From Acute to Chronic: Unraveling the Pathophysiological Mechanisms of the Progression from Acute Kidney Injury to Acute Kidney Disease to Chronic Kidney Disease.

This article provides a thorough overview of the biomarkers, pathophysiology, and molecular pathways involved in the transition from acute kidney injury (AKI) and acute kidney disease (AKD) to chronic kidney disease (CKD). It categorizes the biomarkers of AKI into stress, damage, and functional markers, highlighting their importance in early detection, prognosis, and clinical applications. This review also highlights the links between renal injury and the pathophysiological mechanisms underlying AKI and AKD, including renal hypoperfusion, sepsis, nephrotoxicity, and immune responses. In addition, various molecules play pivotal roles in inflammation and hypoxia, triggering maladaptive repair, mitochondrial dysfunction, immune system reactions, and the cellular senescence of renal cells. Key signaling pathways, such as Wnt/ß-catenin, TGF-ß/SMAD, and Hippo/YAP/TAZ, promote fibrosis and impact renal function. The renin-angiotensin-aldosterone system (RAAS) triggers a cascade leading to renal fibrosis, with aldosterone exacerbating the oxidative stress and cellular changes that promote fibrosis. The clinical evidence suggests that RAS inhibitors may protect against CKD progression, especially post-AKI, though more extensive trials are needed to confirm their full impact.

Association of Dip in eGFR With Clinical Outcomes in Unilateral Primary Aldosteronism Patients After Adrenalectomy.

CONTEXT: Primary aldosteronism (PA) leads to kidney function deterioration after treatment, but the effects of the estimated glomerular filtration rate (eGFR) dip following adrenalectomy and its long-term implications are unclear. OBJECTIVE: This study aims to examine eGFR dip in patients with unilateral PA (uPA) after adrenalectomy and clarify their long-term prognosis. METHODS: This multicenter prospective population-based cohort study, enrolled patients with uPA who underwent adrenalectomy. Patients were divided into 4 groups based on their eGFR dip ratio. Outcomes investigated included mortality, cardiovascular composite events, and major adverse kidney events (MAKEs). RESULTS: Among 445 enrolled patients, those with an eGFR dip ratio worse than -30% (n = 74, 16.6%) were older, had higher blood pressure, higher aldosterone concentration, and lower serum potassium levels. During 5.0 ± 3.6 years of follow-up, 2.9% died, 14.6% had cardiovascular composite events, and 17.3% had MAKEs. The group with eGFR dip worse than -30% had a higher risk of MAKEs (P < .001), but no significant differences in mortality (P = .295) or new-onset cardiovascular composite outcomes (P = .373) were found. Multivariate analysis revealed that patients with an eGFR dip ratio worse than -30% were significantly associated with older age (odds ratio [OR], 1.04), preoperative eGFR (OR, 1.02), hypokalemia (OR, 0.45), preoperative systolic blood pressure (OR, 1.03), and plasma aldosterone concentration (OR, 0.99). CONCLUSION: Within 5 years post adrenalectomy, 17.3% of patients had reduced kidney function. Notably, individuals with an eGFR dip ratio worse than -30% faced higher MAKE risks, underscoring the need to monitor kidney function in PA patients after surgery.

Acute kidney injury in patients with COVID-19 compared to those with influenza: a systematic review and meta-analysis.

Background: COVID-19 and influenza can both lead to acute kidney injury (AKI) as a common complication. However, no meta-analysis has been conducted to directly compare the incidence of AKI between hospitalized patients with COVID-19 and influenza. The objective of our study aims to investigate the incidence and outcomes of AKI among hospitalized patients between these two groups. Materials and methods: A systematic search of PubMed, Embase, and Cochrane databases was conducted from December 2019 to August 2023 to identify studies examining AKI and clinical outcomes among hospitalized patients with COVID-19 and influenza. The primary outcome of interest was the incidence of AKI, while secondary outcomes included in-hospital mortality, recovery from AKI, hospital and ICU stay duration. The quality of evidence was evaluated using Cochrane and GRADE methods. Results: Twelve retrospective cohort studies, involving 17,618 hospitalized patients with COVID-19 and influenza, were analyzed. COVID-19 patients showed higher AKI incidence (29.37% vs. 20.98%, OR: 1.67, 95% CI 1.56-1.80, p < 0.01, I2 = 92.42%), and in-hospital mortality (30.95% vs. 5.51%, OR: 8.16, 95% CI 6.17-10.80, p < 0.01, I2 = 84.92%) compared to influenza patients with AKI. Recovery from AKI was lower in COVID-19 patients (57.02% vs., 80.23%, OR: 0.33, 95% CI 0.27-0.40, p < 0.01, I2 = 85.17%). COVID-19 patients also had a longer hospital stay (SMD: 0.69, 95% CI 0.65-0.72, p < 0.01, I2 = 98.94%) and longer ICU stay (SMD: 0.61, 95% CI 0.50-0.73, p < 0.01, I2 = 94.80%) than influenza patients. In our study, evidence quality was high (NOS score 7-9), with low certainty for AKI incidence and moderate certainty for recovery form AKI by GRADE assessment. Conclusion: COVID-19 patients had higher risk of developing AKI, experiencing in-hospital mortality, and enduring prolonged hospital/ICU stays in comparison to influenza patients. Additionally, the likelihood of AKI recovery was lower among COVID-19 patients.

Predicting the number of citations of polycystic kidney disease with 100 top-cited articles since 2010: Bibliometric analysis.

BACKGROUND: Polycystic kidney disease (PKD) is a genetic disorder in which the renal tubules become structurally abnormal, resulting in the development and growth of multiple cysts within the kidneys. Numerous studies on PKD have been published in the literature. However, no such articles used medical subject headings (MeSH terms) to predict the number of article citations. This study aimed to predict the number of article citations using 100 top-cited PKD articles (T100PKDs) and dissect the characteristics of influential authors and affiliated counties since 2010. METHODS: We searched the PubMed Central® (PMC) database and downloaded 100PKDs from 2010. Citation analysis was performed to compare the dominant countries and authors using social network analysis (SNA). MeSh terms were analyzed by referring to their citations in articles and used to predict the number of article citations using its correlation coefficients (CC) to examine the prediction effect. RESULTS: We observed that the top 3 countries and journals in 100PKDs were the US (65%), Netherlands (7%), France (5%), J Am Soc Nephrol (21%), Clin J Am Soc Nephrol (8%), and N Engl J Med (6%); the most cited article (PMID = 23121377 with 473 citations) was authored by Vicente Torres from the US in 2012; and the most influential MeSH terms were drug therapy (3087.2), genetics (2997.83), and therapeutic use (2760.7). MeSH terms were evident in the prediction power of the number of article citations (CC = 0.37; t = 3.92; P < .01, n = 100). CONCLUSIONS: A breakthrough was made by developing a method using MeSH terms to predict the number of article citations based on 100PKDs. MeSH terms are evident in predicting article citations that can be applied to future research, not limited to PKD, as we did in this study.

Authors who contributed most to the fields of hemodialysis and peritoneal dialysis since 2011 using the hT-index: Bibliometric analysis.

BACKGROUND: The h-index does not take into account the full citation list of a researcher to evaluate individual research achievements (IRAs). As a generalization of the h-index, the hT-index takes all citations into account to evaluate IRAs. Compared to other bibliometric indices, it is unclear whether the hT-index is more closely associated with the h-index. We utilized articles published on hemodialysis and peritoneal dialysis (HD/PD) to validate the hT-index as a measure of the most significant contributions to HD/PD. METHODS: Using keywords involving HD/PD in titles, subject areas, and abstracts since 2011, we obtained 7702 abstracts and their associated metadata (e.g., citations, authors, research institutes, countries of origin). In total, 4752 first or corresponding authors with hT-indices >0 were evaluated. To present the author's IRA, the following 4 visualizations were used: radar, Sankey, impact beam plot, and choropleth map to investigate whether the hT-index was more closely associated with the h-index than other indices (e.g., g-/x-indices and author impact factors), whether the United States still dominates the majority of publications concerning PD/HD, and whether there was any difference in research features between 2 prolific authors. RESULTS: In HD/PD articles, we observed that (a) the hT-index was closer to and associated with the h-index; (b1) the United States (37.15), China (34.63), and Japan (28.09) had the highest hT-index; (b2) Sun Yat Sen University (Chian) earned the highest hT-index (=20.02) among research institutes; (c1) the authors with the highest hT-indices (=15.64 and 14.39, respectively) were David W Johnson (Australia) and Andrew Davenport (UK); and (c2) their research focuses on PD and HD, respectively. CONCLUSION: The hT-index was demonstrated to be appropriate for assessing IRAs along with visualizations. The hT-index is recommended in future bibliometric analyses of IRAs as a complement to the h-index.

Research features between Urology and Nephrology authors in articles regarding UTI related to CKD, HD, PD, and renal transplantation.

BACKGROUND: A urinary tract infection (UTI) is one of the most common types of infections affecting the urinary tract. When bacteria enter the bladder or kidney and multiply in the urine, a URI can occur. The urethra is shorter in women than in men, which makes it easier for bacteria to reach the bladder or kidneys and cause infection. A comparison of the research differences between Urology and Nephrology (UN) authors regarding UTI pertaining to the 4 areas (i.e., Chronic Kidney Disease, Hemodialysis, Peritoneal Dialysis, and Renal Transplantation [CHPR]) is thus necessary. We propose and verify 2 hypotheses: CHPR-related articles on UTI have equal journal impact factors (JIFs) in research achievements (RAs) and UN authors have similar research features (RFs). METHODS: Based on keywords associated with UTI and CHPR in titles, subject areas, and abstracts since 2013, we obtained 1284 abstracts and their associated metadata (e.g., citations, authors, research institutes, departments, countries of origin) from the Web of Science core collection. There were 1030 corresponding and first (co-first) authors with hT-JIF-indices (i.e., JIF was computed using hT-index rather than citations as usual). The following 5 visualizations were used to present the author's RA: radar, Sankey, time-to-event, impact beam plot, and choropleth map. The forest plot was used to distinguish RFs by observing the proportional counts of keyword plus in Web of Science core collection between UN authors. RESULTS: It was observed that CHPR-related articles had unequal JIFs (χ2 = 13.08, P = .004, df = 3, n = 1030) and UN departments had different RFs (Q = 53.24, df = 29, P = .004). In terms of countries, institutes, departments, and authors, the United States (hT-JIF = 38.30), Mayo Clinic (12.9), Nephrology (19.14), and Diana Karpman (10.34) from Sweden had the highest hT-JIF index. CONCLUSION: With the aid of visualizations, the hT-JIF-index and keyword plus were demonstrated to assess RAs and distinguish RFs between UN authors. A replication of this study under other topics and in other disciplines is recommended in the future, rather than limiting it to UN authors only, as we did in this study.

Using the forest plot to compare citation achievements in bibliographic and meta-analysis studies since 2011 using data on PubMed Central: A retrospective study.

BACKGROUND: We saw a steady increase in the number of bibliographic studies published over the years. The reason for this rise is attributed to the better accessibility of bibliographic data and software packages that specialize in bibliographic analyses. Any difference in citation achievements between bibliographic and meta-analysis studies observed so far need to be verified. In this study, we aimed to identify the frequently observed MeSH terms in these 2 types of study and investigate whether the highlighted MeSH terms are strongly associated with one of the study types. METHODS: By searching the PubMed Central database, 5121 articles relevant to bibliometric and meta-analysis studies were downloaded since 2011. Social network analysis was applied to highlight the major MeSH terms of quantitative and statistical methods in these 2 types of studies. MeSH terms were then individually tested for any differences in event counts over the years between study types using odds of 95% confidence intervals for comparison. RESULTS: In these 2 studies, we found that the most productive countries were the United States (19.9%), followed by the United Kingdom (8.8%) and China (8.7%); the most number of articles were published in PLoS One (2.9%), Stat Med (2.5%), and Res Synth (2.4%); and the most frequently observed MeSH terms were statistics and numerical data in bibliographic studies and methods in meta-analysis. Differences were found when compared to the event counts and the citation achievements in these 2 study types. CONCLUSION: The breakthrough was made by developing a dashboard using forest plots to display the difference in event counts. The visualization of the observed MeSH terms could be replicated for future academic pursuits and applications in other disciplines using the odds of 95% confidence intervals.

An online time-to-event dashboard comparing the effective control of COVID-19 among continents using the inflection point on an ogive curve: Observational study.

BACKGROUND: During the COVID-19 pandemic, one of the frequently asked questions is which countries (or continents) are severely hit. Aside from using the number of confirmed cases and the fatality to measure the impact caused by COVID-19, few adopted the inflection point (IP) to represent the control capability of COVID-19. How to determine the IP days related to the capability is still unclear. This study aims to (i) build a predictive model based on item response theory (IRT) to determine the IP for countries, and (ii) compare which countries (or continents) are hit most. METHODS: We downloaded COVID-19 outbreak data of the number of confirmed cases in all countries as of October 19, 2020. The IRT-based predictive model was built to determine the pandemic IP for each country. A model building scheme was demonstrated to fit the number of cumulative infected cases. Model parameters were estimated using the Solver add-in tool in Microsoft Excel. The absolute advantage coefficient (AAC) was computed to track the IP at the minimum of incremental points on a given ogive curve. The time-to-event analysis (a.k.a. survival analysis) was performed to compare the difference in IPs among continents using the area under the curve (AUC) and the respective 95% confidence intervals (CIs). An online comparative dashboard was created on Google Maps to present the epidemic prediction for each country. RESULTS: The top 3 countries that were hit severely by COVID-19 were France, Malaysia, and Nepal, with IP days at 263, 262, and 262, respectively. The top 3 continents that were hit most based on IP days were Europe, South America, and North America, with their AUCs and 95% CIs at 0.73 (0.61-0.86), 0.58 (0.31-0.84), and 0.54 (0.44-0.64), respectively. An online time-event result was demonstrated and shown on Google Maps, comparing the IP probabilities across continents. CONCLUSION: An IRT modeling scheme fitting the epidemic data was used to predict the length of IP days. Europe, particularly France, was hit seriously by COVID-19 based on the IP days. The IRT model incorporated with AAC is recommended to determine the pandemic IP.

Wave-vector-encoded nonlinear endomicroscopy.

Based on a rigid square fiber for wave vector delivery, we present a novel (to the best of our knowledge) wave-vector-encoded nonlinear-optical endomicroscopy (WENE). WENE overcomes three tangled issues, including femtosecond pulse broadening induced signal degradation, complexity of packaging miniaturized scanners in the distal end, and pixel-like images, which cannot be fully addressed by current distal scanning nonlinear endomicroscopy (NE) or fiber-bundle-based proximal scanning NE. Due to the advantages of its simplicity in overall configuration and package in the distal end, the capability of addressing the issue of pulse broadening, and offering continuous wave vector delivery, the demonstrated WENE shows great promise for future basic research on biomedical processes and minimally invasive utilization for clinical diagnosis.

Comparison of Ferguson's δ and the Gini coefficient used for measuring the inequality of data related to health quality of life outcomes.

BACKGROUND: Ferguson's δ and Gini coefficient (GC) are defined as contrasting statistical measures of inequality among members within populations. However, the association and cutting points for these two statistics are still unclear; a visual display is required to inspect their similarities and differences. METHODS: A simulation study was conducted to illustrate the pertinent properties of these statistics, along with Cronbach's α and dimension coefficient (DC) to assess inequality. We manipulated datasets containing four item lengths with two number combinations (0 and 33%) in item length if two domains exist. Each item difficulty with five-point polytomous responses was uniformly distributed across a ± 2 logit range. A simulated response questionnaire was designed along with known different structures of true person scores under Rasch model conditions. This was done for 20 normally distributed sample sizes. A total of 320 scenarios were administered. Four coefficients (Ferguson's δ, GC, test reliability Cronbach's α, and DC) were simultaneously calculated for each simulation dataset. Box plots were drawn to examine which of these presented the correct property of inequality on data. Two examples were illustrated to present the index on Google Maps for securing the discriminatory power of individuals. RESULTS: We found that 1-Ferguson's δ coefficient has a high correlation (0.95) with GC. The cutting points of Ferguson's δ, GC, test reliability Cronbach's α, and the DC are 0.15, 0.50, 0.70, and 0.67, respectively. Two applications are shown on Google Maps with GCs of 0.14 and 0.42, respectively. Histogram legends and Lorenz curves are used to display the results. CONCLUSION: The GC is recommended to readers as an index for measuring the extent of inequality (or lower discrimination power) in a given dataset. It can also show the study results of person measures to determine the inequality in the health-related quality of life outcomes.

Choropleth map legend design for visualizing the most influential areas in article citation disparities: A bibliometric study.

BACKGROUND: Disparities in health outcomes across countries/areas are a central concern in public health and epidemiology. However, few authors have discussed legends that can be complemental to choropleth maps (CMs) and merely linked differences in outcomes to other factors like density in areas. Thus, whether health outcome rates on CMs showing the geographical distribution can be applied to publication citations in bibliometric analyses requires further study. The legends for visualizing the most influential areas in article citation disparities should have sophisticated designs. This paper illustrates the use of cumulative frequency (CF) map legends along with Lorenz curves and Gini coefficients (GC) to characterize the disparity of article citations in areas on CMs, based on the quantile classification method for classes. METHODS: By searching the PubMed database (pubmed.com), we used the keyword "Medicine" [journal] and downloaded 7042 articles published from 1945 to 2016. A total number of 41,628 articles were cited in Pubmed Central (PMC). The publication outputs based on the author's x-index were applied to plot CM about research contributions. The approach uses two methods (i.e., quantiles and equal total values for each class) with CF legends, in order to highlight the difference in x-indices across geographical areas on CMs. GC was applied to observe the x-index disparities in areas. Microsoft Excel Visual Basic for Application (VBA) was used for creating the CMs. RESULTS: Results showed that the most productive and cited countries in Medicine (Baltimore) were China and the US. The most-cited states and cities were Maryland (the US) and Beijing (China). Taiwan (x-index = 24.38) ranked behind Maryland (25.97), but ahead of Beijing (16.9). China earned lower disparity (0.42) than the US (0.49) and the rest of the world (0.53) when the GCs were applied. CONCLUSION: CF legends, particularly using the quantile classification for classes, can be useful to complement CMs. They also contain more information than those in standard CM legends that are commonly used with other classification methods. The steps of creating CM legends are described and introduced. Bibliometric analysts on CM can be replicated in the future.

Whether article types of a scholarly journal are different in cited metrics using cluster analysis of MeSH terms to display: A bibliometric analysis.

BACKGROUND: Many authors are concerned which types of peer-review articles can be cited most in academics and who were the highest-cited authors in a scientific discipline. The prerequisites are determined by: (1) classifying article types; and (2) quantifying co-author contributions. We aimed to apply Medical Subject Headings (MeSH) with social network analysis (SNA) and an authorship-weighted scheme (AWS) to meet the prerequisites above and then demonstrate the applications for scholars. METHODS: By searching the PubMed database (pubmed.com), we used the keyword "Medicine" [journal] and downloaded 5,636 articles published from 2012 to 2016. A total number of 9,758 were cited in Pubmed Central (PMC). Ten MeSH terms were separated to represent the journal types of clusters using SNA to compare the difference in bibliometric indices, that is, h, g, and x as well as author impact factor(AIF). The methods of Kendall coefficient of concordance (W) and one-way ANOVA were performed to verify the internal consistency of indices and the difference across MeSH clusters. Visual representations with dashboards were shown on Google Maps. RESULTS: We found that Kendall W is 0.97 (χ = 26.22, df = 9, P < .001) congruent with internal consistency on metrics across MeSH clusters. Both article types of methods and therapeutic use show higher frequencies than other 8 counterparts. The author Klaus Lechner (Austria) earns the highest research achievement(the mean of core articles on g = Ag = 15.35, AIF = 21, x = 3.92, h = 1) with one paper (PMID: 22732949, 2012), which was cited 23 times in 2017 and the preceding 5 years. CONCLUSION: Publishing article type with study methodology and design might lead to a higher IF. Both classifying article types and quantifying co-author contributions can be accommodated to other scientific disciplines. As such, which type of articles and who contributes most to a specific journal can be evaluated in the future.

Detecting hospital behaviors of up-coding on DRGs using Rasch model of continuous variables and online cloud computing in Taiwan.

BACKGROUND: This work aims to apply data-detection algorithms to predict the possible deductions of reimbursement from Taiwan's Bureau of National Health Insurance (BNHI), and to design an online dashboard to send alerts and reminders to physicians after completing their patient discharge summaries. METHODS: Reimbursement data for discharged patients were extracted from a Taiwan medical center in 2016. Using the Rasch model of continuous variables, we applied standardized residual analyses to 20 sets of norm-referenced diagnosis-related group (DRGs), each with 300 cases, and compared these to 194 cases with deducted records from the BNHI. We then examine whether the results of prediction using the Rasch model have a high probability associated with the deducted cases. Furthermore, an online dashboard was designed for use in the online monitoring of possible deductions on fee items in medical settings. RESULTS: The results show that 1) the effects deducted by the NHRI can be predicted with an accuracy rate of 0.82 using the standardized residual approach of the Rasch model; 2) the accuracies for drug, medical material and examination fees are not associated among different years, and all of those areas under the ROC curve (AUC) are significantly greater than the randomized probability of 0.50; and 3) the online dashboard showing the possible deductions on fee items can be used by hospitals in the future. CONCLUSION: The DRG-based comparisons in the possible deductions on medical fees, along with the algorithm based on Rasch modeling, can be a complementary tool in upgrading the efficiency and accuracy in processing medical fee applications in the discernable future.

Developing an Intuitive Intelligent Inpatient Medical Record System.

Chimei Medical Center developed an intuitive intelligent inpatient medical record system which integrates structured, unstructured, and textual medical records and provides health insurance payment suggestions from the collaborative medical record writer's point of view. Combined with the use of intelligent technology (Python language with the smart Content Difference Recognition software component), the system can ensure medical record quality by focusing on the differencse in recorded progress notes.

Using Google Maps to display the pattern of coauthor collaborations on the topic of schizophrenia: A systematic review between 1937 and 2017.

Schizophrenia is a severe mental disorder affecting more than 21 million people worldwide. Scientific collaborations are required to research schizophrenia. However, there have been limited publications to date investigating scientific collaborations in schizophrenia research or reporting individual researchers' achievements(IRA) for authors. This study aimed to investigate the pattern of coauthor collaborations in schizophrenia research. We conducted a bibliometric study of international scientific publications on schizophrenia. About 57,964 abstracts were identified and downloaded from MEDLINE. All were examined using social network analysis (SNA) on February 20, 2018. The clusters of author nationalities, the authors, and the medical subject headings (MESH) terms were presented on Google Maps. A total of 36,934 articles met the inclusion criteria. The mean number of authors per article increased from 4.5 in 2008 to 6.4 in 2017. The proportion of published articles decreased in North America from 46.7% in 2008, to 32.3% in 2017. In contrast, the proportion of published articles in Asia increased from 14.5% in 1998 to 23.9% in 2017. Among the countries generating schizophrenia research the most prominent is China (corr. = 0.98), followed by India (corr. = 0.94), and France (corr. = 0.93). The representative of the biggest cluster is the author Michael F Green from the United States. The top three MESH terms are physiopathology, schizophrenic psychology, and complications. The scientific interest in schizophrenia remains significant. The application of bibliometric indicators of production is evident in the growth of scientific literature on the topic of schizophrenia.

Second-generation antipsychotic medications and risk of chronic kidney disease in schizophrenia: population-based nested case-control study.

OBJECTIVES: The study aims to compare the risk of chronic kidney diseases (CKDs) between patients with schizophrenia using first and second-generation antipsychotics. SETTING: Datasets of 2000-2013 National Health Insurance in Taiwan were used. PARTICIPANTS: The National Health Insurance reimbursement claims data have been transferred to and managed by the National Health Research Institute in Taiwan since 1996. We used the Psychiatric Inpatient Medical Claims database, a subset of the National Health Insurance Research Database, comprising a cohort of patients hospitalised for psychiatric disorders between 2000 and 2013 (n=2 67 807). The database included patients with at least one psychiatric inpatient record and one discharge diagnosis of mental disorders coded by the International Classification of Diseases, Ninth Revision (ICD-9) codes 290-319. The age of patients at first admission was restricted to 18-65 years. PRIMARY OUTCOME: CKD (ICD-9 code 582, 583, 585, 586, 588) requiring hospitalisation or three outpatient visits. The diagnosis of CKD follows the criteria of 'Kidney Disease: Improving Global Outcomes' in Taiwan. CKD is defined as a kidney damage as albumin-to-creatinine ratio >30 mg/g in two of three spot urine specimens or glomerular filtration rate <60 mL/min/1.73 m2 for 3 months or more. RESULTS: We found that the risks for CKD were higher for those who used second-generation antipsychotics (SGAs) longer cumulatively than those who did not. Using non-users, patients did not have any SGA records, as reference group, the risks for CKD comparing those using SGAs for 90 to 180 days with non-users and those using SGAs for more than 1000 days were 1.42 (1.06-1.91) and 1.30 (1.13-1.51), respectively. CONCLUSIONS: The current study suggests the relationship between using SGAs and risk of CKD.

Understanding the productive author who published papers in medicine using National Health Insurance Database: A systematic review and meta-analysis.

Many researchers used National Health Insurance database to publish medical papers which are often retrospective, population-based, and cohort studies. However, the author's research domain and academic characteristics are still unclear.By searching the PubMed database (Pubmed.com), we used the keyword of [Taiwan] and [National Health Insurance Research Database], then downloaded 2913 articles published from 1995 to 2017. Social network analysis (SNA), Gini coefficient, and Google Maps were applied to gather these data for visualizing: the most productive author; the pattern of coauthor collaboration teams; and the author's research domain denoted by abstract keywords and Pubmed MESH (medical subject heading) terms.Utilizing the 2913 papers from Taiwan's National Health Insurance database, we chose the top 10 research teams shown on Google Maps and analyzed one author (Dr. Kao) who published 149 papers in the database in 2015. In the past 15 years, we found Dr. Kao had 2987 connections with other coauthors from 13 research teams. The cooccurrence abstract keywords with the highest frequency are cohort study and National Health Insurance Research Database. The most coexistent MESH terms are tomography, X-ray computed, and positron-emission tomography. The strength of the author research distinct domain is very low (Gini < 0.40).SNA incorporated with Google Maps and Gini coefficient provides insight into the relationships between entities. The results obtained in this study can be applied for a comprehensive understanding of other productive authors in the field of academics.

Cisplatin Nephrotoxicity Might Have a Sex Difference. An analysis Based on Women's Sex Hormone Changes.

Background: A sex difference in cisplatin-induced nephrotoxicity (CIN) has been reported in human and animal studies. We examined in humans whether it is associated with sex-hormone changes. Methods: In this retrospective nationwide cohort study, we used Taiwan's National Health Insurance Research Database (NHIRD) to identify patients with a history of malignancy and cisplatin treatment. Patients diagnosed with kidney disease before cisplatin treatment and those with sex-organ malignancies were excluded. A diagnosis of kidney disease within 90 days after the first administration of cisplatin was the study outcome. Risk factors were estimated using a Cox regression model. Subgroup analyses were performed based on different women's estrogen levels in phases of childbearing, perimenopause, and postmenopause. Results: A retrospective analysis of the records of 3973 men (mean age: 56.15 ± 12.85 years) and 1154 women (mean age: 56.31 ± 12.40 years) showed that 1468 (36.95%) men and 451 (39.08%) women had a new diagnosis of kidney disease. The risk factors were being > 55 years old, a high comorbidity score, and a history of aminoglycoside treatment. Only postmenopausal women had a significantly higher risk of kidney injury (hazard ratio: 1.28; 95% CI: 1.02-1.61) than did men. Conclusions: Perimenopausal women have a significantly higher risk of CIN than do men, which might be explained by women's higher levels of estrogen. Additional studies on the underlying mechanisms of the sex difference of CIN are needed.

In situ high-resolution thermal microscopy on integrated circuits.

The miniaturization of metal tracks in integrated circuits (ICs) can cause abnormal heat dissipation, resulting in electrostatic discharge, overvoltage breakdown, and other unwanted issues. Unfortunately, locating areas of abnormal heat dissipation is limited either by the spatial resolution or imaging acquisition speed of current thermal analytical techniques. A rapid, non-contact approach to the thermal imaging of ICs with sub-µm resolution could help to alleviate this issue. In this work, based on the intensity of the temperature-dependent two-photon fluorescence (TPF) of Rhodamine 6G (R6G) material, we developed a novel fast and non-invasive thermal microscopy with a sub-µm resolution. Its application to the location of hotspots that may evolve into thermally induced defects in ICs was also demonstrated. To the best of our knowledge, this is the first study to present high-resolution 2D thermal microscopic images of ICs, showing the generation, propagation, and distribution of heat during its operation. According to the demonstrated results, this scheme has considerable potential for future in situ hotspot analysis during the optimization stage of IC development.

Widely wavelength tunable fast intensity-modulated light source for biophotonic applications.

High modulation depth, fast (megahertz to gigahertz), intensity-modulated light sources of various wavelengths within the 0.7-1.35 µm bio-penetration window are highly desirable for many biophotonic diagnosis systems. In this Letter, we present a novel scheme of a wavelength tunable, ultra-broadband light source which simply consists of a pump laser, a nonlinear fiber, and demodulation circuits. The working wavelength range of the light source is from 0.7 to 1.35 µm which covers a vast part of the bio-penetration window, and its modulation frequencies extends from tens of megahertz to gigahertz. The performances of the proposed light source in either working wavelength range or modulation frequency bandwidth are much superior to any typical laser diodes or solid state lasers currently employed in the frequency-domain or other biophotonic utilization. The wide applicability of this novel light source in diverse biophotonic applications can be observed from our carefully designed diffused optical spectroscopy phantom measurement.