration and enhancing the delivery of RNPs into melanoma cells for gene editing purposes. Our research demonstrates that these LNPs enhance drug penetration through the skin, successfully delivering the Cas9 RNPs system and specifically targeting the Braf gene. Cas9 RNPs loaded LNPs exert a notable impact on gene editing in melanoma cells, significantly suppressing their proliferation. Furthermore, in mice experiments, the LNPs exhibited skin penetration and tumor targeting capabilities. This innovative LNPs delivery system offers a promising gene therapy approach for melanoma treatment and provides fresh insights into the development of safe and effective delivery systems for Cas9 RNPs in vivo. STATEMENT OF SIGNIFICANCE: CRISPR/Cas9 technology brings new hope for cancer treatment. Cas9 ribonucleoprotein offers direct genome editing, yet delivery challenges persist. For melanoma, transdermal delivery minimizes toxicity but faces skin barrier issues. We designed multifunctional lipid nanoparticles (LNPs) for Cas9 RNP delivery targeting the Braf gene. With metal microneedle pretreatment, our LNPs effectively edited melanoma cells, reducing Braf expression and inhibiting tumor growth. Our study demonstrates LNPs' potential for melanoma therapy and paves the way for efficient in vivo Cas9 RNP delivery systems in cancer therapy.

Geometric phase-encoded stimuli-responsive cholesteric liquid crystals for visualizing real-time remote monitoring: humidity sensing as a proof of concept.

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.

Acupuncture modulates emotional network resting-state functional connectivity in patients with insomnia disorder: a randomized controlled trial and fMRI study.

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.