L-Asparaginase delivered by Salmonella typhimurium suppresses solid tumors

Bacteria can be engineered to deliver anticancer proteins to tumors via a controlled expression system that maximizes the concentration of the therapeutic agent in the tumor. L-asparaginase (L-ASNase), which primarily converts asparagine to aspartate, is an anticancer protein used to treat acute lymphoblastic leukemia. In this study, Salmonellae were engineered to express L-ASNase selectively within tumor tissues using the inducible araBAD promoter system of Escherichia coli. Antitumor efficacy of the engineered bacteria was demonstrated in vivo in solid malignancies. This result demonstrates the merit of bacteria as cancer drug delivery vehicles to administer cancer-starving proteins such as L-ASNase to be effective selectively within the microenvironment of cancer tissue

Development and Verification of Time-Series Deep Learning for Drug-Induced Liver Injury Detection in Patients Taking Angiotensin II Receptor Blockers: A Multicenter Distributed Research Network Approach

Objectives The objective of this study was to develop and validate a multicenter-based, multi-model, time-series deep learning model for predicting drug-induced liver injury (DILI) in patients taking angiotensin receptor blockers (ARBs). The study leveraged a national-level multicenter approach, utilizing electronic health records (EHRs) from six hospitals in Korea. Methods A retrospective cohort analysis was conducted using EHRs from six hospitals in Korea, comprising a total of 10,852 patients whose data were converted to the Common Data Model. The study assessed the incidence rate of DILI among patients taking ARBs and compared it to a control group. Temporal patterns of important variables were analyzed using an interpretable time-series model. Results The overall incidence rate of DILI among patients taking ARBs was found to be 1.09%. The incidence rates varied for each specific ARB drug and institution, with valsartan having the highest rate (1.24%) and olmesartan having the lowest rate (0.83%). The DILI prediction models showed varying performance, measured by the average area under the receiver operating characteristic curve, with telmisartan (0.93), losartan (0.92), and irbesartan (0.90) exhibiting higher classification performance. The aggregated attention scores from the models highlighted the importance of variables such as hematocrit, albumin, prothrombin time, and lymphocytes in predicting DILI. Conclusions Implementing a multicenter-based time-series classification model provided evidence that could be valuable to clinicians regarding temporal patterns associated with DILI in ARB users. This information supports informed decisions regarding appropriate drug use and treatment strategies

Nonlinear causal effects of estimated glomerular filtration rate on myocardial infarction risks: Mendelian randomization study

Abstract Background Previous observational studies suggested that a reduction in estimated glomerular filtration rate (eGFR) or a supranormal eGFR value was associated with adverse cardiovascular risks. However, a previous Mendelian randomization (MR) study under the linearity assumption reported null causal effects from eGFR on myocardial infarction (MI) risks. Further investigation of the nonlinear causal effect of kidney function assessed by eGFR on the risk of MI by nonlinear MR analysis is warranted. Methods In this MR study, genetic instruments for log-eGFR based on serum creatinine were developed from European samples included in the CKDGen genome-wide association study (GWAS) meta-analysis (N=567,460). Alternate instruments for log-eGFR based on cystatin C were developed from a GWAS of European individuals that included the CKDGen and UK Biobank data (N=460,826). Nonlinear MR analysis for the risk of MI was performed using the fractional polynomial methodand thepiecewise linear method on data from individuals of white British ancestry in the UK Biobank (N=321,024, with 12,205 MI cases). Results Nonlinear MR analysis demonstrated a U-shaped (quadratic P value < 0.001) association between MI risk and genetically predicted eGFR (creatinine) values, as MI risk increased as eGFR declined in the low eGFR range and the risk increased as eGFR increased in the high eGFR range. The results were similar even after adjustment for clinical covariates, such as blood pressure, diabetes mellitus, dyslipidemia, or urine microalbumin levels, or when genetically predicted eGFR (cystatin C) was included as the exposure. Conclusion Genetically predicted eGFR is significantly associated with the risk of MI with a parabolic shape, suggesting that kidney function impairment, either by reduced or supranormal eGFR, may be causally linked to a higher MI risk

System of integrating biosignals during hemodialysis: the CONTINUAL (Continuous mOnitoriNg viTal sIgN dUring hemodiALysis) registry

Background Appropriate monitoring of intradialytic biosignals is essential to minimize adverse outcomes because intradialytic hypotension and arrhythmia are associated with cardiovascular risk in hemodialysis patients. However, a continuous monitoring system for intradialytic biosignals has not yet been developed. Methods This study investigated a cloud system that hosted a prospective, open-source registry to monitor and collect intradialytic biosignals, which was named the CONTINUAL (Continuous mOnitoriNg viTal sIgN dUring hemodiALysis) registry. This registry was based on real-time multimodal data acquisition, such as blood pressure, heart rate, electrocardiogram, and photoplethysmogram results. Results We analyzed session information from this system for the initial 8 months, including data for some cases with hemodynamic complications such as intradialytic hypotension and arrhythmia. Conclusion This biosignal registry provides valuable data that can be applied to conduct epidemiological surveys on hemodynamic complications during hemodialysis and develop artificial intelligence models that predict biosignal changes which can improve patient outcomes

Genetic variations in HMGCR and PCSK9 and kidney function: a Mendelian randomization study

Background The genetically predicted lipid-lowering effect of HMGCR or PCSK9 variant can be used to assess drug proxy effects on kidney function. Methods Mendelian randomization (MR) analysis-identified HMGCR and PCSK9 genetic variants were used to predict the low-density lipoprotein (LDL) cholesterol-lowering effects of medications targeting related molecules. Primary summary-level outcome data for log-estimated glomerular filtration rate (eGFR; creatinine) were provided by the CKDGen Consortium (n = 1,004,040 European) from a meta-analysis of CKDGen and UK Biobank data. We also conducted a separate investigation of summary-level data from CKDGen (n = 567,460, log-eGFR [creatinine]) and UK Biobank (n = 436,581, log-eGFR [cystatin C]) samples. Summary-level MRs using an inverse variance weighted method and pleiotropy-robust methods were performed. Results Summary-level MR analysis indicated that the LDL-lowering effect predicted genetically by HMGCR variants (50-mg/dL decrease) was significantly associated with a decrease in eGFR (–1.67%; 95% confidence interval [CI], –2.20% to –1.13%). Similar significance was found in results from the pleiotropy-robust MR methods when the CKDGen and UK Biobank data were analyzed separately. However, the LDL-lowering effect predicted genetically by PCSK9 variants was significantly associated with an increase in eGFR (+1.17%; 95% CI, 0.10%–2.25%). The results were similarly supported by the weighted median method and in each CKDGen and UK Biobank dataset, but the significance obtained by MR-Egger regression was attenuated. Conclusion Genetically predicted HMG-CoA reductase inhibition was associated with low eGFR, while genetically predicted PCSK9 inhibition was associated with high eGFR. Clinicians should consider that the direct effect of different types of lipid-lowering medication on kidney function can vary

Causal effects of atrial fibrillation on brain white and gray matter volume: a Mendelian randomization study

Background Atrial fibrillation (AF) and brain volume loss are prevalent in older individuals. We aimed to assess the causal effect of atrial fibrillation on brain volume phenotypes by Mendelian randomization (MR) analysis. Methods The genetic instrument for AF was constructed from a previous genome-wide association study (GWAS) meta-analysis (15,993 AF patients and 113,719 controls of European ancestry). The outcome summary statistics for head-size-normalized white or gray matter volume measured by magnetic resonance imaging were provided by a previous GWAS of 33,224 white British participants in the UK Biobank. Two-sample MR by the inverse variance–weighted method was performed, supported by pleiotropy-robust MR sensitivity analysis. The causal estimates for the effect of AF on ischemic stroke were also investigated in a dataset that included the findings from the MEGASTROKE study (34,217 stroke patients and 406,111 controls of European ancestry). The direct effects of AF on brain volume phenotypes adjusted for the mediating effect of ischemic stroke were studied by multivariable MR. Results A higher genetic predisposition for AF was significantly associated with lower grey matter volume [beta −0.040, standard error (SE) 0.017, P=0.017], supported by pleiotropy-robust MR sensitivity analysis. Significant causal estimates were identified for the effect of AF on ischemic stroke (beta 0.188, SE 0.026, P=1.03E−12). The total effect of AF on lower brain grey matter volume was attenuated by adjusting for the effect of ischemic stroke (direct effects, beta −0.022, SE 0.033, P=0.528), suggesting that ischemic stroke is a mediator of the identified causal pathway. The causal estimates were nonsignificant for effects on brain white matter volume as an outcome. Conclusions This study identified that genetic predisposition for AF is significantly associated with lower gray matter volume but not white matter volume. The results indicated that the identified total effect of AF on gray matter volume may be mediated by ischemic stroke.This work was supported by a grant from the Korea Health Technology R&D Project through the Korea Health Industry Development Institute (KHIDI), funded by the Ministry of Health & Welfare, Republic of Korea (grant number: HW20C2066). The funder played no role in the conduct of the study, and the study was performed independently by the authors

Long-term risk of all-cause mortality in live kidney donors: a matched cohort study

Background Long-term outcomes of live kidney donors remain controversial, although this information is crucial for selecting potential donors. Thus, this study compared the long-term risk of all-cause mortality between live kidney donors and healthy control. Methods We performed a retrospective cohort study including donors from seven tertiary hospitals in South Korea. Persons who underwent voluntary health screening were included as controls. We created a matched control group considering age, sex, era, body mass index, baseline hypertension, diabetes, estimated glomerular filtration rate, and dipstick albuminuria. The study outcome was progression to end-stage kidney disease (ESKD), and all-cause mortality as identified in the linked claims database. Results We screened 1,878 kidney donors and 78,115 health screening examinees from 2003 to 2016. After matching, 1,701 persons remained in each group. The median age of the matched study subjects was 44 years, and 46.6% were male. Among the study subjects, 2.7% and 16.6% had underlying diabetes and hypertension, respectively. There were no ESKD events in the matched donor and control groups. There were 24 (1.4%) and 12 mortality cases (0.7%) in the matched donor and control groups, respectively. In the age-sex adjusted model, the risk for all-cause mortality was significantly higher in the donor group than in the control group. However, the significance was not retained after socioeconomic status was included as a covariate (adjusted hazard ratio, 1.82; 95% confidence interval, 0.87–3.80). Conclusion All-cause mortality was similar in live kidney donors and matched non-donor healthy controls with similar health status and socioeconomic status in the Korean population

Metabolic risks in living kidney donors in South Korea

Background Considering the growing prevalence of Western lifestyles and related chronic diseases occurring in South Korea, this study aimed to explore the progression of metabolic risk factors in living kidney donors. Methods This study enrolled living kidney donors from seven hospitals from 1982 to 2016. The controls were individuals that voluntarily received health check-ups from 1995 to 2016 that were matched with donors according to age, sex, diabetes status, baseline estimated glomerular filtration rate, and date of the medical record. Data on hyperuricemia, hypertension, hypercholesterolemia, and overweight/obesity were collected to determine metabolic risks. Logistic regressions with interaction terms between the medical record date and donor status were used to compare the trends in metabolic risks over time in the two groups. Results A total of 2,018 living kidney donors and matched non-donors were included. The median age was 44.0 years and 54.0% were women. The living kidney donors showed a lower absolute prevalence for all metabolic risk factors, except for those that were overweight/obese, than the non-donors. The proportion of subjects that were overweight/obese was consistently higher over time in the donor group. The changes over time in the prevalence of each metabolic risk were not significantly different between groups, except for a lower prevalence of metabolic risk factors ≥ 3 in donors. Conclusion Over time, metabolic risks in living kidney donors are generally the same as in non-donors, except for a lower prevalence of metabolic risk factors ≥3 in donors

R&amp;D Project Valuation Considering Changes of Economic Environment: A Case of a Pharmaceutical R&amp;D Project

R&amp;D project valuation is important for effective R&amp;D portfolio management through decision making, related to the firm’s R&amp;D productivity, sustainable management. In particular, scholars have emphasized the necessities of capturing option value in R&amp;D and developed methods of real option valuation. However, despite suggesting various real option models, there are few studies on simultaneously employing mean-reverting stochastic process and Markov regime switching to describe the evolution of cash flow and to reflect time-varying parameters resulting from changes of economic environment. Therefore, we suggest a mean-reverting binomial lattice model under Markov regime switching and apply it to evaluate clinical development with project cases of the pharmaceutical industry. This study finds that decision making can be different according to the regime condition, thus the suggested model can capture risks caused by the uncertainty of the economic environment, represented by regime switching. Further, this study simulates the model according to rate parameter from 0.00 to 1.00 and risk-free interest rates for regimes 1 and 2 from ( r 1 = 4%, r 2 = 2%) to ( r 1 = 7%, r 2 = 5%), and confirms the rigidity of the model. Therefore, in practice, the mean-reverting binomial lattice model under Markov regime switching proposed in this study for R&amp;D project valuation contributes to assisting company R&amp;D project managers make effective decision making considering current economic environment and future changes

Customer Complaints Analysis Using Text Mining and Outcome-Driven Innovation Method for Market-Oriented Product Development

The rapid increase in the quantity of customer data has promoted the necessity to analyse these data. Recent progress in text mining has enabled analysis of unstructured text data such as customer suggestions, customer complaints and customer feedback. Much research has been attempted to use insights gained from text mining to identify customer needs to guide development of market-oriented products. However, the previous research has a drawback that identifies limited customer needs based on product features. To overcome the limitation, this paper presents application of text mining analysis of customer complaints to identify customers&rsquo; true needs by using the Outcome-Driven Innovation (ODI) method. This paper provides a method to analyse customer complaints by using the concept of job. The ODI-based analysis contributes to identification of customer latent needs during the pre-execution and post-execution steps of product use by customers that previous methods cannot discover. To explain how the proposed method can identify customer requirements, we present a case study of stand-type air conditioners. The analysis identified two needs that experts had not identified but regarded as important. This research helps to identify requirements of all the points at which customers want to obtain help from the product