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Nowadays, the integration of blockchain technology with Ciphertext-Policy Attribute-Based Encryption (CP-ABE) has drawn the researcher attention because it can provide key security auditing and transaction traceability in the context of data sharing. However, in a majority of existing blockchain-based CP-ABE schemes, private keys were still issued by one central authority that would lead to heavy computation, higher transaction costs, and restricted scalability within the decentralized system. To address these challenges, we present an enhancement approach towards utilizing distributed key management service (KMS) and zero-knowledge paradigms. In our improved novel blockchain system model, we define two types of blockchain nodes for the CP-ABE scheme through staking mechanism. Firstly, the proxy re-encryption nodes are introduced to offer secure multi-party management and distribution of the CP-ABE's master secret key, eliminating dependence on a central authority and producing proofs of re-encryption correctness. Secondly, the operator nodes can collect all transactional information in blockchain-based CP-ABE scheme and then send the Zero-Knowledge Succinct Non-Interactive Argument of Knowledge (zk-SNARKs) proofs to verify the batch’s integrity via smart contract. Subsequently, we employ the staking economic incentive model with reward determination and slashing in the decentralized blockchain system to ensure network security. Finally, simulation results validate the effectiveness of our proposed scheme in achieving secure and efficient data sharing. Even amidst the pressure of 100 simultaneous transactions, the average response time for a single node remains at an approximate 28 s. Additionally, there is a notable decrease in on-chain gas consumption, with a gas reduction exceeding 61%. Comparative analyses further indicate that our blockchain-based CP-ABE scheme, in conjunction with a decentralized KMS, offers a superior balance between computational efficiency and functional capability.

Abstract The rapid development of healthcare big data has brought certain convenience to medical research and health management, but privacy protection of healthcare big data is an issue that must be considered in the process of data application. Access control is one of the methods for privacy protection, but traditional access control models cannot adapt to the dynamic, continuous, and real-time characteristics of healthcare big data scenarios. In this paper, we propose an access control model based on risk quantification and usage control (RQ-UCON). The model adds a risk quantification module to the traditional UCON model to achieve privacy protection of medical data. This module classifies risks into direct and indirect risks and quantifies them based on the physician's visit history. The model stores the quantified risk values as subject attributes. The RQ-UCON model uses an improved Exponentially Weighted Moving Average (EWMA) and penalty factors to predict risk value and to update the risk values of the subject attributes in real-time. The RQ-UCON model uses agglomerative hierarchical clustering to cluster the risk values of physicians within the department, resulting in risk intervals for each physician's operational behavior. Each risk interval is stored as a condition in the RQ-UCON model. Finally, according to the model whether the subject attributes meet the model conditions to determine whether the subject has the corresponding access rights, and according to the risk interval to grant the subject the corresponding access rights. Through the final experiment, it can be seen that the access control model proposed in this paper has a certain control on the excessive access behavior of doctors and has a certain limitation on the privacy leakage of healthcare big data.

Background: Almost one-third of patients with major depressive disorder (MDD) do not respond to conventional antidepressants, and new treatments for MDD are urgently needed. Objectives: This phase IIb clinical trial was designed to evaluate the efficacy and safety of Anyu Peibo capsules in the treatment of adults with MDD. Design: A multicenter, randomized, double-blind, placebo-controlled, parallel-group, fixed-dose study. Methods: A total of 172 patients with MDD from nine study centers were randomized (1:1) to receive placebo ( n = 86) or oral Anyu Peibo capsules (0.8 g) twice per day ( n = 86) for 6 weeks. The primary endpoint was the change in the Montgomery Åsberg Depression Rating Scale (MADRS) total score from baseline to week 6, analyzed using an analysis of covariance (ANCOVA) approach with the baseline MADRS score, center effect and center by group interaction as the covariates. Other efficacy endpoints and variables included clinical response and remission rates according to the MADRS and the 17-item Hamilton Depression Rating Scale (HAMD-17) scores, the change in the HAMD-17, Clinical Global Impression – Severity scale and Clinical Global Impression – Improvement scale scores and the reduction in the Hamilton Anxiety Scale from baseline to week 6. Results: The mean baseline MADRS total scores were 29.20 and 29.72 in the Anyu Peibo ( n = 82) and placebo groups ( n = 81), respectively. The least squares mean change in the MADRS score from baseline to week 6 was 16.59 points in the Anyu Peibo group and 14.51 points in the placebo group. Although there were greater reductions in the MADRS score from baseline to week 6 in the Anyu Peibo capsule group compared to the placebo group, the difference did not reach statistical significance (least-squares mean difference, 2.07 points; 95% confidence interval, −0.27 to 4.41; p = 0.0819). The results of sensitivity analyses by ANCOVA with the last observation carried forward method for missing data indicated that the administration of Anyu Peibo capsules may lead to a significant reduction in depressive symptoms compared to the placebo (least-squares mean difference: 3.29 points; 95% confidence interval: 0.64–5.93; p = 0.0152). Furthermore, Anyu Peibo capsules showed significant benefits over placebo when the change in the HAMD-17 score from baseline to week 6 was evaluated as the secondary analysis ( t = 2.01; 95% confidence interval, 0.03–4.23; p = 0.0464). Conclusion: Anyu Peibo capsules may have an effective and safe antidepressant effect, which warrants further research.

Currently, research predominantly focuses on evaluating clinical effects at specific time points while neglecting underlying patterns within the treatment process. This study aims to analyze the dynamic alterations in PANSS total scores and prolactin levels in patients with schizophrenia treated with risperidone, along with the influencing covariates. Using data from an 8-week randomized, double-blind, multicenter clinical trial, a population pharmacodynamic model was established for the PANSS total scores of and prolactin levels in patients treated with risperidone. The base model employed was the Emax model. Covariate selection was conducted using a stepwise forward inclusion and backward elimination approach. A total of 144 patients were included in this analysis, with 807 PANSS total scores and 531 prolactin concentration values. The PANSS total scores of the patients treated with risperidone decreased over time, fitting a proportionally parameterized sigmoid Emax model with covariates including baseline score, course of the disease, gender, plasma calcium ions, and lactate dehydrogenase levels. The increase in prolactin levels conformed to the ordinary Emax model, with covariates encompassing course of the disease, gender, weight, red blood cell count, and triglyceride levels. The impacts of the baseline scores and the course of the disease on the reduction of the PANSS scores, as well as the influence of gender on the elevation of prolactin levels, each exceeded 20%. This study provides valuable quantitative data regarding PANSS total scores and prolactin levels among patients undergoing risperidone treatment across various physiological conditions.

Abstract Background Major depressive disorder is the second leading cause of years lost to disability worldwide. Anyu Peibo Capsule has been shown to be effective and safe in phase II trials. Methods This clinical study is a multi-center, randomized, double-blinded, placebo-controlled, parallel-group, phase III trial of Anyu Peibo Capsule in China. The aim is to test whether the administration of Anyu Peibo Capsule compared to placebo improves clinical outcomes in adults (aged 18 to 65 years) with MDD. Patients will receive an 8-week treatment of Anyu Peibo Capsule 1.6 g per day or placebo. The primary outcome will be the change from baseline in the total score for the Montgomery-Asberg Depression Rating Scale at the end of the 8-week treatment. Discussion The trial aims to provide pivotal evidence for the efficacy and safety of Anyu Peibo Capsule in patients with major depressive disorder. Trial registration ClinicalTrials.gov NCT04210973 . Registered on December 26, 2019

To address the quantum attacks on number theory-based ciphertext policy attribute-based encryption (CP-ABE), and to avoid private key leakage problems by relying on a trustworthy central authority, we propose a lattice-inspired CP-ABE scheme for data access and sharing based on blockchain in this paper. Firstly, a CP-ABE-based algorithm using learning with errors (LWE) assumption is constructed, which is selective security under linear independence restriction in the random oracle model. Secondly, the blockchain nodes can act as a distributed key management server to offer control over master keys used to generate private keys for different data users that reflect their attributes through launching transactions on the blockchain system. Finally, we develop smart contracts for proving the correctness of proxy re-encryption (PRE) and provide auditability for the whole data-sharing process. Compared with the traditional CP-ABE algorithm, the post-quantum CP-ABE algorithm can significantly improve the computation speed according to the result of the functional and experimental analysis. Moreover, the proposed blockchain-based CP-ABE scheme provides not only multi-cryptography collaboration to enhance the security of data access and sharing but also reduces average transaction response time and throughput.

Background: Discontinuation of antipsychotic treatment is a common problem in patients with schizophrenia and could reduce the effectiveness of treatment. Time to discontinuation (TTD) is one of the indicators of compliance and may also be an effective indicator of medication efficacy. The aim of the study was to compare the clinical effectiveness of quetiapine, olanzapine, risperidone, and aripiprazole in the real-world treatment of schizophrenia with 3-years follow-up.Method: A multi-center, open, cohort, prospective, real-world study was conducted. 706 patients were analyzed without intervention in medication selection and use, followed up for 3 years. Kaplan-Meier survival curves were used to draw the treatment discontinuation rates (TDR) curves at each time point. Cox proportional hazard regression model was used to assess the relative risk of TTD of antipsychotics.Results: There was a significant difference among monotherapy groups in all-cause antipsychotic treatment discontinuation (p = 0.0057). Among the four medications, the TDR of risperidone was the highest. Compared with polypharmacy, except for aripiprazole, the TDR of other three monotherapy medications were lower than that of polypharmacy, and olanzapine was statistically different (p = 0.0325). The cox regression analysis showed that after correction of Hochberg with multiple tests, only olanzapine had a relative risk lower than risperidone (p < 0.0083).Conclusions: The findings indicated that risperidone monotherapy and polypharmacy had the highest TDR and the shortest TTD. Olanzapine monotherapy had a relative risk lower than risperidone and was superior to polypharmacy.

Objective: This study aimed to investigate the characteristics and spectrum of cardiotoxicity induced by various antipsychotics based on the United States Food and Drug Administration (FDA) Adverse Event Reporting System (FAERS) database.Methods: Data of the FAERS database from the first quarter of 2015 to the fourth quarter of 2020 were downloaded for disproportionality analysis. The significant signal was evaluated by reporting odds ratios and information components with statistical shrinkage transformation.Results: A total of 2,361,487 records were extracted for disproportionality analysis. Among the 10 antipsychotics, clozapine and amisulpride performed strong cardiotoxicity. Cardiomyopathy, cardiac arrhythmia, and Torsade de pointes/QT prolongation were the common cardiac adverse event induced by antipsychotics. Different characteristics of the spectrum of cardiotoxicity in various APs were discovered after further data mining. Moreover, evidence of the association between antipsychotics and eosinophilic myocarditis, peripartum cardiomyopathy was provided in this study.Conclusion: Antipsychotics presented cardiotoxicity in different degrees, and more cardiac examinations should be monitored in patients with antipsychotics.

Based on our previous studies and other evidence, miR-124 is an important biomarker and therapeutic target for major depressive disorder (MDD). The aim of this study was to clarify the role of miR-124 methylation in MDD and antidepressant effects from the perspective of epigenetics. MethylTarget™ was used to detect methylation levels of the three miR-124 precursor genes (MIR124-1, MIR124-2, and MIR124-3) in 33 pre- and post-treatment MDD patients and 33 healthy controls. A total of 11 cytosine-phosphate-guanine (CpG) islands in the three miR-124 precursor genes, including 222 CpG sites, were detected. All CpG islands were hypomethylated in MDD patients when compared to healthy controls and seven CpG regions were still identified with a statistically significant difference after Bonferroni correction. In addition, 137 of 222 CpG sites were found a statistical difference between MDD patients and controls, and 40 CpG sites were still statistically significant after Bonferroni correction. After performing the LASSO regression model, seven biomarkers with differential methylation among 40 CpG sites were identified. Mean methylation score was lower in MDD patients (z = −5.84, p = 5.16E-9). The AUC value reached 0.917 (95% CI: 0.854–0.981) to discriminate MDD and controls. No changes in methylation of the three miR-124 precursor genes were found in MDD patients following antidepressant treatment. The methylation of miR-124 could be a promising diagnostic biomarker for MDD.

Technologies such as machine learning and artificial intelligence have brought about a tremendous change to biomedical computing and intelligence health care. As a principal component of the intelligence healthcare system, the hospital information system (HIS) has provided great convenience to hospitals and patients, but incidents of leaking private information of patients through HIS occasionally occur at times. Therefore, it is necessary to properly control excessive access behavior. To reduce the risk of patient privacy leakage when medical data are accessed, this article proposes a dynamic permission intelligent access control model that introduces credit line calculation. According to the target given by the doctor in HIS and the actual access record, the International Classification of Diseases (ICD)-10 code is used to describe the degree of correlation, and the rationality of the access is formally described by a mathematical formula. The concept of intelligence healthcare credit lines is redefined with relevance and time Windows. The access control policy matches the corresponding credit limit and credit interval according to the authorization rules to achieve the purpose of intelligent control. Finally, with the actual data provided by a Grade-III Level-A hospital in Kunming, the program code is written through machine learning and biomedical computing-related technologies to complete the experimental test. The experiment proves that the intelligent access control model based on credit computing proposed in this study can play a role in protecting the privacy of patients to a certain extent.

Major depressive disorder (MDD) is a leading cause of disability worldwide, although its etiology and mechanism remain unknown. The aim of our study was to identify hub genes associated with MDD and to illustrate the underlying mechanisms. A weighted gene co-expression network analysis (WGCNA) was performed to identify significant gene modules and hub genes associated with MDD in peripheral blood mononuclear cells (PBMCs) (n = 45). In the blue module (R2 = 0.95), five common hub genes in both co-expression network and protein–protein interaction (PPI) network were regarded as “real” hub genes. In another independent dataset, GSE52790, four genes were still significantly down-regulated in PBMCs from MDD patients compared with the controls. Furthermore, these four genes were validated by quantitative real-time polymerase chain reaction (qRT-PCR) in PBMCs from 33 MDD patients and 41 healthy controls. The qRT-PCR analysis showed that ATP synthase membrane subunit c locus 1 (ATP5G1) was significantly down-regulated in samples from MDD patients than in control samples (t = −2.89, p-value = 0.005). Moreover, this gene was significantly differentially expressed between patients and controls in the prefrontal cortex (z = −2.83, p-value = 0.005). Highly significant differentially methylated positions were identified in the Brodmann area 25 (BA25), with probes in the ATP5G1 gene being significantly associated with MDD: cg25495775 (t = 2.82, p-value = 0.008), cg25856120 (t = −2.23, p-value = 0.033), and cg23708347 (t = −2.24, p-value = 0.032). These findings indicate that the ATP5G1 gene is associated with the pathogenesis of MDD and that it could serve as a peripheral biomarker for MDD.

In wireless sensor networks (WSNs), the energy hole problem is a key factor affecting the network lifetime. In a circular multi-hop sensor network (modeled as concentric coronas), the optimal transmission ranges of all coronas can effectively improve network lifetime. In this paper, we investigate WSNs with non-uniform maximum transmission ranges, where sensor nodes deployed in different regions may differ in their maximum transmission range. Then, we propose an Energy-efficient algorithm for Non-uniform Maximum Transmission range (ENMT), which can search approximate optimal transmission ranges of all coronas in order to prolong network lifetime. Furthermore, the simulation results indicate that ENMT performs better than other algorithms.

Introduction: Ectopic expression of transcription factor-mediated in vivo neuronal reprogramming provides promising strategy to compensate for neuronal loss, while its further clinical application may be hindered by delivery and safety concerns. As a novel and attractive alternative, small molecules may offer a non-viral and non-integrative chemical approach for reprogramming cell fates. Recent definitive evidences have shown that small molecules can convert non-neuronal cells into neurons in vitro. However, whether small molecules alone can induce neuronal reprogramming in vivo remains largely unknown. Objectives: To identify chemical compounds that can induce in vivo neuronal reprogramming in the adult spinal cord. Methods: Immunocytochemistry, immunohistochemistry, qRT-PCR and fate-mapping are performed to analyze the role of small molecules in reprogramming astrocytes into neuronal cells in vitro and in vivo. Results: By screening, we identify a chemical cocktail with only two chemical compounds that can directly and rapidly reprogram cultured astrocytes into neuronal cells. Importantly, this chemical cocktail can also successfully trigger neuronal reprogramming in the injured adult spinal cord without introducing exogenous genetic factors. These chemically induced cells showed typical neuronal morphologies and neuron-specific marker expression and could become mature and survive for more than 12 months. Lineage tracing indicated that the chemical compound-converted neuronal cells mainly originated from post-injury spinal reactive astrocytes. Conclusion: Our proof-of-principle study demonstrates that in vivo glia-to-neuron conversion can be manipulated in a chemical compound-based manner. Albeit our current chemical cocktail has a low reprogramming efficiency, it will bring in vivo cell fate reprogramming closer to clinical application in brain and spinal cord repair. Future studies should focus on further refining our chemical cocktail and reprogramming approach to boost the reprogramming efficiency.