RER-YOLO: improved method for surface defect detection of aluminum ingot alloy based on YOLOv5.

Aluminum ingot alloy is one of the commonly used materials in industrial production and intelligent manufacturing, whose quality directly affects the performance of aluminum processed products. Therefore, the inspection of surface defects of aluminum ingot alloy is extremely valuable for actual industrial engineering. Aiming at the issues of low detecting precision and the slowly processing rate thatexisted in the traditional target detection methods for aluminum ingot alloy dataset, the YOLOv5-based improvement model RER-YOLO is proposed. Firstly, the aluminum ingot alloy dataset is coped with the image pretreatment methods of rotation, translation, contrast and brightness transformations in a random combination so as to boost the capacity of generalization for model training. Secondly, a multi-scale characteristic extraction network block (Res2Net) is utilized to take the place of the C3 block in the previous YOLOv5 to augment the model's ability that can accurately extract rich features. Finally, an over-parameterization-based re-parameterized convolutional block is utilized in place of the 3×3 convolutional blocks in the Res2Net residual block and baseline model, enlarging the search space of the network and boosting the model's fitting ability while maintaining inference rate. The comparison experimental results demonstrate that the RER-YOLO reaches a mean average precision of 75.1% on the aluminum ingot alloy dataset, which is higher 4.9% than the conventional YOLOv5 and does not increase the inference delay. It also improves the detection accuracy by 12.7% for burr defects, which are fewer in number in the dataset and the defect features are difficult to extract. It can be seen that the presented model in this study has an important reference value towards detecting surface defects in aluminum ingot alloy.

Global, regional and national burden of CKD in children and adolescents from 1990 to 2019.

BACKGROUND: Chronic kidney disease(CKD) is one of the most prevalent non-communicable health concerns in children and adolescents worldwide; however, data on its incidence, prevalence, disability-adjusted life years (DALYs) and trends in the population are limited. We aimed to assess the global, regional and national trends in CKD burden in children and adolescents. METHODS: In this trend analysis based on the 2019 Global Diseases, Injuries, and Risk Factors Study, CKD incidence, prevalence and DALYs rates per 100 000 population for children and adolescents were reported at the global, regional and national levels, as well as the average annual percentage change (AAPC). These global trends were analyzed by age, sex, region and socio-demographic index (SDI). RESULTS: Globally, the overall incidence of CKD (all stages including kidney replacement therapy) in children and adolescents showed an increasing trend [AAPC 0.44 (95% confidence interval 0.36-0.52)] between 1990 and 2019. Similarly, the overall prevalence of CKD also showed an upward trend [AAPC 0.46 (0.42-0.51)]. However, the DALYs of CKD showed a continuous decreasing trend [AAPC -1.18 (-1.37 to -0.99)]. The population aged 15-19 years had the largest CKD incidence increase during this period. The largest increase in age-standardized incidence rate (ASIR) was in middle SDI countries [AAPC 0.56 (0.45-0.67)]. The relationship between the ASIR and SDI showed an inverse U-shaped correlation while the relationship between the age-standardized DALYs rate (ASDR) and SDI showed an inverse trend with SDI. Among adolescents (15-19 years), the ASIR continued to increase for five causes of CKD, owing to type 2 diabetes mellitus and hypertension. Most of the disease burden was concentrated in countries with a lower SDI. Andean Latin America and Central Latin America showed the largest increases in CKD ASIR between 1990 and 2019. CONCLUSION: The burden of CKD in children and adolescents has increased worldwide, especially in regions and countries with a lower SDI.

Joint effects of sleep disturbance and renal function impairment on incident new-onset severe metabolic dysfunction-associated steatotic liver disease.

AIM: To elucidate the effects of sleep parameters and renal function on the risk of developing new-onset severe metabolic dysfunction-associated steatotic liver disease (MASLD). MATERIALS AND METHODS: The primary analysis involved a cohort of 305 257 participants. Multivariable Cox models were employed to calculate hazard ratios and 95% confidence intervals. Traditional mediation and two-step Mendelian randomization (MR) analyses were conducted to assess the associations and mediating roles of renal function indicators between sleep and new-onset severe MASLD. RESULTS: Poor sleep score and renal function biomarker score (RFS) were associated with an increased risk of new-onset severe MASLD (all ptrend <0.001). Participants with poor sleep patterns and the highest RFS had a 5.45-fold higher risk of new-onset severe MASLD, compared to those with healthy sleep patterns and the lowest RFS (p < 0.001). The RFS could explain 10.08% of the correlations between poor sleep score and risk of new-onset severe MASLD. Additionally, MR analyses supported a causal link between insomnia and new-onset severe MASLD and revealed a mediating role of chronic kidney disease in the connection between insomnia and new-onset severe MASLD risk. CONCLUSIONS: This study highlights the independent and combined associations of sleep parameters and renal function indicators with new-onset severe MASLD, underscoring the bidirectional communication of the liver-kidney axis and providing modifiable strategies for preventing MASLD.

SGLT2 inhibition, high-density lipoprotein, and kidney function: a mendelian randomization study.

BACKGROUND: Sodium-glucose cotransporter 2 (SGLT2) inhibition is recognized for its evident renoprotective benefits in diabetic renal disease. Recent data suggest that SGLT2 inhibition also slows down kidney disease progression and reduces the risk of acute kidney injury, regardless of whether the patient has diabetes or not, but the mechanism behind these observed effects remains elusive. The objective of this study is to utilize a mendelian randomization (MR) methodology to comprehensively examine the influence of metabolites in circulation regarding the impact of SGLT2 inhibition on kidney function. METHODS: We used a MR study to obtain associations between genetic proxies for SGLT2 inhibition and kidney function. We retrieved the most recent and comprehensive summary statistics from genome-wide association studies (GWAS) that have been previously published and involved kidney function parameters such as estimated glomerular filtration rate (eGFR), urine albumin-to-creatinine ratio (UACR), and albuminuria. Additionally, we included blood metabolite data from 249 biomarkers in the UK Biobank for a more comprehensive analysis. We performed MR analyses to explore the causal relationships between SGLT2 inhibition and kidney function and two-step MR to discover potential mediating metabolites. RESULTS: The study found that a decrease in HbA1c levels by one standard deviation, which is genetically expected to result in SGLT2 inhibition, was linked to a decreased likelihood of developing type 2 diabetes mellitus (T2DM) (odds ratio [OR] = 0.55 [95% CI 0.35, 0.85], P = 0.007). Meanwhile, SGLT2 inhibition also protects eGFR (ß = 0.05 [95% CI 0.03, 0.08], P = 2.45 × 10- 5) and decreased UACR (-0.18 [95% CI -0.33, -0.02], P = 0.025) and albuminuria (-1.07 [95% CI -1.58, -0.57], P = 3.60 × 10- 5). Furthermore, the study found that of the 249 metabolites present in the blood, only one metabolite, specifically the concentration of small high-density lipoprotein (HDL) particles, was significantly correlated with both SGLT2 inhibition and kidney function. This metabolite was found to play a crucial role in mediating the improvement of renal function through the use of SGLT2 inhibition (ß = 0.01 [95% CI 0.005, 0.018], P = 0.001), with a mediated proportion of 13.33% (95% CI [5.71%, 26.67%], P = 0.020). CONCLUSIONS: The findings of this investigation provide evidence in favor of a genetically anticipated biological linkage between the inhibition of SGLT2, the presence of circulating metabolites, and renal function. The findings demonstrate that the protective effect of SGLT2 inhibition on renal function is mostly mediated by HDL particle concentrations in circulating metabolites. These results offer significant theoretical support for both the preservation of renal function and a better comprehension of the mechanisms underlying SGLT2 inhibition.

Development and validation of a nomogram for predicting in-hospital death in cirrhotic patients with acute kidney injury.

BACKGROUND: The purpose of this study was to develop a nomogram for predicting in-hospital mortality in cirrhotic patients with acute kidney injury (AKI) in order to identify patients with a high risk of in-hospital death early. METHODS: This study collected data on cirrhotic patients with AKI from 2008 to 2019 using the Medical Information Mart for Intensive Care IV. Multivariate logistic regression was used to identify confounding factors related to in-hospital mortality, which were then integrated into the nomogram. The concordance index (C-Index) was used to evaluate the accuracy of the model predictions. The area under the curve (AUC) and decision curve analysis (DCA) was used to assess the predictive performance and clinical utility of the nomogram. RESULTS: The final study population included 886 cirrhotic patients with AKI, and 264 (29.8%) died in the hospital. After multivariate logistic regression, age, gender, cerebrovascular disease, heart rate, respiration rate, temperature, oxygen saturation, hemoglobin, blood urea nitrogen, serum creatinine, international normalized ratio, bilirubin, urine volume, and sequential organ failure assessment score were predictive factors of in-hospital mortality. In addition, the nomogram showed good accuracy in estimating the in-hospital mortality of patients. The calibration plots showed the best agreement with the actual presence of in-hospital mortality in patients. In addition, the AUC and DCA curves showed that the nomogram has good prediction accuracy and clinical value. CONCLUSIONS: We have created a prognostic nomogram for predicting in-hospital death in cirrhotic patients with AKI, which may facilitate timely intervention to improve prognosis in these patients.

Prevalence and risk factors of sarcopenia in patients on maintenance hemodialysis: a retrospective cohort study.

BACKGROUND: This study aimed to explore the prevalence and related risk factors of sarcopenia in patients on maintenance hemodialysis (MHD). METHODS: This cohort study enrolled 165 patients on MHD. The patients were divided into sarcopenia and non-sarcopenia groups based on the presence of sarcopenia or not. Sarcopenia was diagnosed according to the consensus of the Asian Sarcopenia Working Group that considers reduced muscle mass and decreased muscle strength (19). The muscle mass was measured using the multi-frequency bioelectrical impedance (Inbody260) and skeletal muscle index (SMI) was used: <7.0 kg/m2 (male); <5.7 kg/m2 (female) - with muscle mass reduction. The electronic grip dynamometer was used for measuring dominant handgrip strength (HGS) to reflect muscle strength. Male patients with HGS < 28 kg and female patients with HGS < 18 kg were considered with a decrease in muscle strength. The demographic characteristics, laboratory indexes, anthropometrical measurements, body compositions, and InBody score were compared between groups. The multivariate logistic regression was used to explore the risk factors for sarcopenia. RESULTS: Of the 165 patients on MHD, 36 had sarcopenia, and the prevalence was 21.82%. Patients in the sarcopenia group had higher ages and lower body mass index, serum albumin level, circumference of waist, hip, and biceps, handgrip strength, total water content, protein inorganic salt concentrations, skeletal muscle mass, basal metabolic rate, obesity degree, SMI, and body fat content. The multivariate logistic regression showed that age, waist circumference, handgrip strength, and InBody score were influencing factors for sarcopenia in patients on hemodialysis. CONCLUSION: The prevalence of sarcopenia was high in patients on MHD. Higher age, lower waist circumference, lower handgrip strength, and lower InBody score were independent risk factors for sarcopenia in such patients.

Interpretable machine learning model for predicting acute kidney injury in critically ill patients.

BACKGROUND: This study aimed to create a method for promptly predicting acute kidney injury (AKI) in intensive care patients by applying interpretable, explainable artificial intelligence techniques. METHODS: Population data regarding intensive care patients were derived from the Medical Information Mart for Intensive Care IV database from 2008 to 2019. Machine learning (ML) techniques with six methods were created to construct the predicted models for AKI. The performance of each ML model was evaluated by comparing the areas under the curve (AUC). Local Interpretable Model-Agnostic Explanations (LIME) method and Shapley Additive exPlanation values were used to decipher the best model. RESULTS: According to inclusion and exclusion criteria, 53,150 severely sick individuals were included in the present study, of which 42,520 (80%) were assigned to the training group, and 10,630 (20%) were allocated to the validation group. Compared to the other five ML models, the eXtreme Gradient Boosting (XGBoost) model greatly predicted AKI following ICU admission, with an AUC of 0.816. The top four contributing variables of the XGBoost model were SOFA score, weight, mechanical ventilation, and the Simplified Acute Physiology Score II. An AKI and Non-AKI cases were predicted separately using the LIME algorithm. CONCLUSION: Overall, the constructed clinical feature-based ML models are excellent in predicting AKI in intensive care patients. It would be constructive for physicians to provide early support and timely intervention measures to intensive care patients at risk of AKI.

Development and validation of a nomogram for predicting refractory peritoneal dialysis related peritonitis.

OBJECTIVE: To identify the risk factors of refractory peritoneal dialysis related peritonitis (PDRP) and construct a nomogram to predict the occurrence of refractory PDRP. METHODS: Refractory peritonitis was defined as the peritonitis episode with persistently cloudy bags or persistent dialysis effluent leukocyte count >100 × 109/L after 5 days of appropriate antibiotic therapy. The study dataset was randomly divided into a 70% training set and a 30% validation set. Univariate logistic analysis, LASSO regression analysis, and random forest algorithms were utilized to identify the potential risk factors for refractory peritonitis. Independent risk factors identified using multivariate logistic analysis were used to construct a nomogram. The discriminative ability, calibrating ability, and clinical practicality of the nomogram were evaluated using the receiver operating characteristic curve, Hosmer-Lemeshow test, calibration curve, and decision curve analysis. RESULTS: A total of 294 peritonitis episodes in 178 patients treated with peritoneal dialysis (PD) were enrolled, of which 93 were refractory peritonitis. C-reactive protein, serum albumin, diabetes mellitus, PD duration, and type of causative organisms were independent risk factors for refractory peritonitis. The nomogram model exhibited excellent discrimination with an area under the curve (AUC) of 0.781 (95% CI: 0.716-0.847) in the training set and 0.741 (95% CI: 0.627-0.855) in the validation set. The Hosmer-Lemeshow test and calibration curve indicated satisfactory calibration ability of the predictive model. Decision curve analysis revealed that the nomogram model had good clinical utility in predicting refractory peritonitis. CONCLUSION: This nomogram can accurately predict refractory peritonitis in patients treated with PD.

Machine learning algorithm for predict the in-hospital mortality in critically ill patients with congestive heart failure combined with chronic kidney disease.

BACKGROUND: The objective of this study was to develop and validate a machine learning (ML) model for predict in-hospital mortality among critically ill patients with congestive heart failure (CHF) combined with chronic kidney disease (CKD). METHODS: After employing least absolute shrinkage and selection operator regression for feature selection, six distinct methodologies were employed in the construction of the model. The selection of the optimal model was based on the area under the curve (AUC). Furthermore, the interpretation of the chosen model was facilitated through the utilization of SHapley Additive exPlanation (SHAP) values and the Local Interpretable Model-Agnostic Explanations (LIME) algorithm. RESULTS: This study collected data and enrolled 5041 patients on CHF combined with CKD from 2008 to 2019, utilizing the Medical Information Mart for Intensive Care Unit. After selection, 22 of the 47 variables collected post-intensive care unit admission were identified as mortality-associated and subsequently utilized in the development of ML models. Among the six models generated, the eXtreme Gradient Boosting (XGBoost) model demonstrated the highest AUC at 0.837. Notably, the SHAP values highlighted the sequential organ failure assessment score, age, simplified acute physiology score II, and urine output as the four most influential variables in the XGBoost model. In addition, the LIME algorithm explains the individualized predictions. CONCLUSIONS: In conclusion, our study accomplished the successful development and validation of ML models for predicting in-hospital mortality in critically ill patients with CHF combined with CKD. Notably, the XGBoost model emerged as the most efficacious among all the ML models employed.

Standard sine motion video-based virtual traceability method for monocular vision low-frequency vibration measurements.

The low-frequency vibration exists in building structures, mechanical devices, instrument manufacturing, and other fields, and is the key to modal analysis, steady-state control, and precision machining. At present, the monocular vision (MV) method has gradually become the primary choice to measure the low-frequency vibration because of its distinctive advantages in efficiency, non-contact, simplicity, flexibility, low cost, etc. Although many literature reports have demonstrated that this method has the capability to reach high measurement repeatability and resolution, its metrological traceability and uncertainty evaluation are difficult to be unified. In this study, a novel, to the best of our knowledge, virtual traceability method is presented to evaluate the measurement performance of the MV method for the low-frequency vibration. This presented method achieves traceability by adopting the standard sine motion videos and the precise position error correction model. Simulations and experiments confirm that the presented method can evaluate the amplitude and phase measurement accuracy of the MV-based low-frequency vibration in the frequency range from 0.01 to 20 Hz.

Dialysis vintage is associated with a high prevalence and severity of unpleasant symptoms in patients on hemodialysis.

Background: The burden of physical and emotional symptoms caused by somatic illness is present in most dialysis patients. However, it's unclear how symptom burden varies among patients with different dialysis vintages. We sought to examine differences in the prevalence and severity of unpleasant symptoms in hemodialysis patients with diverse dialysis vintage cohorts.Methods: This cross-sectional study included patients on maintenance hemodialysis at the Second Hospital of Anhui Medical University. We used the Dialysis Symptom Index (DSI) to determine the associated unpleasant symptoms, which is a validated survey to assess symptom burden/severity (higher scores indicate more severe symptoms), over June 2022 - September 2022.Results: We studied 146 patients: 35 (24%) had a dialysis vintage of ≤12 months (group 1) and 111 (76%) had a dialysis vintage of >12 months (group 2). Concerning Group 1 patients, the prevalence and severity of unpleasant symptoms were significantly higher in Group 2, the most common individual symptoms included feeling tired or lack of energy and trouble falling asleep (i.e., 75-85% of patients in each group), with dialysis vintage being an independent influencing factor (adjusted OR, 0.19; 95% CI, 0.16 to 0.23). Lower hemoglobin levels, iron stores, and dialysis adequacy levels are correlated with longer dialysis vintage.Conclusion: We observed a high prevalence of unpleasant symptoms and symptom clusters in a diverse dialysis vintages hemodialysis cohort. Further studies are needed to accurately and routinely define the symptom burden of chronic patients with chronic kidney disease (CKD).

Machine learning algorithm to predict the in-hospital mortality in critically ill patients with chronic kidney disease.

BACKGROUND: This study aimed to establish and validate a machine learning (ML) model for predicting in-hospital mortality in critically ill patients with chronic kidney disease (CKD). METHODS: This study collected data on CKD patients from 2008 to 2019 using the Medical Information Mart for Intensive Care IV. Six ML approaches were used to build the model. Accuracy and area under the curve (AUC) were used to choose the best model. In addition, the best model was interpreted using SHapley Additive exPlanations (SHAP) values. RESULTS: There were 8527 CKD patients eligible for participation; the median age was 75.1 (interquartile range: 65.0-83.5) years, and 61.7% (5259/8527) were male. We developed six ML models with clinical variables as input factors. Among the six models developed, the eXtreme Gradient Boosting (XGBoost) model had the highest AUC, at 0.860. According to the SHAP values, the sequential organ failure assessment score, urine output, respiratory rate, and simplified acute physiology score II were the four most influential variables in the XGBoost model. CONCLUSIONS: In conclusion, we successfully developed and validated ML models for predicting mortality in critically ill patients with CKD. Among all ML models, the XGBoost model is the most effective ML model that can help clinicians accurately manage and implement early interventions, which may reduce mortality in critically ill CKD patients with a high risk of death.

Randomized controlled trial of nalfurafine for refractory pruritus in hemodialysis patients.

Background: Chronic kidney disease-associated pruritus (CKD-aP) is very common and sometimes refractory to treatment in hemodialysis patients. In a trial conducted in Japan, nalfurafine, effectively reduced itching of treatment-resistant CKD-aP. Our present bridging study aimed to evaluate the efficacy and safety of nalfurafine in Chinese cohort with refractory CKD-aP.Methods: In this phase III, multicenter bridging study conducted at 22 sites in China, 141 Chinese cases with refractory CKD-aP were randomly (2:2:1) assigned to receive 5 µg, 2.5 µg of nalfurafine or a placebo orally for 14 days in a double-blind manner. The primary end point was the mean decrease in the mean visual analogue scale (VAS) from baseline.Results: A total of 141 patients were included. The primary endpoint analysis based on full analysis set (FAS), the difference of mean VAS decrease between 5 µg nalfurafine and placebo group was 11.37 mm (p = .041); the difference of mean VAS decrease between 2.5 µg and placebo group was 8.81 mm, but not statistically significantly different. Both differences were greater than 4.13 mm, which met its predefined success criterion of at least 50% efficacy of the key Japanese clinical trial. The per protocol set (PPS) analysis got similar results. The incidence of adverse drug reactions (ADRs) was 49.1% in 5µg, 38.6% in 2.5 µg and 33.3% in placebo group. The most common ADR was insomnia, seen in 21 of the 114 nalfurafine patients.Conclusions: Oral nalfurafine effectively reduced itching with few significant ADRs in Chinese hemodialysis patients with refractory pruritus.

Could long-term dialysis vintage and abnormal calcium, phosphorus and iPTH control accelerate aging among the maintenance hemodialysis population?

OBJECTIVE: Aging is a complex process of physiological dysregulation of the body system and is common in hemodialysis patients. However, limited studies have investigated the links between dialysis vintage, calcium, phosphorus, and iPTH control and aging. The purpose of the current study was to examine these associations. METHODS: During 2020, a cross-sectional study was conducted in 3025 hemodialysis patients from 27 centers in Anhui Province, China. Biological age was calculated by a formula using chronological age and clinical indicators. The absence of the target range for serum phosphorus (0.87-1.45 mmol/L), corrected calcium (2.1-2.5 mmol/L) and iPTH (130-585 pg/mL) were identified as abnormal calcium, phosphorus, and iPTH control. RESULTS: A total of 1131 hemodialysis patients were included, 59.2% of whom were males (669/1131). The mean (standard deviation) of actual age and biological age were 56.07 (12.79) years and 66.94 (25.88), respectively. The median of dialysis vintage was 4.3 years. After adjusting for the confounders, linear regression models showed patients with abnormal calcium, phosphorus, and iPTH control and on hemodialysis for less than 4.3 years (B = 0.211, p = .002) or on hemodialysis for 4.3 years or more (B = 0.302, p < .001), patients with normal calcium, phosphorus, and iPTH control and on hemodialysis for 4.3 years or more (B = 0.087, p = .013) had a higher biological age. CONCLUSION: Our findings support the hypothesis that long-term hemodialysis and abnormal calcium, phosphorus, and iPTH control may accelerate aging in the hemodialysis population. Further studies are warrant to verify the significance of maintaining normal calcium-phosphorus metabolism in aging.

Air pollution exposure and auto-inflammatory and autoimmune diseases of the musculoskeletal system: a review of epidemiologic and mechanistic evidence.

Auto-inflammatory and autoimmune diseases of the musculoskeletal system can be perceived as a spectrum of rheumatic diseases, with the joints and connective tissues are eroded severely that progressively develop chronic inflammation and lesion. A wide range of risk factors represented by genetic and environmental factors have been uncovered by population-based surveys and experimental studies. Lately, the exposure to air pollution has been found to be potentially involved in the mechanisms of occurrence or development of such diseases, principally manifest in oxidative stress, local and systemic inflammation, and epigenetic modifications, as well as the mitochondrial dysfunction, which has been reported to participate in the intermediate links. The lungs might serve as a starting area of air pollutants, which would cause oxidative stress-induced bronchial-associated lymphoid tissue (iBALT) to further to influence T, B cells, and the secretion of pro-inflammatory cytokines. The binding of aromatic hydrocarbon receptor (AhR) to the corresponding contaminant ligands tends to regulate the reaction of Th17 and Tregs. Furthermore, air pollution components might spur on immune and inflammatory responses by damaging mitochondria that could interact with and exacerbate oxidative stress and pro-inflammatory cytokines. In this review, we focused on the association between air pollution and typical auto-inflammatory and autoimmune diseases of the musculoskeletal system, mainly including osteoarthritis (OA), rheumatoid arthritis (RA), spondyloarthritis (SpA) and juvenile idiopathic arthritis (JIA), and aim to collate the mechanisms involved and the potential channels. A complete summary and in-depth understanding of the autoimmune and inflammatory effects of air pollution exposure should hopefully contribute new perspectives on how to formulate better public health policies to alleviate the adverse health effects of air pollutants.

Imbalance of circulating innate lymphoid cell subpopulations in patients with chronic kidney disease.

Innate lymphoid cells (ILCs) are a newly identified heterogeneous family of innate immune cells. We conducted this study to investigate the frequency of circulating ILC subsets in various chronic kidney diseases (CKD). In DN, the proportion of total ILCs and certain ILC subgroups increased significantly. Positive correlations between proportion of total ILCs, ILC1s and body mass index, glycated hemoglobin were observed in DN. In LN, a significantly increased proportion of ILC1s was found in parallel with a reduced proportion of ILC2s. The proportions of total ILCs and ILC1s were correlated with WBC count and the level of C3. In all enrolled patients, the proportion of total ILCs and ILC1s was significantly correlated with the levels of ACR and GFR. In the present study, the proportion of circulating ILC subsets increased significantly in various types of CKD and correlated with clinico-pathological features, which suggests a possible role for ILCs in CKD.

Axial and frontal X-ray fluoroscopy technique of the sustentaculum tali can improve the accuracy of sustentacular screw placement.

INTRODUCTION: Calcaneal fractures, especially those involving the articular surface, should be anatomically reduced as much as possible. Fixing the fracture by placing a screw into the sustentaculum tali from the lateral side of the calcaneus is generally considered to be the key to successful surgery. However, due to the limited visibility during surgery, it is not easy to place screws into the sustentaculum tali accurately. The purpose of this study was to explore a new fluoroscopy method for the sustentaculum tali and verify the value of this method in improving screw placement accuracy. METHODS: In this study, a total of 42 human foot and ankle specimens were dissected and measured. The shape and position of the sustentaculum tali were observed, and the influence of adjacent bones on imaging findings was analysed. The axial and frontal X-ray fluoroscopy method to view the sustentaculum tali was formulated, and the appropriate projection angle through anatomical and image measurements was explored. Thirty specimens were randomly selected for screw placement, and the direction of the screw was dynamically adjusted under the new imaging method. The success rate of sustentacular screw placement was evaluated. RESULTS: The anteversion angles of the sustentaculum tali were 30.81 ± 2.21° and 30.68 ± 2.86° by anatomical and imaging measurements, respectively. There was no statistically significant difference in the anteversion angle between the two measurement methods. Harris heel views should be obtained at 30° to identify the sustentaculum tali on axial X-ray images. Frontal X-ray imaging was performed perpendicular to this projection angle. Through frontal and axial X-ray imaging, the position and shape of the sustentaculum tali can be clearly observed, and these factors are seldom affected by adjacent bones. Under the new fluoroscopy method, the screws were placed from the anterior region of the lateral wall of the calcaneus to the sustentaculum tali. A total of 60 screws were placed in the 30 specimens; of these, 54 screws were in good position, 2 screws penetrated the cortical bone, and 4 screws did not enter the sustentaculum tali. The success rate of sustentacular screw placement was 90% (54/60). CONCLUSIONS: Axial and frontal X-ray images of the sustentaculum tali can clearly show the shape of the structure, which improves sustentacular screw placement accuracy.

Hybrid-Flash Butterfly Optimization Algorithm with Logistic Mapping for Solving the Engineering Constrained Optimization Problems.

Only the smell perception rule is considered in the butterfly optimization algorithm (BOA), which is prone to falling into a local optimum. Compared with the original BOA, an extra operator, i.e., color perception rule, is incorporated into the proposed hybrid-flash butterfly optimization algorithm (HFBOA), which makes it more in line with the actual foraging characteristics of butterflies in nature. Besides, updating the strategy of the control parameters by the logistic mapping is used in the HFBOA for enhancing the global optimal ability. The performance of the proposed method was verified by twelve benchmark functions, where the comparison experiment results show that the HFBOA converges quicker and has better stability for numerical optimization problems, which are compared with six state-of-the-art optimization methods. Additionally, the proposed HFBOA is successfully applied to six engineering constrained optimization problems (i.e., tubular column design, tension/compression spring design, cantilever beam design, etc.). The simulation results reveal that the proposed approach demonstrates superior performance in solving complex real-world engineering constrained tasks.

Circulating adiponectin levels and systemic lupus erythematosus: a two-sample Mendelian randomization study.

OBJECTIVES: Several studies have reported increased serum/plasma adiponectin levels in SLE patients. This study was performed to estimate the causal effects of circulating adiponectin levels on SLE. METHODS: We selected nine independent single-nucleotide polymorphisms that were associated with circulating adiponectin levels (P < 5 × 10-8) as instrumental variables from a published genome-wide association study (GWAS) meta-analysis. The corresponding effects between instrumental variables and outcome (SLE) were obtained from an SLE GWAS analysis, including 7219 cases with 15 991 controls of European ancestry. Two-sample Mendelian randomization (MR) analyses with inverse-variance weighted, MR-Egger regression, weighted median and weight mode methods were used to evaluate the causal effects. RESULTS: The results of inverse-variance weighted methods showed no significantly causal associations of genetically predicted circulating adiponectin levels and the risk for SLE, with an odds ratio (OR) of 1.38 (95% CI 0.91, 1.35; P = 0.130). MR-Egger [OR 1.62 (95% CI 0.85, 1.54), P = 0.195], weighted median [OR 1.37 (95% CI 0.82, 1.35), P = 0.235) and weighted mode methods [OR 1.39 (95% CI 0.86, 1.38), P = 0.219] also supported no significant associations of circulating adiponectin levels and the risk for SLE. Furthermore, MR analyses in using SLE-associated single-nucleotide polymorphisms as an instrumental variable showed no associations of genetically predicted risk of SLE with circulating adiponectin levels. CONCLUSION: Our study did not find evidence for a causal relationship between circulating adiponectin levels and the risk of SLE or of a causal effect of SLE on circulating adiponectin levels.

U-Net combined with multi-scale attention mechanism for liver segmentation in CT images.

BACKGROUND: The liver is an important organ that undertakes the metabolic function of the human body. Liver cancer has become one of the cancers with the highest mortality. In clinic, it is an important work to extract the liver region accurately before the diagnosis and treatment of liver lesions. However, manual liver segmentation is a time-consuming and boring process. Not only that, but the segmentation results usually varies from person to person due to different work experience. In order to assist in clinical automatic liver segmentation, this paper proposes a U-shaped network with multi-scale attention mechanism for liver organ segmentation in CT images, which is called MSA-UNet. Our method makes a new design of U-Net encoder, decoder, skip connection, and context transition structure. These structures greatly enhance the feature extraction ability of encoder and the efficiency of decoder to recover spatial location information. We have designed many experiments on publicly available datasets to show the effectiveness of MSA-UNet. Compared with some other advanced segmentation methods, MSA-UNet finally achieved the best segmentation effect, reaching 98.00% dice similarity coefficient (DSC) and 96.08% intersection over union (IOU).