OBJECTIVE To explore a new model of intelligent medication education for pharmaceutical outpatient clinics by constructing dynamic HTML web pages through the DeepSeek-V3-0324 large-scale model. METHODS Clinical pharmacists integrated key clinical information such as patients’ basic information， medication history and medication precautions in real time， and generated a standardized medication education list through the DeepSeek-V3-0324 large-scale model and manual review. RESULTS The DeepSeek-V3-0324 large-scale model was applied in the pharmaceutical outpatient clinics to generate a personalized medication education list， which could effectively solve the disunity of pharmacy guidance caused by the lack of standardization of medication education and the difference of individualized experience of pharmacists in the traditional pharmaceutical outpatient clinics in the face of complex cases， and medication errors caused by forgetting or misremembering information among certain special patient populations after receiving medication education. CONCLUSIONS The transformation and application of artificial intelligence technology in pharmaceutical outpatient clinics is an innovation of pharmaceutical outpatient service means， which can provide patients with immediate and personalized medication education and improve the quality of pharmaceutical care. However， it is also necessary to face the lag of database update and the lack of risk management， as well as the lack of diversification of medication education lists.

Objective: To examine the association between incidence of hand-foot-mouth disease (HFMD) and meteorological factors, so as to provide the basis for the prevention and control of HFMD. Methods: The number of HFMD cases in Jiading District, Shanghai Municipality from 2016 to 2023 were collected through the Chinese Disease Prevention and Control Information System, and meteorological data were obtained from the Shanghai Meteorological Bureau. The associations of daily average temperature, daily average relative humidity, and daily average atmospheric pressure with the daily number of HFMD cases were analyzed using a distributed lag non-linear model (DLNM). Results: A total of 21 555 HFMD cases were reported in Jiading District from 2016 to 2023, with an average annual incidence of 132.57/100 000. There were 12 762 male cases (59.21%) and 8 793 female cases (40.79%). The main peak of incidence occurred from June to August, and the secondary peak was from October to December. DLNM analysis showed that the incidence risk of HFMD first increased and then decreased with the increase of daily average temperature, and first decreased and then increased with the prolongation of the lag time. The cumulative lag risk was higher when the daily average temperature ranged from 18.4 to 35.1 ℃, and the maximum cumulative lag effect was observed at 27.8 ℃ (RR=5.522, 95%CI: 4.751-6.370). The incidence risk of HFMD first increased and then decreased with the increase of daily average relative humidity, and first decreased, then increased and then decreased again with the prolongation of the lag time. The cumulative lag risk was higher when the daily average relative humidity ranged from 71.7% and 90.8%, and the maximum cumulative lag effect was observed at 81.8% (RR=1.603, 95%CI: 1.321-1.995). The incidence risk of HFMD decreased with the increase of daily average atmospheric pressure, and decreased with the prolongation of the lag time when the daily average atmospheric pressure was greater than 1 015.80 hPa. When the daily average atmospheric pressure was less than 1 015.80 hPa, the incidence risk of HFMD increased with the prolongation of the lag time. The maximum cumulative lag effect was observed at 986.80 hPa (RR=8.513, 95%CI: 1.401-36.625). Conclusion The incidence risk of HFMD in Jiading District initially increases and then decreases with increasing temperature and relative humidity, while it decreases with increasing atmospheric pressure, and these effects exhibit a lagged response.

The interactions between microorganisms and medicinal plants are crucial to the quality improvement of medicinal plants. Medicinal plants attract microorganisms to colonize by secreting specific compounds and provide niche and nutrient support for these microorganisms, with a symbiotic network formed. These microorganisms grow in the rhizosphere, phyllosphere, and endophytic tissues of plants and significantly improve the growth performance and medicinal component accumulation of medicinal plants by promoting nutrient uptake, enhancing disease resistance, and regulating the synthesis of secondary metabolites. Microorganisms are also widely used in the ecological planting of medicinal plants, and the growth conditions of medicinal plants are optimized by simulating the microbial effects in the natural environment. The interactions between microorganisms and medicinal plants not only significantly improve the yield and quality of medicinal plants but also enhance their geoherbalism, which is in line with the concept of green agriculture and eco-friendly development. This study reviewed the research results on the interactions between medicinal plants and microorganisms in recent years and focused on the analysis of the great potential of microorganisms in optimizing the growth environment of medicinal plants, regulating the accumulation of secondary metabolites, inducing systemic resistance, and promoting the ecological planting of medicinal plants. It provides a scientific basis for the research on the interactions between medicinal plants and microorganisms, the research and development of microbial agents, and the application of microorganisms in the ecological planting of medicinal plants and is of great significance for the quality improvement of medicinal plants and the green and sustainable development of TCM resources.
Plants, Medicinal/metabolism* ; Bacteria/genetics* ; Symbiosis

Objective： The aim of this study is to explore the predictive value of biparametric magnetic resonance imaging(bpMRI)for pelvic lymph node metastasis in prostate cancer patients with PSA≤20 μg/L and establish a nomogram. Methods： The imaging data and clinical data of 363 patients undergoing radical prostatectomy and pelvic lymph node dissection in the First Affiliated Hospital of Nanjing Medical University from July 2018 to December 2023 were retrospectively analyzed. Univariate analysis and multivariate logistic regression were used to screen independent risk factors for pelvic lymph node metastasis in prostate cancer, and a nomogram of the clinical prediction model was established. Calibration curves were drawn to evaluate the accuracy of the model. Results： Multivariate logistic regression analysis showed extrocapusular extension (OR=8.08,95%CI=2.62－24.97, P<0.01), enlargement of pelvic lymph nodes (OR=4.45,95%CI=1.16－17.11,P=0.030), and biopsy ISUP grade(OR=1.97,95%CI=1.12－3.46, P=0.018)were independent risk factors for pelvic lymph node metastasis. The C-index of the prediction model was 0.834, which indicated that the model had a good prediction ability. The actual value of the model calibration curve and the prediction probability of the model fitted well, indicating that the model had a good accuracy. Further analysis of DCA curve showed that the model had good clinical application value when the risk threshold ranged from 0.05 to 0.70.Conclusion： For prostate cancer patients with PSA≤20 μg/L, bpMRI has a good predictive value for the pelvic lymph node metastasis of prostate cancer with extrocapusular extension, enlargement of pelvic lymph nodes and ISUP grade≥4.
Humans ; Male ; Prostatic Neoplasms/diagnostic imaging* ; Lymphatic Metastasis ; Retrospective Studies ; Nomograms ; Prostate-Specific Antigen/blood* ; Lymph Nodes/pathology* ; Pelvis ; Predictive Value of Tests ; Prostatectomy ; Lymph Node Excision ; Risk Factors ; Magnetic Resonance Imaging ; Logistic Models ; Middle Aged ; Aged

OBJECTIVE: Myocardial inflammation during myocardial infarction (MI) could be inhibited by regulating arachidonic acid (AA) metabolism. Recent studies demonstrated that Sini Decoction (SND) was identified to be an effective prescription for treating heart failure (HF) caused by MI. But the anti-inflammatory mechanism of SND remained unclear. The work was designed to investigate the anti-inflammatory mechanism of SND through the AA metabolism pathway in vitro and in vivo experiments. METHODS: An inflammatory injury model of H9c2 cells was established by lipopolysaccharide (LPS)-stimulated macrophage-conditioned medium (CM). The MI model was built by the ligation of left anterior descending (LAD) branch of coronary artery in rat. Meanwhile, the rats were divided into five groups: sham group, MI group, MI + Celecoxib group, MI + low-dose SND group (SND-L) and MI + high-dose SND group (SND-H). Cardiac function, histopathological changes and serum cytokines were examined four weeks later. Western blot analysis was conducted to verify the key enzymes levels in the AA metabolic pathway, including phospholipase A2 (PLA2), cyclooxygenases (COXs) and lipoxygenases (LOXs). RESULTS: These in vivo results demonstrated that SND could improve the cardiac function and pathological changes of rats with MI, and regulate the key inflammatory molecules in the AA metabolism pathway, including sPLA2, COX-1, COX-2, 5-LOX and 15-LOX. In vitro, SND could decrease the release of pro-inflammatory cytokines including TNF-α and IL-6 and inhibit cell apoptosis in CM-induced H9c2 cells. Moreover, SND could protect H9c2 cells from the damage of CM by regulating nuclear factor kappa-B (NF-κB) signal pathway and the expression of COX-2. CONCLUSION SND may be a drug candidate for anti-inflammatory treatment during MI by regulating the multiple targets in the AA metabolism pathway.

OBJECTIVE: Exposure to polycyclic aromatic hydrocarbons (PAHs) or metal(loid)s individually has been associated with neural tube defects (NTDs). However, the impacts of PAH and metal(loid) co-exposure and potential interaction effects on NTD risk remain unclear. We conducted a case-control study in China among population with a high prevalence of NTDs to investigate the combined effects of PAH and metal(loid) exposures on the risk of NTD. METHODS: Cases included 80 women who gave birth to offspring with NTDs, whereas controls were 50 women who delivered infants with no congenital malformations. We analyzed the levels of placental PAHs using gas chromatography and mass spectrometry, PAH-DNA adducts with ³²P-post-labeling method, and metal(loid)s with an inductively coupled plasma mass spectrometer. Unconditional logistic regression was employed to estimate the associations between individual exposures and NTDs. Least absolute shrinkage and selection operator (LASSO) penalized regression models were used to select a subset of exposures, while additive interaction models were used to identify interaction effects. RESULTS: In the single-exposure models, we found that eight PAHs, PAH-DNA adducts, and 28 metal(loid)s were associated with NTDs. Pyrene, selenium, molybdenum, cadmium, uranium, and rubidium were selected through LASSO regression and were statistically associated with NTDs in the multiple-exposure models. Women with high levels of pyrene and molybdenum or pyrene and selenium exhibited significantly increased risk of having offspring with NTDs, indicating that these combinations may have synergistic effects on the risk of NTDs. CONCLUSION Our findings suggest that individual PAHs and metal(loid)s, as well as their interactions, may be associated with the risk of NTDs, which warrants further investigation.
Humans ; Neural Tube Defects/chemically induced* ; Polycyclic Aromatic Hydrocarbons/adverse effects* ; Female ; Case-Control Studies ; China/epidemiology* ; Adult ; Pregnancy ; Environmental Pollutants ; Maternal Exposure/adverse effects* ; Metals/toxicity* ; Young Adult ; Risk Factors

OBJECTIVE: To reveal the effects and potential mechanisms by which synaptic vesicle glycoprotein 2A (SV2A) influences the distribution of amyloid precursor protein (APP) in the trans-Golgi network (TGN), endolysosomal system, and cell membranes and to reveal the effects of SV2A on APP amyloid degradation. METHODS: Colocalization analysis of APP with specific tagged proteins in the TGN, ensolysosomal system, and cell membrane was performed to explore the effects of SV2A on the intracellular transport of APP. APP, β-site amyloid precursor protein cleaving enzyme 1 (BACE1) expressions, and APP cleavage products levels were investigated to observe the effects of SV2A on APP amyloidogenic processing. RESULTS: APP localization was reduced in the TGN, early endosomes, late endosomes, and lysosomes, whereas it was increased in the recycling endosomes and cell membrane of SV2A-overexpressed neurons. Moreover, Arl5b (ADP-ribosylation factor 5b), a protein responsible for transporting APP from the TGN to early endosomes, was upregulated by SV2A. SV2A overexpression also decreased APP transport from the cell membrane to early endosomes by downregulating APP endocytosis. In addition, products of APP amyloid degradation, including sAPPβ, Aβ _1-42, and Aβ _1-40, were decreased in SV2A-overexpressed cells. CONCLUSION These results demonstrated that SV2A promotes APP transport from the TGN to early endosomes by upregulating Arl5b and promoting APP transport from early endosomes to recycling endosomes-cell membrane pathway, which slows APP amyloid degradation.
Amyloid beta-Protein Precursor/genetics* ; Membrane Glycoproteins/genetics* ; Animals ; Protein Transport ; Nerve Tissue Proteins/genetics* ; Humans ; Mice ; Endosomes/metabolism* ; trans-Golgi Network/metabolism*

Humans ; Vascular Stiffness ; Coal Mining ; Occupational Diseases/epidemiology* ; Risk Factors ; Coal ; Miners

Objective:To analyze the correlation among human retinol binding protein 4(RBP4),miR-1-3p,re-combinant human vitamin D binding protein(VDBP)and severity of ST-segment elevation myocardial infarction(STEMI)based on SYNTAX score and their diagnostic value for it.Methods:A total of 162 STEMI patients admit-ted to our hospital for treatment from January 2020 to June 2021 were selected as STEMI group,and another 67 healthy subjects who received physical examination in our hospital simultaneously were selected as healthy control group.According to SYNTAX score,STEMI group was further divided into low risk group(n=68,＜22 points),medium risk group(n=53,23 points≤SYNTAX score≤32 points)and high risk group(n=41,＞33 points).Lev-els of RBP4,VDBP and miR-1-3p expression were compared among above groups.Diagnostic value of single and combined detection of above indexes for severity of STEMI were analyzed.Correlation between SYNTAX score and above indexes were analyzed in STEMI patients.Results:Compared with healthy control group,there were signifi-cant rise in levels of RBP4[(47.18±5.24)μg/L vs.(81.40±6.11)μg/L]and VDBP[(2.36±0.75)μg/ml vs.(3.55±1.04)μg/ml],and significant reduction in miR-1-3p expression[(1.18±0.13)vs.(0.72±0.07)]in STEMI group,P＜0.001 all.Compared with low risk group,there were significant rise in levels of RBP4[(69.97±2.98)μg/L vs.(85.23±3.76)μg/L vs.(95.41±3.16)μg/L]and VDBP[(3.19±0.50)μg/ml vs.(3.55±0.52)μg/ml vs.(4.16±0.64)μg/ml],and significant reduction in miR-1-3p expression[(0.80±0.05)vs.(0.70±0.04)vs.(0.61±0.03)]in medium risk group and high risk group,and levels of RBP4 and VDBP in high risk group were significantly higher than those of medium risk group,miR-1-3p expression in high risk group was significantly lower than that of medium risk group(P＜0.001 all).ROC analysis indicated that area under the curve(AUC)of combined detection of above indexes was 0.892,which was significantly higher than each single detection(Z=4.787,3.078,2.495,P＜0.05 or＜0.01).Spearman correlation analysis indicated that STEMI severity was significant positively correlated with levels of RBP4 and VDBP(r=0.849,0.776,P＜0.001 both),and significant inversely correla-ted with miR-1-3p expression(r=-0.790,P＜0.001).Conclusion:Levels of RBP4 and VDBP,and miR-1-3p ex-pression can reflect severity of STEMI patients.Triple combined detection possesses high diagnostic efficacy for disease se-verity,which can be used as clinical important indexes for assessing patient's condition.

Objective:To explore the predictive value of admission serum homocysteine levels and quantitative electroencephalogram (qEEG) indicators for adverse outcomes in patients with cerebral hemorrhage.Methods:A retrospective study was conducted on 89 patients, who were collected as the study objects with hemorrhagic stroke treated in the neurology intensive care unit at Kailuan General Hospital from January 2017 to December 2022. Patients were categorized into two groups based on modified Rankin Scale (mRS) scores at discharge: a good prognosis group (mRS≤2) and a poor prognosis group (mRS 3-6). Clinical data and qEEG monitoring of various brain regions were collected. The impact factors of hemorrhagic prognosis were analyzed using multifactorial logistic regression. ROC curve analysis was performed to assess the predictive value of qEEG and admission homocysteine levels for adverse outcomes in hemorrhagic stroke patients.Results:(1) The age of the poor prognosis group was higher than that of the good prognosis group((66.51+13.64) to (60.53+11.69), t=2.15, P=0.034) and admission serum homocysteine levels were significantly higher in the poor prognosis group than in the good prognosis group (17.28(15.52,24.72)mmol/L to 14.50(10.28,16.00)mmol/L, Z=4.14, P<0.001). (2) In the poor prognosis group, power values of δ brain waves in leads Fp1-2, F4, C4, P4, F8, and T4 were higher than those in the good prognosis group (87.99(41.57,196.69) to 50.67(26.64,54.75), Z=2.76, P=0.006); (79.17(40.71,200.00) to 45.06(20.22,61.00), Z=2.10, P=0.036); (72.64(34.97,219.78) to 34.42(19.81,63.4), Z=2.03, P=0.043); (65.06(33.36,177.45) to 28.12(15.88,63.36), Z=2.08, P=0.038); (52.92(25.64,187.91) to 23.61(11.67,43.26), Z=2.21, P=0.027); (66.67(32.56,180.76) to 36.31(17.2,53.78), Z=2.46, P=0.014); (57.30(25.24,127.04) to 29.57(11.91,41.89), Z=2.26, P=0.024). Power values of θ brain waves in leads Fp1-2, F3, F4, C3, C4, P3-4, O1, F7-8, and T3-4 were higher in the poor prognosis group(77.45(47.63,138.72)比35.88(20.92,44.81), Z=3.50, P<0.001); (77.05(35.16,120.22) to 38.74(19.86,58.09), Z=2.27, P=0.023); (85.24(52.53,147.90) to 35.42(14.7,52.59), Z=2.61, P=0.009); (75.81(37.90,124.97) to 36.85(17.92,55.43), Z=2.30, P=0.021); (72.00(43.92,123.54) to 28.37(14.02,51.9), Z=2.22, P=0.027); (67.08(32.01,104.05) to 31.32(17.98,45.28), Z=2.10, P=0.035); (55.33(32.29,94.30) to 25.64(11.87,34.01), Z=2.24, P=0.025); (48.84(20.64,96.28) to 19.85(9.83,28.58), Z=2.30, P=0.022);(48.46(25.06,81.78) to 23.95(8.80,29.16), Z=2.51, P=0.012); (64.46(39.38,112.44) to 26.85(15.74,39.58), Z=2.80, P=0.005); (65.68(31.78,102.00) to 31.09(15.98,46.96), Z=2.38, P=0.017); (45.26(28.34,73.14) to 21.45(10.57,36.59), Z=2.04, P=0.042); (43.50(22.58,78.67) to 25.45(11.91,32.26), Z=2.22, P=0.027). Power values of slow-wave index in leads Fp1-2, F3-4, C3-4, P4, F7-8, and T4, as well as the overall brain average, were higher in the poor prognosis group (6.64(2.98,10.42) to 3.65(2.31,4.30), Z=2.65, P=0.01); (6.53(3.96,11.65) to 3.53(2.56,4.51), Z=2.30, P=0.022); (7.38(4.62,13.12) to 3.83(1.70,4.71), Z=2.38, P=0.017); (5.88(4.02,12.15) to 3.18(2.21,4.46), Z=2.29, P=0.022); (6.13(3.83,11.22) to 2.97(1.53,4.58), Z=2.01, P=0.044); (6.07(3.53,9.39) to 2.74(2.00,3.81), Z=2.40, P=0.016);(4.11(2.51,9.23) to 2.18(1.37,2.82), Z=2.25, P=0.024); (5.71(3.81,10.44) to 3.22(1.86,4.04), Z=2.28, P=0.023); (6.00(3.65,10.37) to 3.04(2.00,4.00), Z=2.39, P=0.017); (4.08(2.56,8.33) to 2.08(1.60,3.14), Z=2.50, P=0.013), with significant statistical differences noted (5.45(3.31,10.08) to 3.17(2.02,4.88), Z=3.62, P=0.005). (3) Logistic regression results showed that admission homocysteine levels ( OR 1.311,95% CI 1.008-1.705, P=0.044), admission NIHSS scores ( OR 1.588,95% CI 1.074-2.349, P=0.020), and overall brain average slow-wave index were influencing factors for poor prognosis in cerebral hemorrhage ( OR 8.596,95% CI 1.088-67.889, P=0.041). (4) ROC curve analysis revealed that the AUC for predicting adverse outcomes in cerebral hemorrhage was 0.768 (95% CI (0.665, 0.872)) for admission homocysteine levels, 0.743 (95% CI (0.634, 0.852)) for the overall brain average slow-wave index, and 0.896 (95% CI (0.827, 0.965)) for admission NIHSS. The cutoff values were 15.67, 3.62, and 8.5, respectively. Sensitivity was 77.8%, 71.1%, and 68.9%, and specificity was 59.4%, 68.7%, and 100%, respectively. The Youden indices were 0.372, 0.398, and 0.689. Conclusion:In the acute phase of cerebral hemorrhage, electroencephalographic physiological changes manifest shows an increase in the δ, θ, and slow-wave index throughout the entire brain. Higher admission homocysteine levels suggest a worse prognosis in patients with cerebral hemorrhage. Admission homocysteine levels and overall brain average slow-wave index have certain predictive value for adverse outcomes in acute cerebral hemorrhage.