OBJECTIVE To summarize the current status of position training for clinical pharmacists in China and provide references for the continuous optimization of such training programs. METHODS SinoMed， CNKI，VIP and Wanfang Data were electronically searched to collect position training of clinical pharmacists studies from the inception until November 5th 2024. After data extraction and quality evaluation， descriptive analysis was performed on the results of the included studies. RESULTS & A total of 68 pieces of relevant literature were included in the study. Among them， 50 studies reported on training content， 49 involved the allocation of teaching resources in the bases， 48 addressed training methods， and 39 focused on training evaluation； only 2 studies mentioned faculty development. There were notable variations in the clinical pharmacist training programs across different bases， particularly in the allocation of teaching resources， such as the composition of the teaching team and the utilization of auxiliary teaching tools. Additionally， differences existed in training approaches， such as those employing a single method versus a blended approach. Conversely， the core training content of each base generally revolved around clinical pharmacy practice， demonstrating a degree of consistency. Moreover， the overall emphasis on teacher training and assessment tended to be obviously insufficient. Each base can focus on enhancing the competence of clinical pharmacists by allocating teaching resources， selecting training methods， improving training content， and using evaluation tools， to further enhance the quality of clinical pharmacist training.

ObjectivePrimary liver cancer, predominantly hepatocellular carcinoma (HCC), is a significant global health issue, ranking as the sixth most diagnosed cancer and the third leading cause of cancer-related mortality. Accurate and early diagnosis of HCC is crucial for effective treatment, as HCC and non-HCC malignancies like intrahepatic cholangiocarcinoma (ICC) exhibit different prognoses and treatment responses. Traditional diagnostic methods, including liver biopsy and contrast-enhanced ultrasound (CEUS), face limitations in applicability and objectivity. The primary objective of this study was to develop an advanced, light-weighted classification network capable of distinguishing HCC from other non-HCC malignancies by leveraging the automatic analysis of brightness changes in CEUS images. The ultimate goal was to create a user-friendly and cost-efficient computer-aided diagnostic tool that could assist radiologists in making more accurate and efficient clinical decisions. MethodsThis retrospective study encompassed a total of 161 patients, comprising 131 diagnosed with HCC and 30 with non-HCC malignancies. To achieve accurate tumor detection, the YOLOX network was employed to identify the region of interest (ROI) on both B-mode ultrasound and CEUS images. A custom-developed algorithm was then utilized to extract brightness change curves from the tumor and adjacent liver parenchyma regions within the CEUS images. These curves provided critical data for the subsequent analysis and classification process. To analyze the extracted brightness change curves and classify the malignancies, we developed and compared several models. These included one-dimensional convolutional neural networks (1D-ResNet, 1D-ConvNeXt, and 1D-CNN), as well as traditional machine-learning methods such as support vector machine (SVM), ensemble learning (EL), k-nearest neighbor (KNN), and decision tree (DT). The diagnostic performance of each method in distinguishing HCC from non-HCC malignancies was rigorously evaluated using four key metrics: area under the receiver operating characteristic (AUC), accuracy (ACC), sensitivity (SE), and specificity (SP). ResultsThe evaluation of the machine-learning methods revealed AUC values of 0.70 for SVM, 0.56 for ensemble learning, 0.63 for KNN, and 0.72 for the decision tree. These results indicated moderate to fair performance in classifying the malignancies based on the brightness change curves. In contrast, the deep learning models demonstrated significantly higher AUCs, with 1D-ResNet achieving an AUC of 0.72, 1D-ConvNeXt reaching 0.82, and 1D-CNN obtaining the highest AUC of 0.84. Moreover, under the five-fold cross-validation scheme, the 1D-CNN model outperformed other models in both accuracy and specificity. Specifically, it achieved accuracy improvements of 3.8% to 10.0% and specificity enhancements of 6.6% to 43.3% over competing approaches. The superior performance of the 1D-CNN model highlighted its potential as a powerful tool for accurate classification. ConclusionThe 1D-CNN model proved to be the most effective in differentiating HCC from non-HCC malignancies, surpassing both traditional machine-learning methods and other deep learning models. This study successfully developed a user-friendly and cost-efficient computer-aided diagnostic solution that would significantly enhances radiologists’ diagnostic capabilities. By improving the accuracy and efficiency of clinical decision-making, this tool has the potential to positively impact patient care and outcomes. Future work may focus on further refining the model and exploring its integration with multimodal ultrasound data to maximize its accuracy and applicability.

OBJECTIVE: Humans are exposed to complex mixtures of environmental chemicals and other factors that can affect their health. Analysis of these mixture exposures presents several key challenges for environmental epidemiology and risk assessment, including high dimensionality, correlated exposure, and subtle individual effects. METHODS: We proposed a novel statistical approach, the generalized functional linear model (GFLM), to analyze the health effects of exposure mixtures. GFLM treats the effect of mixture exposures as a smooth function by reordering exposures based on specific mechanisms and capturing internal correlations to provide a meaningful estimation and interpretation. The robustness and efficiency was evaluated under various scenarios through extensive simulation studies. RESULTS: We applied the GFLM to two datasets from the National Health and Nutrition Examination Survey (NHANES). In the first application, we examined the effects of 37 nutrients on BMI (2011-2016 cycles). The GFLM identified a significant mixture effect, with fiber and fat emerging as the nutrients with the greatest negative and positive effects on BMI, respectively. For the second application, we investigated the association between four pre- and perfluoroalkyl substances (PFAS) and gout risk (2007-2018 cycles). Unlike traditional methods, the GFLM indicated no significant association, demonstrating its robustness to multicollinearity. CONCLUSION GFLM framework is a powerful tool for mixture exposure analysis, offering improved handling of correlated exposures and interpretable results. It demonstrates robust performance across various scenarios and real-world applications, advancing our understanding of complex environmental exposures and their health impacts on environmental epidemiology and toxicology.
Humans ; Environmental Exposure/analysis* ; Linear Models ; Nutrition Surveys ; Environmental Pollutants ; Body Mass Index

Objective To investigate the differences of characteristic of bone marrow plasma cells in patients of different plasma cell dyscrasias according to International Myeloma Working Group(IMWG)criteria.Methods We analyzed the serological and bone marrow flow cytometry results of patients with plasma cell dyscrasias diagnosed and treated in Department of Hematology,Zhongshan Hospital(Xiamen Branch),Fudan University from Jun 12,2019 to Sep 5,2023 retrospectively.Results A total of 102 patients,63 males and 39 females,aged 22 to 85 years,were included,including 46 patients with monoclonal gammaglobulinemia of unknown significance,39 patients with multiple myeloma,5 patients with smoldering multiple myeloma and 12 patients with light-chain amyloidosis.All patients had M proteinemia,including 58 patients with IgG type and 44 patients with non-IgG type.Plasma cells were detected in the bone marrow of all patients.Clonal plasma cells were detected in the bone marrow of 79 patients.Normal plasma cells were detected in the bone marrow of 63 patients.Both clonal and normal plasma cells were detected in the bone marrow of 40 patients.Clonal plasma cells from bone marrow of 52 patients expressed CD56 and 12 patients expressed CD117.There were no significant differences in gender,age among different disease groups.There were statistical differences in M protein type,the concentration of M protein,serum involved/uninvolved free light chain ratio,the proportion of plasma cells in bone marrow nucleated cells,the proportion of clonal plasma cells in bone marrow nucleated cells,the proportion of clonal plasma cells in all bone marrow plasma cells,and the proportion of normal plasma cells in all bone marrow plasma cells among different disease groups(P<0.05).There was statistical difference in the expression of CD56 in clonal plasma cells among different disease groups(P=0.009),but no statistical difference in the expression of CD117.Conclusion The proportion of clonal plasma cells to all nucleated cells,the proportion of clonal plasma cells to all plasma cells,and the proportion of CD56 expression in abnormal plasma cells in the bone marrow of patients with light amyloidosis were similar to those of monoclonal gammopathy of undetermined significance,but significantly different from those of patients with multiple myeloma.

Community-acquired pneumonia (CAP) is the third leading cause of death worldwide and one of the most commonly infectious diseases. Its epidemiological characteristics vary with host and immune status, and corresponding pathogen spectrums migrate over time and space distribution. Meanwhile, with the outbreak of COVID-19, some unconventional treatment strategies are on the rise. This article reviewed the epidemiological characteristics, pathogen spectrum and treatment direction of CAP in China over the years, and aimed to provide guidance for the diagnosis and treatment of CAP in clinical practice.
Humans ; COVID-19 ; Pneumonia/diagnosis* ; Community-Acquired Infections/drug therapy* ; Causality ; Risk Factors

Objective: To investigate the value of uterine morphological parameters and endometrial T₂ signal intensity (T₂-SI) in evaluating the degree of the fibrotic repair secondary to endometrial injury. Methods: From Sep. 2018 to Feb. 2023, this study prospectively enrolled 29 patients with fibrotic repair secondary to severe endometrial injury (severe group), 17 patients with fibrotic repair secondary to mild to moderate endometrial injury (mild to moderate group), and 40 healthy women of reproductive age (control group) in Nanjing Drum Tower Hospital. The length of uterine cavity (LUC), length of cervix and isthmus (LCI), width of upper uterine cavity (WUUC) and width of lower uterine cavity (WLUC) were measured using magnetic resonance imaging. T₂-SI of endometrium and subcutaneous fat of buttocks were measured, and endometrial normalized T₂-SI (nT₂-SI; T₂-SI of endometrium/T₂-SI of subcutaneous fat of buttocks) was calculated. Statistical analyses of data were performed using one-way analysis of variance, Mann-Whitney U test, intraclass correlation coefficient, Spearman rho test, area under the receiver operating characteristic curve (AUC). Results: LUC, WUUC, WLUC and endometrial nT₂-SI of severe group [(19.7±3.5) mm, (26.9±6.4) mm, (7.9±1.4) mm, 0.73±0.11, respectively] were significantly lower than those of the control group (all P<0.01), while LCI and WUUC/LUC [(51.3±7.3) mm and 1.38±0.34] were significantly higher than those of the control group (all P<0.001). LUC and WLUC of severe group were significantly lower than those of mild to moderate group [(32.4±5.1) mm and (8.8±1.2) mm; all P<0.05], while LCI and WUUC/LUC were significantly higher than those of mild to moderate group [(41.8±8.6) mm and 0.94±0.16; all P<0.001]. LUC and endometrial nT₂-SI of mild to moderate group were significantly lower than those of the control group [ (32.4±5.1) vs (35.3±3.5) mm, 0.68±0.13 vs 0.80±0.12; all P<0.01]. LUC, WUUC, WLUC and endometrial nT₂-SI were significantly negatively correlated to the degree of the fibrotic repair secondary to endometrial injury (Spearman rho:-0.794, -0.441, -0.471 and -0.316, respectively; all P<0.05), while LCI and WUUC/LUC were significantly positively correlated to the degree of the fibrotic repair secondary to endometrial injury (Spearman rho: 0.481 and 0.674, respectively; all P<0.05). LUC and WUUC/LUC showed high value in distinguishing severe group from the control group or mild to moderate group (all AUC>0.9, all P<0.001). Conclusion: As noninvasive and quantitative biomarkers, uterine morphological parameters and endometrial nT₂-SI could evaluate the degree of the fibrotic repair secondary to endometrial injury.
Humans ; Female ; Uterus ; Endometrium ; Health Status ; Hospitals ; ROC Curve

Anti-N-methyl-D-aspartate receptor (NMDAR) encephalitis is an autoimmune inflammatory disease of the central nervous system, and little is known about its immune mechanism at present. There is a lack of disease-related biomarkers in cerebrospinal fluid except anti-NMDAR antibody, which leads to delayed diagnosis and treatment in some patients. Therefore, there has been an increasing number of studies on related cytokines in recent years to assess whether they can be used as new biomarkers for evaluating disease conditions and assisting diagnosis and treatment. Current studies have shown that some cytokines may be associated with the progression of anti-NMDAR encephalitis, and this article reviews the research advances in such cytokines associated with anti-NMDAR encephalitis.
Humans ; Cytokines ; Anti-N-Methyl-D-Aspartate Receptor Encephalitis/therapy* ; Biomarkers

The global COVID-19 coronavirus pandemic has infected over 109 million people, leading to over 2 million deaths up to date and still lacking of effective drugs for patient treatment. Here, we screened about 1.8 million small molecules against the main protease (M^pro) and papain like protease (PL^pro), two major proteases in severe acute respiratory syndrome-coronavirus 2 genome, and identified 1851M^pro inhibitors and 205 PL^pro inhibitors with low nmol/l activity of the best hits. Among these inhibitors, eight small molecules showed dual inhibition effects on both M^pro and PL^pro, exhibiting potential as better candidates for COVID-19 treatment. The best inhibitors of each protease were tested in antiviral assay, with over 40% of M^pro inhibitors and over 20% of PL^pro inhibitors showing high potency in viral inhibition with low cytotoxicity. The X-ray crystal structure of SARS-CoV-2 M^pro in complex with its potent inhibitor 4a was determined at 1.8 Å resolution. Together with docking assays, our results provide a comprehensive resource for future research on anti-SARS-CoV-2 drug development.
Humans ; Antiviral Agents/chemistry* ; COVID-19 ; COVID-19 Drug Treatment ; High-Throughput Screening Assays ; Molecular Docking Simulation ; Protease Inhibitors/chemistry* ; SARS-CoV-2/enzymology* ; Viral Nonstructural Proteins

Humans ; Gemcitabine ; Neoplasms

ObjectiveTo explore the effect and mechanism of Sishenwan-containing serum on aerobic glycolysis in human colon cancer HCT116 cells. MethodCell counting kit-8 （CCK-8） was used to detect the cell viability of colon cancer HCT116 cells after treatment with Sishenwan-containing serum （2.5%， 5%， and 10%） for 24， 48， 72 h. The concentration of lactic acid， the content of intracellular glucose， and the activity of hexokinase （HK） and fructose-6-phosphate kinase （PFK） in the cell culture medium were detected by the micro-method. The content of glucose transporter 1 （GluT1） mRNA was detected by Real-time quantitative polymerase chain reaction （Real-time PCR）. The protein expression of GluT1 and methyltransferase-like 3 （MettL3） was detected by Western blot. The expression of GluT1 in cells was detected by immunofluorescence and the level of N6-methyladenosine （m⁶A） RNA methylation was detected by colorimetry. ResultCompared with the normal serum， 2.5%， 5%， and 10% Sishenwan-containing serum had no significant effect on the viability of HCT116 cells at 24 h， while 10% Sishenwan-containing serum showed a significant inhibitory effect on the viability of HCT116 cells at 48 h （P<0.05）. Hence， 10% Sishenwan-containing serum was used in subsequent experiments， and the intervention time was 48 h. Compared with the normal serum， 10% Sishenwan-containing serum could reduce lactate production （P<0.05）， down-regulate glucose uptake （P<0.05）， and blunt the activities of HK and PFK， the key rate-limiting enzymes of glycolysis （P<0.05）. Meanwhile， 10% Sishenwan-containing serum could decrease the expression of GluT1 protein （P<0.01） and mRNA （P<0.05） and reduce the proportion of cells expressing GluT1 （P<0.01）. Compared with the normal serum， Sishenwan-containing serum also decreased the protein content of MettL3 （P<0.05） and the methylation level of m⁶A RNA （P<0.01）. ConclusionSishenwan can inhibit glycolysis in colon cancer cells， and its inhibitory mechanism may be related to reducing MettL3 overexpression， inhibiting m⁶A RNA methylation， and down-regulating GluT1 and the activities of intracellular aerobic glycolysis-related enzymes such as HK and PFK.