Exploring the progression of drug dependence in a methamphetamine self-administration rat model through targeted and non-targeted metabolomics analyses.

Persistent neurochemical and biological disturbances resulting from repeated cycles of drug reward, withdrawal, and relapse contribute to drug dependence. Methamphetamine (MA) is a psychostimulant with substantial abuse potential and neurotoxic effects, primarily affecting monoamine neurotransmitter systems in the brain. In this study, we aimed to explore the progression of drug dependence in rat models of MA self-administration, extinction, and reinstatement through targeted and non-targeted metabolomics analyses. Metabolic profiles were examined in rat plasma during the following phases: after 16 days of MA self-administration (Group M); after 16 days of self-administration followed by 14 days of extinction (Group MS); and after self-administration and extinction followed by a reinstatement injection of MA (Group MSM). Each group of MA self-administration, extinction, and reinstatement induces distinct changes in the metabolic pathways, particularly those related to the TCA cycle, arginine and proline metabolism, and arginine biosynthesis. Additionally, the downregulation of glycerophospholipids and sphingomyelins in Group MSM suggests their potential role in MA reinstatement. These alterations may signify the progressive deterioration of these metabolic pathways, possibly contributing to drug dependence following repeated cycles of drug reward, withdrawal, and relapse. These results provide valuable insights into the metabolic changes associated with MA use at various stages, potentially facilitating the discovery of early diagnostic biomarkers and therapeutic targets for MA use disorders.

Morphology of Copper Nanofoams for Radiation Hydrodynamics and Fusion Applications Investigated by 3D Ptychotomography.

The performance of metal and polymer foams used in inertial confinement fusion (ICF), inertial fusion energy (IFE), and high-energy-density (HED) experiments is currently limited by our understanding of their nanostructure and its variation in bulk material. We utilized an X-ray-free electron laser (XFEL) together with lensless X-ray imaging techniques to probe the 3D morphology of copper foams at nanoscale resolution (28 nm). The observed morphology of the thin shells is more varied than expected from previous characterizations, with a large number of them distorted, merged, or open, and a targeted mass density 14% less than calculated. This nanoscale information can be used to directly inform and improve foam modeling and fabrication methods to create a tailored material response for HED experiments.

Structural Evolution of Photoexcited Methylcobalamin toward a CarH-like Metastable State: Evidence from Time-Resolved X-ray Absorption and X-ray Emission.

CarH is a protein photoreceptor that uses a form of B12, adenosylcobalamin (AdoCbl), to sense light via formation of a metastable excited state. Aside from AdoCbl bound to CarH, methylcobalamin (MeCbl) is the only other example─to date─of photoexcited cobalamins forming metastable excited states with lifetimes of nanoseconds or longer. The UV-visible spectra of the excited states of MeCbl and AdoCbl bound to CarH are similar. We have used transient Co K-edge X-ray absorption and X-ray emission spectroscopies in conjunction with transient absorption spectroscopy in the UV-visible region to characterize the excited states of MeCbl. These data show that the metastable excited state of MeCbl has a slightly expanded corrin ring and increased electron density on the cobalt, but only small changes in the axial bond lengths.

Neoadjuvant Chemotherapy Response in Triple-Negative Apocrine Carcinoma: Comparing Apocrine Morphology, Androgen Receptor, and Immune Phenotypes.

CONTEXT.­: Apocrine differentiation and androgen receptor (AR) positivity represent a specific subset of triple-negative breast cancer (TNBC) and are often considered potential prognostic or predictive factors. OBJECTIVE.­: To evaluate the response of TNBC to neoadjuvant chemotherapy (NAC) and to assess the impact of apocrine morphology, AR status, Ki-67 labeling index (Ki-67LI), and tumor-infiltrating lymphocytes (TILs). DESIGN.­: A total of 232 TNBC patients who underwent NAC followed by surgical resection in a single institute were analyzed. The study evaluated apocrine morphology and AR and Ki-67LI expression via immunohistochemistry from pre-NAC biopsy samples. Additionally, pre-NAC intratumoral TILs and stromal TILs (sTILs) were quantified from biopsies using a deep learning model. The response to NAC after surgery was assessed based on residual cancer burden. RESULTS.­: Both apocrine morphology and high AR expression correlated with lower Ki-67LI (P < .001 for both). Apocrine morphology was associated with lower postoperative pathologic complete response (pCR) rates after NAC (P = .02), but the difference in TILs between TNBC cases with and without apocrine morphology was not statistically significant (P = .09 for sTILs). In contrast, AR expression did not significantly affect pCR (P = .13). Pre-NAC TILs strongly correlated with postoperative pCR in TNBCs without apocrine morphology (P < .001 for sTILs), whereas TNBC with apocrine morphology demonstrated an indeterminate trend (P = .82 for sTILs). CONCLUSIONS.­: Although TIL counts did not vary significantly based on apocrine morphology, apocrine morphology itself was a more reliable predictor of NAC response than AR expression. Consequently, although apocrine morphology is a rare subtype of TNBC, its identification is clinically important.

An artificial intelligence-powered PD-L1 combined positive score (CPS) analyser in urothelial carcinoma alleviating interobserver and intersite variability.

AIMS: Immune checkpoint inhibitors targeting programmed death-ligand 1 (PD-L1) have shown promising clinical outcomes in urothelial carcinoma (UC). The combined positive score (CPS) quantifies PD-L1 22C3 expression in UC, but it can vary between pathologists due to the consideration of both immune and tumour cell positivity. METHODS AND RESULTS: An artificial intelligence (AI)-powered PD-L1 CPS analyser was developed using 1,275,907 cells and 6175.42 mm2 of tissue annotated by pathologists, extracted from 400 PD-L1 22C3-stained whole slide images of UC. We validated the AI model on 543 UC PD-L1 22C3 cases collected from three institutions. There were 446 cases (82.1%) where the CPS results (CPS ≥10 or <10) were in complete agreement between three pathologists, and 486 cases (89.5%) where the AI-powered CPS results matched the consensus of two or more pathologists. In the pathologist's assessment of the CPS, statistically significant differences were noted depending on the source hospital (P = 0.003). Three pathologists reevaluated discrepancy cases with AI-powered CPS results. After using the AI as a guide and revising, the complete agreement increased to 93.9%. The AI model contributed to improving the concordance between pathologists across various factors including hospital, specimen type, pathologic T stage, histologic subtypes, and dominant PD-L1-positive cell type. In the revised results, the evaluation discordance among slides from different hospitals was mitigated. CONCLUSION: This study suggests that AI models can help pathologists to reduce discrepancies between pathologists in quantifying immunohistochemistry including PD-L1 22C3 CPS, especially when evaluating data from different institutions, such as in a telepathology setting.

Inflamed immune phenotype predicts favorable clinical outcomes of immune checkpoint inhibitor therapy across multiple cancer types.

BACKGROUND: The inflamed immune phenotype (IIP), defined by enrichment of tumor-infiltrating lymphocytes (TILs) within intratumoral areas, is a promising tumor-agnostic biomarker of response to immune checkpoint inhibitor (ICI) therapy. However, it is challenging to define the IIP in an objective and reproducible manner during manual histopathologic examination. Here, we investigate artificial intelligence (AI)-based immune phenotypes capable of predicting ICI clinical outcomes in multiple solid tumor types. METHODS: Lunit SCOPE IO is a deep learning model which determines the immune phenotype of the tumor microenvironment based on TIL analysis. We evaluated the correlation between the IIP and ICI treatment outcomes in terms of objective response rates (ORR), progression-free survival (PFS), and overall survival (OS) in a cohort of 1,806 ICI-treated patients representing over 27 solid tumor types retrospectively collected from multiple institutions. RESULTS: We observed an overall IIP prevalence of 35.2% and significantly more favorable ORRs (26.3% vs 15.8%), PFS (median 5.3 vs 3.1 months, HR 0.68, 95% CI 0.61 to 0.76), and OS (median 25.3 vs 13.6 months, HR 0.66, 95% CI 0.57 to 0.75) after ICI therapy in IIP compared with non-IIP patients, respectively (p<0.001 for all comparisons). On subgroup analysis, the IIP was generally prognostic of favorable PFS across major patient subgroups, with the exception of the microsatellite unstable/mismatch repair deficient subgroup. CONCLUSION: The AI-based IIP may represent a practical, affordable, clinically actionable, and tumor-agnostic biomarker prognostic of ICI therapy response across diverse tumor types.

Augmented interpretation of HER2, ER, and PR in breast cancer by artificial intelligence analyzer: enhancing interobserver agreement through a reader study of 201 cases.

BACKGROUND: Accurate classification of breast cancer molecular subtypes is crucial in determining treatment strategies and predicting clinical outcomes. This classification largely depends on the assessment of human epidermal growth factor receptor 2 (HER2), estrogen receptor (ER), and progesterone receptor (PR) status. However, variability in interpretation among pathologists pose challenges to the accuracy of this classification. This study evaluates the role of artificial intelligence (AI) in enhancing the consistency of these evaluations. METHODS: AI-powered HER2 and ER/PR analyzers, consisting of cell and tissue models, were developed using 1,259 HER2, 744 ER, and 466 PR-stained immunohistochemistry (IHC) whole-slide images of breast cancer. External validation cohort comprising HER2, ER, and PR IHCs of 201 breast cancer cases were analyzed with these AI-powered analyzers. Three board-certified pathologists independently assessed these cases without AI annotation. Then, cases with differing interpretations between pathologists and the AI analyzer were revisited with AI assistance, focusing on evaluating the influence of AI assistance on the concordance among pathologists during the revised evaluation compared to the initial assessment. RESULTS: Reevaluation was required in 61 (30.3%), 42 (20.9%), and 80 (39.8%) of HER2, in 15 (7.5%), 17 (8.5%), and 11 (5.5%) of ER, and in 26 (12.9%), 24 (11.9%), and 28 (13.9%) of PR evaluations by the pathologists, respectively. Compared to initial interpretations, the assistance of AI led to a notable increase in the agreement among three pathologists on the status of HER2 (from 49.3 to 74.1%, p < 0.001), ER (from 93.0 to 96.5%, p = 0.096), and PR (from 84.6 to 91.5%, p = 0.006). This improvement was especially evident in cases of HER2 2+ and 1+, where the concordance significantly increased from 46.2 to 68.4% and from 26.5 to 70.7%, respectively. Consequently, a refinement in the classification of breast cancer molecular subtypes (from 58.2 to 78.6%, p < 0.001) was achieved with AI assistance. CONCLUSIONS: This study underscores the significant role of AI analyzers in improving pathologists' concordance in the classification of breast cancer molecular subtypes.

A versatile pressure-cell design for studying ultrafast molecular-dynamics in supercritical fluids using coherent multi-pulse x-ray scattering.

Supercritical fluids (SCFs) can be found in a variety of environmental and industrial processes. They exhibit an anomalous thermodynamic behavior, which originates from their fluctuating heterogeneous micro-structure. Characterizing the dynamics of these fluids at high temperature and high pressure with nanometer spatial and picosecond temporal resolution has been very challenging. The advent of hard x-ray free electron lasers has enabled the development of novel multi-pulse ultrafast x-ray scattering techniques, such as x-ray photon correlation spectroscopy (XPCS) and x-ray pump x-ray probe (XPXP). These techniques offer new opportunities for resolving the ultrafast microscopic behavior in SCFs at unprecedented spatiotemporal resolution, unraveling the dynamics of their micro-structure. However, harnessing these capabilities requires a bespoke high-pressure and high-temperature sample system that is optimized to maximize signal intensity and address instrument-specific challenges, such as drift in beamline components, x-ray scattering background, and multi-x-ray-beam overlap. We present a pressure cell compatible with a wide range of SCFs with built-in optical access for XPCS and XPXP and discuss critical aspects of the pressure cell design, with a particular focus on the design optimization for XPCS.

Artificial intelligence-powered spatial analysis of tumor-infiltrating lymphocytes for prediction of prognosis in resected colon cancer.

Tumor-infiltrating lymphocytes (TIL) have been suggested as an important prognostic marker in colorectal cancer, but assessment usually requires additional tissue processing and interpretational efforts. The aim of this study is to assess the clinical significance of artificial intelligence (AI)-powered spatial TIL analysis using only a hematoxylin and eosin (H&E)-stained whole-slide image (WSI) for the prediction of prognosis in stage II-III colon cancer treated with surgery and adjuvant therapy. In this retrospective study, we used Lunit SCOPE IO, an AI-powered H&E WSI analyzer, to assess intratumoral TIL (iTIL) and tumor-related stromal TIL (sTIL) densities from WSIs of 289 patients. The patients with confirmed recurrences had significantly lower sTIL densities (mean sTIL density 630.2/mm2 in cases with confirmed recurrence vs. 1021.3/mm2 in no recurrence, p < 0.001). Additionally, significantly higher recurrence rates were observed in patients having sTIL or iTIL in the lower quartile groups. Risk groups defined as high-risk (both iTIL and sTIL in the lowest quartile groups), low-risk (sTIL higher than the median), or intermediate-risk (not high- or low-risk) were predictive of recurrence and were independently associated with clinical outcomes after adjusting for other clinical factors. AI-powered TIL analysis can provide prognostic information in stage II/III colon cancer in a practical manner.

Effect of glycopyrrolate on the postoperative urine output of patients following kidney transplantation: a retrospective observational study.

OBJECTIVES: To compare the urine output and estimated glomerular filtration rate (eGFR) of patients postoperatively administered sugammadex or glycopyrrolate 7 days following kidney transplantation (KT). METHODS: We retrospectively enrolled 134 consecutive patients who underwent KT under general anesthesia. Their urine output and eGFR were recorded every 24 hours between postoperative day (POD) 1 and 7. We used regression analysis to evaluate the relationship between the reversal agent administered and the outcomes of the participants. RESULTS: The urine output and eGFR of the participants did not differ between the two groups. Multivariate analysis showed that body mass index (BMI) (odds ratio (OR) 1.21; 95% confidence interval (CI) 1.05-1.40), diabetes mellitus (OR 3.14; 95% CI 1.07-9.16), neurovascular disease (OR 7.00; 95% CI 1.61-30.42), and the duration of surgery (OR 1.01; 95% CI 1.00-1.01) were associated with lower urine output on POD 7. In addition, only BMI (OR 1.25; 95% CI 1.09-1.42) was associated with low eGFR on POD 7. CONCLUSIONS: The urine output and eGFR of patients administered sugammadex or glycopyrrolate following KT did not differ 7 days later. Moreover, glycopyrrolate does not affect urine output or eGFR on POD 7, according to multivariate regression analysis.

Ultrafast X-Ray Scattering Reveals Composite Amplitude Collective Mode in the Weyl Charge Density Wave Material (TaSe_{4})_{2}I.

We report ultrafast x-ray scattering experiments of the quasi-1D charge density wave (CDW) material (TaSe_{4})_{2}I following ultrafast infrared photoexcitation. From the time-dependent diffraction signal at the CDW sidebands we identify a 0.11 THz amplitude mode derived primarily from a transverse acoustic mode of the high-symmetry structure. From our measurements we determine that this mode interacts with the valence charge indirectly through another collective mode, and that the CDW system in (TaSe_{4})_{2}I has a composite nature supporting multiple dynamically active structural degrees of freedom.

Hard x-ray - optical four-wave mixing using a split-and-delay line.

New, hard x-ray free electron lasers (FEL) produce intense femtosecond-to-attosecond pulses at angstrom wavelengths, giving access to the fundamental spatial and temporal scales of matter. These revolutionary light sources open the door to applying the suite of nonlinear, optical spectroscopy methods at hard x-ray photon energies. Nonlinear spectroscopy with hard x-rays can allow for measuring the coherence properties of short wavelength excitations with atomic specificity and for understanding how high energy excitations couple to other degrees of freedom in atomic, molecular or condensed-phase systems. As a step in this direction, here we present hard x-ray, optical four-wave mixing (4WM) measurements done at 9.8 keV at the split-and-delay line at the x-ray correlation spectroscopy (XCS) hutch of the Linac Coherent Light Source (LCLS). In this work, we create an x-ray transient grating (TG) from a pair of crossing x-ray beams and diffract optical laser pulses at 400 nm from the TG. The key technical advance here is being able to independently vary the delays of the x-ray pulses. Measurements were made in 3 different solid samples: bismuth germinate (BGO), zinc oxide (ZnO) and yttrium aluminum garnet (YAG). The resulting phase-matched, 4WM signal is measured in two different ways: by varying the x-ray, x-ray pulse delay which can reveal both material and light source coherence properties and also by varying the optical laser delay with respect to the x-ray TG to study how the x-ray excitation couples to the optical properties. Although no coherent 4WM signal was seen in these measurements, the absence of this signal gives important information on experimental requirements for detecting this in future work. Also, our laser-delay scans, although not a new measurement, were applied to different materials than in past work and reveal new examples x-ray induced lattice dynamics in solids. This work represents a key step towards extending nonlinear optics and time-resolved spectroscopy into the hard x-ray regime.

Deep learning model improves tumor-infiltrating lymphocyte evaluation and therapeutic response prediction in breast cancer.

Tumor-infiltrating lymphocytes (TILs) have been recognized as key players in the tumor microenvironment of breast cancer, but substantial interobserver variability among pathologists has impeded its utility as a biomarker. We developed a deep learning (DL)-based TIL analyzer to evaluate stromal TILs (sTILs) in breast cancer. Three pathologists evaluated 402 whole slide images of breast cancer and interpreted the sTIL scores. A standalone performance of the DL model was evaluated in the 210 cases (52.2%) exhibiting sTIL score differences of less than 10 percentage points, yielding a concordance correlation coefficient of 0.755 (95% confidence interval [CI], 0.693-0.805) in comparison to the pathologists' scores. For the 226 slides (56.2%) showing a 10 percentage points or greater variance between pathologists and the DL model, revisions were made. The number of discordant cases was reduced to 116 (28.9%) with the DL assistance (p < 0.001). The DL assistance also increased the concordance correlation coefficient of the sTIL score among every two pathologists. In triple-negative and human epidermal growth factor receptor 2 (HER2)-positive breast cancer patients who underwent the neoadjuvant chemotherapy, the DL-assisted revision notably accentuated higher sTIL scores in responders (26.8 ± 19.6 vs. 19.0 ± 16.4, p = 0.003). Furthermore, the DL-assistant revision disclosed the correlation of sTIL-high tumors (sTIL ≥ 50) with the chemotherapeutic response (odd ratio 1.28 [95% confidence interval, 1.01-1.63], p = 0.039). Through enhancing inter-pathologist concordance in sTIL interpretation and predicting neoadjuvant chemotherapy response, here we report the utility of the DL-based tool as a reference for sTIL scoring in breast cancer assessment.

Considerations on the methane correction factor and fraction of methane parameters in the IPCC first-order decay model for active aeration landfills.

Understanding the behavior of organic carbon in municipal solid waste landfills is a major challenge for estimating methane (CH4) emissions using the Intergovernmental Panel on Climate Change (IPCC) first-order decay (FOD) model. According to the IPCC guidelines, the default values of CH4 correction factor (MCF) and fraction of CH4 (F) for active aeration landfills are set as 0.4 and 0.5, respectively. However, whether it is reasonable to apply the default values of MCF and F to active aeration landfills is questionable. This study aims to estimate the MCF and develop a method to determine the F value for active aeration landfills. In this investigation, three landfill sites were operated as active aeration landfills to estimate the MCF and the F. The study results indicate that MCF values were lower than the default value of 0.4 provided in the IPCC guidelines under aerobic conditions with a CH4 concentration of less than 5%. According to the carbon balance analyses, there was a mismatch between the theoretical CH4/CO2 ratio based on the F default value of 0.5 and the measured CH4/CO2 ratio. Using the F calculation method proposed in this study, the theoretical CH4/CO2 ratio and the measured CH4/CO2 ratio was calculated equally. The F values during air injection ranged from 0.25 to 0.93 at three landfill sites, suggesting that adapting the F default value of 0.5 for active aeration landfills may lead to significant errors in the estimation of CH4 emissions using the IPCC FOD model.

Control of Tire Wear Particulate Matter through Tire Tread Prescription.

This study aims to analyze tire wear particulate matter (TWP) from tread rubber with different formulations and to compare the concentration of TWP with different wear devices. The TWP generated during the abrasion of truck and bus radial (TBR) tires were examined, and the effect of using different types of rubber and carbon black (CB) were investigated. When natural rubber (NR) was solely used as the tire tread rubber material, there was a higher concentration of 5-10 µm TWP. However, when the tread formulation consisted of NR mixed with butadiene rubber, the TWP concentration decreased. Changing the type of CB also reduced the amount of TWP in the 2.5 µm size range. The TWP concentration in the specimens increased with increasing speed and vertical load. The TWP generated during the abrasion tests using wear testers and tire simulators exhibited similar trends. These findings suggest that modifying tire tread formulations can effectively control the distribution and amount of TWP generation.

Optimized End Functionality of Silane-Terminated Liquid Butadiene Rubber for Silica-Filled Rubber Compounds.

As the world is shifting from internal combustion engine vehicles to electric vehicles in response to environmental pollution, the tire industry has been conducting research on tire performance to meet the requirements of electric vehicles. In this experiment, functionalized liquid butadiene rubber (F-LqBR) with triethoxysilyl groups at both ends was introduced into a silica-filled rubber compound as a substitute for treated distillate aromatic extract (TDAE) oil, and comparative evaluation was conducted according to the number of triethoxysilyl groups. The results showed that F-LqBRs improved silica dispersion in the rubber matrix through the formation of chemical bonds between silanol groups and the base rubber, and reduced rolling resistance by limiting chain end mobility and improving filler-rubber interaction. However, when the number of triethoxysilyl groups in F-LqBR was increased from two to four, self-condensation increased, the reactivity of the silanol groups decreased, and the improvement of properties was reduced. As a result, the optimized end functionality of triethoxysilyl groups for F-LqBR in silica-filled rubber compound was two. The 2-Azo-LqBR with the optimized functionality showed an improvement of 10% in rolling resistance, 16% in snow traction, and 17% in abrasion resistance when 10 phr of TDAE oil was substituted.

Papillary thyroid cancer immune phenotypes via tumor-infiltrating lymphocyte spatial analysis.

Standard-of-care treatment options provide an excellent prognosis for papillary thyroid cancers (PTCs); however, approximately 10% of cases are advanced PTCs, resulting in less than 50% 5-year survival rates. Understanding the tumor microenvironment is essential for understanding cancer progression and investigating potential biomarkers for treatment, such as immunotherapy. Our study focused on tumor-infiltrating lymphocytes (TILs), which are the main effectors of antitumor immunity and related to the mechanism of immunotherapy. Using an artificial intelligence model, we analyzed the density of intratumoral and peritumoral TILs in the pathologic slides of The Cancer Genome Atlas PTC cohort. Tumors were classified into three immune phenotypes (IPs) based on the spatial distribution of TILs: immune-desert (48%), immune-excluded (34%), and inflamed (18%). Immune-desert IP was mostly characterized by RAS mutations, high thyroid differentiation score, and low antitumor immune response. Immune-excluded IP predominantly consisted of BRAF V600E-mutated tumors and had a higher rate of lymph node metastasis. Inflamed IP was characterized by a high antitumor immune response, as demonstrated by a high cytolytic score, immune-related cell infiltrations, expression of immunomodulatory molecules (including immunotherapy target molecules), and enrichment of immune-related pathways. This study is the first to investigate IP classification using TILs in PTC through a tissue-based approach. Each IP had unique immune and genomic profiles. Further studies are warranted to assess the predictive value of IP classification in advanced PTC patients treated with immunotherapy.

Identification of Potential Biomarkers for Diagnosis of Patients with Methamphetamine Use Disorder.

The current method for diagnosing methamphetamine use disorder (MUD) relies on self-reports and interviews with psychiatrists, which lack scientific rigor. This highlights the need for novel biomarkers to accurately diagnose MUD. In this study, we identified transcriptome biomarkers using hair follicles and proposed a diagnostic model for monitoring the MUD treatment process. We performed RNA sequencing analysis on hair follicle cells from healthy controls and former and current MUD patients who had been detained in the past for illegal use of methamphetamine (MA). We selected candidate genes for monitoring MUD patients by performing multivariate analysis methods, such as PCA and PLS-DA, and PPI network analysis. We developed a two-stage diagnostic model using multivariate ROC analysis based on the PLS-DA method. We constructed a two-step prediction model for MUD diagnosis using multivariate ROC analysis, including 10 biomarkers. The first step model, which distinguishes non-recovered patients from others, showed very high accuracy (prediction accuracy, 98.7%). The second step model, which distinguishes almost-recovered patients from healthy controls, showed high accuracy (prediction accuracy, 81.3%). This study is the first report to use hair follicles of MUD patients and to develop a MUD prediction model based on transcriptomic biomarkers, which offers a potential solution to improve the accuracy of MUD diagnosis and may lead to the development of better pharmacological treatments for the disorder in the future.

Effect of anesthetics on postoperative nausea and vomiting after peripheral vascular surgery in end-stage renal disease patients: A retrospective observational study.

Background: Propofol-based total intravenous anesthesia (TIVA) is considered a prophylactic approach to decrease postoperative nausea and vomiting (PONV). Despite general anesthesia commonly being performed in end-stage renal disease (ESRD) patients, PONV in ESRD patients has not been well-described. We investigated PONV in peripheral vascular surgery under general anesthesia in ESRD patients. Methods: To compare PONV between propofol-based TIVA and anesthesia with volatile anesthetics, we collected retrospective data from patients who underwent peripheral vascular surgery under general anesthesia from July 2018 to April 2020. We performed univariable and multivariable analyses, including factors that could be associated with PONV and those previously shown to affect PONV. Result: A total of 1,699 peripheral vascular surgeries under general anesthesia in ESRD patients were eligible for analysis. Based on the multivariable analysis, TIVA (odds ratio [OR], 0.45; 95% confidence interval [CI], 0.35-0.60; P < 0.001) significantly decreased PONV. Female sex (OR, 1.85; 95% CI, 1.44-2.38; P < 0.001) and anesthetic duration (OR, 1.01; 95% CI, 1.00-1.01; P < 0.001) were associated with increased PONV. Conclusion: Propofol-based TIVA is the most influential factor decreasing PONV after peripheral vascular surgery in ESRD patients. Anesthesiologists can apply propofol-based TIVA as an alternative to anesthesia with volatile anesthetics.

Postoperative mortality in patients with end-stage renal disease according to the use of sugammadex: a single-center retrospective propensity score matched study.

BACKGROUND: Clearance of the sugammadex-rocuronium complex is limited to renal excretion. There are restrictions on the use of sugammadex in patients with severe renal impairment. A paucity of data supports the clinical safety of sugammadex in patients with renal impairment. We analyzed mortality after using sugammadex in patients with end-stage renal disease to establish evidence of safety for sugammadex. METHODS: We retrospectively collected the medical records of 2,134 patients with end-stage renal disease who were dependent on hemodialysis and underwent surgery under general anesthesia between January 2018 and December 2019. Propensity score matching was used. The primary outcome was the 30-day mortality rate, and secondary outcomes were the 1-year mortality rate and causes of death. RESULTS: A total of 2,039 patients were included in the study. Sugammadex was administered as a reversal agent for rocuronium in 806 (39.5%) patients; the remaining 1,233 (60.5%) patients did not receive sugammadex. After matching, 1,594 patients were analyzed; 28 (3.5%) of the 797 patients administered sugammadex, and 28 (3.5%) of the 797 patients without sugammadex, died within 30 days after surgery (P > 0.99); 38 (4.8%) of the 797 patients administered sugammadex, and 45 (5.7%) of the 797 patients without sugammadex, died within 1 year after surgery (P = 0.499). No significant differences in the causes of 30-day mortality were observed between the two groups after matching (P = 0.860). CONCLUSIONS: In this retrospective study, sugammadex did not increase the 30-day and 1-year mortality rate after surgery in end-stage renal disease patients.