Skill Mastery and Learning Curve Analysis in Fenestrated Endovascular Aortic Repair with Physician-Modified Endograft.

Objectives: This study aims to delineate the unique learning curve for fenestrated endovascular aortic repair (FEVAR) at our institution. Materials and Methods: We measured the FEVAR-specific procedure time (FSPT) as the duration from device deployment to bridging stent completion. To maintain consistency in technical complexity, the study focused on 38 cases with four-fenestration FEVAR for juxtarenal abdominal aortic aneurysms, selected from 103 of all FEVAR procedures between June 2011 and February 2024. In these cases, superior mesenteric and bilateral renal arteries were preserved with fenestration with bridging stents insertion, while celiac arteries fenestrations without fenestrations. Learning curve and cumulative sum (CUSUM) analyses assessed FSPT reduction against increased FEVAR experiences. Results: A significant learning curve was observed, with the procedure time (y) and experience (X) correlation given by y = -39.95 log(X) + 283.6 (R2 = 0.5758). CUSUM indicated that 30 to 50 cases were required for skill stabilization and maturation. Conclusion: Our endovascular team required 30-50 cases to establish reliable FEVAR proficiency. Beyond cumulative experiences, pivotal elements in the learning trajectory seemed to include technological advancements and team augmentation.

Comprehensive evaluation of the implementation of close-type county medical alliance in Shandong Province using entropy weight TOPSIS method and non-integer rank sum ratio method.

BACKGROUND: To improve the capacity of county medical and health services, China encourages all localities to pilot the close-type county medical alliance. In Shandong Province, medical alliances have been piloted in 47 counties, ranking first in the country. The objective of this study is to comprehensively evaluate the implementation of close-type county medical alliance in Shandong Province, identify the differences between different county regions, and analyze the reasons, so as to provide a reference for the construction of a new county medical and health service system with clear goals, powers and responsibilities, and division of labor. METHODS: The implementation of the close-type county medical alliance was comprehensively evaluated in 47 national pilot counties in Shandong Province using entropy weight TOPSIS method and non-integer rank sum ratio method. Variance analysis was used for comparison of the comprehensive evaluation results. RESULTS: The weight coefficient of evaluation indicators was highest for information interconnection, at 18.06%, and lowest for orderly referral of patients, at 3.64%. There was no difference in results of the comprehensive evaluation of entropy weight TOPSIS method and non-integer rank sum ratio method. Comprehensively order the implementation status of each pilot county according to the relative paste progress, 13 counties Y5, Y11, Y14, Y16, Y20, Y25, Y26, Y27, Y28, Y32, Y33, Y40 and Y42 were ranked highest, whereas county Y37 was ranked lowest. Non-integer rank sum ratio method graded counties into three grades: excellent, good and average. Kruskal-Wallis nonparametric test showed that the difference between the grades was statistically significant (H[Formula: see text]37.099, p[Formula: see text]0.001). Variance analysis based on comprehensive evaluation results showed that implementation status was not correlated with the county economic development level, the level of health resources input and the medical service ability of the lead hospital. CONCLUSIONS: Our findings indicated that the implementation of the close-type county medical alliance is significantly different between pilot counties, with a marked differentiation within the same urban area. Therefore, effective measures are recommended to reduce this gap, including promoting informatization empowerment of the county medical community, strengthening government responsibility and improving policy effectiveness.

Strategy investments in zero-sum games.

We propose an extension of two-player zero-sum games, where one player may select available actions for themselves and the opponent, subject to a budget constraint. We present a mixed-integer linear programming (MILP) formulation for the problem, provide analytical results regarding its solution, and discuss applications in the security and advertising domains. Our computational experiments demonstrate that heuristic approaches, on average, yield suboptimal solutions with at least a 20% relative gap with those obtained by the MILP formulation.

Complex-valued soft-log threshold reweighting for sparsity of complex-valued convolutional neural networks.

Complex-valued convolutional neural networks (CVCNNs) have been demonstrated effectiveness in classifying complex signals and synthetic aperture radar (SAR) images. However, due to the introduction of complex-valued parameters, CVCNNs tend to become redundant with heavy floating-point operations. Model sparsity is emerged as an efficient method of removing the redundancy without much loss of performance. Currently, there are few studies on the sparsity problem of CVCNNs. Therefore, a complex-valued soft-log threshold reweighting (CV-SLTR) algorithm is proposed for the design of sparse CVCNN to reduce the number of weight parameters and simplify the structure of CVCNN. On one hand, considering the difference between complex and real numbers, we redefine and derive the complex-valued log-sum threshold method. On the other hand, by considering the distinctive characteristics of complex-valued convolutional (CConv) layers and complex-valued fully connected (CFC) layers of CVCNNs, the complex-valued soft and log-sum threshold methods are respectively developed to prune the weights of different layers during the forward propagation, and the sparsity thresholds are optimized during the backward propagation by inducing a sparsity budget. Furthermore, different optimizers can be integrated with CV-SLTR. When stochastic gradient descent (SGD) is used, the convergence of CV-SLTR is proved if Lipschitzian continuity is satisfied. Experiments on the RadioML 2016.10A and S1SLC-CVDL datasets show that the proposed algorithm is efficient for the sparsity of CVCNNs. It is worth noting that the proposed algorithm has fast sparsity speed while maintaining high classification accuracy. These demonstrate the feasibility and potential of the CV-SLTR algorithm.

A novel hybrid LFC scheme for multi-area interconnected power systems considering coupling attenuation.

In this paper, a hybrid load frequency control (LFC) scheme is proposed for multi-area interconnected power systems to decouple the intricate double control objectives, by dividing all subareas into the responsible areas and the free areas. The LFC in the responsible area has the function of regulating both the local frequency and the tie-line power, while the control objective of the LFC in the free area is thus simplified to regulate the local frequency only. Then, addressing the complex network coupling and uncertain dynamics, an integrated LFC controller is proposed for the free areas, which consists of two parts, namely, the coupling attenuation baseline controller and the disturbance compensation controller. The coupling attenuation baseline controller satisfying the predefined bounded L2-Gain condition is derived based on the solution to a multi-player zero-sum differential game. Additionally, a novel generalized integral observer is designed to estimate the system's integrated disturbance, and the corresponding disturbance compensation controller is derived. After that, the ultimately uniformly bounded (UUB) stability of the integrated LFC controller combining baseline controller and disturbance compensation controller is proven rigorously. Finally, the performance superiority of the proposed hybrid LFC scheme is validated by the simulations in challenging operating modes.

Pollutants-mediated viral hepatitis in different types: assessment of different algorithms and time series models.

The escalating frequency of environmental pollution incidents has raised significant concerns regarding the potential health impacts of pollutant fluctuations. Consequently, a comprehensive study on the role of pollutants in the prevalence of viral hepatitis is indispensable for the advancement of innovative prevention strategies. Monthly incidence rates of viral hepatitis from 2005 to 2020 were sourced from the Chinese Center for Disease Control and Prevention Infectious Disease Surveillance Information System. Pollution data spanning 2014-2020 were obtained from the National Oceanic and Atmospheric Administration (NOAA), encompassing pollutants such as CO, NO2, and O3. Time series analysis models, including seasonal auto-regressive integrated moving average (SARIMA), Holt-Winters model, and Generalized Additive Model (GAM), were employed to explore prediction and synergistic effects related to viral hepatitis. Spearman correlation analysis was utilized to identify pollutants suitable for inclusion in these models. Concurrently, machine learning (ML) algorithms were leveraged to refine the prediction of environmental pollutant levels. Finally, a weighted quantile sum (WQS) regression framework was developed to evaluate the singular and combined impacts of pollutants on viral hepatitis cases across different demographics, age groups, and environmental strata. The incidence of viral hepatitis in Beijing exhibited a declining trend, primarily characterized by HBV and HCV types. In predicting hepatitis prevalence trends, the Holt-Winters additive seasonal model outperformed the SARIMA multiplicative model ((1,1,0) (2,1,0) [12]). In the prediction of environmental pollutants, the SVM model demonstrated superior performance over the GPR model, particularly with Polynomial and Besseldot kernel functions. The combined pollutant risk effect on viral hepatitis was quantified as ßWQS (95% CI) = 0.066 (0.018, 0.114). Among different groups, PM2.5 emerged as the most sensitive risk factor, notably impacting patients with HCV and HEV, as well as individuals aged 35-64. CO predominantly affected HAV patients, showing a risk effect of ßWQS (95% CI) = - 0.0355 (- 0.0695, - 0.0016). Lower levels of PM2.5 and PM10 were associated with heightened risk of viral hepatitis incidence with a lag of five months, whereas elevated levels of PM2.5 (100-120 µg/m3) and CO correlated with increased hepatitis incidence risk with a lag of six months. The Holt-Winters model outperformed the SARIMA model in predicting the incidence of viral hepatitis. Among machine learning algorithms, SVM and GPR models demonstrated superior performance for analyzing pollutant data. Patients infected with HAV and HEV were primarily influenced by PM10 and CO, whereas SO2 and PM2.5 significantly impacted others. Individuals aged 35-64 years appeared particularly susceptible to these pollutants. Mixed pollutant exposures were found to affect the development of viral hepatitis with a notable lag of 5-6 months. These findings underscore the importance of long-term monitoring of pollutants in relation to viral hepatitis incidence.

The learning curve for modified hand-assisted retroperitoneoscopic living donor nephrectomy.

BACKGROUND: We aimed to introduce our modified hand-assisted retroperitoneoscopic living donor nephrectomy (HARPLDN) technique and define the learning curve. METHODS: One hundred thirty-eight kidney donors who underwent modified HARPLDN by the same surgeon between May 2015 and March 2022 were included. A cumulative sum (CUSUM) learning curve analysis was performed with the total operation time as the study outcome. RESULTS: In total, the mean operative time was 138.2 ± 32.1 min. The median warm ischemic time (WIT) and estimated blood loss were 90 s and 50 ml, respectively. The learning curve for the total operative time was best modeled as a second-order polynomial with the following equation: CUSUMOT (min) = (-0.09 case number2) + (12.88 case number) - 67.77 (R2 = 0.7875; p<0.05). The CUSUM learning curve included the following three unique phases: phase 1 (the initial 41 cases), representing the initial learning curve; phase 2 (the middle 43 cases), representing expert competence; and phase 3 (the final 54 cases), representing mastery. The overall 6-month graft survival rate was 99.3%, with 94.9% immediate onset of graft function without delayed graft function and 0.7% ureteral complications. CONCLUSIONS: Our modified method is safe and effective for living donor nephrectomy and has the advantages of a shorter operating time and optimized WIT. The surgeon can become familiar with the modified HARPLDN after 41 cases and effectively perform the next 97 cases.

Improvement and application of back random response detection: Based on cumulative sum and change point analysis.

In educational and psychological assessments, benefiting from back random response (BRR) is a major type of rapid guessing in misfitting item score patterns. Person-fit statistics (PFS) based on cumulative sum (CUSUM) and change point analysis (CPA) from statistical process control (SPC) are better than other PFS for detecting aberrant response. In this study, we developed new person-fit statistics based on three algorithms from CPA procedure and CUSUM method for detection of person misfit with dichotomous or polytomous items. By means of simulated data, the effectiveness of the new statistics to detect test-takers with BRR was investigated.

Modeling cheatgrass distribution, abundance, and response to climate change as a function of soil microclimate.

Exotic annual grass invasions in water-limited systems cause degradation of native plant and animal communities and increased fire risk. The life history of invasive annual grasses allows for high sensitivity to interannual variability in weather. Current distribution and abundance models derived from remote sensing, however, provide only a coarse understanding of how species respond to weather, making it difficult to anticipate how climate change will affect vulnerability to invasion. Here, we derived germination covariates (rate sums) from mechanistic germination and soil microclimate models to quantify the favorability of soil microclimate for cheatgrass (Bromus tectorum L.) establishment and growth across 30 years at 2662 sites across the sagebrush steppe system in the western United States. Our approach, using four bioclimatic covariates alone, predicted cheatgrass distribution with accuracy comparable to previous models fit using many years of remotely-sensed imagery. Accuracy metrics from our out-of-sample testing dataset indicate that our model predicted distribution well (72% overall accuracy) but explained patterns of abundance poorly (R2 = 0.22). Climatic suitability for cheatgrass presence depended on both spatial (mean) and temporal (annual anomaly) variation of fall and spring rate sums. Sites that on average have warm and wet fall soils and warm and wet spring soils (high rate sums during these periods) were predicted to have a high abundance of cheatgrass. Interannual variation in fall soil conditions had a greater impact on cheatgrass presence and abundance than spring conditions. Our model predicts that climate change has already affected cheatgrass distribution with suitable microclimatic conditions expanding 10%-17% from 1989 to 2019 across all aspects at low- to mid-elevation sites, while high- elevation sites (>2100 m) remain unfavorable for cheatgrass due to cold spring and fall soils.

On the Benefits of Using Maximal Reliability in Educational and Behavioral Research.

This note is concerned with the benefits that can result from the use of the maximal reliability and optimal linear combination concepts in educational and psychological research. Within the widely used framework of unidimensional multi-component measuring instruments, it is demonstrated that the linear combination of their components that possesses the highest possible reliability can exhibit a level of consistency considerably exceeding that of their overall sum score that is nearly routinely employed in contemporary empirical research. This optimal linear combination can be particularly useful in circumstances where one or more scale components are associated with relatively large error variances, but their removal from the instrument can lead to a notable loss in validity due to construct underrepresentation. The discussion is illustrated with a numerical example.

2D spatiotemporal passive cavitation imaging and evaluation during ultrasound thrombolysis based on diagnostic ultrasound platform.

Acoustic cavitation plays a critical role in various biomedical applications. However, uncontrolled cavitation can lead to undesired damage to healthy tissues. Therefore, real-time monitoring and quantitative evaluation of cavitation dynamics is essential for understanding underlying mechanisms and optimizing ultrasound treatment efficiency and safety. The current research addressed the limitations of traditionally used cavitation detection methods by developing introduced an adaptive time-division multiplexing passive cavitation imaging (PCI) system integrated into a commercial diagnostic ultrasound platform. This new method combined real-time cavitation monitoring with B-mode imaging, allowing for simultaneous visualization of treatment progress and 2D quantitative evaluation of cavitation dosage within targeted area. An improved delay-and-sum (DAS) algorithm, optimized with a minimum variance (MV) beamformer, is utilized to minimize the side lobe effect and improve the axial resolution typically associated with PCI. In additional to visualize and quantitatively assess the cavitation activities generated under varied acoustic pressures and microbubble concentrations, this system was specifically applied to perform 2D cavitation evaluation for ultrasound thrombolysis mediated by different solutions, e.g., saline, nanodiamond (ND) and nitrogen-annealed nanodiamond (N-AND). This research aims to bridge the gap between laboratory-based research systems and real-time spatiotemporal cavitation evaluation demands in practical uses. Results indicate that this improved 2D cavitation monitoring and evaluation system could offer a useful tool for comprehensive evaluating cavitation-mediated effects (e.g., ultrasound thrombolysis), providing valuable insights into in-depth understanding of cavitation mechanisms and optimization of cavitation applications.

Relationship between mixed exposure to phenyl hydroxides, polycyclic aromatic hydrocarbons, and phthalates and the risk of arthritis.

BACKGROUND: To determine the relationship between mixed exposure to three types of endocrine-disrupting chemicals (EDCs), namely phenyl hydroxides, polycyclic aromatic hydrocarbons (PAHs), and phthalates (PAEs), and risk of arthritis. METHODS: Participants were selected from National Health and Nutrition Examination Survey (NHANES). The relationships between the urinary concentrations of phenyl hydroxides, PAHs, and PAEs and the risk of arthritis were analyzed by generalized linear regression model. The mixed exposure to these EDCs and the risk of arthritis was analyzed by weighted quantile sums (WQSs) and Bayesian kernel machine regression (BKMR) model. RESULTS: Our analysis showed that participants with urinary benzophenone-3 and methylparaben concentrations in the highest quartile (Q4) had an increased risk of arthritis compared with those in Q1. For each one-unit increase in the natural logarithm-converted urinary concentrations of 1-hydroxynapthalene and 2-hydroxynapthalene, the risk of arthritis increased by 5% and 8%, respectively. Chemical mixing index coefficients were significantly associated with risk of arthritis in both WQS positive- and negative-constraint models. In the BKMR model, there was a significant positive correlation between mixed exposure and the risk of arthritis. CONCLUSION: Mixed exposure to phenyl hydroxides, PAHs, and PAEs increased the risk of arthritis, with exposure to PAHs being the key factor.

Sex and obesity influence the relationship between perfluoroalkyl substances and lean body mass: NHANES 2011-2018.

Objective: Polyfluoroalkyl substances (PFAS) are known endocrine disruptors, that have been the subject of limited research regarding their impact on human lean body mass. The aim of this study was to investigate the effects of PFAS exposure on lean body mass. Methods: We performed a cross-sectional data analysis involving 1022 adolescents and 3274 adults from the National Health and Nutrition Examination Survey (NHANES) 2011-2018, whose lean body mass was measured by dual-energy X-ray absorptiometry. The lean mass index (LMI) was calculated as lean body mass dividing by the square of height. The association between PFAS and LMI was examined through a multivariate-adjusted weighted generalized linear model. Moreover, weighted quantile sum (WQS) regression models were employed to futher examine the relationship between the mixture of PFAS and LMI. Results: Regression analyses revealed an inverse correlation between PFAS exposure and LMI after adjusting for potential covariates. Adults with higher serum PFAS concentrations manifested a reduction in whole LMI ( ß  = -0.193, 95 % confidence interval (CI): -0.325 to -0.06). Notably, this correlation was particularly significant in adult females and individuals with obesity, and it was observed across diverse anatomical regions, including lower limbs, right arm, trunk, and whole lean body mass. In adult females, the association between PFAS and whole LMI was statistically significant ( ß  = -0.294, 95 % CI: -0.495 to -0.094), and a similar trend was found in obese individuals ( ß  = -0.512, 95 % CI: -0.762 to -0.261). WQS regression analyses supported the results obtained from weighted linear regression analyses. Conclusions: Our study suggests that exposure to PFAS, whether individually or in combination, is associated with decreased lean body mass in specific body areas, with sex and obesity serving as major influencing factors.

Breakdown of TMS evoked EEG signal propagation within the default mode network in Alzheimer's disease.

BACKGROUND: The neural activity of the Default Mode Network (DMN) is disrupted in patients with In Alzheimer's disease (AD). OBJECTIVES: We used a novel multimodal approach to track neural signal propagation within the DMN in AD patients. METHODS: Twenty mild to moderate AD patients were recruited. We used transcranial magnetic stimulation (TMS) pulses to probe with a millisecond time resolution the propagation of evoked electroencephalography (EEG) signal following the neural activation of the Precuneus (PC), which is a key hub area of the DMN. Moreover, functional and structural magnetic resonance imaging (MRI) data were collected to reconstruct individual features of the DMN. RESULTS: In AD patients a probe TMS pulse applied over the PC evokes an increased local activity unmasking underlying hyperexcitability. In contrast, the EEG evoked neural signal did not propagate efficiently within the DMN showing a remarkable breakdown of signal propagation. fMRI and structural tractography showed that impaired signal propagation was related to the same connectivity matrices derived from DMN BOLD signal and transferred by specific white matter bundles forming the cingulum. These features were not detectable stimulating other areas (left dorsolateral prefrontal cortex) or for different networks (fronto-parietal network). Finally, connectivity breakdown was associated with cognitive impairment, as measured with the Clinical Dementia Rating Scale sum of boxes (CDR-SB). CONCLUSIONS: TMS-EEG in AD shows both local hyperexcitability and a lack of signal propagation within the DMN. These neurophysiological features also correlate with structural and cognitive attributes of the patients. SIGNIFICANCE: Neuronavigated TMS-EEG may be used as a novel neurophysiological biomarker of DMN connectivity in AD patients.

Within-hospital Temporal Clustering of Postoperative Complications and Implications for Safety Monitoring and Benchmarking Using ACS-NSQIP Data.

Objective: To determine the extent to which within-hospital temporal clustering of postoperative complications is observed in the American College of Surgeons, National Surgical Quality Improvement Program (ACS-NSQIP). Background: ACS-NSQIP relies on periodic and on-demand reports for quality benchmarking. However, if rapid increases in postoperative complication rates (clusters) are common, other reporting methods might be valuable additions to the program. This article focuses on estimating the incidence of within-hospital temporal clusters. Methods: ACS-NSQIP data from 1,547,440 patients, in 425 hospitals, over a 2-year period was examined. Hospital-specific Cox proportional hazards regression was used to estimate the incidence of mortality, morbidity, and surgical site infection (SSI) over a 30-day postoperative period, with risk adjustment for patient and procedure and with additional adjustments for linear trend, day-of-week, and season. Clusters were identified using scan statistics, and cluster counts were compared, using unpaired and paired t tests, for different levels of adjustment and when randomization of cases across time eliminated all temporal influences. Results: Temporal clusters were rarely observed. When clustering was adjusted only for patient and procedure risk, an annual average of 0.31, 0.85, and 0.51 clusters were observed per hospital for mortality, morbidity, and SSI, respectively. The number of clusters dropped after adjustment for linear trend, day-of-week, and season (0.31-0.24; P = 0.012; 0.85-0.80; P = 0.034; and 0.51-0.36; P < 0.001; using paired t tests) for mortality, morbidity, and SSI, respectively. There was 1 significant difference in the number of clusters when comparing data with all adjustments and after data were randomized (0.24 and 0.25 for mortality; P = 0.853; 0.80 and 0.82 for morbidity; P = 0.529; and 0.36 and 0.46 [randomized data had more clusters] for SSI; P = 0.001; using paired t tests) for mortality, morbidity, and SSI, respectively. Conclusions: Temporal clusters of postoperative complications were rarely observed in ACS-NSQIP data. The described methodology may be useful in assessing clustering in other surgical arenas.

Accuracy Validation of the New Barrett True Axial Length Formula and the Optimized Lens Factor Using Sum-of-Segment Biometry.

Objectives: This study aims to verify the accuracy of a new calculation formula, Barrett true axial length formula (T-AL), and the optimized lens factor (LF) for predicting postoperative refraction after cataract surgery. Methods: We included 156 Japanese patients who underwent cataract surgery using Clareon monofocal intraocular lenses at our clinic between January 2022 and June 2023. Postoperative spherical equivalent was calculated using subjective refraction values obtained 1 month post-surgery. The LFs were optimized so that the mean prediction error (PE) of each calculation formula was zero (zero optimization). We calculated the mean absolute PE (MAE) to assess accuracy and used a Friedman test for statistical comparisons. The accuracy of T-AL and the optimized LFs was compared with that of the conventional Barrett Universal II formula for ARGOS (AR-B) and OA-2000 (OA-B) with equivalent refractive index. Results: For T-AL, AR-B, and OA-B, the MAEs ± standard deviations were 0.225 ± 0.179, 0.219 ± 0.168, and 0.242 ± 0.206 D, respectively. The Friedman test showed no statistically significant differences among the three groups. The device-optimized LFs were 2.248-2.289 (T-AL), 2.236-2.246 (AR-B), and 2.07-2.08 (OA-B); the corresponding zero-optimized LFs were 2.262-2.287 (T-AL), 2.287-2.303 (AR-B), and 2.160-2.170 (OA-B). Conclusion: There were no significant differences in prediction accuracy among the formulas. However, the accuracy of LF optimization varied by device, with T-AL being closest to the value under zero optimization. This suggests that T-AL is clinically useful for predicting an accurate postoperative refraction without zero optimization.

Associations of the intake of individual and multiple fatty acids with depressive symptoms among adults in NHANES 2007-2018.

BACKGROUND: Previous studies have mainly focused on the effects of individual fatty acids on depressive symptoms, while the combined effect of fatty acids on the risk of depressive symptoms has not yet been extensively reported. This study evaluate the associations between individual and multiple fatty acids with depressive symptoms in U.S. adults. METHODS: Data sets were obtained from the National Health and Nutrition Examination Survey (NHANES) 2007-2018 cycles. Both males and females aged above 18 years with complete information about dietary fatty acids intake, depression symptoms, and covariates were included. Weighted linear regression models were conducted to evaluate the relationships between individual fatty acid intake and depressive symptoms, and restricted cubic spline (RCS) models were utilized to explore the corresponding dose-response relationships. Additionally, we implemented the weighted quantile sum (WQS) regression and quantile g-computation (QGC) models to estimate the mixed effects of 19 fatty acids and identify the predominant types. RESULTS: After multivariable adjustments, an increase of one unit in Linoleic acid (LA), Alpha-Linolenic Acid (ALA), Arachidonic acid (AA), Docosapentaenoic acid(DPA), Docosahexaenoic acid(DHA), was associated with a decrease in depressive scores by -0.021 (95 % CI: -0.039,-0.003, p = 0.021),-0.028 (95 % CI: -0.045,-0.011, p = 0.002),-0.026 (95 % CI: -0.044,-0.008, p = 0.005), -0.026 (95 % CI: -0.042,-0.009, p = 0.003), and - 0.022 (95 % CI: -0.041,-0.003, p = 0.022), respectively. However, a per unit increase in Hexanoic acid and Octanoic acid was associated with an increase in depressive scores of 0.020 (95 % CI: 0.002,0.038, p = 0.029) and 0.026 (95 % CI: 0.004,0.048, p = 0.020), respectively. Meanwhile, significant dose-response relationships were supported by the RCS models. As for the mixed effects, both WQS and QGC models demonstrated that the mixture of polyunsaturated fatty acids (PUFAs) was inversely related to depressive symptoms, and ALA and DPA were the most critical contributors. DHA was negatively correlated with depressive symptoms in WQS analysis, but positively correlated with depressive symptoms in QGC analysis. LIMITATIONS: The cross-sectional design limits our ability to establish causality, and 24-hour dietary recall can lead to potential inaccuracies reflecting participants' true eating habits. CONCLUSION: Our study suggests that the single effects of each PUFA were inversely associated with depressive symptoms, except for octadecatetraenoic acid. Moreover, higher combined intake of dietary PUFAs is inversely associated with depressive symptoms in U.S. adults. Among the mixed effects of PUFAs, ALA and DPA may play predominant roles. However, DHA mixed with other fatty acids may have different effects on depressive symptoms, and further study is needed.

The third-order nonlinear optical responses of zinc porphyrin oligomers: Cycles vs linear chains.

Porphyrins are widely used as potential nonlinear optical (NLO) materials because of their highly delocalized π electrons and feasible synthesis and functionalization with broad biological applications. A variety of linear and cyclic porphyrin derivatives have been synthesized, and the correlation between their structures and NLO properties awaits being disclosed. In this work, the electronic structures and third-order NLO properties of linear and cyclic butadiyne-linked zinc porphyrin oligomers have been studied by quantum chemical methods and sum-over-states model. The static second hyperpolarizability (<γ0>) increases exponentially with the number of zinc porphyrin units ([<γ0>n] = 0.67[<γ0>1]n2.63, n = 2 âˆ¼ 6) in linear π-conjugated oligomers, and the <γ0> of the linear hexamer is about 74 times that of the monomer. Such enhancement of <γ0> in linear oligomers originates from closely-lying frontier molecular orbitals available for low energy electron excitations and strong charge transfer-based excitations across porphyrins. The <γ0>s of cyclic porphyrins are lower than that of the linear hexamer, though the interaction between the ring and the ligand enhances the <γ0> of some cyclic zinc porphyrin complexes. The large two-photon absorption cross sections confer on these zinc porphyrin derivatives excellent candidates for two-photon absorption applications.

From Novice to Mastery: Learning Curve and Efficacy Analysis of Short-Term Spinal Cord Stimulation for Diabetic Foot Ulcers.

BACKGROUND: To analyze the learning curve of novices in mastering short-term Spinal cord stimulation (st-SCS) for diabetic foot, evaluating the efficacy, safety, and difficulty of this technique. METHODS: A retrospective review of diabetic foot patients treated with st-SCS at our hospital was conducted. All procedures were performed by the same physician and patients were sequentially numbered according to the order of surgery. Learning curves were plotted using segmented linear regression and cumulative sum curves based on surgery duration. Patients were divided into 2 groups according to the inflection points on the learning curve: the learning group and the mastery group. Pre- and postoperative efficacy indicators were recorded and compared, along with general patient data, perioperative parameters, and incidence of complications. RESULTS: A total of 36 patients were included. Significant improvements were observed post-st-SCS in ulcer size (from 7.00 cm2 to 4.00 cm2), visual analog scale (from 7.00 to 3.00), foot temperature (from 30.06°C to 32.37°C), and Pittsburgh Sleep Quality Index (from 14.42 to 8.36) (P < 0.05). The physician could proficiently perform st-SCS after 9 cases. Surgery time was significantly shorter in the mastery group (1-9 cases) compared to the learning group (10-36 cases) (28.04 vs. 43.56 min, P < 0.05). There were no significant differences between the 2 groups in baseline data, improvement in efficacy indicators, or complications (P > 0.05). CONCLUSIONS: St-SCS is beneficial for wound healing, pain relief, improving peripheral circulation, and improving sleep quality. Surgeons can master this simple and safe technique in about 9 cases.

Optimizing entropy weight model to accurately predict variation of pharmaceuticals and personal care products in the estuaries and coasts of the South China.

Pharmaceuticals and personal care products (PPCPs) have raised increasing concern worldwide due to their continuous release and potential hazards to the ecosystem and human health. This study optimized the entropy weight model (EW-WRSR) that combines entropy weight with multi-criteria decision analysis to investigate pollution patterns of PPCPs in the coasts and estuaries. The results revealed that occurrences of PPCPs from the 1940s to the present were consistent with using PPCPs, different types of human activities, and local urban development. This helped better understand the history of PPCP contamination and evaluate the uncertainty of EW-WRSR. The model predicted hotspots of PPCPs that were consistent with the actual situation, indicating that PPCPs mainly enter the nearshore ecosystem by the form of sewage discharge and residual aquaculture. This study can provide method that identifying highly contaminated regions on a global scale.