Development and Validation of the Pharmacological Statin-Associated Muscle Symptoms Risk Stratification Score Using Electronic Health Record Data.

Statin-associated muscle symptoms (SAMS) can lead to statin nonadherence. This paper aims to develop a pharmacological SAMS risk stratification (PSAMS-RS) score using a previously developed PSAMS phenotyping algorithm that distinguishes objective vs. nocebo SAMS using electronic health record (EHR) data. Using our PSAMS phenotyping algorithm, SAMS cases and controls were identified from Minnesota Fairview EHR, with the statin user cohort divided into derivation (January 1, 2010, to December 31, 2018) and validation (January 1, 2019, to December 31, 2020) cohorts. A Least Absolute Shrinkage and Selection Operator regression model was applied to identify significant features for PSAMS. PSAMS-RS scores were calculated and the clinical utility of stratifying PSAMS risk was assessed by comparing hazard ratios (HRs) between fourth vs. first score quartiles. PSAMS cases were identified in 1.9% (310/16,128) of the derivation and 1.5% (64/4,182) of the validation cohorts. Sixteen out of 38 clinical features were determined to be significant predictors for PSAMS risk. Patients within the fourth quartile of the PSAMS scores had an over sevenfold (HR: 7.1, 95% confidence interval (CI): 4.03-12.45, derivation cohort) or sixfold (HR: 6.1, 95% CI: 2.15-17.45, validation cohort) higher hazard of developing PSAMS vs. those in their respective first quartile. The PSAMS-RS score is a simple tool to stratify patients' risk of developing PSAMS after statin initiation which could inform clinician-guided pre-emptive measures to prevent PSAMS-related statin nonadherence.

Development and application of pharmacological statin-associated muscle symptoms phenotyping algorithms using structured and unstructured electronic health records data.

Importance: Statins are widely prescribed cholesterol-lowering medications in the United States, but their clinical benefits can be diminished by statin-associated muscle symptoms (SAMS), leading to discontinuation. Objectives: In this study, we aimed to develop and validate a pharmacological SAMS clinical phenotyping algorithm using electronic health records (EHRs) data from Minnesota Fairview. Materials and Methods: We retrieved structured and unstructured EHR data of statin users and manually ascertained a gold standard set of SAMS cases and controls using the published SAMS-Clinical Index tool from clinical notes in 200 patients. We developed machine learning algorithms and rule-based algorithms that incorporated various criteria, including ICD codes, statin allergy, creatine kinase elevation, and keyword mentions in clinical notes. We applied the best-performing algorithm to the statin cohort to identify SAMS. Results: We identified 16 889 patients who started statins in the Fairview EHR system from 2010 to 2020. The combined rule-based (CRB) algorithm, which utilized both clinical notes and structured data criteria, achieved similar performance compared to machine learning algorithms with a precision of 0.85, recall of 0.71, and F1 score of 0.77 against the gold standard set. Applying the CRB algorithm to the statin cohort, we identified the pharmacological SAMS prevalence to be 1.9% and selective risk factors which included female gender, coronary artery disease, hypothyroidism, and use of immunosuppressants or fibrates. Discussion and Conclusion: Our study developed and validated a simple pharmacological SAMS phenotyping algorithm that can be used to create SAMS case/control cohort to enable further analysis which can lead to the development of a SAMS risk prediction model.

Development and validation of the pharmacological statin-associated muscle symptoms risk stratification (PSAMS-RS) score using real-world electronic health record data.

Introduction: Statin-associated muscle symptoms (SAMS) contribute to the nonadherence to statin therapy. In a previous study, we successfully developed a pharmacological SAMS (PSAMS) phenotyping algorithm that distinguishes objective versus nocebo SAMS using structured and unstructured electronic health records (EHRs) data. Our aim in this paper was to develop a pharmacological SAMS risk stratification (PSAMS-RS) score using these same EHR data. Method: Using our PSAMS phenotyping algorithm, SAMS cases and controls were identified using University of Minnesota (UMN) Fairview EHR data. The statin user cohort was temporally divided into derivation (1/1/2010 to 12/31/2018) and validation (1/1/2019 to 12/31/2020) cohorts. First, from a feature set of 38 variables, a Least Absolute Shrinkage and Selection Operator (LASSO) regression model was fitted to identify important features for PSAMS cases and their coefficients. A PSAMS-RS score was calculated by multiplying these coefficients by 100 and then adding together for individual integer scores. The clinical utility of PSAMS-RS in stratifying PSAMS risk was assessed by comparing the hazard ratio (HR) between 4th vs 1st score quartile. Results: PSAMS cases were identified in 1.9% (310/16128) of the derivation and 1.5% (64/4182) of the validation cohort. After fitting LASSO regression, 16 out of 38 clinical features were determined to be significant predictors for PSAMS risk. These factors are male gender, chronic pulmonary disease, neurological disease, tobacco use, renal disease, alcohol use, ACE inhibitors, polypharmacy, cerebrovascular disease, hypothyroidism, lymphoma, peripheral vascular disease, coronary artery disease and concurrent uses of fibrates, beta blockers or ezetimibe. After adjusting for statin intensity, patients in the PSAMS score 4th quartile had an over seven-fold (derivation) (HR, 7.1; 95% CI, 4.03-12.45) and six-fold (validation) (HR, 6.1; 95% CI, 2.15-17.45) higher hazard of developing PSAMS versus those in 1st score quartile. Conclusion: The PSAMS-RS score can be a simple tool to stratify patients' risk of developing PSAMS after statin initiation which can facilitate clinician-guided preemptive measures that may prevent potential PSAMS-related statin non-adherence.

Development and Application of Pharmacological Statin-Associated Muscle Symptoms Phenotyping Algorithms Using Structured and Unstructured Electronic Health Records Data.

Background: Statins are widely prescribed cholesterol-lowering medications in the US, but their clinical benefits can be diminished by statin-associated muscle symptoms (SAMS), leading to discontinuation. In this study, we aimed to develop and validate a pharmacological SAMS clinical phenotyping algorithm using electronic health records (EHRs) data from Minnesota Fairview. Methods: We retrieved structured and unstructured EHR data of statin users and manually ascertained a gold standard set of SAMS cases and controls using the SAMS-CI tool from clinical notes in 200 patients. We developed machine learning algorithms and rule-based algorithms that incorporated various criteria, including ICD codes, statin allergy, creatine kinase elevation, and keyword mentions in clinical notes. We applied the best performing algorithm to the statin cohort to identify SAMS. Results: We identified 16,889 patients who started statins in the Fairview EHR system from 2010-2020. The combined rule-based (CRB) algorithm, which utilized both clinical notes and structured data criteria, achieved similar performance compared to machine learning algorithms with a precision of 0.85, recall of 0.71, and F1 score of 0.77 against the gold standard set. Applying the CRB algorithm to the statin cohort, we identified the pharmacological SAMS prevalence to be 1.9% and selective risk factors which included female gender, coronary artery disease, hypothyroidism, use of immunosuppressants or fibrates. Conclusion: Our study developed and validated a simple pharmacological SAMS phenotyping algorithm that can be used to create SAMS case/control cohort for further analysis such as developing SAMS risk prediction model.

Recent Progress in Bi-Based Anodes for Magnesium Ion Batteries.

Rechargeable magnesium ion batteries (MIBs) have attracted increasing interest due to abundant reserves, high theoretical specific capacities and safety. However, the incompatibility between Mg metal and conventional electrolytes, among the most serious challenges, restrains their development. Replacing Mg metal with alloy-type anodes offers an effective strategy to circumvent the surface passivation issue of Mg metal in conventional electrolytes. Among them, Bi has the most potential in Mg storage owing to its unique characteristics. Herein, the advantages/challenges and progress of Bi-based anodes in MIBs are summarized. The theoretical evaluations, battery configurations, electrode designs, electrochemical properties as well as Mg storage mechanisms are summarized and discussed. Moreover, the key issues and some views on the future development of Bi-based anodes in MIBs are provided.

Biological Prototype Acquisition Based on Biological Coupling in Bionic Design.

Because the judgment basis in the process of biological prototype screening is highly subjective, and because it is difficult to generate a scheme when using multiple biological prototypes for bionic design, this work proposes a biological prototype retrieval and matching method for multibiological prototype bionic design. Using BioTRIZ in combination with biological coupling mechanism analysis, orthogonal analysis, and the calculation of the goodness value of the scheme, a multibiological prototype bionic design model is constructed. First, the biological prototype contradictions matrix is obtained by BioTRIZ. Then, a biological coupling mechanism analysis is carried out to calculate the goodness value of the auxiliary scheme to further evaluate the advantages and disadvantages of the biological prototype. The orthogonal analysis is then conducted to select the optimal biological prototype combination scheme. Finally, the best biological prototype combination scheme is transformed into the final design scheme according to the biological coupling mode prompts. According to this process, the innovative design of an automatic food threading machine was carried out, and an experiment was conducted for verification. The results demonstrate that the machine after bionic improvement could meet the design requirements, and the feasibility and effectiveness of the established design model were verified.

Modified Harris Hawks Optimization Algorithm with Exploration Factor and Random Walk Strategy.

One of the most popular population-based metaheuristic algorithms is Harris hawks optimization (HHO), which imitates the hunting mechanisms of Harris hawks in nature. Although HHO can obtain optimal solutions for specific problems, it stagnates in local optima solutions. In this paper, an improved Harris hawks optimization named ERHHO is proposed for solving global optimization problems. Firstly, we introduce tent chaotic map in the initialization stage to improve the diversity of the initialization population. Secondly, an exploration factor is proposed to optimize parameters for improving the ability of exploration. Finally, a random walk strategy is proposed to enhance the exploitation capability of HHO further and help search agent jump out the local optimal. Results from systematic experiments conducted on 23 benchmark functions and the CEC2017 test functions demonstrated that the proposed method can provide a more reliable solution than other well-known algorithms.

Composition- and size-modulated porous bismuth-tin biphase alloys as anodes for advanced magnesium ion batteries.

Rechargeable magnesium batteries have huge potential for applications in large scale energy storage systems due to their low cost and abundant sources. Nevertheless, not much attention has been paid to the development of alternative anodes for magnesium ion batteries (MIBs). Herein, we demonstrate a scalable strategy to fabricate bismuth (Bi)-tin (Sn) biphase anodes with a porous (P) structure and controllable compositions/sizes, through the design of triphase precursors with immiscible elements (with a positive enthalpy of mixing between elements) and selective phase corrosion. Here, one phase was selected as the sacrificial component to form three-dimensional porous channels, which differs from the mechanism by which a porous structure is generated in a classical dealloying process. We systematically investigate how the composition and size of P-Bi-Sn anodes affect their Mg storage properties. As an anode for MIBs, the P-Bi3Sn2 electrode exhibits an excellent reversible capacity retention of over 93% for 200 cycles at 1000 mA g-1. Most importantly, the Mg storage mechanism of P-Bi-Sn anodes was unveiled by operando X-ray diffraction.

Scalable Fabrication of Core-Shell Sb@Co(OH)2 Nanosheet Anodes for Advanced Sodium-Ion Batteries via Magnetron Sputtering.

Antimony (Sb) has captured extensive attention as a promising anode for sodium-ion batteries (SIBs) due to its high theoretical capacity and moderate sodiated potential but is held back from practical applications owing to its pulverization induced by dramatic volumetric variations during the (de)sodiation process. Herein, we report a core-shell Sb@Co(OH)2 nanosheet anode fabricated via magnetron sputtering Sb onto the mass-productive Co(OH)2 substrate anchored on stainless-steel mesh, which is scalable and suitable for flow-line production. In SIBs, the Sb@Co(OH)2 anode displays superior rate performance (383.5 mAh/g at 30 A/g), high discharge capacity, and excellent stability. Compared with the sputtered Sb film electrode, the improved performance of the core-shell Sb@Co(OH)2 nanosheet anode can be attributed to the open framework of the Co(OH)2 substrate, not only accelerating the ion and electron transfer but also serving as the buffer for alleviating the volumetric variation and the supporting scaffold for prohibiting the aggregation. More importantly, the (de)sodiation mechanism of the Sb@Co(OH)2 anode was explored by operando ( in situ) X-ray diffraction, and the similar alloying-dealloying processes (Sb ↔ Na xSb ↔ Na3Sb) for the 1st, 13th, and 30th cycles illustrate the excellent stability of the electrode.

A shell-crosslinked polymeric micelle system for pH/redox dual stimuli-triggered DOX on-demand release and enhanced antitumor activity.

Based on targeted amphiphilic block copolymer N-acetyl glucosamine-poly (styrene-alt-maleic anhydride)58-b-polystyrene130 (NAG-P(St-alt-MA)58-b-PSt130), a pH/redox dual-triggered shell-crosslinked polymeric micelle system was constructed. The shell-crosslinked micelles (CLM) were prepared by post-crosslinking method to regulate drug release kinetics using cystamine as linkers between carboxy groups of the shell. Compared with non-crosslinked micelles (NCLM), CLM showed spherical shapes with little increased mean diameter of 102.40±0.54nm, low polydispersity index (PDI) of 0.19±0.36, enlarged zeta potential value from -41.46±0.99 to -9.31±0.50mV, indicating the successful modification of disulfide bonds in shell. In vitro drug release study clearly exhibited a pH and redox dual-sensitive drug release profile with significantly accelerated drug release under pH 5.0 and 10mM GSH conditions (46.84% in 96h) without burst release. Both CLM and NCLM showed quite different release profiles between physiological (pH 7.4) and tumoral microenvironment (pH 5.0), effectively avoiding the premature drug leakage and realizing on-demand drug release. The MTT assay implied that CLM presented a time- and concentration-dependent manner to inhibit proliferation of A549 and MCF-7 cells and much lower IC50 values in comparison with that of NCLM after 72h incubation. Both FCM and CLSM results showed that CLM displayed much higher cellular uptake efficiency and anti-tumor activities than NCLM and free DOX. CLM and NCLM could be internalized by energy-dependent endocytosis mechanism due to similar surface properties. Overall, this dual-stimuli triggered micelle system provided a promising tumor-responsive platform for cancer therapy.

Cardioprotection by combination of three compounds from ShengMai preparations in mice with myocardial ischemia/reperfusion injury through AMPK activation-mediated mitochondrial fission.

GRS is a drug combination of three active components including ginsenoside Rb1, ruscogenin and schisandrin. It derived from the well-known TCM formula ShengMai preparations, a widely used traditional Chinese medicine for the treatment of cardiovascular diseases in clinic. The present study explores the cardioprotective effects of GRS on myocardial ischemia/reperfusion (MI/R) injury compared with ShengMai preparations and investigates the underlying mechanisms. GRS treatment significantly attenuated MI/R injury and exhibited similar efficacy as Shengmai preparations, as evidenced by decreased myocardium infarct size, ameliorated histological features, the decrease of LDH production and improved cardiac function, and also produced a significant decrease of apoptotic index. Mechanistically, GRS alleviated myocardial apoptosis by inhibiting the mitochondrial mediated apoptosis pathway as reflected by inhibition of caspase-3 activity, normalization of Bcl-2/Bax levels and improved mitochondrial function. Moreover, GRS prevented cardiomyocytes mitochondrial fission and upregulated AMPKα phosphorylation. Interestingly, AMPK activation prevented hypoxia and reoxygenation induced mitochondrial fission in cardiomyocytes and GRS actions were significantly attenuated by knockdown of AMPKα. Collectively, these data show that GRS is effective in mitigating MI/R injury by suppressing mitochondrial mediated apoptosis and modulating AMPK activation-mediated mitochondrial fission, thereby providing a rationale for future clinical applications and potential therapeutic strategy for MI/R injury.