A stacked machine learning-based classification model for endometriosis and adenomyosis: a retrospective cohort study utilizing peripheral blood and coagulation markers.

Introduction: Endometriosis (EMs) and adenomyosis (AD) are common gynecological diseases that impact women's health, and they share symptoms such as dysmenorrhea, chronic pain, and infertility, which adversely affect women's quality of life. Current diagnostic approaches for EMs and AD involve invasive surgical procedures, and thus, methods of noninvasive differentiation between EMs and AD are needed. This retrospective cohort study introduces a novel, noninvasive classification methodology employing a stacked ensemble machine learning (ML) model that utilizes peripheral blood and coagulation markers to distinguish between EMs and AD. Methods: The study included a total of 558 patients (329 with EMs and 229 with AD), in whom key hematological and coagulation markers were analyzed to identify distinctive profiles. Feature selection was conducted through ML (logistic regression, support vector machine, and K-nearest neighbors) to determine significant hematological markers. Results: Red cell distribution width, mean corpuscular hemoglobin concentration, activated partial thromboplastin time, international normalized ratio, and antithrombin III were proved to be the key distinguishing indexes for disease differentiation. Among all the ML classification models developed, the stacked ensemble model demonstrated superior performance (area under the curve = 0.803, 95% credibility interval = 0.701-0.904). Our findings demonstrate the effectiveness of the stacked ensemble ML model for classifying EMs and AD. Discussion: Integrating biomarkers into this multi-algorithm framework offers a novel approach to noninvasive diagnosis. These results advocate for the application of stacked ensemble ML utilizing cost-effective and readily available peripheral blood and coagulation indicators for the early, rapid, and noninvasive differential diagnosis of EMs and AD, offering a potentially transformative approach for clinical decision-making and personalized treatment strategies.

Catalytic activity of Setd2 is essential for embryonic development in mice: establishment of a mouse model harboring patient-derived Setd2 mutation.

SETD2 is the only enzyme responsible for transcription-coupled histone H3 lysine 36 trimethylation (H3K36me3). Mutations in SETD2 cause human diseases including cancer and developmental defects. In mice, Setd2 is essential for embryonic vascular remodeling. Given that many epigenetic modifiers have recently been found to possess noncatalytic functions, it is unknown whether the major function(s) of Setd2 is dependent on its catalytic activity or not. Here, we established a site-specific knockin mouse model harboring a cancer patient-derived catalytically dead Setd2 (Setd2-CD). We found that the essentiality of Setd2 in mouse development is dependent on its methyltransferase activity, as the Setd2CD/CD and Setd2-/- mice showed similar embryonic lethal phenotypes and largely comparable gene expression patterns. However, compared with Setd2-/-, the Setd2CD/CD mice showed less severe defects in allantois development, and single-cell RNA-seq analysis revealed differentially regulated allantois-specific 5' Hoxa cluster genes in these two models. Collectively, this study clarifies the importance of Setd2 catalytic activity in mouse development and provides a new model for comparative study of previously unrecognized Setd2 functions.

Study on the key ecological factors for the reproduction of Gymnocypris eckloni in the upper reaches of the Yellow River.

The development of hydroelectric projects has adversely affected the reproductive activities of downstream fish species. To facilitate the natural reproduction of fish and restore spawning grounds post-dam construction, it is imperative to explore the ecological factors crucial for their reproduction. Currently, various research methods with different advantages and limitations are employed for this purpose. Using identified spawning locations and periods as clues, we quantitatively investigate the flow velocity, water depth, water temperature, and riverbed substrate required for spawning. The results are validated using habitat simulation methods, aiming to establish a more scientific approach to explore ecological factors affecting fish reproduction. This study provides a more scientific, systematic, and detailed report on the ecological factors required for the spawning of Gymnocypris eckloni: flow velocity ranging from 0.19 to 0.97 m/s, water depth from 0.28 to 1.12 m, water temperature between 11.4 and 15.2°C, and predominantly gravel substrate. The reliability of the results was verified in another spawning ground, with good verification results. This research provides crucial data for the bio-mimetic reproductive technology of Gymnocypris eckloni and the restoration of spawning grounds for natural fish reproduction post-dam construction. It addresses the lack of suitable ecological factor data for protective fish species in the upper reaches of the Yellow River. The method exhibits strong scientific, accurate, and implementable characteristics.

Quantitative determination of flow rate variations in reservoir Eco-scheduling: A case study of Yangqu dam in the upper yellow river.

Accurate quantification of flow dynamics during reservoir ecological scheduling hinders the maintenance of normal reproductive activities in downstream riverine fish. This study proposed a quantitative method for determining the flow rate changes in reservoir ecological scheduling. The approach utilized the daily flow rate and daily flow-rate increment to characterize the flow process. Adopting the perspective of shifting spawning grounds of adhesive egg-laying fish species in response to flow rate variations, we introduced the Spawning Ground Overlap Rate as an indicator and utilized it to determine flow rate changes. Focusing on the downstream area of the Yangqu Hydropower Station in the upper reaches of the Yellow River, we calculated the distribution of spawning grounds and the Spawning Ground Overlap Rate in the region. We set a threshold for the Spawning Ground Overlap Rate to restrict the flow rate changes. The results indicated that during the fish spawning period, the ecological flow range in the downstream area of the Yangqu Dam was 480-1200 m3/s. It was required to maintain a daily flow rate change of less than 49.45 m3/(s·d) and a maximum seven-day flow difference of less than 227.76 m3/s to maintain the optimal level of spawning ground overlap rate. Additionally, it was necessary to keep the daily flow rate change below 123.83 m3/(s·d) and the maximum seven-day flow difference below 368.84 m3/s to maintain the minimum spawning ground overlap rate. The findings provide foundational data for determining flow dynamics during the ecological scheduling of the spawning period for viscous-spawning fish.

Rheological Performance Analysis of Different Preventive Maintenance Materials in Porous High-Viscosity Asphalt Pavements.

Porous asphalt pavements are widely used in rainy and wet areas for their skid resistance, noise reduction, runoff minimization and environmental sustainability. Long-term moisture vapor erosion and the destabilization of large pore structures can easily result in pavement problems such as fragmentation, spalling, cracking, and excessive permanent deformation. To this end, four different preventive maintenance materials, including the rejuvenation (RJ), cohesion reinforcement (CEM), polymerization reaction, and emulsified asphalt (EA) types, were selected in this paper to improve the high-viscosity porous asphalt pavement. The effects of the different preventive maintenance materials on the temperature sensitivity, rheological properties and fatigue performance of high-viscosity modified asphalt were evaluated through temperature sweep, frequency sweep, multi-stress creep recovery (MSCR), linear amplitude sweep (LAS), and bending beam rheometer (BBR) tests. The results showed that the four preventive maintenance materials exhibit different enhancement mechanisms and effects. RJ improves the fatigue properties, deformation resistance and low-temperature cracking resistance of aged asphalt by adding elastomeric components; CEM materials are more conducive to increasing the low-temperature crack resistance of aged asphalt; while GL1 and EA improve the viscoelastic behavior of aged asphalt, but the effect of the dosing ratio needs to be considered.

Real-world persistence and costs among patients with chronic migraine treated with onabotulinumtoxinA or calcitonin gene-related peptide monoclonal antibodies.

BACKGROUND: Chronic migraine (CM) is a common neurologic disorder that imposes substantial burden on payers, patients, and society. Low rates of persistence to oral migraine preventive medications have been previously documented; however, less is known about persistence and costs associated with innovative nonoral migraine preventive medications. OBJECTIVE: To evaluate real-world persistence and costs among adults with CM treated with onabotulinumtoxinA (onabotA) or calcitonin gene-related peptide monoclonal antibodies (CGRP mAbs). METHODS: This was a retrospective, longitudinal, observational study analyzing the IBM MarketScan Commercial and Medicare databases. The study sample included adults with CM initiating treatment with either onabotA or a CGRP mAb on or after January 1, 2018. Persistence and costs over 12 months after treatment initiation were evaluated using chi-square and Student's t-tests. Persistence to onabotA was compared with CGRP mAbs as a weighted average of the class and by individual CGRP mAbs. Mean pharmacy (acute and preventive), medical (inpatient, emergency department, and outpatient), and total costs are reported. Multivariate regression analyses were conducted to generate adjusted estimates of persistence and costs after controlling for potential confounders (age, sex, region, insurance type, number of baseline comorbidities, Charlson Comorbidity Index, and number of previously used oral migraine preventive medications). RESULTS: Of 66,303 individuals with onabotA or CGRP mAb claims, 2,697 with CM met the inclusion/exclusion criteria. In the total population, individuals were primarily female (85.5%), lived in the South (48.5%), and had a mean (SD) age of 44 (12) years, which was consistent across the onabotA and CGRP mAb cohorts. Common comorbid conditions included anxiety (23.9%), depression (18.2%), hypertension (16.5%), and sleep disorders (16.9%). After adjusting for potential confounding variables, persistence to onabotA during the 12-month follow-up period was 40.7% vs 27.8% for CGRP mAbs (odds ratio [OR] = 0.683; 95% CI = 0.604-0.768; P < 0.0001). Persistence to erenumab, fremanezumab, and galcanezumab was 25.5% (OR = 0.627; 95% CI = 0.541-0.722; P < 0.0001), 30.3% (OR = 0.746; 95% CI = 0.598-0.912; P = 0.0033), and 33.7% (OR = 0.828; 95% CI = 0.667-1.006; P = 0.058). All-cause ($18,292 vs $18,275; P = 0.9739) and migraine-related ($8,990 vs $9,341; P = 0.1374) costs were comparable between the onabotA and CGRP mAb groups. CONCLUSIONS: Among adults with CM receiving onabotA and CGRP mAbs, individuals initiating onabotA treatment had higher persistence compared with those receiving CGRP mAbs. Total all-cause and migraine-related costs over 12 months were comparable between those receiving onabotA and CGRP mAbs. DISCLOSURES: This study was sponsored by Allergan (prior to its acquisition by AbbVie), they contributed to the design and interpretation of data and the writing, reviewing, and approval of final version. Writing and editorial assistance was provided to the authors by Dennis Stancavish, MS, of Peloton Advantage, LLC, an OPEN Health company, Parsippany, NJ, and was funded by AbbVie. The opinions expressed in this article are those of the authors. The authors received no honorarium/fee or other form of financial support related to the development of this article. Dr Schwedt serves on the Board of Directors for the American Headache Society and the American Migraine Foundation. Within the prior 12 months he has received research support from Amgen, Henry Jackson Foundation, Mayo Clinic, National Institutes of Health, Patient-Centered Outcomes Research Institute, SPARK Neuro, and US Department of Defense. Within the past 12 months, he has received personal compensation for serving as a consultant or advisory board member for AbbVie, Allergan, Axsome, BioDelivery Science, Biohaven, Collegium, Eli Lilly, Ipsen, Linpharma, Lundbeck, and Satsuma. He holds stock options in Aural Analytics and Nocira. He has received royalties from UpToDate. Dr Lee and Ms Shah are employees of AbbVie and may hold AbbVie stock. Dr Gillard was an employee of AbbVie and may hold AbbVie stock. Dr Knievel has served as a consultant for AbbVie, Amgen, Eli Lilly, and Biohaven; conducted research for AbbVie, Amgen, and Eli Lilly; and is on speaker programs for AbbVie and Amgen. Dr McVige has served as a speaker and/or received research support from Allergan (now AbbVie Inc.), Alder, Amgen/Novartis, Avanir, Biohaven, Eli Lilly, Lundbeck, and Teva. Ms Wang and Ms Wu are employees of Genesis Research, which provides consulting services to AbbVie. Dr Blumenfeld, within the past 12 months, has served on advisory boards for Allergan, AbbVie, Aeon, Alder, Amgen, Axsome, BDSI, Biohaven, Impel, Lundbeck, Lilly, Novartis, Revance, Teva, Theranica, and Zosano; as a speaker for Allergan, AbbVie, Amgen, BDSI, Biohaven, Lundbeck, Lilly, and Teva; as a consultant for Allergan, AbbVie, Alder, Amgen, Biohaven, Lilly, Lundbeck, Novartis, Teva, and Theranica; and as a contributing author for Allergan, AbbVie, Amgen, Biohaven, Novartis, Lilly, and Teva. He has received grant support from AbbVie and Amgen. AbbVie is committed to responsible data sharing regarding the clinical trials we sponsor. This includes access to anonymized, individual, and trial-level data (analysis data sets), as well as other information (eg, protocols, clinical study reports, or analysis plans), as long as the trials are not part of an ongoing or planned regulatory submission. This includes requests for clinical trial data for unlicensed products and indications. These clinical trial data can be requested by any qualified researchers who engage in rigorous, independent scientific research, and will be provided following review and approval of a research proposal and Statistical Analysis Plan and execution of a Data Sharing Agreement. Data requests can be submitted at any time after approval in the United States and Europe and after acceptance of this manuscript for publication. The data will be accessible for 12 months, with possible extensions considered. For more information on the process, or to submit a request, visit the following link: https://www.abbvieclinicaltrials.com/hcp/data-sharing/.

A new model for predicting the occurrence of polycystic ovary syndrome: Based on data of tongue and pulse.

Background and objective: Polycystic ovary syndrome is one of the most common types of endocrine and metabolic diseases in women of reproductive age that needs to be screened early and assessed non-invasively. The objective of the current study was to develop prediction models for polycystic ovary syndrome based on data of tongue and pulse using machine learning techniques. Methods: A dataset of 285 polycystic ovary syndrome patients and 201 healthy women were investigated to identify the significant tongue and pulse parameters for predicting polycystic ovary syndrome. In this study, feature selection was performed using least absolute shrinkage and selection operator regression. Several machine learning algorithms (multilayer perceptron classifier, eXtreme gradient boosting classifier, and support vector machine) were used to construct the classification models to predict the presence of polycystic ovary syndrome. Results: TB-L, TB-a, TB-b, TC-L, TC-a, h3, and h4/h1 in tongue and pulse parameters were statistically associated with polycystic ovary syndrome presence. Among the several machine learning techniques, the support vector machine model was optimal for the comprehensive evaluation of this dataset and deduced the area under the receiver operating characteristic curve, DeLong test, calibration curve, and decision curve analysis. Conclusion: The machine learning model with tongue and pulse factors can predict the existence of polycystic ovary syndrome precisely.

Parameter study on characteristic pulse diagram of polycystic ovary syndrome based on logistic regression analysis.

This study aimed to explore the parameters of the independent predictive characteristic pulse diagram of polycystic ovary syndrome (PCOS) by analysing the pulse characteristics between healthy women and the PCOS group. A total of 278 women were recruited for this study. Pulse wave parameters were collected by the pulse spectrum analyser. The single-factor analysis of the pulse diagram parameters was used to identify significant indicators, and the logistic regression analysis was carried out on the above indicators with statistical differences to obtain independent predictors. According to the single-factor and multi-factor analyses, h1, h5, h3/h1, t, t1 and t5 were independent predictors of PCOS diagnosis. The results showed that PCOS patients had a faster heart rate, decreased left ventricular systolic function and decreased aortic compliance compared to healthy individuals. These findings suggested that the characteristic pulse parameters screened out are valuable for the diagnosis of PCOS.IMPACT STATEMENTWhat is already known on this subject? Polycystic ovary syndrome (PCOS) is a common gynecological reproductive endocrine and metabolic disease, which is significant for screening and early intervention in the disease. However, due to the lack of pulse's diagnostic evidence of PCOS, there is still an unknown area in the research on the correlation between PCOS and pulse diagram parameters.What do the results of this study add? This study fills the gap between the research on PCOS and pulse wave. The study also shows that the pulse characteristic parameters h1, h5, h3/h1, t, t1, and t5 are independent predictors of PCOS, suggesting that the patients have a higher heart rate, lower ventricular systolic function, and aortic compliance than healthy individuals.What are the implications of these findings for clinical practice and/or further research? Prominent risk factors for pulse parameters associated with the occurrence of PCOS facilitate early screening and diagnosis of the disease. The objectification of pulse diagnosis helps to establish a health management model, which can be used for the accurate assessment and treatment of PCOS by traditional Chinese medicine (TCM). It provides a clinical reference for the study of pulse diagnosis objectification.

Use of common blood parameters for the differential diagnosis of childhood infections.

BACKGROUND: Routine laboratory investigations are not rapidly available to assist clinicians in the diagnosis of pediatric acute infections. Our objective was to evaluate some common blood parameters and use them for the differential diagnosis of childhood infections. METHODS: This retrospective study was conducted between October 2019 and September 2020 at Guangzhou Women and Children's Medical Center, China. We performed blood tests in patients infected with DNA viruses (n = 402), RNA viruses (n = 602), gram-positive organisms (G+; n = 421), gram-negative organisms (G-; n = 613), or Mycoplasma pneumoniae (n = 387), as well as in children without infection (n = 277). The diagnostic utility of blood parameters to diagnose various infections was evaluated by logistic regression analysis. RESULTS: The most common G+ organism, G- organism, and virus were Streptococcus pneumoniae (39.7%), Salmonella typhimurium (18.9%), and influenza A virus (40.2%), respectively. The value of logit (P) = 0.003 × C-reactive protein (CRP) - 0.011 × hemoglobin (HGB) + 0.001 × platelets (PLT) was significantly different between the control, RNA virus, DNA virus, M. pneumoniae, G- organism, and G+ organism groups (2.46 [95% CI, 2.41-2.52], 2.60 [2.58-2.62], 2.70 [2.67-2.72], 2.78 [2.76-2.81], 2.88 [2.85-2.91], and 2.97 [2.93-3.00], respectively; p = 0.00 for all). The logistic regression-based model showed significantly greater accuracy than the best single discriminatory marker for each group (logit [Pinfection] vs. CRP, 0.90 vs. 0.84, respectively; logit [PRNA] vs. lymphocytes, 0.83 vs. 0.77, respectively; p = 0.00). The area under curve values were 0.72 (0.70-0.74) for HGB and 0.81 (0.79-0.82) for logit (Pvirus/bacteria) to diagnose bacterial infections, whereas they were 0.72 (0.68-0.74) for eosinophils and 0.80 (0.78-0.82) for logit (Pvirus/bacteria) to diagnose viral infections. Logit (Pvirus/bacteria) < -0.45 discriminated bacterial from viral infection with 78.9% specificity and 70.7% sensitivity. CONCLUSIONS: The combination of CRP, HGB, PLT, eosinophil, monocyte, and lymphocyte counts can distinguish between the infectious pathogens in children.

Clinical Events and Renal Function in the First Year Predict Long-Term Kidney Transplant Survival.

Background: Estimated glomerular filtration rate (eGFR) at 1 year post transplantation has been shown to be a strong predictor of long-term graft survival. However, intercurrent events (ICEs) may affect the relationship between eGFR and failure risk. Methods: The OPTN and USRDS databases on single-organ kidney transplant recipients from 2012 to 2016 were linked. Competing risk regressions estimated adjusted subhazard ratios (SHRs) of 12-month eGFR on long-term graft failure, considering all-cause mortality as the competing risk, for deceased donor (DD) and living donor (LD) recipients. Additional predictors included recipient, donor, and transplant characteristics. ICEs examined were acute rejection, cardiovascular events, and infections. Results: Cohorts comprised 25,131 DD recipients and 7471 LD recipients. SHRs for graft failure increased rapidly as 12-month eGFR values decreased from the reference 60 ml/min per 1.73 m2. At an eGFR of 20 ml/min per 1.73 m2, SHRs were 13-15 for DD recipients and 12-13 for LD recipients; at an eGFR of 30 ml/min per 1.73 m2, SHRs were 5.0-5.7 and 5.0-5.5, respectively. Among first-year ICEs, acute rejection was a significant predictor of long-term graft failure in both DD (SHR=1.63, P<0.001) and LD (SHR=1.51, P=0.006) recipients; cardiovascular events were significant in DD (SHR=1.24, P<0.001), whereas non-CMV infections were significant in the LD cohort (SHR=1.32, P=0.03). Adjustment for ICEs did not significantly reduce the association of eGFR with graft failure. Conclusions: Twelve-month eGFR is a strong predictor of long-term graft failure after accounting for clinical events occurring from discharge to 1 year. These findings may improve patient management and clinical evaluation of novel interventions.

Costs in the Year Following Deceased Donor Kidney Transplantation: Relationships With Renal Function and Graft Failure.

Relationships between renal function and medical costs for deceased donor kidney transplant recipients are not fully quantified post-transplant. We describe these relationships with renal function measured by estimated glomerular filtration rate (eGFR) and graft failure. The United States Renal Data System identified adults receiving single-organ deceased donor kidneys 2012-2015. Inpatient, outpatient, other facility costs and eGFRs at discharge, 6 and 12 months were included. A time-history of costs was constructed for graft failures and monthly costs in the first year post-transplant were compared to those without failure. The cohort of 24,021 deceased donor recipients had a 2.4% graft failure rate in the first year. Total medical costs exhibit strong trends with eGFR. Recipients with 6-month eGFRs of 30-59 ml/min/1.73m2 have total costs 48% lower than those <30 ml/min/1.73m2. For recipients with graft failure monthly costs begin to rise 3-4 months prior to failure, with incremental costs of over $38,000 during the month of failure. Mean annual total incremental costs of graft failure are over $150,000. Total costs post-transplant are strongly correlated with eGFR. Graft failure in the first year is an expensive, months-long process. Further reductions in early graft failures could yield significant human and economic benefits.

Falls, healthcare resources and costs in older adults with insomnia treated with zolpidem, trazodone, or benzodiazepines.

BACKGROUND: Falls are the leading cause of injury-related death among older Americans. While some research has found that insomnia heightens falls, health care resource utilization (HCRU) and costs, the impact of insomnia treatments on fall risk, mortality, HCRU and costs in the elderly population, which could be of substantial interest to payers, has not been fully elucidated. This study evaluated the risk of falls and related consequences among adults ≥ 65 years of age treated with common prescription medications for insomnia compared with non-sleep disordered controls. METHODS: This was a retrospective cohort analysis of deidentified Medicare claims from January 2011 through December 2017. Medicare beneficiaries treated for insomnia receiving zolpidem extended-release, zolpidem immediate-release, trazodone, or benzodiazepines were matched with non-sleep disordered controls. The main outcomes were falls, mortality, healthcare resource utilization (HCRU), and medical costs during the 12 months following the earliest fill date for the insomnia medication of interest. Generalized linear models controlled for several key covariates, including age, race, sex, geographic region and Charlson Comorbidity Index score. RESULTS: The study included 1,699,913 Medicare beneficiaries (59.9% female, mean age 75 years). Relative to controls, adjusted analyses showed that beneficiaries receiving insomnia medication experienced over twice as many falls (odds ratio [OR] = 2.34, 95% CI: 2.31-2.36). In adjusted analyses, patients receiving benzodiazepines or trazodone had the greatest risk. Crude all-cause mortality rates were 15-times as high for the insomnia-treated as controls. Compared with controls, beneficiaries receiving insomnia treatment demonstrated higher estimated adjusted mean number of inpatient, outpatient, and emergency department visits and longer length of inpatient stay. All-cause total adjusted mean costs were higher among insomnia treated patients ($967 vs $454). CONCLUSIONS: Individuals receiving insomnia treatment had an increased risk of falls and mortality and higher HCRU and costs compared with matched beneficiaries without sleep disorders. Trazodone and benzodiazepines were associated with the greatest risk of falls. This analysis suggests that significant risks are associated with common, older generation insomnia medication treatments in the elderly. Nonetheless, these results should be interpreted with caution as the use of these medications may be indicative of underlying morbidity with potential for residual confounding.

Fall Risk, Healthcare Resource Use, and Costs Among Adult Patients in the United States Treated for Insomnia with Zolpidem, Trazodone, or Benzodiazepines: A Retrospective Cohort Study.

INTRODUCTION: Falls are a common cause for morbidity and mortality among patients taking prescription insomnia medication. The objective of this study is to compare the risk of falls, all-cause healthcare resource utilization (HCRU), and costs among patients treated with commonly used, older generation insomnia medications and non-sleep-disordered controls. METHODS: This retrospective cohort study used the IBM® MarketScan® Commercial and Medicare Supplemental Databases to identify patients aged at least 18 years treated with commonly prescribed medications for insomnia (zolpidem, trazodone, benzodiazepines) between 1 January 2012 and 30 September 2017. The insomnia-treated cohort were age- and sex-matched (1:1) to non-sleep-disordered controls. Odds ratios (ORs) compared risk of falls in each cohort, adjusting for covariates. Costs were adjusted to 2018 dollars, the most recent year for the study data. RESULTS: Relative to matched controls (n = 313,086), the insomnia-treated cohort had a higher rate of falls (3.34% vs. 1.33%), and higher risk of falls [OR = 2.36 (95% confidence interval 2.27-2.44)]. Relative to other index treatments, patients treated with trazodone had the greatest risk of falls. Compared with matched controls, the estimated mean number of inpatient visits, emergency department visits, outpatient visits, and mean length of inpatient stay were all significantly higher among patients treated for insomnia. Such patients incurred greater total costs per patient per month than matched controls ($2100 versus $888; estimated mean ratio, 2.36; 95% CI 2.35-2.38; p < 0.0001). CONCLUSIONS: Relative to matched controls, the insomnia-treated cohort showed higher risk of falls with greater HCRU and costs. Each outcome measured was highest among patients treated with trazodone, relative to other index treatments. Findings suggest the need for new treatment options to optimize quality of care for patients with insomnia.

Improved Procedure for the 3D Reconstruction of Asphalt Concrete Mesostructures Considering the Similarity of Aggregate Phase Geometry between Adjacent CT Slices.

Existing image segmentation algorithms used for the computed tomography (CT) images of asphalt concrete mostly ignore the similarity of aggregate phase geometry between adjacent CT slices, thus increasing the variability in the aggregate phase pixel values between adjacent slices and leading to a large number of model defects, e.g., interconnected aggregates, flaky aggregates, and incomplete aggregates. The developed mesostructural models with these defects pose a challenge to following simulation operations. To address this issue, an improved procedure for the 3D reconstruction of asphalt concrete mesostructures considering the similarity of aggregate phase geometry between adjacent slices was developed, which includes two adjacent-slice pixel-value-correction algorithms, a multi-directional multiple-correction method, and an image pixel interpolation process. First, the bilinear interpolation algorithm was employed to improve the pixel density of 2D CT images and the average filtering algorithm was used to reduce the noise of the CT images. Subsequently, the OTSU method was employed to separate the asphalt mortar matrix phase from the aggregate phase, and the marker-based watershed segmentation method was used to separate the interconnected aggregates. Finally, the adjacent-slice pixel-value-correction algorithm was used to recover the similarity of aggregate phase geometry between adjacent CT slices, and the multi-directional multiple-correction method was used to further enhance the geometric similarity. The results show that the developed 3D reconstruction procedure removes most of the model defects in the 3D mesostructural model of asphalt concrete, thus realistically maintaining the 3D spatial distribution features and contour characteristics.

Nomogram for predicting survival of patients with metastatic nonfunctioning pancreatic neuroendocrine tumors: A SEER based study.

ABSTRACT: More attention has been placed on nonfunctioning pancreatic neuroendocrine tumors due to the increase in its incidence in recent years. Whether tumor resection at the primary site of metastatic NFpNET is effective remains controversial. Moreover, clinicians need a more precise prognostic tool to estimate the survival of these patients.Patients with metastatic NFpNET were extracted from the Surveillance, Epidemiology, and End Results (SEER) database. Significant prognostic factors were identified using a multivariate Cox regression model and included in the nomogram. Coarsened exact matching analysis was used to balance the clinical variables between the non-surgical and surgical groups in our study.A total of 1464 patients with metastatic nonfunctioning pancreatic neuroendocrine tumors (NFpNETs) were included in our cohort. Multivariate analysis identified age, sex, tumor size, differentiated grade, lymph node metastases, resection of primary tumors, and marital status as independent predictors of metastatic NFpNET. The nomogram showed excellent accuracy in predicting 1-, 3-, and 5-year overall survival, with a C-index of 0.812. The calibration curve revealed good consistency between the predicted and actual survival.Coarsened exact matching analysis using SEER data indicated the survival advantages of resection of primary tumors. Our study is the first to build a nomogram model for patients with metastatic NFpNETs. This predictive tool can help clinicians identify high-risk patients and more accurately assess patient survival times.

Comparative Evaluation of Microbiota Dynamics and Metabolites Correlation Between Spontaneous and Inoculated Fermentations of Nanfeng Tangerine Wine.

Understanding the evolution of microorganisms and metabolites during wine fermentation is essential for controlling its production. The structural composition and functional capacity of the core microbiota determine the quality and quantity of fruit wine. Nanfeng tangerine wine fermentation involves a complex of various microorganisms and a wide variety of metabolites. However, the microbial succession and functional shift of the core microbiota in this product fermentation remain unclear. Therefore, high-throughput sequencing (HTS) and headspace-gas chromatography-mass spectrometry (HS/GC-MS) were employed to reveal the core functional microbiota for the production of volatile flavors during spontaneous fermentation (SF) and inoculated fermentation (IF) with Saccharomyces cerevisiae of Nanfeng tangerine wine. A total of 13 bacterial and 8 fungal genera were identified as the core microbiota; Lactobacillus and Acetobacter were the dominant bacteria in SF and IF, respectively. The main fungal genera in SF and IF were Hanseniaspora, Pichia, and Saccharomyces with a clear succession. In addition, the potential correlations analysis between microbiota succession and volatile flavor dynamics revealed that Lactobacillus, Acetobacter, Hanseniaspora, and Saccharomyces were the major contributors to the production of the volatile flavor of Nanfeng tangerine wine. The results of the present study provide insight into the effects of the core functional microbiota in Nanfeng tangerine wine and can be used to develop effective strategies for improving the quality of fruit wines.

In vitro preparation of uniform and nucleic acid free hepatitis B core particles through an optimized disassembly-purification-reassembly process.

Structure heterogeneity and host nucleic acids contamination are two major problems for virus-like particles (VLPs) produced by various host cells. In this study, an in vitro optimized disassembly-purification-reassembly process was developed to obtain uniform and nucleic acid free hepatitis B core (HBc) based VLPs from E. coli fermentation. The process started with ammonium sulfate precipitation of all heterogeneous HBc structures after cell disintegration. Then, dissolution and disassembly of pellets into basic subunits were carried out under the optimized disassembly condition. All contaminants, including host nucleic acids and proteins, were efficiently removed with affinity chromatography. The purified subunits reassembled into VLPs by final removal of the chaotropic agent. Two uniform and nucleic acid free HBc-based VLPs, truncated HBc149 and chimeric HBc183-MAGE3 I, were successfully prepared. It was found that disassembly degree of HBc-based VLPs had a great influence on the protein yield, nucleic acid removal and reassembly efficiency. 4 M urea was optimal because lower concentration would not disassemble the particles completely while higher concentration would further denature the subunits into disordered aggregate and could not be purified and reassembled efficiently. For removal of strong binding nucleic acids such as in the case of HBc183-MAGE3 I, benzonase nuclease was added to the disassembly buffer before affinity purification. Through the optimized downstream process, uniform and nucleic acid free HBc149 VLPs and HBc183-MAGE3 I VLPs were obtained with purities above 90% and yields of 55.2 and 43.0 mg/L, respectively. This study would be a reference for efficient preparation of other VLPs.

Financial impact of delayed graft function in kidney transplantation.

Increased utilization of suboptimal organs in response to organ shortage has resulted in increased incidence of delayed graft function (DGF) after transplantation. Although presumed increased costs associated with DGF are a deterrent to the utilization of these organs, the financial burden of DGF has not been established. We used the Premier Healthcare Database to conduct a retrospective analysis of healthcare resource utilization and costs in kidney transplant patients (n = 12 097) between 1/1/2014 and 12/31/2018. We compared cost and hospital resource utilization for transplants in high-volume (n = 8715) vs low-volume hospitals (n = 3382), DGF (n = 3087) vs non-DGF (n = 9010), and recipients receiving 1 dialysis (n = 1485) vs multiple dialysis (n = 1602). High-volume hospitals costs were lower than low-volume hospitals ($103 946 vs $123 571, P < .0001). DGF was associated with approximately $18 000 (10%) increase in mean costs ($130 492 vs $112 598, P < .0001), 6 additional days of hospitalization (14.7 vs 8.7, P < .0001), and 2 additional ICU days (4.3 vs 2.1, P < .0001). Multiple dialysis sessions were associated with an additional $10 000 compared to those with only 1. In conclusion, DGF is associated with increased costs and length of stay for index kidney transplant hospitalizations and payment schemes taking this into account may reduce clinicians' reluctance to utilize less-than-ideal kidneys.

Light-controlled cooperative catalysis of asymmetric sulfoxidation based on azobenzene-bridged chiral salen TiIV catalysts.

Incorporation of azobenzene into the linker of bimetallic chiral salen TiIV catalysts allowed the photoswitchable arrangement of the two Ti(salen) units through cis/trans photoisomerization of azobenzene. The differently arranged Ti(salen) units changed their cooperative function to reflect the positional relationships, as a result, their efficiency as cooperative catalysts in asymmetric sulfoxidation could be readily controlled by light stimuli.