Amyotrophic lateral sclerosis (ALS) is an irreversible, fatal neurodegenerative disorder whose incidence is positively correlated with the aging population. ALS is characterized by the progressive loss of motor neurons, leading to muscle weakness, atrophy, and ultimately respiratory failure. The pathogenesis of ALS involves multiple factors, including genetic and environmental influences, with genetic factors playing a particularly significant role. To date, several causative genes have been identified in ALS, such as the Cu/Zn superoxide dismutase 1 (Cu/Zn SOD1, also known as SOD1) gene, transactive response DNA-binding protein 43 (TDP-43) gene, fused in sarcoma (FUS) gene, and chromosome open reading frame 72 (C9orf72). Mutations in these genes have been found not only in familial ALS but also in sporadic ALS. Based on the identified ALS risk genes, various ALS animal models have been established through multiple approaches, including transgenic models, gene knockout/knock-in models, and adeno-associated virus-mediated overexpression models. These models simulate some typical pathological features of human ALS, such as motor neuron loss, ubiquitinated inclusions, and neuromuscular junction degeneration. However, these models still have limitations: (1) single-gene mutation models are insufficient to fully replicate the complex multi-factorial pathogenesis of sporadic ALS; (2) significant differences in microenvironmental regulation mechanisms and the rate of neurodegeneration between model organisms and humans may affect the accurate reproduction of disease phenotypes and the reliable evaluation of drug efficacy. To better understand the pathogenesis of ALS and promote the development of effective therapies, constructing and optimizing ALS animal models is crucial. This review aims to summarize commonly used ALS gene mutation mouse models, analyze their phenotypes and pathological characteristics, including transgenic mouse models, gene knockout/knock-in mouse models, and adeno-associated virus-mediated overexpression mouse models, and further discuss their specific applications in ALS pathogenesis research and drug development by comparing the advantages and limitations of each model.

Amyotrophic lateral sclerosis (ALS) is an irreversible, fatal neurodegenerative disorder whose incidence is positively correlated with the aging population. ALS is characterized by the progressive loss of motor neurons, leading to muscle weakness, atrophy, and ultimately respiratory failure. The pathogenesis of ALS involves multiple factors, including genetic and environmental influences, with genetic factors playing a particularly significant role. To date, several causative genes have been identified in ALS, such as the Cu/Zn superoxide dismutase 1 (Cu/Zn SOD1, also known as SOD1) gene, transactive response DNA-binding protein 43 (TDP-43) gene, fused in sarcoma (FUS) gene, and chromosome open reading frame 72 (C9orf72). Mutations in these genes have been found not only in familial ALS but also in sporadic ALS. Based on the identified ALS risk genes, various ALS animal models have been established through multiple approaches, including transgenic models, gene knockout/knock-in models, and adeno-associated virus-mediated overexpression models. These models simulate some typical pathological features of human ALS, such as motor neuron loss, ubiquitinated inclusions, and neuromuscular junction degeneration. However, these models still have limitations: (1) single-gene mutation models are insufficient to fully replicate the complex multi-factorial pathogenesis of sporadic ALS; (2) significant differences in microenvironmental regulation mechanisms and the rate of neurodegeneration between model organisms and humans may affect the accurate reproduction of disease phenotypes and the reliable evaluation of drug efficacy. To better understand the pathogenesis of ALS and promote the development of effective therapies, constructing and optimizing ALS animal models is crucial. This review aims to summarize commonly used ALS gene mutation mouse models, analyze their phenotypes and pathological characteristics, including transgenic mouse models, gene knockout/knock-in mouse models, and adeno-associated virus-mediated overexpression mouse models, and further discuss their specific applications in ALS pathogenesis research and drug development by comparing the advantages and limitations of each model.

OBJECTIVE To systematically investigate the current status and effectiveness of the standardized on-the-job training program for community clinical pharmacists in Shanghai， and to provide a scientific basis for optimizing the training scheme. METHODS A questionnaire survey was conducted to collect the data from trainees and mentor pharmacists who participated in the program between 2016 and 2024. The survey examined their basic information， evaluations of the training scheme， satisfaction with training outcomes， and suggestions for improvement. Statistical analyses were also conducted. RESULTS A total of 420 valid responses were collected， including 340 from trainees and 80 from mentor pharmacists. Before training， only 30.29% of trainees were engaged in clinical pharmacy-related work， whereas this proportion increased to 73.24% after training. Most mentor pharmacists had extensive experience in clinical pharmacy （76.25% with ≥5 years of experience） and mentoring （78.75% with ≥3 teaching sessions）. Totally 65.59% of trainees and 55.00% of mentor pharmacists believed that blended training yielded the best learning outcomes. Over 80.00% of both trainees and mentor pharmacists considered the overall training duration， theoretical study time， and practical training time to be reasonable. More than 95.00% of trainees and mentor pharmacists agreed that the homework and assessment schemes were appropriate. Trainees rated the relevance of training content to their actual work highly （with an average relevance score ＞4.5）， though they perceived the chronic disease medication therapy management module as significantly more challenging than the prescription review and evaluation module and the home-based pharmaceutical care module. The average satisfaction score of trainees and mentor pharmacists with the training effectiveness of each project was above 4 points， indicating a high overall satisfaction. Inadequate provision of teaching resources was unanimously recognized by trainees and mentor pharmacists as the key area requiring improvement. CONCLUSIONS The standardized on-the-job training program for community clinical pharmacists in Shanghai has contributed to improving pharmaceutical services in community healthcare settings. However， ongoing improvements must concentrate on content design， resource development， and faculty cultivation.

Objective To explore the effect of community pharmacy outpatient service on patients with type 2 diabetes mellitus. Methods A non-randomized controlled study was conducted, and type 2 diabetes patients managed in the community were divided into an intervention group of 112 cases and a control group of 110 cases. The control group received routine medication guidance during general practice outpatient visits, while the intervention group received comprehensive pharmacy outpatient service intervention based on routine medication guidance in general practice. Follow-up visits were conducted every 3 months. Repeated measurement analysis of variance and multivariate linear regression analysis were used to evaluate the intervention effect of the pharmacy outpatient service. Results Fasting blood glucose and glycosylated hemoglobin levels in the intervention group showed a decreasing trend with the increase of intervention time compared to pre-intervention time （P<0.01）, with increased duration of weekly exercise, decreased staple food intake, increased vegetable intake, and increased medication adherence score （P<0.01）. After adjusting for confounding factors through multivariate linear regression model, pharmacy outpatient intervention was found to be an independent protective factor for fasting blood glucose level （β=−0.891, P<0.01） and glycosylated hemoglobin level （β=−0.760, P<0.01） in the study subjects. Conclusion The community pharmacy outpatient service could enhance the self-management ability of patients with type 2 diabetes mellitus, and effectively improve patients’ fasting blood glucose and glycosylated hemoglobin.

The use of wastewater analysis, or wastewater-based epidemiology, to assess and monitor the situation of drug abuse is now widely used at home and abroad. However, there is currently a lack of effective evaluation methods and effective ways of comparison, supervision and standardization, which is not conducive to the analysis and comparisons of data in different countries and regions. Quality control techniques can control the laboratory's analytical errors, safeguard the consistency and comparability of identification conclusions, and promote the further improvement of the level and capacity of urban drug governance, thus playing significant roles. This paper provides an overview of sample collection, sample preservation and transportation, laboratory analysis, back-calculation of drug use and external laboratory quality control in the process of wastewater analysis, with a view to exploring more comprehensive scientific and objective methods and approaches suitable for examining and evaluating qualitative and quantitative analysis of drugs in wastewater among laboratories.

ObjectiveTo establish a model that can quickly identify the aroma components in different parts of Nardostachys jatamansi， so as to provide a quality control basis for the market circulation and clinical use of N. jatamansi. MethodsHeadspace solid-phase microextraction-gas chromatography-mass spectrometry（HS-SPME-GC-MS） combined with Smart aroma database and National Institute of Standards and Technology（NIST） database were used to characterize the aroma components in different parts of N. jatamansi， and the aroma components were quantified according to relative response factor（RRF） and three internal standards， and the markers of aroma differences in different parts of N. jatamansi were identified by orthogonal partial least squares-discriminant analysis（OPLS-DA） and cluster thermal analysis based on variable importance in the projection（VIP） value >1 and P<0.01. The odor data of different parts of N. jatamansi were collected by Heracles Ⅱ Neo ultra-fast gas phase electronic nose， and the correlation between compound types of aroma components collected by the ultra-fast gas phase electronic nose and the detection results of HS-SPME-GC-MS was investigated by drawing odor fingerprints and odor response radargrams. Chromatographic peak information with distinguishing ability≥0.700 and peak area≥200 was selected as sensor data， and the rapid identification model of different parts of N. jatamansi was established by principal component analysis（PCA）， discriminant factor alysis（DFA）， soft independent modeling of class analogies（SIMCA） and statistical quality control analysis（SQCA）. ResultsThe HS-SPME-GC-MS results showed that there were 28 common components in the underground and aboveground parts of N. jatamansi， of which 22 could be quantified and 12 significantly different components were screened out. Among these 12 components， the contents of five components（ethyl isovalerate， 2-pentylfuran， benzyl alcohol， nonanal and glacial acetic acid，） in the aboveground part of N. jatamansi were significantly higher than those in the underground part（P<0.01）， the contents of β-ionone， patchouli alcohol， α-caryophyllene， linalyl butyrate， valencene， 1，8-cineole and p-cymene in the underground part of N. jatamansi were significantly higher than those in the aboveground part（P<0.01）. Heracles Ⅱ Neo electronic nose results showed that the PCA discrimination index of the underground and aboveground parts of N. jatamansi was 82， and the contribution rates of the principal component factors were 99.94% and 99.89% when 2 and 3 principal components were extracted， respectively. The contribution rate of the discriminant factor 1 of the DFA model constructed on the basis of PCA was 100%， the validation score of the SIMCA model for discrimination of the two parts was 99， and SQCA could clearly distinguish different parts of N. jatamansi. ConclusionHS-SPME-GC-MS can clarify the differential markers of underground and aboveground parts of N. jatamansi. The four analytical models provided by Heracles Ⅱ Neo electronic nose（PCA， DFA， SIMCA and SQCA） can realize the rapid identification of different parts of N. jatamansi. Combining the two results， it is speculated that terpenes and carboxylic acids may be the main factors contributing to the difference in aroma between the underground and aboveground parts of N. jatamansi.

Tumor drug resistance is an important problem in the failure of chemotherapy and targeted drug therapy, which is a complex process involving chromatin remodeling. SWI/SNF is one of the most studied ATP-dependent chromatin remodeling complexes in tumorigenesis, which plays an important role in the coordination of chromatin structural stability, gene expression, and post-translation modification. However, its mechanism in tumor drug resistance has not been systematically combed. SWI/SNF can be divided into 3 types according to its subunit composition: BAF, PBAF, and ncBAF. These 3 subtypes all contain two mutually exclusive ATPase catalytic subunits (SMARCA2 or SMARCA4), core subunits (SMARCC1 and SMARCD1), and regulatory subunits (ARID1A, PBRM1, and ACTB, etc.), which can control gene expression by regulating chromatin structure. The change of SWI/SNF complex subunits is one of the important factors of tumor drug resistance and progress. SMARCA4 and ARID1A are the most widely studied subunits in tumor drug resistance. Low expression of SMARCA4 can lead to the deletion of the transcription inhibitor of the BCL2L1 gene in mantle cell lymphoma, which will result in transcription up-regulation and significant resistance to the combination therapy of ibrutinib and venetoclax. Low expression of SMARCA4 and high expression of SMARCA2 can activate the FGFR1-pERK1/2 signaling pathway in ovarian high-grade serous carcinoma cells, which induces the overexpression of anti-apoptosis gene BCL2 and results in carboplatin resistance. SMARCA4 deletion can up-regulate epithelial-mesenchymal transition (EMT) by activating YAP1 gene expression in triple-negative breast cancer. It can also reduce the expression of Ca²⁺ channel IP3R3 in ovarian and lung cancer, resulting in the transfer of Ca²⁺ needed to induce apoptosis from endoplasmic reticulum to mitochondria damage. Thus, these two tumors are resistant to cisplatin. It has been found that verteporfin can overcome the drug resistance induced by SMARCA4 deletion. However, this inhibitor has not been applied in clinical practice. Therefore, it is a promising research direction to develop SWI/SNF ATPase targeted drugs with high oral bioavailability to treat patients with tumor resistance induced by low expression or deletion of SMARCA4. ARID1A deletion can activate the expression of ANXA1 protein in HER2+ breast cancer cells or down-regulate the expression of progesterone receptor B protein in endometrial cancer cells. The drug resistance of these two tumor cells to trastuzumab or progesterone is induced by activating AKT pathway. ARID1A deletion in ovarian cancer can increase the expression of MRP2 protein and make it resistant to carboplatin and paclitaxel. ARID1A deletion also can up-regulate the phosphorylation levels of EGFR, ErbB2, and RAF1 oncogene proteins.The ErbB and VEGF pathway are activated and EMT is increased. As a result, lung adenocarcinoma is resistant to epidermal growth factor receptor tyrosine kinase inhibitors (EGFR-TKIs). Although great progress has been made in the research on the mechanism of SWI/SNF complex inducing tumor drug resistance, most of the research is still at the protein level. It is necessary to comprehensively and deeply explore the detailed mechanism of drug resistance from gene, transcription, protein, and metabolite levels by using multi-omics techniques, which can provide sufficient theoretical basis for the diagnosis and treatment of poor tumor prognosis caused by mutation or abnormal expression of SWI/SNF subunits in clinical practice.

BACKGROUND:At present,modern medical treatment has certain limitations on the treatment of knee osteoarthritis.Traditional Chinese medicine alkaloids can effectively prevent and treat knee osteoarthritis through various mechanisms. OBJECTIVE:To review the mechanism of alkaloids in traditional Chinese medicine in the prevention and treatment of knee osteoarthritis,providing a scientific basis for the clinical development of drugs for the treatment of knee osteoarthritis. METHODS:CNKI,WanFang,PubMed,Web of Science and Google Scholar were retrieved for relevant literature published from database inception to May 2024.The key words were"knee osteoarthritis""osteoarthritis""osteoclast""chondrocyte""alkaloids"in Chinese and English.Duplicates and obsolete non-referenced literature were excluded,and a total of 68 eligible papers were included for further review. RESULTS AND CONCLUSION:Although traditional Chinese medicine has a long history of treating knee osteoarthritis,only a small number of natural compounds are in the preclinical stage of research against knee osteoarthritis.Alkaloids have a greater potential for the prevention and treatment of knee osteoarthritis,among which,sophocarpidine,oxymatrine,sinomenine,and betaine have been shown to be effective in the prevention and treatment of knee osteoarthritis by modulating multiple signaling pathways.Alkaloids can delay the progression of knee osteoarthritis by inhibiting inflammatory response,exerting antioxidant response,inhibiting chondrocyte apoptosis,promoting chondrocyte proliferation,and inhibiting osteoclast formation and differentiation.

The plants of the genus Caragana Fabr. are desert plants of the Leguminosae family, which are widely used in traditional ethnomedicine, with the effects of nourishing Yin and nourishing blood, dispelling wind and removing dampness, clearing heat and detoxifying toxins. The anti-inflammatory effect of Caragana Fabr. has attracted much attention, the research on the related mechanism has made some progress, but it lacks systematic collation. By summarising the anti-inflammatory effects of the active ingredients of Caragana Fabr., the authors found that they have good anti-inflammatory activities on different inflammatory cell models in vitro, and have good improvement effects on rheumatoid arthritis, colitis, complex nephritis, and acute lung injury and other disease models. The anti-inflammatory effects of the active components of this genus mainly include the reduction of the levels of a variety of pro-inflammatory factors, and the signalling pathways involved mainly include TLR4/NF-κB, TLR4/MAPK, TLR4/NF-κB/IRF3, JAK/STAT-1, ERK/STAT-1 and so on. Therefore, the paper mainly reviews the research progress on the anti-inflammatory effects and mechanisms of the Caragana Fabr. plants and their active components according to disease types, with a view to providing reference for the in-depth study of their anti-inflammatory activities and the development of new products with related functions.

Objective: To analyze the temporal variation patterns of sickness absenteeism among primary and secondary school students in Shanghai, so as to explore models suitable for predicting peaks and intensity of absenteeism rates. Methods: The seasonal and trend decomposition using loess (STL) method was used to analyze the seasonal and long term trend changes in sickness absenteeism among primary and secondary school students from September 1 in 2010 to June 30 in 2018, in Shanghai. A hierarchical clustering method based on Dynamic Time Warping (DTW) was employed to classify absenteeism symptoms with similar temporal patterns. Based on historical data, the study constructed and evaluated different time series algorithms and machine learning models to optimize the accuracy of predicting the trend of sickness absenteeism. Results: During the research period, the average new absenteeism rate due to illness was 16.86 per 10 000 person day for every academic year, and the trend of sickness absenteeism exhibited both seasonality and a long term upward trend, reaching its highest point in the 2017 academic year (22.47 per 10 000 person day). The symptoms of absenteeism were divided into three categories: high incidence in winter and spring (respiratory symptoms, fever and general discomfort, etc.), high incidence in summer (eye symptoms, nosebleeds, etc.) and those without obvious seasonality (skin symptoms, accidental injuries, etc.).The constructed time series models effectively predicted the trend of absenteeism due to illness, although the accuracy of predicting peak intensity was relatively low. Among them, the multi layer perceptron (MLP) model performed the best, with an root mean squared error (RMSE) of 8.96 and an mean absolute error (MAE) of 4.37, reducing 36.51% and 39.02% compared to the baseline model. Conclusion Time series models and machine learning algorithms could effectively predict the trend of sickness absenteeism, and corresponding prevention and control measures can be taken for absenteeism caused by different symptoms during peak periods.