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Clustered regularly interspaced short palindromic repeats (CRISPR)-Cas system has recently gained growing attention as a diagnostic tool due to its capability of specific gene targeting. It consists of Cas enzymes and a guide RNA (gRNA) that can cleave the target DNA or RNA based on the sequence of the gRNA, making it an attractive genetic engineering technique. In addition to the target-specific binding and cleavage, the trans-cleavage activity was reported for some Cas proteins, including Cas12a and Cas13a, which is to cleave the surrounding single-stranded DNA or RNA upon the target binding of Cas-gRNA complex. All these activities of the CRISPR-Cas system are based on its target-specific binding, making it applied to develop diagnostic methods by detecting the disease-related gene as well as microRNAs and the genetic variations such as single nucleotide polymorphism and DNA methylation. Moreover, it can be applied to detect the non-nucleic acids target such as proteins. In this review, we cover the various CRISPR-based diagnostic methods by focusing on the activity of the CRISPR-Cas system and the form of the target. The CRISPR-based diagnostic methods without target amplification are also introduced briefly.

Cancer immunotherapy is fast rising as a prominent new pillar of cancer treatment, harnessing the immune system to fight against numerous types of cancer. Rho-kinase (ROCK) pathway is involved in diverse cellular activities, and is therefore the target of interest in various diseases at the cellular level including cancer. Indeed, ROCK is well-known for its involvement in the tumor cell and tumor microenvironment, especially in its ability to enhance tumor cell progression, migration, metastasis, and extracellular matrix remodeling. Importantly, ROCK is also considered to be a novel and effective modulator of immune cells, although further studies are needed. In this review article, we describe the various activities of ROCK and its potential to be utilized in cancer treatment, particularly in cancer immunotherapy, by shining a light on its activities in the immune system.

The cluster of differentiation 1 (CD1) molecule differs from major histocompatibility complex class I and II because it presents glycolipid/lipid antigens. Moreover, the CD1-restricted T cells that recognize these self and foreign antigens participate in both innate and adaptive immune responses. CD1s are constitutively expressed by professional and nonprofessional antigen-presenting cells in mucosal tissues, namely, the skin, lung, and intestine. This suggests that CD1-reactive T cells are involved in the immune responses of these tissues. Indeed, evidence suggests that these cells play important roles in diverse diseases, such as inflammation, autoimmune disease, and infection. Recent studies elucidating the molecular mechanisms by which CD1 presents lipid antigens suggest that defects in these mechanisms could contribute to the activities of CD1-reactive T cells. Thus, improving our understanding of these mechanisms could lead to new and effective therapeutic approaches to CD1-associated diseases. In this review, we discuss the CD1-mediated antigen presentation system and its roles in mucosal tissue immunity. Immunity: T cell responses to lipid antigens Further research is needed into how certain proteins present self and foreign lipid antigens from mucosal tissues (skin, lungs, intestines) to a specific group of T cells, triggering immune responses, and how disruption to that system triggers inflammation and disease. In a review paper, Ji Hyung Kim and colleagues at Korea University in Seoul, South Korea, examined the lipid antigen presentation system mediated by CD1 glycoproteins, and highlighted how finely tuned the system is. A defect or mutation in a single factor as a result of metabolic or environmental stresses can destabilize the system, potentially driving the development of auto-immune and mucosal tissue diseases. Improved understanding of CD1-mediated immunity could provide valuable therapeutic targets for multiple health conditions. Advances in 3D cultures and organ models will boost research efforts.

Background: This study aims to identify the status of continuous glucose monitoring (CGM) use among individuals with type 1 diabetes mellitus (T1DM) in South Korea and to investigate whether age-related disparities exist.Methods: Individuals with T1DM receiving intensive insulin therapy were identified from the Korean National Health Insurance Cohort (2019–2022). Characteristics of CGM users and non-users were compared, and the prescription rates of CGM and sensor- augmented pump (SAP) or automated insulin delivery (AID) systems according to age groups (<19, 19–39, 40–59, and ≥60 years) were analyzed using chi-square tests. Glycosylated hemoglobin (HbA1c) levels and coefficients of variation (CV) among CGM users were also examined.Results: Among the 56,908 individuals with T1DM, 10,822 (19.0%) used CGM at least once, and 6,073 (10.7%) used CGM continuously. Only 241 (0.4%) individuals utilized either SAP or AID systems. CGM users were younger than non-users. The continuous prescription rate of CGM was highest among individuals aged <19 years (37.0%), followed by those aged 19–39 years (15.8%), 40–59 years (10.7%), and ≥60 years (3.9%) (P<0.001 for between-group differences). Among CGM users, HbA1c levels decreased from 8.7%±2.4% at baseline to 7.2%±1.2% at 24 months, and CV decreased from 36.6%±11.9% at 3 months to 34.1%±12.7% at 24 months.Conclusion: Despite national reimbursement for CGM devices, the prescription rates of CGM remain low, particularly among older adults. Given the improvements in HbA1c and CV following CGM initiation, more efforts are needed to increase CGM utilization and reduce age-related disparities.

We investigated the additive interaction of diabetes mellitus (DM) and chronic kidney disease (CKD) on the risk of mortality in cancer patients and evaluated the impact of diabetic kidney disease (DKD) on mortality in cancer patients with DM. We retrospectively analyzed 101,684 cancer patients. A multivariable Cox regression model was used for assessing mortality risk. Relative excess risk due to interaction (RERI), attributable proportion (AP), and synergy index (SI) were used to evaluate the additive interactive effect. The adjusted hazard ratio (aHR, 95%CI) for mortality was significant for those with CKD alone (1.53, 1.39–1.68), DM alone (1.25, 1.2–1.3), and both CKD and DM (1.99, 1.84–2.17) compared to non-CKD and non-DM cancer patients. The additive interaction between CKD and DM was significant (RERI 0.22[95%CI = 0.01–0.42], AP 0.11[0.01–0.21], SI 1.28[1.01–1.62]). Among cancer patients with DM, the presence of DKD raised the aHR for mortality (1.55, 95%CI = 1.33–1.81) compared to those without DKD. Coexistence of DM and CKD at the time of cancer diagnosis was significantly associated with an increased risk of mortality, and their interaction exerted an additive interactive effect on mortality. DKD was significantly associated with an increased risk of mortality in cancer patients with DM.

In recent years, the dance field has been able to create diverse content by leveraging technical advancements such as deep learning models, generating content beyond the unique artistic creations that only humans can create. However, in terms of dance data, there are still a lack of video and label datasets or datasets that contain multiple tags for videos. To address this gap, this paper explores the feasibility of generating dance captions from tags using a pseudo-captioning approach, inspired by the significant improvements large language models (LLMs) have shown in other domains. Various tags are generated from features extracted from videos and audio, and LLMs are then instructed to produce dance captions based on these tags. Captions were generated using both the open dance dataset and Internet dance videos, followed by user evaluations of randomly sampled captions. Participants found the captions effective in describing dance movements, of expert quality, and consistent with video content. Additionally, positive feedback was received on the evaluation of the gap in image extraction and the inclusion of tag data. This paper introduces and validates a novel pseudo-captioning method for generating dance captions using predefined tags, contributing to the expansion of data available for dance research and offering a practical solution to the current lack of datasets in this field.

Background Self-monitoring blood glucose (SMBG) is an important factor for glycemic control in people with diabetes. Many countries have implemented policies to support SMBG for better glycemic control. The long-term impact of supporting the cost of SMBG strips for glycemic control in type 1 diabetes mellitus (T1DM) and insulin-treated type 2 DM (T2DM) have been determined in only a few studies. Methods People with T1DM and insulin-treated T2DM for whom the cost of blood glucose strips was and was not supported were included in the present study. Longitudinal changes in HbA1c were repeatedly measured in people with T1DM and insulin-treated T2DM at baseline, and 3, 6, 9, and 12 months. The inverse probability of treatment weighting (IPTW) linear mixed effect model (LMM) analysis was used. Results Among people with T1DM and insulin-treated T2DM, subjects who received support for strip prescriptions showed significantly decreased hemoglobin A1c (HbA1c) levels (0.78% and 0.92% at 12 months, respectively), with significant between-group differences compared with subjects who did not receive support ( p  < 0.001). When using the IPTW-LMM after adjusting for covariates, the mean reduction in HbA1c every 3 months in T1DM and insulin-treated T2DM subjects with strip prescriptions was − 0.105% (95% CI: −0.197 to − 0.014) and − 0.112% (95% CI: −0.143 to − 0.082), respectively, compared with their respective control groups without strip prescriptions. Conclusions In conclusion, the reimbursement policy supporting the cost of blood glucose strip prescriptions was associated with significant reduction in HbA1c levels in people with T1DM or insulin-treated T2DM. Graphical Abstract

Background: This study compares the association between real-time continuous glucose monitoring (rtCGM) and intermittently- scanned CGM (isCGM) and glycemic control in individuals with type 1 diabetes mellitus (T1DM) in a real-world setting.Methods: Using data from the Korean National Health Insurance Service Cohort, individuals with T1DM managed by intensive insulin therapy were followed at 3-month intervals for 2 years after the initiation of CGM. The glycosylated hemoglobin (HbA1c) levels and coefficients of variation (CVs) of rtCGM and isCGM users were compared using independent two-sample t-test and a linear mixed model.Results: The analyses considered 7,786 individuals (5,875 adults aged ≥19 years and 1,911 children and adolescents aged <19 years). Overall, a significant reduction in HbA1c level was observed after 3 months of CGM, and the effect was sustained for 2 years. The mean HbA1c level at baseline was higher in rtCGM users than in isCGM users (8.9%±2.7% vs. 8.6%±2.2%, P<0.001). However, from 3 to 24 months, rtCGM users had lower HbA1c levels than isCGM users at every time point (7.1%±1.2% vs. 7.5%±1.3% at 24 months, P<0.001 for all time points). In both adults and children, the greater reduction in HbA1c with rtCGM remained significant after adjusting for the baseline characteristics of the users. The CV also showed greater decrease with rtCGM than with isCGM.Conclusion: In this large nationwide cohort study, the use of rtCGM was associated with a greater improvement in glycemic control, including HbA1c reduction, than the use of isCGM in both adults and children with T1DM.

Disease characterization of Post-Acute Sequelae of SARS-CoV-2 (PASC) does not account for pre-existing conditions and time course of incidence. We utilized longitudinal data and matching to a COVID PCR-negative population to discriminate PASC conditions over time within our patient population during 2020. Clinical Classification Software was used to identify PASC condition groupings. Conditions were specified acute and persistent (occurring 0-30 days post COVID PCR and persisted 30–120 days post-test) or late (occurring initially 30-120 days post-test). We matched 3:1 COVID PCR-negative COVIDPCR-positive by age, sex, testing month and service area, controlling for pre-existing conditions up to four years prior; 28,118 PCR-positive to 70,293 PCR-negative patients resulted. We estimated PASC risk from the matched cohort. Risk of any PASC condition was 12% greater for PCR-positive patients in the late period with a significantly higher risk of anosmia, cardiac dysrhythmia, diabetes, genitourinary disorders, malaise, and nonspecific chest pain. Our findings contribute to a more refined PASC definition which can enhance clinical care. In this study, the authors use electronic health record data from the US to characterise post-acute sequelae of SARS-CoV-2 infection (PASC). They identify 17 common PASC conditions and find an overall ~12% increase in risk of PASC conditions in the post-acute period among people with a SARS-CoV-2 positive test compared to matched test-negative controls.

The past few years were marked by increased online offensive strategies perpetrated by state and non-state actors to promote their political agenda, sow discord, and question the legitimacy of democratic institutions in the US and Western Europe. In 2016, the US congress identified a list of Russian state-sponsored Twitter accounts that were used to try to divide voters on a wide range of issues. Previous research used latent Dirichlet allocation (LDA) to estimate latent topics in data extracted from these accounts. However, LDA has characteristics that may limit the effectiveness of its use on data from social media: The number of latent topics must be specified by the user, interpretability of the topics can be difficult to achieve, and it does not model short-term temporal dynamics. In the current paper, we propose a new method to estimate latent topics in texts from social media termed Dynamic Exploratory Graph Analysis (DynEGA). In a Monte Carlo simulation, we compared the ability of DynEGA and LDA to estimate the number of simulated latent topics. The results show that DynEGA is substantially more accurate than several different LDA algorithms when estimating the number of simulated topics. In an applied example, we performed DynEGA on a large dataset with Twitter posts from state-sponsored right- and left-wing trolls during the 2016 US presidential election. DynEGA revealed topics that were pertinent to several consequential events in the election cycle, demonstrating the coordinated effort of trolls capitalizing on current events in the USA. This example demonstrates the potential power of our approach for revealing temporally relevant information from qualitative text data.