Exploring the Role of Extracellular Vesicles in the Pathogenesis of Tuberculosis.

Tuberculosis (TB) remains a significant global health concern, necessitating accurate diagnosis and treatment monitoring. Extracellular vesicles (EVs), including exosomes, play crucial roles in disease progression, with their associated genes serving as potential biomarkers and therapeutic targets. Leveraging publicly available RNA-Seq datasets of TB patients and healthy controls (HCs), to identify differentially expressed genes (DEGs) and their associated protein-protein interaction networks and immune cell profiles, the common EV-related DEGs were identified and validated in the GSE42830 and GSE40553 datasets. We have identified nine common EV-related DEGs (SERPINA1, TNFAIP6, MAPK14, STAT1, ITGA2B, VAMP5, CTSL, CEACAM1, and PLAUR) upregulated in TB patients. Immune cell infiltration analysis revealed significant differences between TB patients and HCs, highlighting increased proportions of various immune cells in TB patients. These DEGs are involved in crucial cellular processes and pathways related to exocytosis and immune response regulation. Notably, VAMP5 exhibited excellent diagnostic performance (AUC-0.993, sensitivity-93.8%, specificity-100%), with potential as a novel biomarker for TB. The EV-related genes can serve as novel potential biomarkers that can distinguish between TB and HCs. VAMP5, which functions in exosome biogenesis and showed significant upregulation in TB, can be targeted for therapeutic interventions and treatment outcomes.

Antibiotic Resistance in Plant Pathogenic Bacteria: Recent Data and Environmental Impact of Unchecked Use and the Potential of Biocontrol Agents as an Eco-Friendly Alternative.

The economic impact of phytopathogenic bacteria on agriculture is staggering, costing billions of US dollars globally. Pseudomonas syringae is the top most phytopathogenic bacteria, having more than 60 pathovars, which cause bacteria speck in tomatoes, halo blight in beans, and so on. Although antibiotics or a combination of antibiotics are used to manage infectious diseases in plants, they are employed far less in agriculture compared to human and animal populations. Moreover, the majority of antibiotics used in plants are immediately washed away, leading to environmental damage to ecosystems and food chains. Due to the serious risk of antibiotic resistance (AR) and the potential for environmental contamination with antibiotic residues and resistance genes, the use of unchecked antibiotics against phytopathogenic bacteria is not advisable. Despite the significant concern regarding AR in the world today, there are inadequate and outdated data on the AR of phytopathogenic bacteria. This review presents recent AR data on plant pathogenic bacteria (PPB), along with their environmental impact. In light of these findings, we suggest the use of biocontrol agents as a sustainable, eco-friendly, and effective alternative to controlling phytopathogenic bacteria.

Exploring the Potential of Exosomes as Biomarkers in Tuberculosis and Other Diseases.

Tuberculosis (TB) is a major cause of morbidity and mortality and remains an important public health issue in developing countries worldwide. The existing methods and techniques available for the diagnosis of TB are based on combinations of laboratory (chemical and biological), radiological, and clinical tests. These methods are sophisticated and laborious and have limitations in terms of sensitivity, specificity, and accuracy. Clinical settings need improved diagnostic biomarkers to accurately detect biological changes due to pathogen invasion and pharmacological responses. Exosomes are membrane-bound vesicles and mediators of intercellular signaling processes that play a significant role in the pathogenesis of various diseases, such as tuberculosis, and can act as promising biomarkers for the monitoring of TB infection. Compared to conventional biomarkers, exosome-derived biomarkers are advantageous because they are easier to detect in different biofluids, are more sensitive and specific, and may be useful in tracking patients' reactions to therapy. This review provides insights into the types of biomarkers, methods of exosome isolation, and roles of the cargo (proteins) present in exosomes isolated from patients through omics studies, such as proteomics. These findings will aid in developing new prognostic and diagnostic biomarkers and could lead to the identification of new therapeutic targets in the clinical setting.

Spatial transcriptomics data and analytical methods: An updated perspective.

Spatial transcriptomics (ST) is a newly emerging field that integrates high-resolution imaging and transcriptomic data to enable the high-throughput analysis of the spatial localization of transcripts in diverse biological systems. The rapid progress in this field necessitates the development of innovative computational methods to effectively tackle the distinct challenges posed by the analysis of ST data. These platforms, integrating AI techniques, offer a promising avenue for understanding disease mechanisms and expediting drug discovery. Despite significant advances in the development of ST data analysis techniques, there is an ongoing need to enhance these models for increased biological relevance. In this review, we briefly discuss the ST-related databases and current deep-learning-based models for spatial transcriptome data analyses and highlight their roles and future perspectives in biomedical applications.

Evaluation of accuracies of genomic predictions for body conformation traits in Korean Holstein.

OBJECTIVE: This study aimed to assess the genetic parameters and accuracy of genomic predictions for twenty-four linear body conformation traits and overall conformation scores in Korean Holstein dairy cows. METHODS: A dataset of 2,206 Korean Holsteins was collected, and genotyping was performed using the Illumina Bovine 50K single nucleotide polymorphism (SNP) chip. The traits investigated included body traits (stature, height at front end, chest width, body depth, angularity, body condition score, and locomotion), rump traits (rump angle, rump width, and loin strength), feet and leg traits (rear leg set, rear leg rear view, foot angle, heel depth, and bone quality), udder traits (udder depth, udder texture, udder support, fore udder attachment, front teat placement, front teat length, rear udder height, rear udder width, and rear teat placement), and overall conformation score. Accuracy of genomic predictions was assessed using the single-trait animal model genomic best linear unbiased prediction method implemented in the ASReml-SA v4.2 software. RESULTS: Heritability estimates ranged from 0.10 to 0.50 for body traits, 0.21 to 0.35 for rump traits, 0.13 to 0.29 for feet and leg traits, and 0.05 to 0.46 for udder traits. Rump traits exhibited the highest average heritability (0.29), while feet and leg traits had the lowest estimates (0.21). Accuracy of genomic predictions varied among the twenty-four linear body conformation traits, ranging from 0.26 to 0.49. The heritability and prediction accuracy of genomic estimated breeding value (GEBV) for the overall conformation score were 0.45 and 0.46, respectively. The GEBVs for body conformation traits in Korean Holstein cows had low accuracy, falling below the 50% threshold. CONCLUSION: The limited response to selection for body conformation traits in Korean Holsteins may be attributed to both the low heritability of these traits and the lower accuracy estimates for GEBVs. Further research is needed to enhance the accuracy of GEBVs and improve the selection response for these traits.

From cancer big data to treatment: Artificial intelligence in cancer research.

In recent years, developing the idea of "cancer big data" has emerged as a result of the significant expansion of various fields such as clinical research, genomics, proteomics and public health records. Advances in omics technologies are making a significant contribution to cancer big data in biomedicine and disease diagnosis. The increasingly availability of extensive cancer big data has set the stage for the development of multimodal artificial intelligence (AI) frameworks. These frameworks aim to analyze high-dimensional multi-omics data, extracting meaningful information that is challenging to obtain manually. Although interpretability and data quality remain critical challenges, these methods hold great promise for advancing our understanding of cancer biology and improving patient care and clinical outcomes. Here, we provide an overview of cancer big data and explore the applications of both traditional machine learning and deep learning approaches in cancer genomic and proteomic studies. We briefly discuss the challenges and potential of AI techniques in the integrated analysis of omics data, as well as the future direction of personalized treatment options in cancer.

Revolutionizing Drug Targeting Strategies: Integrating Artificial Intelligence and Structure-Based Methods in PROTAC Development.

PROteolysis TArgeting Chimera (PROTAC) is an emerging technology in chemical biology and drug discovery. This technique facilitates the complete removal of the target proteins that are "undruggable" or challenging to target through chemical molecules via the Ubiquitin-Proteasome System (UPS). PROTACs have been widely explored and outperformed not only in cancer but also in other diseases. During the past few decades, several academic institutes and pharma companies have poured more efforts into PROTAC-related technologies, setting the stage for several major degrader trial readouts in clinical phases. Despite their promising results, the formation of robust ternary orientation, off-target activity, poor permeability, and binding affinity are some of the limitations that hinder their development. Recent advancements in computational technologies have facilitated progress in the development of PROTACs. Researchers have been able to utilize these technologies to explore a wider range of E3 ligases and optimize linkers, thereby gaining a better understanding of the effectiveness and safety of PROTACs in clinical settings. In this review, we briefly explore the computational strategies reported to date for the formation of PROTAC components and discuss the key challenges and opportunities for further research in this area.

Genome-Wide Association Study to Identify QTL for Carcass Traits in Korean Hanwoo Cattle.

This study aimed to identify genetic associations with carcass traits in Hanwoo cattle using a genome-wide association study. A total of 9302 phenotypes were analyzed, and all animals were genotyped using the Illumina Bovine 50K v.3 SNP chip. Heritabilities for carcass weight (CWT), eye muscle area (EMA), backfat thickness (BF), and marbling score (MS) were estimated as 0.42, 0.36, 0.36, and 0.47, respectively, using the GBLUP model, and 0.47, 0.37, 0.36, and 0.42, respectively, using the Bayes B model. We identified 129 common SNPs using DGEBV and 118 common SNPs using GEBV on BTA6, BTA13, and BTA14, suggesting their potential association with the traits of interest. No common SNPs were found between the GBLUP and Bayes B methods when using residuals as a response variable in GWAS. The most promising candidate genes for CWT included SLIT2, PACRGL, KCNIP4, RP1, XKR4, LYN, RPS20, MOS, FAM110B, UBXN2B, CYP7A1, SDCBP, NSMAF, TOX, CA8, LAP3, FAM184B, and NCAPG. For EMA, the genes IBSP, LAP3, FAM184B, LCORL, NCAPG, SLC30A9, and BEND4 demonstrated significance. Similarly, CYP7B1, ARMC1, PDE7A, and CRH were associated with BF, while CTSZ, GNAS, VAPB, and RAB22A were associated with MS. This finding offers valuable insights into genomic regions and molecular mechanisms influencing Hanwoo carcass traits, aiding efficient breeding strategies.

Genome-Wide Association Studies for Body Conformation Traits in Korean Holstein Population.

The objective of this study was to identify quantitative trait loci (QTL) and nearby candidate genes that influence body conformation traits. Phenotypic data for 24 body conformation traits were collected from a population of 2329 Korean Holstein cattle, and all animals were genotyped using the 50 K Illumina bovine SNP chip. A total of 24 genome-wide significant SNPs associated with 24 body conformation traits were identified by genome-wide association analysis. The selection of the most promising candidate genes was based on gene ontology (GO) terms and the previously identified functions that influence various body conformation traits as determined in our study. These genes include KCNA1, RYBP, PTH1R, TMIE, and GNAI3 for body traits; ANGPT1 for rump traits; MALRD1, INHBA, and HOXA13 for feet and leg traits; and CDK1, RHOBTB1, and SLC17A1 for udder traits, respectively. These findings contribute to our understanding of the genetic basis of body conformation traits in this population and pave the way for future breeding strategies aimed at enhancing desirable traits in dairy cattle.

Assessment of genomic breeding values and their accuracies for carcass traits in Jeju Black cattle using whole-genome SNP chip panels.

The objective of the present study was to evaluate the breeding value and accuracy of genomic estimated breeding values (GEBVs) of carcass traits in Jeju Black cattle (JBC) using Hanwoo steers and JBC as a reference population using the single-trait animal model. Our research included genotype and phenotype information on 19,154 Hanwoo steers with 1097 JBC acting as the reference population. Likewise, the test population consisted of 418 genotyped JBC individuals with no phenotypic records for those carcass traits. For estimating the accuracy of GEBV, we divided the entire population into three groups. Hanwoo and JBC make up the first group; Hanwoo and JBC, who has both the genotype and phenotypic records, are referred to as the reference (training) population, and JBC, who lacks phenotypic information is referred to as the test (validation) population. The second group consists of the JBC (without phenotype) as the test population and Hanwoo as a reference population with phenotype and genotypic data. The only JBCs in the third group are those who have genotypic and phenotypic data on them as a reference population but no phenotypic data on them as a test population. The single-trait animal model was used in all three groups for statistical purposes. The reference populations estimated heritabilities for carcass weight (CWT), eye muscle area (EMA), backfat thickness (BF), and marbling score (MS) as 0.30, 0.26, 0.26, and 0.34 for the Hanwoo steer and 0.42, 0.27, 0.26, and 0.48 for JBC. The average accuracy for carcass traits in Group 1 was 0.80 for the Hanwoo and JBC reference population compared with 0.73 for the JBC test population. Although the average accuracy for carcass traits in Group 2 was 0.80, it was 0.80 for the Hanwoo reference population and only 0.56 for the JBC test population. The average accuracy for the JBC reference and test populations was 0.68 and 0.50, respectively, when they were included in the accuracy comparison without the Hanwoo reference population. Groups 1 and 2 used Hanwoo as reference population, which led to a better average accuracy; however, Group 3 only used the JBC reference and test population, which led to a lower average accuracy. This might be due to the fact that Group 3 used a smaller reference size than the group that came before it and that the genetic makeup of the Hanwoo and JBC breeds differed. The GEBV accuracy for MS was higher than that of other traits across all three analysis groups, followed by CWT, EMA, and BF, which may be partially explained by the MS traits' higher heritability. This study suggests that in order to achieve more accuracy, a large reference population particular to a breed should be established. Therefore, to increase the accuracy of GEBV prediction and the genetic benefit from genomic selection in JBC, individual reference breeds, and large populations are required.

Genomic Predictions in Korean Hanwoo Cows: A Comparative Analysis of Genomic BLUP and Bayesian Methods for Reproductive Traits.

This study aimed to predict the accuracy of genomic estimated breeding values (GEBVs) for reproductive traits in Hanwoo cows using the GBLUP, BayesB, BayesLASSO, and BayesR methods. Accuracy estimates of GEBVs for reproductive traits were derived through fivefold cross-validation, analyzing a dataset comprising 11,348 animals and employing an Illumina Bovine 50K SNP chip. GBLUP showed an accuracy of 0.26 for AFC, while BayesB, BayesLASSO, and BayesR demonstrated values of 0.28, 0.29, and 0.29, respectively. For CI, GBLUP attained an accuracy of 0.19, whereas BayesB, BayesLASSO, and BayesR scored 0.21, 0.24, and 0.25, respectively. The accuracy for GL was uniform across GBLUP, BayesB, and BayesR at 0.31, whereas BayesLASSO showed a slightly higher accuracy of 0.33. For NAIPC, GBLUP showed an accuracy of 0.24, while BayesB, BayesLASSO, and BayesR recorded 0.22, 0.27, and 0.30, respectively. The variation in genomic prediction accuracy among methods indicated Bayesian approaches slightly outperformed GBLUP. The findings suggest that Bayesian methods, notably BayesLASSO and BayesR, offer improved predictive capabilities for reproductive traits. Future research may explore more advanced genomic approaches to enhance predictive accuracy and genetic gains in Hanwoo cattle breeding programs.

Tracing the Origin of the RSPO2 Long-Hair Allele and Epistatic Interaction between FGF5 and RSPO2 in Sapsaree Dog.

Genetic analysis of the hair-length of Sapsaree dogs, a Korean native dog breed, showed a dominant mode of inheritance for long hair. Genome-Wide Association Study (GWAS) analysis and subsequent Mendelian segregation analysis revealed an association between OXR1, RSPO2, and PKHD1L1 on chromosome 13 (CFA13). We identified the previously reported 167 bp insertion in RSPO2 3' untranslated region as a causative mutation for hair length variations. The analysis of 118 dog breeds and wolves revealed the selection signature on CFA13 in long-haired breeds. Haplotype analysis showed the association of only a few specific haplotypes to the breeds carrying the 167 bp insertion. The genetic diversity in the neighboring region linked to the insertion was higher in Sapsarees than in other Asian and European dog breeds carrying the same variation, suggesting an older history of its insertion in the Sapsaree genome than in that of the other breeds analyzed in this study. Our results show that the RSPO2 3' UTR insertion is responsible for not only the furnishing phenotype but also determining the hair length of the entire body depending on the genetic background, suggesting an epistatic interaction between FGF5 and RSPO2 influencing the hair-length phenotype in dogs.

Risk of Cardiovascular Events and Medical Cost of Dapagliflozin and Dipeptidyl Peptidase-4 Inhibitors.

This study compared dapagliflozin, a sodium-glucose co-transporter 2 inhibitor, and dipeptidyl peptidase-4 inhibitors (DPP-4i) with regard to cardiovascular (CV) event incidence and direct medical costs during type 2 diabetes treatment. A retrospective cohort study was conducted using national health insurance claims data from September 1, 2014, to June 30, 2018, of patients in Korea. Patients who were prescribed dapagliflozin and DPP-4i for the first time were included. The primary outcome was the incidence of a composite of major adverse CV events (MACEs)-nonfatal myocardial infarction, nonfatal stroke, or in-hospital CV death. Proportional hazard models after propensity score weighting were used to determine hazard ratios (HRs) and 95% confidence intervals (CIs) for MACE in the dapagliflozin and DPP-4i groups. A decision analytic model was used to compare direct medical costs between the two treatment groups from a healthcare provider's perspective. Of the 260,336 patients in the cohort, 23,147 and 237,189 received dapagliflozin and DPP-4i, respectively. During the follow-up, 184 patients receiving dapagliflozin and 3,674 receiving DPP-4i (incidence, 6.47 and 11.33 events/1,000 person-years, respectively) had MACE. The adjusted HR of MACE for dapagliflozin compared with that for DPP-4i was 0.69 (95% CI 0.57-0.83). The corresponding HRs were consistent among patients with and without underlying CV disease. The estimated direct medical cost appeared to be lower by $68,452 in the dapagliflozin group than that in the DPP-4i group for 3 years, in 1,000 hypothetical patients. In this population-based cohort study, the use of dapagliflozin instead of DPP-4i was associated with a reduced risk of MACE, which subsequently reduced direct medical costs. These data provide valuable information to patients, practitioners, and authorities regarding the risk of CV events associated with dapagliflozin versus DPP-4i use in clinical practice.

Safety profile of D-penicillamine: a comprehensive pharmacovigilance analysis by FDA adverse event reporting system.

BACKGROUND: D-penicillamine (D-pen) is a copper-chelating drug and has immune-modulatory properties. D-pen is used to treat rheumatoid arthritis, Wilson's disease, and kidney stones (cystinuria). However, associated adverse events (AEs) of D-pen treatment are frequent and often serious. Therefore, a comprehensive assessment of the safety profile of D-pen is urgently needed. RESEARCH DESIGN AND METHODS: We identified and analyzed AEs associated with D-pen between April-1970 to July-2020 from the U.S. Food and Drug Administration Adverse Event Reporting System (FAERS) databases and calculated the reported odds ratio (ROR) with 95% confidence intervals (CI) using the disproportionality analysis. RESULTS: A total of 9,150,234 AEs related to drugs were reported in the FAERS database, of which 542 were related to D-Pen. We report that D-pen was associated with dystonia (ROR: 20.52; 95%CI: 12.46-33.80), drug hypersensitivity (ROR: 5.42; 95%CI: 3.72-7.90), pancytopenia (ROR: 10.20; 95%CI: 5.61-18.56), joint swelling (ROR: 9.07; 95%CI: 5.51-14.94), renal-impairment (ROR: 6.68; 95%CI: 3.67-12.15), dysphagia (ROR: 5.05; 95%CI: 2.76-8.89), aggravation of condition (ROR: 4.16; 95%CI: 2.60-6.67), congestive cardiac failure (ROR: 4.04; 95%CI: 2.22-7.35), peripheral edema (ROR: 3.77; 95%CI: 2.17-6.55), tremor (ROR: 3.46; 95%CI: 2.00-6.01), pyrexia (ROR: 3.46; 95%CI: 2.00-6.01), and gait disturbance (ROR: 2.41; 95%CI: 1.29-4.52). CONCLUSIONS: Patients taking D-pen require close monitoring of renal function, blood counts, immunity, liver, cardiac function, and neurological function. D-pen suppresses immune system which maximizes the risk of infection.

Flavonoids: Potential Candidates for the Treatment of Neurodegenerative Disorders.

Neurodegenerative disorders, such as Parkinson's disease (PD), Alzheimer's disease (AD), Amyotrophic lateral sclerosis (ALS), and Huntington's disease (HD), are the most concerning disorders due to the lack of effective therapy and dramatic rise in affected cases. Although these disorders have diverse clinical manifestations, they all share a common cellular stress response. These cellular stress responses including neuroinflammation, oxidative stress, proteotoxicity, and endoplasmic reticulum (ER)-stress, which combats with stress conditions. Environmental stress/toxicity weakened the cellular stress response which results in cell damage. Small molecules, such as flavonoids, could reduce cellular stress and have gained much attention in recent years. Evidence has shown the potential use of flavonoids in several ways, such as antioxidants, anti-inflammatory, and anti-apoptotic, yet their mechanism is still elusive. This review provides an insight into the potential role of flavonoids against cellular stress response that prevent the pathogenesis of neurodegenerative disorders.

Neurobiology of zinc and its role in neurogenesis.

BACKGROUND: Zinc (Zn) has a diverse role in many biological processes, such as growth, immunity, anti-oxidation system, homeostatic, and repairing. It acts as a regulatory and structural catalyst ion for activities of various proteins, enzymes, and signal transcription factors, as well as cell proliferation, differentiation, and survival. The Zn ion is essential for neuronal signaling and is mainly distributed within presynaptic vesicles. Zn modulates neuronal plasticity and synaptic activity in both neonatal and adult stages. Alterations in brain Zn status results in a dozen neurological diseases including impaired brain development. Numerous researchers are working on neurogenesis, however, there is a paucity of knowledge about neurogenesis, especially in neurogenesis in adults. Neurogenesis is a multifactorial process and is regulated by many metal ions (e.g. Fe, Cu, Zn, etc.). Among them, Zn has an essential role in neurogenesis. At the molecular level, Zn controls cell cycle, apoptosis, and binding of DNA and several proteins including transcriptional and translational factors. Zn is needed for protein folding and function and Zn acts as an anti-apoptotic agent; organelle stabilizer; and an anti-inflammatory agent. Zn deficiency results in aging, neurodegenerative disease, immune deficiency, abnormal growth, cancer, and other symptoms. Prenatal deficiency of Zn results in developmental disorders in humans and animals. CONCLUSION: Both in vitro and in vivo studies have shown an association between Zn deficiency and increased risk of neurological disorders. This article reviews the existing knowledge on the role of Zn and its importance in neurogenesis.

Proteotoxicity: A Fatal Consequence of Environmental Pollutants-Induced Impairments in Protein Clearance Machinery.

A tightly regulated protein quality control (PQC) system maintains a healthy balance between correctly folded and misfolded protein species. This PQC system work with the help of a complex network comprised of molecular chaperones and proteostasis. Any intruder, especially environmental pollutants, disrupt the PQC network and lead to PQCs disruption, thus generating damaged and infectious protein. These misfolded/unfolded proteins are linked to several diseases such as Parkinson's disease, Alzheimer's disease, Huntington's disease, and cataracts. Numerous studies on proteins misfolding and disruption of PQCs by environmental pollutants highlight the necessity of detailed knowledge. This review represents the PQCs network and environmental pollutants' impact on the PQC network, especially through the protein clearance system.

Genetic characteristics of Korean Jeju Black cattle with high density single nucleotide polymorphisms.

OBJECTIVE: Conservation and genetic improvement of cattle breeds require information about genetic diversity and population structure of the cattle. In this study, we investigated the genetic diversity and population structure of the three cattle breeds in the Korean peninsula. METHODS: Jeju Black, Hanwoo, Holstein cattle in Korea, together with six foreign breeds were examined. Genetic diversity within the cattle breeds was analyzed with minor allele frequency (MAF), observed and expected heterozygosity (HO and HE), inbreeding coefficient (FIS) and past effective population size. Molecular variance and population structure between the nine breeds were analyzed using a model-based clustering method. Genetic distances between breeds were evaluated with Nei's genetic distance and Weir and Cockerham's FST. RESULTS: Our results revealed that Jeju Black cattle had lowest level of heterozygosity (HE = 0.21) among the studied taurine breeds, and an average MAF of 0.16. The level of inbreeding was -0.076 for Jeju Black, while -0.018 to -0.118 for the other breeds. Principle component analysis and neighbor-joining tree showed a clear separation of Jeju Black cattle from other local (Hanwoo and Japanese cattle) and taurine/indicine cattle breeds in evolutionary process, and a distinct pattern of admixture of Jeju Black cattle having no clustering with other studied populations. The FST value between Jeju Black cattle and Hanwoo was 0.106, which was lowest across the pair of breeds ranging from 0.161 to 0.274, indicating some degree of genetic closeness of Jeju Black cattle with Hanwoo. The past effective population size of Jeju Black cattle was very small, i.e. 38 in 13 generation ago, whereas 209 for Hanwoo. CONCLUSION: This study indicates genetic uniqueness of Jeju Black cattle. However, a small effective population size of Jeju Black cattle indicates the requirement for an implementation of a sustainable breeding policy to increase the population for genetic improvement and future conservation.

Association between proton pump inhibitor use and risk of fracture: A population-based case-control study.

OBJECTIVES: The purpose of this study was to reconfirm the association between the risk of fracture and proton pump inhibitor use and to establish evidence for defining a high-risk group of patients among proton pump inhibitor users. METHODS: A nested case-control study was performed using data from the National Health Insurance Sharing Service database from the period January 2007 to December 2017. The study population included elderly women aged ≥65 years with claims for peptic ulcer or gastro-esophageal reflux disease. The cases were all incidental osteoporotic fractures, and up to two controls were matched to each case by age, osteoporosis, and Charlson comorbidity index. Conditional logistic regression was used to calculate the adjusted odds ratio and 95% confidence interval (CI). RESULTS: A total of 21,754 cases were identified, and 43,508 controls were matched to the cases. The adjusted odds ratio of osteoporotic fractures related to the use of proton pump inhibitors was 1.15 (95% CI: 1.11-1.20). There was a statistically significant interaction between proton pump inhibitor and bisphosphonate use (p<0.01). The risk of fracture in patients using proton pump inhibitors was 1.15 (95% CI: 1.08-1.92) in bisphosphonate users and 1.11 (95% CI: 1.03-1.20) in bisphosphonate non-users. CONCLUSION: Concomitant use of bisphosphonates and proton pump inhibitors will likely increase the risk of osteoporotic fractures in women aged 65 and over, and caution should be exercised in this high-risk group of patients.

Comparison of heart failure risk and medical costs between patients with type 2 diabetes mellitus treated with dapagliflozin and dipeptidyl peptidase-4 inhibitors: a nationwide population-based cohort study.

BACKGROUND: Dapagliflozin is one of the novel glucose-lowering agents, which has recently been reported to reduce the risk of hospitalization for heart failure (hHF). The present study aimed to compare the differences between the risk of hHF after using dapagliflozin and dipeptidyl peptidase-4 inhibitors (DPP-4i) as second-line drugs for the treatment of type 2 diabetes mellitus using the latest nationwide population data in Korea. Additionally, we aimed to examine the impact of clinical outcomes on direct medical costs in the two groups. METHODS: The present population-based, retrospective cohort study was conducted using the nationwide claims data between September 01, 2014 and June 30, 2018. New users of dapagliflozin and DPP-4i were identified from the database and the differences in patients' characteristics between the two groups were analyzed using propensity score-weighted analysis. Cox proportional hazards regression analysis was used to estimate the risk of hHF. A simple model was used for the estimation of direct medical costs for 3 years. RESULTS: In total, 23,147 patients in the dapagliflozin group and 237,187 patients in the DPP-4i group were selected for the analysis. The incidence rates of hHF were 3.86 and 6.79 per 1000 person-years in the dapagliflozin and DPP-4i groups, respectively. In the entire study population, the hazard ratio for hHF in the dapagliflozin group compared to the DPP-4i group was 0.58 (95% confidence interval 0.46-0.74), with 0.55 (95% confidence interval 0.41-0.74) among patients with underlying cardiovascular disease and 0.66 (95% confidence interval 0.46-0.95) among patients without underlying cardiovascular disease. The direct medical costs were $57,787 lower in the dapagliflozin group than in the DPP-4i group for 3 years. CONCLUSIONS: This study showed that dapagliflozin lowers the risk for hHF and subsequently reduces direct medical costs compared to DPP-4i. The protective effect against hHF was more evident among patients with underlying cardiovascular disease.