[This corrects the article DOI: 10.1016/j.apsb.2022.05.019.].

The etiology of nervous system diseases is complicated, posing significant harm to patients and often resulting in poor prognoses. In recent years, the role of dopaminergic system in nervous system diseases has attracted much attention, and its complex regulatory mechanism and therapeutic potential have been gradually revealed. This paper reviews the role of dopaminergic neurons, the neurotransmitter dopamine, dopamine receptors and dopamine transporters in neurological diseases (including Alzheimer's disease, Parkinson's disease and schizophrenia), with a view to further elucidating the disease mechanism and providing new insights and strategies for the treatment of neurological diseases.
Humans ; Dopamine/metabolism* ; Nervous System Diseases/physiopathology* ; Parkinson Disease/physiopathology* ; Receptors, Dopamine/metabolism* ; Dopaminergic Neurons/physiology* ; Dopamine Plasma Membrane Transport Proteins/metabolism* ; Alzheimer Disease/physiopathology* ; Schizophrenia/physiopathology* ; Animals

OBJECTIVE To explore comprehensive management and potential issues associated with long-term prescriptions medications of psychiatry， in order to provide a reference for the comprehensive management of long-term prescriptions of psychiatry in psychiatric hospitals and other medical institutions’ pharmacies. METHODS Starting from the applicable principles for long-term prescriptions of psychiatry， this study introduced the standardized assessment and precautions before issuing long-term prescriptions， the formulation and adjustment of the drug list， as well as the rational management of the long-term prescriptions. It also analyzed potential issues that may arise in the comprehensive management of long-term prescription medications and proposed corresponding countermeasures and suggestions. RESULTS & CONCLUSIONS Prior to initiating long-term prescriptions， a standardized assessment should be conducted on patients from the aspects of their psychiatric condition and long-term potential risk factors， pharmacological treatment plans and other non-pharmacological therapies， physical illnesses. Additionally， healthcare providers should fulfill their obligation to inform patients or their family members. The comprehensive management of long-term prescription medications should be jointly established and improved by multiple departments， and the formulation of drug catalogs should avoid including drugs with potential social harm or medication risks while complying with policy requirements. Furthermore， measures such as adding special identifiers to long-term prescriptions， providing patients with reminders about （No.YGLX202537） prescription expiration， or offering online consultations can also effectively enhance the rationality of medication use under long-term prescriptions. Currently， the implementation of long-term prescriptions in psychiatry remains challenged by inconsistencies in prescription duration， incomplete coverage of diagnostic categories， poor patient adherence， and the risk of deviation in clinical assessments. In this regard， measures such as collaborating with multiple departments to strengthen long-term prescription information management， providing matching pharmaceutical services， ensuring the quality and rationality of long-term prescription implementation， and using modern methods to screen high-risk patients can be taken to improve patient medication compliance and safety.

Objective:To develop a treatment-related quality of life scale for Chinese patients with multiple myeloma (MM) and to test its reliability and validity.Methods:The initial scale was constructed through a literature search, Delphi expert correspondence, and cognitive testing. This study conducted a preliminary survey of 379 patients with MM and a formal survey of 865 patients from the hematology departments of 155 hospitals nationwide from February 2024 to March 2024. The final scale was obtained after conducting item analysis and reliability and validity tests on the initial scale.Results:The constructed scale contains 36 items covering six domains: physiological, psychological, social, treatment side effects, general health, and others. In the preliminary survey, the Cronbach’s alpha coefficient of each item ranged from 0.597 to 0.939, and the test-retest reliability was 0.747 ( P<0.001). Exploratory factor analysis extracted eight common factors with a cumulative variance contribution of 60.058%. In the formal survey, the Cronbach’s alpha coefficient of each item ranged from 0.484 to 0.930, and the test-retest reliability was 0.835 ( P<0.001). Confirmatory factor analysis revealed a comparative fit index of 0.750, a root-mean-square error of approximation of 0.090, and a root-mean-square residual of 0.067. Conclusion:The treatment-related quality of life scale for Chinese patients with MM designed in this study exhibited good reliability and validity, reflecting the impact of treatment on the quality of life of patients. This scale can provide a reference to clinicians for assessing the disease status of patients.

In the context of aging population, the issue of polypharmacy among elderly patients with mental disorders has become increasingly prominent.Cognitive decline and depressive symptoms render these patients more vulnerable to medication-related risks, while poorly managed physical illnesses further complicate their treatment.To address these challenges, this paper proposes a series of management strategies that emphasize the critical role of pharmacists in conducting medication reviews.A comprehensive assessment of drug risks, benefits, and patient adherence is essential.The proposed strategies not only require careful consideration of patients' clinical needs and individual preferences but also highlight the importance of multidisciplinary team collaboration to reach a consensus on medication therapy.The use of clinical decision support systems as an auxiliary tool is recommended to enhance the scientific rigor of medication decision-making.Furthermore, pharmacists can optimize medication regimens through scientifically validated methods and promote patient or family involvement in self-management to improve acceptance and adherence to treatment adjustments.

OBJECTIVE: To assess the value of the Prostate Imaging Reporting and Data System version 2.1 (PI-RADS v2.1) score combined with PSA density (PSAD) in the diagnosis of clinically significant prostate cancer (CSPCa) in the PSA grey zone by MRI-TRUS cognitive fusion-guided transperineal targeted prostate biopsy. METHODS: This retrospective study included 327 male patients with total PSA (tPSA) levels of 4-10 μg/L undergoing MRI-TRUS cognitive fusion-guided transperineal targeted prostate biopsy in our hospital between January 2021 and December 2023. According to the pathological results, we divided the patients into a CSPCa (n = 44) and a non-CSPCa group (n = 283), collected their clinical and imaging data, and subjected them to statistical analysis. RESULTS: The age, tPSA level, PSAD and PI-RADS score were significantly higher, while the free PSA (fPSA) level, f/tPSA ratio and prostate volume remarkably lower in the CSPCa than in the non-CSPCa group (P<0.05). The areas under the curve (AUCs) of PSAD, PI-RADS score and their combination were 0.772, 0.730 and 0.801, with sensitivities of 63.63%, 70.45% and 72.73%, and specificities of 84.10%, 75.62% and 83.75%, respectively (P<0.01). With PSAD 0.2 μg/(ml·cm3) as the best cut-off value and based on the PI-RADS scores, the patients were divided into two groups for analysis. In the patients with PI-RADS scores 2 and 5, the AUCs were 0.534 and 0.643, with sensitivities of 16.67% and 63.64%, and specificities of 85.14% and 64.29%, with no statistically significant differences (P= 0.784, P= 0.228), and in those with PI-RADS scores 3 and 4, the AUCs were 0.794 and 0.843, with sensitivities of 57.14% and 80.00%, and specificities of 87.14% and 81.82%, with statistically significant differences (P= 0.009, P<0.001). CONCLUSION PI-RADS v2.1 score combined with PSAD can effectively improve the diagnostic efficiency of CSPCa in the PSA grey zone by MRI-TRUS cognitive fusion-guided transperineal targeted prostate biopsy and serve as a guide for selection of prostate biopsy.
Humans ; Male ; Prostatic Neoplasms/diagnostic imaging* ; Retrospective Studies ; Prostate-Specific Antigen ; Magnetic Resonance Imaging ; Image-Guided Biopsy ; Prostate/pathology* ; Aged ; Middle Aged

Delirium is a common cause and complication of hospitalization in the elderly and is associated with higher risk of future dementia and progression of existing dementia, of which 70% is Alzheimer's disease (AD). AD and delirium, which are known to be aggravated by one another, represent significant societal challenges, especially in light of the absence of effective treatments. The intricate biological mechanisms have led to numerous clinical trial setbacks and likely contribute to the limited efficacy of existing therapeutics. Artificial intelligence (AI) presents a promising avenue for overcoming these hurdles by deploying algorithms to uncover hidden patterns across diverse data types. This review explores the pivotal role of AI in revolutionizing drug discovery for AD and delirium from target identification to the development of small molecule and protein-based therapies. Recent advances in deep learning, particularly in accurate protein structure prediction, are facilitating novel approaches to drug design and expediting the discovery pipeline for biological and small molecule therapeutics. This review concludes with an appraisal of current achievements and limitations, and touches on prospects for the use of AI in advancing drug discovery in AD and delirium, emphasizing its transformative potential in addressing these two and possibly other neurodegenerative conditions.

Objective As an important monitoring target for gaseous effluents from nuclear facilities, the radioactive inert gas ⁸⁵Kr requires accurate measurement, which is crucial for nuclear safety and radiation protection. This study organized an inter-laboratory comparison activity for ⁸⁵Kr gamma-ray spectrometry measurements, with the aims of identifying potential problems in ⁸⁵Kr measurement and improving detection capabilities and proficiency. Methods Four laboratories participated in this comparison. Each laboratory employed gamma-ray spectrometry to measure the comparison samples, and the results were evaluated using the E_n value. Results The measurement results from all participating institutions were acceptable, with the maximum E_n value being 0.73. The results demonstrate that despite the low gamma-ray branching ratio of ⁸⁵Kr and its susceptibility to interference from the 511 keV positron annihilation peak, gamma-ray spectrometry can achieve reliable activity measurements of ⁸⁵Kr. Conclusion The comparison for ⁸⁵Kr gamma-ray spectrometry tested the capabilities of the participating laboratories in ⁸⁵Kr measurement. It promoted technical exchange and experience sharing among laboratories, laying a foundation for future comparisons involving more complex radioactive inert gases.

Mid-to-late stage ankle arthritis is a chronic degenerative disease that is extremely common in clinical practice.It is characterized by significant cartilage degeneration and subchondral bone sclerosis,accompanied by the formation of osteophytes around the joint,often leading to joint deformity.This condition causes severe pain in the patients during walking,severely restricts their activities,and affects their qualities of life.In recent years,with the continuous improvement of medical standards,the treatment methods for mid-to-late stage ankle arthritis have shown a diversified development trend.Non-surgical treatments primarily include activity restriction,orthotic devices,oral non-steroidal anti-inflammatory drugs(NSAIDs),and intra-articular injections of the talocrural joint.The surgical treatments primarily include joint distraction arthroplasty,periacetabular osteotomy,total ankle arthroplasty,and ankle arthrodesis.Tissue engineering therapy,as an emerging method,has also received considerable attention.This article systematically reviewed the selection principles and research progress of various treatment options for mid-to-late stage ankle arthritis,including traditional treatments,non-surgical treatments,surgical treatments,and tissue engineering treatments.By deeply analyzing the basic principles and advantages and disadvantages of each treatment method,and combining the latest research findings on clinical outcomes,a scientific and comprehensive clinical decision-making reference system was constructed to provide clearer and more comprehensive treatment choices for both doctors and patients,thereby effectively improving treatment outcomes and enhancing the quality of life for the patients.

Objective:To evaluate the predictive performance of machine learning methods for predicting the risk of death in traumatic hemorrhage, and address the low prediction accuracy of traditional trauma scores, provide a reference for developing a more robust prediction method for severe trauma patients.Methods:Clinical data of severe trauma patients from the National Trauma Medical Center between April 1, 2023, and March 31, 2024 were collected. ElasticNet, Recursive Feature Elimination, and Mutual Information-based feature selection methods were used to screen variables and compared with traditional hypothesis testing methods. Built the prediction models for mortality risk in traumatic hemorrhage using Logistic Regression, ElasticNet, and Support Vector Machine (SVM) and compared the predictive performance.Results:The study included 5,601 trauma patients, the results of the variable screening and importance ranking were consistent with three feature selection methods. The classification accuracy and AUC values for the three models were as follows: Overall accuracy was 83.2%, survival accuracy was 84.0%, death accuracy was 76.3%, and an AUC was 0.86 in logistic regression; Overall accuracy was 78.9%, survival accuracy was 78.5%, death accuracy was 81.7%, and an AUC was 0.88 in ElasticNet; Overall accuracy was 84.7%, survival accuracy was 86.1%, death accuracy was 72.4%, and an AUC was 0.86 in SVM. The prediction performance of three models is quite little.Conclusion:Machine learning methods can effectively improve the prediction of death risk for traumatic hemorrhage,and has wide applications.