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The research of biomedical data is crucial for disease diagnosis, health management, and medicine development. However, biomedical data are usually characterized by high dimensionality and class imbalance, which increase computational cost and affect the classification performance of minority class, making accurate classification difficult. In this paper, we propose a biomedical data classification method based on feature selection and data resampling. First, use the minimal-redundancy maximal-relevance (mRMR) method to select biomedical data features, reduce the feature dimension, reduce the computational cost, and improve the generalization ability; then, a new SMOTE oversampling method (Spectral-SMOTE) is proposed, which solves the noise sensitivity problem of SMOTE by an improved spectral clustering method; finally, the marine predators algorithm is improved using piecewise linear chaotic maps and random opposition-based learning strategy to improve the algorithm's optimization seeking ability and convergence speed, and the key parameters of the spectral-SMOTE are optimized using the improved marine predators algorithm, which effectively improves the performance of the over-sampling approach. In this paper, five real biomedical datasets are selected to test and evaluate the proposed method using four classifiers, and three evaluation metrics are used to compare with seven data resampling methods. The experimental results show that the method effectively improves the classification performance of biomedical data. Statistical test results also show that the proposed PRMPA-Spectral-SMOTE method outperforms other data resampling methods. [ABSTRACT FROM AUTHOR]

The emergence of credit has generated a wealth of data on consumer lending behavior. In recent years, financial institutions have also started to use such data to make informed lending decisions based on fine-grained customer data, but conventional risk assessment models are inadequate in meeting the risk control requirements of the financial industry. Therefore, this paper proposes a credit scoring ensemble model incorporating fuzzy clustering particle swarm optimization (PSO) algorithm to obtain better credit risk prediction capability. First, a weighted outlier detection method based on the Induced Ordered Weighted Average Operator is proposed to preprocess the data to reduce noisy data's misleading effect on model training. Then, an undersampling method combined with fuzzy clustering PSO is proposed to overcome the negative effect of category imbalance on model training by resampling the data. In addition, a hyperparameter optimization framework is introduced to adaptively adjust important parameters in the ensemble model considering the impact of parameter settings on the training performance of the model. Based on the evaluation metrics of F-score, AUC, and Kappa coefficient, an empirical analysis was conducted on five credit risk datasets. The results show that the proposed method outperforms the comparative model with an improvement of 10% to 50% in terms of F-score and AUC. The highest achieved F-score is 0.9488, and the maximum AUC is 0.9807, demonstrating the effectiveness of the proposed method. The kappa coefficient results indicate a high level of consistency in the predicted classification results of the model. [ABSTRACT FROM AUTHOR]

Extreme Learning Machine (ELM) is widely popular for its advantages such as fast training speed and good generalization performance. However, the randomness of hidden layer parameters in ELM leads to unstable prediction performance of the model. We propose a novel two-hidden-layer extreme learning machine (TELM) for complex data classification. Firstly, the idea of weighting is introduced into TELM, and a weighted two-hidden-layer extreme learning machine (WTELM) model is proposed to improve the classification accuracy of the model. Secondly, the convergence factor and position update formula in gray wolf optimization algorithm (GWO) are adjusted to enhance the optimization algorithm's ability to search for optimal parameters. Finally, an improved gray wolf optimization (IGWO) is utilized to search for the optimal parameters of the WTELM model. The impact of different intelligent optimization algorithms on the model's classification results is compared. The experimental results demonstrate that the classification accuracy of the WTELM model has been improved by about 11–18% compared to traditional ELM models. Moreover, compared to the WTELM model, the classification accuracy of the IGWO-WTELM model has improved by about 1–19%. This indicates that the method proposed in this paper is significantly superior to traditional ELM methods and their variants, improving the stability, training speed and classification accuracy of the model. [ABSTRACT FROM AUTHOR]

Purpose: Non-adherence to heart failure (HF) medications is associated with poor outcomes. We used restricted cubic splines (RCS) to assess the continuous relationship between adherence to renin-angiotensin system inhibitors (RASI) and β-blockers and long-term outcomes in senior HF patients. Methods: We identified a population-based cohort of 4234 patients, aged 65–84 years, 56% male, who were hospitalised for HF in Western Australia between 2003 and 2008 and survived to 1-year post-discharge (landmark date). Adherence was calculated using the proportion of days covered (PDC) in the first year post-discharge. RCS Cox proportional-hazards models were applied to determine the relationship between adherence and all-cause death and death/HF readmission at 1 and 3 years after the landmark date. Results: RCS analysis showed a curvilinear adherence-outcome relationship for both RASI and β-blockers which was linear above PDC 60%. For each 10% increase in RASI and β-blocker adherence above this level, the adjusted hazard ratio for 1-year all-cause death fell by an average of 6.6% and 4.8% respectively (trend p < 0.05) and risk of all-cause death/HF readmission fell by 5.4% and 5.8% respectively (trend p < 0.005). Linear reductions in adjusted risk for these outcomes at PDC ≥ 60% were also seen at 3 years after landmark date (all trend p < 0.05). Conclusion: RCS analysis showed that for RASI and β-blockers, there was no upper adherence level (threshold) above 60% where risk reduction did not continue to occur. Therefore, interventions should maximise adherence to these disease-modifying HF pharmacotherapies to improve long-term outcomes after hospitalised HF. [ABSTRACT FROM AUTHOR]

To deal with the randomness, intermittence and fluctuation of wind speed, reasonable dispatch and economic prediction model are necessary. For this purpose, a new composite framework position encoding, feature extraction, quantile regression bidirectional minimal gated memory network (QRBiMGM) and kernel density estimation are proposed in this study. Firstly, numerical weather features are encoded by position encoding and aggregated with historical wind speed data. Then, the multi-head attention mechanism is applied to extract the clustering features. Subsequently, combined with the improved minimum gated memory network and quantile regression, three wind speed datasets are trained to obtain conditional quantiles at different confidence levels. Finally, the probability density function of wind speed is estimated by kernel density estimation. To verify the effectiveness of the proposed composite framework, a comparative test of four related models on three datasets is conducted. It obtains the root-mean-square error values of 2.1876 m/s for Dataset 1, respectively, using mode of conditional distribution. In terms of interval prediction, the Winkler score is 4.9130, 5.3969 and 5.6506 with 95% confidence level, which exhibits higher accuracy than the benchmark. The experimental results reveal that the combination of multi-head attention mechanism and QRBiMGM can better balance the prediction performance and timing computation, and possess strong adaptability that allows them to be widely utilized in the wind speed prediction of various wind power plants. [ABSTRACT FROM AUTHOR]

Passenger flow is an important benchmark for measuring tourism benefits, and accurate tourism passenger flow prediction is of great significance to the government and related tourism enterprises and can promote the sustainable development of China's tourism industry. For daily passenger flow time series data, a passenger flow forecasting method based on convolutional neural network (CNN) and improved quantile regression long short-term memory network (QRLSTM), denoted as CNN-IQRLSTM, is proposed with reconstructed correlation features and in the form of sliding windows as inputs. First, four discrete variables such as whether the day is a weekend and holiday are created by time; then, a sliding window of width 42 is used to pass the passenger flow data into the network sequentially; finally, the loss function of the sparse Laplacian improved QRLSTM is introduced for passenger flow prediction, and the point prediction and interval prediction results under different quartiles are obtained. The application of quantile regression captures the overall picture of the data, enhances the robustness, fit, predictive power and nonlinear processing capability of neural networks, and fills the gap between quantile regression and neural network methods in the field of passenger flow prediction. CNN can effectively handle complex input data, and the improved nonlinear QR model can provide passenger flow quantile prediction information. The method is applied to the tourism traffic prediction of four 5A scenic spots in Jilin Province, and the effectiveness of the method is verified. The results show that the method proposed in this paper fits best in point prediction and has higher prediction accuracy. The MAPE of the Changbai Mountain dataset was 0.07, the MAPE of the puppet palace museum dataset was 0.05, the fit of the Sculpture Park dataset reached 93%, and the fit of the net moon lake dataset was as high as 99%. Meanwhile, the interval prediction results show that the method has a larger interval coverage as well as a smaller interval average width, which improves the prediction efficiency. In 95% of the interval predictions, the interval coverage of Changbai Mountain data is 99% and the interval average width is 0.49. It is a good reference value for the management of different scenic spots. [ABSTRACT FROM AUTHOR]

Accurate classification of gene expression data is crucial for disease diagnosis and drug discovery. However, gene expression data usually has a large number of features, which poses a challenge for accurate classification. In this paper, a novel feature selection method based on minimal redundancy maximal relevance (mRMR) and aquila optimizer is proposed, which introduces the mRMR method in the initialization stage of the population to generate excellent initial populations, effectively improve the quality of the population, and then, the using random opposition-based learning strategy to improve the diversity of aquila population and accelerate the convergence speed of the algorithm, and finally, introducing inertia weight in the position update formula in the late iteration of the aquila optimizer to avoid the algorithm falling into the local optimum and improve the algorithm’s capability to find the optimum. In order to verify the effectiveness of the proposed method, ten real gene expression datasets are selected in this paper and compared with several meta-heuristic algorithms. Experimental results show that the proposed method is significantly superior to other meta-heuristic algorithms in terms of fitness value, classification accuracy and the number of selected features. Compared with the original aquila optimizer, the average classification accuracy of the proposed method on KNN and SVM classifiers is improved by 3.48–12.41% and 0.53–18.63% respectively. The proposed method significantly reduces the feature dimension of gene expression data, retains important features, and obtains higher classification accuracy, providing a new method and idea for feature selection of gene expression data. [ABSTRACT FROM AUTHOR]

Feature Selection (FS) is regarded as an important preprocessing technique with the main aim of discarding irrelevant and redundant features on the premise of improving or retaining classification accuracy. As one of the most popular FS methods, the swarm intelligent (SI) algorithm based FS method faces great challenges in solution quality. Given this, a novel probe mechanism based particle swarm optimization (PPSO) is proposed. The probe mechanism is proposed to test the dataset with different filter methods by employing a part of features to generate high-quality initial population, thus playing a critical role in enhancing the final results of the algorithm. Next, the repeated binarized solution avoid mechanism prevents the same binarized solution from repeating compute the fitness function, which can improve the exploration capability. Additionally, a volcanic eruption mechanism based on the updated information of the global best solution is designed to improve the performance in convergence rate and search precision. In the experiment section, 14 UCI datasets are employed to assess the performance of the PPSO. The outcomes show that the designed strategies can significantly improve the original PSO, and the PPSO is more efficient than the other comparative algorithms for solving the FS problems. In addition, the impact of each strategy is analyzed, and the volcanic eruption mechanism is the most efficient among the three strategies. [ABSTRACT FROM AUTHOR]

Rolling bearing fault diagnosis is a meaningful and challenging task. Most methods first extract statistical features and then carry out fault diagnosis. At present, the technology of intelligent identification of bearing mostly relies on deep neural network, which has high requirements for computer equipment and great effort in hyperparameter tuning. To address these issues, a rolling bearing fault diagnosis method based on the improved deep forest algorithm is proposed. Firstly, the fault feature information of rolling bearing is extracted through multigrained scanning, and then the fault diagnosis is carried out by cascade forest. Considering the fitting quality and diversity of the classifier, the classifier and the cascade strategy are updated. In order to verify the effectiveness of the proposed method, a comparison is made with the traditional machine learning method. The results suggest that the proposed method can identify different types of faults more accurately and robustly. At the same time, it has very few hyperparameters and very low requirements on computer hardware. [ABSTRACT FROM AUTHOR]

Aims: Inconclusive findings of lipid‐lowering medications (LLMs) on cancer survival benefit require more evidence. We tested the hypothesis that adherence to this drug is associated with reduced cancer‐specific mortality in a homogeneous population who had used this drug before cancer diagnosis. Methods: The Australian Cancer Database was linked to the Pharmaceutical Benefits Scheme database, and to the National Death Index (up to 2015). Medication adherence was calculated by proportion of days covered. Cox regression models with time‐varying covariates were used to derive multivariable‐adjusted cause‐specific hazard ratio (HR) and 95% confidence interval (CI) for the associations between adherence to LLMs, statins, lipophilic, and hydrophilic statins and cancer‐specific mortality. Results: From 2003 to 2013, 3 separate cohorts of 20 046, 11 719 and 6430 female patients with newly diagnosed breast, colorectal cancer, and melanoma respectively were identified. The 1‐year adherence was similar at 1‐year prediagnosis in the 3 cohorts, on average 82%. Each 10% increase in 1‐year adherence to LLMs was inversely associated with cancer‐specific mortality among women with breast cancer (fully adjusted HR = 0.92, 95% CI 0.91–0.93), colorectal cancer (fully adjusted HR = 0.92, 95% CI 0.91–0.93), or melanoma (fully adjusted HR = 0.97, 95% CI 0.94–1.00). The reductions in cancer‐specific mortality were more pronounced for women who adhered to lipophilic than hydrophilic statins in all 3 cancers albeit not statistically significant for melanoma. Conclusion: Among LLM users, adherence to this drug is associated with a decrease in cancer‐specific mortality. If confirmed, LLMs could be considered as an adjuvant cancer therapy to improve prognosis in cancer survivors. [ABSTRACT FROM AUTHOR]

Purpose: Inconsistent results of lipid-lowering medications (LLMs) on improved cancer survival need more investigations. We tested the hypothesis that adherence to the drug would be associated with a lower cancer-specific mortality in a homogeneous population who has ever used the drug. Methods: Utilising data from the Australian Cancer database, linked to the Pharmaceutical Benefits Scheme data and the National Death Index, we identified two separate cohorts of 4519 and 3083 women patients with newly diagnosed endometrial and lung cancer respectively between 2003 and 2013. Adherence to this drug was calculated by proportion of days covered. Cox regression models with time-varying covariates were used to estimate the multivariable-adjusted cause-specific hazard ratios (HRs) and 95% confidence intervals (CIs) for the association of adherence to LLMs, statins, lipophilic and hydrophilic statins, and cancer-specific mortality. Results: Each 10% increase in 1-year adherence to LLMs reduced cancer-specific mortality among women with endometrial cancer (adjusted HR=0.93, 95% CI 0.90–0.96) or lung cancer (adjusted HR=0.95, 95% CI 0.93–0.97). The inverse associations remained unchanged in different subgroup analyses. The reductions in lung cancer mortality were not apparent for women who adhered to lipophilic statins albeit better endometrial cancer survival appeared in the lipophilic statin group and borderline statistical improvement in the hydrophilic statin group. Conclusions: Among LLM users, adherence to this drug is inversely associated with reduced cancer-specific mortality. Together with previous evidence, randomised controlled trials are called for to confirm whether LLMs could be considered as an adjuvant treatment to improve prognosis. [ABSTRACT FROM AUTHOR]

In this work, we reported the preparation of a novel type of collagen composite fiber with antibacterial properties by utilizing the hydrodynamic focusing effect of the multichannel microfluidic chip and microfluidic wet spinning technology (MWST). Specifically, tannin (TA)-grafted collagen solution was used as the precursor, ethanol anhydrous was used as the sheath flow, and AgNO 3 solution was used as the spacer flow between the precursor and the sheath flow. The morphologies, structures, and properties of obtained collagen composite fibers were seriously analyzed. These results indicated that TA can not only form strong crosslinking networks of hydrogen bonds with collagen fibrils, but also chelate with Ag+, thus resulting in a synergistic enhancement of the mechanical properties, thermal stability, and antibacterial properties of collagen/TA-Ag composite fibers. These collagen composite fibers with excellent mechanical properties and antibacterial properties might have potential applications in the surgical suture field. [Display omitted] • Collagen/TA-Ag composite fibers were firstly fabricated via the hydrodynamic focusing effect and MWST. • Ag ions can chelate with TA-grafted collagen in the microchannels of the microfluidic chip during the MWST process. • Mechanical properties of composite fibers were enhanced via the synergistic effect of chelation and hydrogen bond effects. • Collagen/TA-Ag composite fibers fabricated through MWST have impressive antibacterial properties. [ABSTRACT FROM AUTHOR]

Rolling bearing is a critical part of machinery, whose failure will lead to considerable losses and disastrous consequences. Aiming at the research of rotating mechanical bearing data, a fault identification method based on Variational Mode Decomposition (VMD) and Iterative Random Forest (iRF) classifier is proposed. Furthermore, EMD and EEMD are used to decompose the data. At the same time, three mainstream classifiers were selected as the benchmark model. The results show that the proposed model has the highest recognition accuracy. [ABSTRACT FROM AUTHOR]

Accurate diagnosis of rolling bearing fault on the normal operation of machinery and equipment has a very important significance. A method combining Ensemble Empirical Mode Decomposition (EEMD) and Random Forest (RF) is proposed. Firstly, the original signal is decomposed into several intrinsic mode functions (IMFs) by EEMD, and the effective IMFs are selected. Then their energy entropy is calculated as the feature. Finally, the classification is performed by RF. In addition, the wavelet method is also used in the proposed process, the same as EEMD. The results of the comparison show that the EEMD method is more accurate than the wavelet method. [ABSTRACT FROM AUTHOR]

We herein report novel polyethersulfone/graphene oxide@titanium dioxide (PES/GO@TiO 2) photocatalytic membranes developed via the layer-by-layer (LBL) self-assembly technique. The synergistic coupling of GO and TiO 2 could result in improved photocatalytic activity and endow the pristine PES membrane with photocatalytic function. Various techniques were adopted to characterize the structure and morphology of PES/GO@TiO 2 photocatalytic membranes, which indicated that GO@TiO 2 was successfully integrated in PES. Contact angle testing proved that the hydrophilicity of PES/GO@TiO 2 photocatalytic membranes was improved with the increase of self-assembled GO@TiO 2 layers. FTIR results indicated that GO@TiO 2 was attached to the pristine PES membrane surface through interfacial interactions in our experimental conditions. And both the thermal stability and mechanical stability of the pristine PES membrane were improved with the incorporation of GO@TiO 2. Meanwhile, the optimized PES/GO@TiO 2 photocatalytic membranes exhibited a water flux up to 109.8 L/(m2 h), more than 4 times that of pristine PES membrane, while kept high bovine serum albumin (BSA) rejection of 99.1%, and flux recovery ratio (FRR) of 86.1% in dark condition and the flux almost tripled after about 3 h of UV irradiation. Specially, the photocatalytic activity of PES/GO@TiO 2 photocatalytic membranes increased with an increase of self-assembled GO@TiO 2 layers. When the self-assembled layers of GO@TiO 2 were three, the membrane showed 95.1% of degradation against methylene blue (MB) dye under UV light irradiation. Thus, the prepared PES/GO@TiO 2 photocatalytic membranes can be suggested as an ideal candidate for photocatalytic templates in wastewater treatment. [Display omitted] • GO@TiO 2 composite photocatalyst were fabricated via hydrothermal method. • PES/GO@TiO 2 photocatalytic membranes with different self-assembled layers were fabricated via LBL self-assembly technique. • GO@TiO 2 composite photocatalyst and the pristine PES membrane can be connected by hydrogen bonds to ensure awesome stability. • PES/GO@TiO 2 photocatalytic membranes had awesome hydrophilicity and wholesome photocatalytic performance of MB. [ABSTRACT FROM AUTHOR]

Objective: Medication adherence is considered an important risk factor for sudden unexpected death in epilepsy (SUDEP), although measurement accuracy is difficult. Using prescription dispensations, this study aims to estimate antiseizure medication (ASM) adherence and identify adherence patterns that influence epilepsy mortality. Methods: This is a retrospective cohort study of tertiary epilepsy outpatients seen at St Vincent's Hospital Melbourne, Victoria, Australia, from January 1, 2012 until December 31, 2017. Privacy‐preserving data linkage with the Australian national prescription, death, and coroner's databases was performed. We fitted a four‐cluster longitudinal group‐based trajectory model for ASM adherence from recurring 90‐day windows of prescription dispensations during a 3‐year "landmark period" from January 1, 2012 to December 31, 2014, using the AdhereR package. We estimated the risk of SUDEP and all‐cause death for each adherence pattern during an "observation period" from January 1, 2015 to December 31, 2017. The Cox proportional hazards and logistic regression models were adjusted for age, sex, socioeconomic status, epilepsy duration, comorbidity, drug resistance, and inadequate seizure control. Results: One thousand one hundred eighty‐seven participants were observed for a median of 3.2 years (interquartile range = 2.4–4.0 years). We observed 66 deaths with 10 SUDEP cases during the observation period. We identified four patterns of ASM adherence: good, 51%; declining, 24%; poor, 16%; and very poor, 9%. Declining adherence was associated with an increased risk for SUDEP, with hazard ratio (HR) = 8.43 (95% confidence interval [CI] = 1.10–64.45) at 1 year and HR = 9.17 (95% CI = 1.16–72.21) at 3 years. Compared to no ASM therapeutic change, the addition of a second to fourth ASM offered increased protection against SUDEP in patients with continuing drug‐resistant epilepsy. Significance: ASM nonadherence was observed in half of outpatients with epilepsy. A declining pattern of adherence, observed in a quarter of patients, was associated with more than eight times increased risk of SUDEP. Any ongoing medication interventions must include strategies to maintain and improve ASM adherence if we are to reduce the risk of SUDEP. [ABSTRACT FROM AUTHOR]

Aims: Equivocal results of association between metformin and cancer-specific survival need more investigation. We tested the hypothesis that adherence to the drug had a lower cancer-specific mortality in a homogeneous population (i.e. regular users).Methods: The Australian Cancer database was linked to the Pharmaceutical Benefits Scheme data and the National Death Index. Adherence to metformin was calculated by proportion of days covered. Cox regression models with time-varying covariates were used to estimate multivariable-adjusted cause-specific hazard ratios (HRs) and 95% confidence intervals (CI) for the association of adherence to metformin and cancer-specific mortality.Results: Between 2003 and 2013, three separate cohorts of 6717, 3121, and 1854 female patients were identified with newly diagnosed breast, colorectal, or endometrial cancer. The 1-year adherence was similar at baseline in three cohorts, on average 75%. Each 10% increase in 1-year adherence to metformin reduced cancer-specific mortality among women with breast cancer (adjusted HR = 0.95; 95% CI, 0.93-0.97), colorectal cancer (adjusted HR = 0.94; 95% CI, 0.91-0.96), or endometrial cancer (adjusted HR = 0.95; 95% CI, 0.90-0.99). The inverse associations remained unchanged in most subgroup analyses.Conclusions: Among metformin users, adherence to this drug is inversely associated with reduced cancer-specific mortality. If confirmed, metformin could be considered as an adjuvant treatment. [ABSTRACT FROM AUTHOR]

The kernel function in SVM enables linear segmentation in a feature space for a large number of linear inseparable data. The kernel function that is selected directly affects the classification performance of SVM. To improve the applicability and classification prediction effect of SVM in different areas, in this paper, we propose a weighted p-norm distance t kernel SVM classification algorithm based on improved polarization. A t-class kernel function is constructed according to the t distribution probability density function, and its theoretical proof is presented. To find a suitable mapping space, the t-class kernel function is extended to the p-norm distance kernel. The training samples are obtained by stratified sampling, and the affinity matrix is redefined. The improved local kernel polarization is established to obtain the optimal kernel weights and kernel parameters so that different kernel functions are weighted combinations. The cumulative optimal performance rate is constructed to evaluate the overall classification performance of different kernel SVM algorithms, and the significant effects of different p-norms on the classification performance of SVM are verified by 10 times fivefold cross-validation statistical comparison tests. In most cases, the results using 6 real datasets show that compared with the traditional kernel function, the proposed weighted p-norm distance t kernel can improve the classification prediction performance of SVM. [ABSTRACT FROM AUTHOR]

Summary: Aim: Hospitalized heart failure (HF) patients have a poor prognosis postdischarge. We determined whether renin‐angiotensin system inhibitors (RASI) and β‐blockers dispensed to patients within 60 days post‐HF hospital discharge are associated with improved 1‐year survival. Methods: A retrospective population‐based study was conducted in 4897 seniors, aged 65‐84 years, alive at 60 days postindex HF hospitalization in Western Australia over 2003‐2008. Dispensing of RASI and β‐blocker dispensing was identified from the Pharmaceutical Benefits Scheme claims database linked to hospital admission and death records. Results: At 1‐year posthospital discharge, the all‐cause mortality and all‐cause death or HF rehospitalization rate was 13.5% (n = 663) and 24.4% (n = 1193), respectively. Postdischarge RASI and β‐blocker were dispensed in 77.4% and 53.0% of patients, respectively. Their use was associated with a lower inverse probability treatment weighted (IPTW) HR for 1‐year mortality of 0.70, 95% CI 0.61‐0.81 and 0.79, 95% CI 0.68‐0.92, respectively (both P < 0.0001), with a survival advantage most evident in the subgroup (70.1%) of patients with ischemic HF. In the overall cohort, these therapies were also associated with reduced IPTW HRs for all‐cause death or HF rehospitalization (both P < 0.005) but not for HF rehospitalization exclusively. Use of a β‐blocker was associated with a reduced IPTW HR for HF rehospitalization in the ischemic HF subgroup only. Conclusions: In a cohort of senior patients hospitalized with HF, dispensing of a RASI or β‐blocker within 60 days postdischarge is associated with a 1‐year survival benefit. Early postdischarge support programs after recent HF hospitalization should include measures to optimize adherence to evidence‐based medications. [ABSTRACT FROM AUTHOR]

Objectives: To (a) compare palliative care needs of lung cancer patients on their final admission to community‐based and inpatient palliative care services; and (b) explore whether and how these care needs affect their utilisation of different palliative care services in the last days of life. Methods: Descriptive study involving 17,816 lung cancer patients who received the last episode of palliative care from specialist services and died between 1 January 2013 and 31 December 2018. Results: Both groups of patients admitted to community‐based and inpatient palliative care services generally experienced relatively low levels of symptom distress, but high levels of functional impairment and dependency. "Unstable" versus "stable" palliative care phase (Odds ratio = 11.66; 95% Confidence Interval: 9.55–14.24), poorer functional outcomes and severe levels of distress from many symptoms predicted greater likelihood of use of inpatient versus community‐based palliative care. Conclusions: Most inpatient palliative care admissions are not associated with high levels of symptom severity. To extend the period of home care and rate of home death for people with lung cancer, additional investment is required to improve their access to sufficiently skilled palliative care staff, multi‐disciplinary teams and 24‐hour home support in community settings. [ABSTRACT FROM AUTHOR]

Using deep learning to improve the performance of finger vein recognition has become a part of mainstream research. Currently, the performance of finger vein recognition systems is limited by insufficient finger vein training samples, which leads to insufficient feature learning and weak model generalization. To solve the above problems, first, we propose a simple and effective finger vein data augmentation strategy named FV-Mix, which uses fine finger vein image region of interest (ROI) for grayscale normalization and linear mixing. It can accomplish exponential-level data augmentation on the training sample, and the augmented data will represent a more complete dataset. Second, a residual Gabor convolutional network (RGCN) is designed for finger vein recognition, which includes a residual Gabor convolutional layer (RGCL) and a dense semantic analysis module (DSAM). The RGCL is designed to replace the shallow convolutional layer in the network by using the characteristics of the Gabor filter to enhance the scale and direction of information in the shallow pattern features. And then, the enhanced deep features are further extracted and analyzed by DSAM, which is used to assist the final model in classifying and recognizing finger vein images. To verify the effectiveness of our work, five publicly available finger vein image datasets are used, and a large number of comparison experiments are designed. The proposed FV-Mix strategy, RGCL module, and DSAM module were fully validated. The experimental results show that on these five datasets, the average recognition accuracy and equal error rate (EER) of the proposed RGCN are 99.22% and 0.188%, respectively, achieves competitive performance compared with current state-of-the-art work. • Novel residual Gabor convolutional network improves recognition system performance. • Novel residual Gabor convolutional layer enhances finger vein pattern features. • Novel dense semantic analysis module assists with model classification. • Exponential level augmentation for finger vein image samples. [ABSTRACT FROM AUTHOR]

Background Polypharmacy among older people represents a global challenge due to its association with adverse drug events. The reported prevalence of polypharmacy varies widely across countries, and is particularly high in Asian countries. However, there is no multinational study using standardised measurements exploring variations in prescribing trends. Objective To compare polypharmacy trends in older people in Asia, Australia and the United Kingdom. Design Multinational, retrospective, time-trend, observational study using a common study protocol. Setting Outpatient and community settings. Subjects All individuals aged ≥ 65 years between 2013 and 2016. Methods We defined polypharmacy as the concomitant use of ≥5 medications for ≥45 days per year. We estimated the annual prevalence of polypharmacy and calculated average annual percentage change (AAPC) to assess the time trends. Results A total of 1.62 million individuals were included in this study. The highest prevalence of polypharmacy was observed in Hong Kong (46.4%), followed by Taiwan (38.8%), South Korea (32.0%), the United Kingdom (23.5%) and Australia (20.1%) in 2016. For the time trend, the Asian region showed a steady increase, particularly in Hong Kong and South Korea (AAPC: Hong Kong, 2.7%; South Korea, 1.8%; Taiwan, 1.0%). However, Australia and the United Kingdom showed a decreasing trend (Australia, −4.9%; the United Kingdom, −1.1%). Conclusions Polypharmacy prevalence in older people was higher in Hong Kong, Taiwan and South Korea, with an increasing trend over time, compared with Australia and the United Kingdom. Our findings underline the necessity to monitor polypharmacy among older people in Asia by conducting government-level interventions and introducing medicine-optimisation strategies. [ABSTRACT FROM AUTHOR]

Background: Multimorbidity is a prevalent risk factor for COVID-19–related complications and death. We sought to evaluate the association of homologous booster vaccination using BNT162b2 (Pfizer-BioNTech) or CoronaVac (Sinovac) with COVID-19–related deaths among people with multimorbidity during the initial Omicron wave of the COVID-19 pandemic. Methods: Using routine clinical records from public health care facilities in Hong Kong, we conducted a territory-wide retrospective cohort study comparing people aged 18 years or older with 2 or more chronic conditions who received a homologous booster (third) dose with those who received only 2 doses, between Nov. 11, 2021, and Mar. 31, 2022. The primary outcome was death related to COVID-19. Results: We included 120 724 BNT162b2 recipients (including 87 289 who received a booster), followed for a median of 34 (interquartile range [IQR] 20–63) days and 127 318 CoronaVac recipients (including 94 977 who received a booster), followed for a median of 38 (IQR 22–77) days. Among BNT162b2 recipients, booster-vaccinated people had fewer COVID-19–related deaths than those who received 2 doses (5 v. 34, incidence rate 1.3 v. 23.4 per million person-days, weighted incidence rate ratio [IRR] 0.05, 95% confidence interval [CI] 0.02–0.16). We observed similar results among recipients of CoronaVac booster vaccination compared with those who received only 2 doses (26 v. 88, incidence rate 5.3 v. 53.1 per million person-days, weighted IRR 0.08, 95% CI 0.05–0.12). Interpretation: Among people with multimorbidity, booster vaccination with BNT162b2 or CoronaVac was associated with reductions of more than 90% in COVID-19–related mortality rates compared with only 2 doses. These results highlight the crucial role of booster vaccination for protecting vulnerable populations as the COVID-19 pandemic continues to evolve. [ABSTRACT FROM AUTHOR]

Background: Although randomized controlled trials (RCTs) have suggested a non-significant increased risk of stroke among proton pump inhibitor (PPI) users, the association has not been confirmed. We evaluated the association between regular use of PPIs and incident stroke and identified population groups at high net risk. Methods: This is a prospective analysis of 492,479 participants free of stroke from the UK biobank. Incident stroke was identified through linkage to hospital admission and death registries using the International Classification of Diseases (ICD)-10 codes (I60, I61, I63, and I64). We evaluated hazard ratios (HRs) adjusting for demographic factors, lifestyle habits, prevalent comorbidities, concomitant use of medications, and indications of PPIs. We assessed the risk differences (RDs) according to the baseline Framingham Stroke Risk Score. In the meta-analysis, we searched PubMed, EMBASE, and the Cochrane Central Register of Controlled Trials (from 1988 to 1 June 2020) for randomized trials comparing PPIs with other interventions, placebo, or no treatment on stroke risk. Results were combined using a fix-effect meta-analysis (Mantel-Haenszel method). Results: We documented 5182 incident strokes over 3,935,030 person-years of follow-up. Regular PPI users had a 16% higher risk of stroke than non-users (HR 1.16, 95% CI 1.06 to 1.27). The estimated effect was similar to our meta-analysis of nine RCTs (case/participants 371/26,642; RR 1.22, 95% CI 1.00 to 1.50; quality of evidence: moderate). The absolute effect of PPI use on stroke increased with the baseline Framingham Stroke Risk Score, with an RD of 1.34‰, 3.32‰, 4.83‰, and 6.28‰ over 5 years for the lowest, quartile 2, quartile 3, and the highest quartile, respectively. Conclusions: Regular use of PPIs was associated with an increased risk of stroke, with a higher absolute risk observed in individuals with high baseline stroke risk. Physicians should therefore exercise caution when prescribing PPIs. An assessment of the underlying stoke risk is recommended for individualized use of PPIs. [ABSTRACT FROM AUTHOR]

Background: Although randomized controlled trials (RCTs) have suggested a non-significant increased risk of stroke among proton pump inhibitor (PPI) users, the association has not been confirmed. We evaluated the association between regular use of PPIs and incident stroke and identified population groups at high net risk.Methods: This is a prospective analysis of 492,479 participants free of stroke from the UK biobank. Incident stroke was identified through linkage to hospital admission and death registries using the International Classification of Diseases (ICD)-10 codes (I60, I61, I63, and I64). We evaluated hazard ratios (HRs) adjusting for demographic factors, lifestyle habits, prevalent comorbidities, concomitant use of medications, and indications of PPIs. We assessed the risk differences (RDs) according to the baseline Framingham Stroke Risk Score. In the meta-analysis, we searched PubMed, EMBASE, and the Cochrane Central Register of Controlled Trials (from 1988 to 1 June 2020) for randomized trials comparing PPIs with other interventions, placebo, or no treatment on stroke risk. Results were combined using a fix-effect meta-analysis (Mantel-Haenszel method).Results: We documented 5182 incident strokes over 3,935,030 person-years of follow-up. Regular PPI users had a 16% higher risk of stroke than non-users (HR 1.16, 95% CI 1.06 to 1.27). The estimated effect was similar to our meta-analysis of nine RCTs (case/participants 371/26,642; RR 1.22, 95% CI 1.00 to 1.50; quality of evidence: moderate). The absolute effect of PPI use on stroke increased with the baseline Framingham Stroke Risk Score, with an RD of 1.34‰, 3.32‰, 4.83‰, and 6.28‰ over 5 years for the lowest, quartile 2, quartile 3, and the highest quartile, respectively.Conclusions: Regular use of PPIs was associated with an increased risk of stroke, with a higher absolute risk observed in individuals with high baseline stroke risk. Physicians should therefore exercise caution when prescribing PPIs. An assessment of the underlying stoke risk is recommended for individualized use of PPIs. [ABSTRACT FROM AUTHOR]

There is a growing emphasis on the importance of availability of specialist palliative care for people living with dementia. However, for people imminently dying with dementia, we still have little knowledge about their palliative care needs and utilization of different specialist services. To (i) assess palliative care needs and other clinical and social characteristics of people imminently dying with dementia on their last admission in the context of community and inpatient palliative care services before death; (ii) compare care needs between patients requiring community-based and inpatient services; (iii) determine how and whether such care needs affect utilization of different palliative care services. Observational study using data from the Australian Palliative Care Outcomes Collaboration. Specialist palliative care services across Australia registered in the Palliative Care Outcomes Collaboration. A total of 3361 people who required specialist palliative care principally for dementia (including Alzheimer's disease and other dementias), and whose death occurred between 1 January 2013 and 31 December 2018. Five validated clinical instruments were used to collect point-of-care outcomes on each individual's function (Resource Utilisation Groups - Activities of Daily Living & Australia-modified Karnofsky Performance Status), symptom distress (Symptoms Assessment Scale & Palliative Care Problem Severity Score) and other clinical characteristics (Palliative Care Phases). We fitted multivariate logistic regression models to examine the association between these clinical outcomes and utilization of different specialist palliative care services. The majority of people imminently dying with dementia had absent or mild levels of symptom distress but experienced high levels of functional decline and needed substantial assistance with basic tasks of daily living in their last days of life. Large disparities in symptoms distress and functional decline between inpatient and community groups were not observed although differences in assessment scores were often statistically significant. Poor functional outcomes (odds ratio = 1.77, 95% confidence interval: 1.24–2.52) and "non-stable" palliative care phases (odds ratio =24.51, 95% confidence interval: 12.03–49.96) were positively associated with use of inpatient versus community palliative care, whereas there was no clear association between the majority of symptoms and use of different care services. The majority of people imminently dying with dementia could potentially benefit from greater access to supportive services in the community. Development of a dementia-specific palliative care pathway is needed to promote needs-based palliative care delivery models. the majority of people imminently dying with dementia could potentially benefit from greater access to supportive services in community. [ABSTRACT FROM AUTHOR]

The aim of this study was to compare incidences of adverse events of special interest (AESI) and delirium in 3 cohorts: after COVID-19 vaccination, prepandemic, and SARS-CoV-2 polymerase chain reaction (PCR) test positive. This is a population-based cohort study using electronic medical records linked with vaccination records in Hong Kong. A total of 17,449 older people with dementia received at least 1 dose of CoronaVac (n = 14,719) or BNT162b2 (n = 2730) between February 23, 2021, and March 31, 2022. Moreover, 43,396 prepandemic and 3592 SARS-CoV-2 test positive patients were also included in this study. The incidences of AESI and delirium up to 28 days after vaccination in the vaccinated dementia cohort were compared with the prepandemic and SARS-CoV-2 test positive dementia cohorts by calculating incidence rate ratios (IRRs). Patients who received multiple doses were followed up separately for each dose, up to the third dose. We did not detect an increased risk of delirium and most AESI following vaccination compared to the prepandemic period and those tested positive for SARS-CoV-2. No AESI group nor delirium incidence exceeded 10 per 1000 person-days in vaccinated individuals. The findings provide evidence for the safe use of COVID-19 vaccines in older patients with dementia. In the short run, benefit appears to outweigh the harm due to vaccine; however, longer follow-up should be continued to identify remote adverse events. [ABSTRACT FROM AUTHOR]

Intracellular sensing of lipopolysaccharide (LPS) by murine caspase-11 or human caspase-4 initiates a protease cascade, termed the non-canonical inflammasome, that results in gasdermin D (GSDMD) processing and subsequent NLRP3 inflammasome activation. In an effort aimed at identifying additional sensors for intracellular LPS by biochemical screening, we identified the nuclear orphan receptor Nur77 as an LPS-binding protein in macrophage lysates. Nr4a1 −/− macrophages exhibited impaired activation of the NLRP3 inflammasome, but not caspase-11, in response to LPS. Biochemical mapping revealed that Nur77 bound LPS directly through a domain in its C terminus. Yeast two-hybrid assays identified NLRP3 as a binding partner for Nur77. The association between Nur77 and NLRP3 required the presence of LPS and dsDNA. The source of dsDNA was the mitochondria, requiring the formation of gasdermin-D pores. In vivo , Nur77 deficiency ameliorated host response to endotoxins. Thus, Nur77 functions as an intracellular LPS sensor, binding mitochondrial DNA and LPS to activate the non-canonical NLRP3 inflammasome. [Display omitted] • Nur77 is screened out as an intracellular LPS-binding protein • Nur77 activates the NLRP3 inflammasome with the help of LPS and dsDNA • Nur77 detects mitochondrial DNA released from the GSDMD pores on the mitochondria • Nur77 exacerbates host-immune responses to endotoxins Cytosolic LPS triggers non-canonical NLRP3 inflammasome activation, but how the NLRP3 inflammasome is ignited is still vague. In this issue of Immunity , Zhu et al. show that Nur77 detects both the dsDNA released from the mitochondria and the intracellular LPS to activate the NLRP3 inflammasome. [ABSTRACT FROM AUTHOR]

Tuft cells are a type of intestinal epithelial cells that exist in epithelial barriers and play a critical role in immunity against parasite infection. It remains insufficiently clear whether Tuft cells participate in bacterial eradication. Here, we identified Sh2d6 as a signature marker for CD45+ Tuft-2 cells. Depletion of Tuft-2 cells resulted in susceptibility to bacterial infection. Tuft-2 cells quickly expanded in response to bacterial infection and sensed the bacterial metabolite N-undecanoylglycine through vomeronasal receptor Vmn2r26. Mechanistically, Vmn2r26 engaged with N-undecanoylglycine activated G-protein-coupled receptor-phospholipase C gamma2 (GPCR-PLCγ2)-Ca2+ signaling axis, which initiated prostaglandin D2 (PGD2) production. PGD2 enhanced the mucus secretion of goblet cells and induced antibacterial immunity. Moreover, Vmn2r26 signaling also promoted SpiB transcription factor expression, which is responsible for Tuft-2 cell development and expansion in response to bacterial challenge. Our findings reveal an additional function of Tuft-2 cells in immunity against bacterial infection through Vmn2r26-mediated recognition of bacterial metabolites. [Display omitted] • Tuft-2 cells exert antibacterial function • Tuft-2 cells sense bacterial metabolites through Vmn2r26 • PGD2 produced by Tuft-2 cells facilitates mucus secretion of goblet cells • SpiB is required for Tuft-2 cell expansion over bacterial infection Tuft cells play a critical role in immunity against parasite infection, but whether Tuft cells participate in bacterial eradication is presently unclear. Xiong et al. reveal that Tuft-2 cells mediate immunity against bacterial infection through Vmn2r26-mediated recognition of bacterial metabolites. [ABSTRACT FROM AUTHOR]

Abstract: Context: Phosphodiesterase type 5 inhibitors (PDE5-Is) are currently the first-line therapy for erectile dysfunction (ED), but available studies investigating the comparative effects of different PDE5-Is are limited. Objective: To compare the efficacy and safety of different classes of oral PDE5-Is for ED. Evidence acquisition: A systematic search was performed in PubMed, Cochrane Library, and Embase to identify randomized controlled trials that compared different PDE5-Is or PDE5-Is with a placebo for ED. The methodological quality of included studies was appraised with the Cochrane Collaboration bias appraisal tool, and the quality of evidence was evaluated using the Grading of Recommendations, Assessment, Development, and Evaluation system. Evidence synthesis: A total of 118 trials (31 195 individuals) were included. There was no major difference in the results between the traditional meta-analysis and the network meta-analysis. Network meta-analysis demonstrated that PDE5-Is were superior to placebo to improve erectile function. Compared with tadalafil (relative risk [RR]: 0.61; 95% confidence interval [CI], 0.33–0.90) and vardenafil (RR: 0.63; 95% CI, 0.35–0.92), avanafil was less effective on Global Assessment Questionnaire question 1. Tadalafil was more effective than vardenafil (mean difference [MD]: 1.49; 95% CI, 0.50–2.50) and udenafil (MD: −1.84; 95% CI, −3.31 to −0.33) as measured by the erectile function domain of the International Index of Erectile Function. For all efficacy outcomes, the absolute effects and the rank tests indicated that tadalafil and vardenafil were the most effective agents. After adjusting for dosage, the conclusion remained the same. Safety analysis showed there was no major difference among different agents. Conclusions: In recommended doses, oral PDE5-Is are more effective than placebo for ED, and tadalafil seems to be the most effective agent, followed by vardenafil. PDE5-Is are generally safe and well tolerated, and there is no major difference on the safety profile. [Copyright &y& Elsevier]

Abstract Objective: A great number of clinical trials and systematic reviews have evaluated the efficacy and safety of [alpha](1) blockers for benign prostatic hyperplasia (BPH). We carried out an overview of reviews to provide an up-to-date summary of evidence regarding the efficacy and safety between different [alpha](1) blockers for BPH. Research design and methods: PubMed, EMBASE, the Cochrane Library, China National Knowledge Infrastructure, Chinese BioMedical Literature Database and VIP were searched for eligible studies. Direct evidence was analyzed narratively. We used a random-effects model within a Bayesian framework to calculate indirect estimates if no direct evidence existed. The GRADE approach was used in summarizing conclusions. Results: A total of 15 systematic reviews involving five [alpha](1) blockers met the inclusion criteria. Direct evidence demonstrated that [alpha](1) blockers were superior to placebo in reducing urinary symptom scores and improving peak urinary flow PUF. Doxazosin could significantly reduce urinary symptom scores compared with tamsulosin mean difference (MD -1.60, 95% CI -1.80 to -1.40) and alfuzosin (MD1.7, 95% CI 0.76-1.64). Indirect evidence suggested that the urinary symptom score and PUF at endpoint in men treated with naftopidil were similar to those treated with other [alpha](1) blockers. [alpha](1) Blockers generally lead to more adverse effects compared with placebo, and those caused by terazosin were more frequent than others. Conclusions: [alpha](1) Blockers are more effective than placebo for BPH, doxazosin and tamsulosin seem to be more effective than other [alpha](1) blockers. The adverse effects caused by [alpha](1) blockers are generally mild and well-tolerated. [ABSTRACT FROM AUTHOR]