ObjectiveTo explore the effects of angle and original moisture content on the moisture distribution， migration and contents of effective components in the drying process of sliced Salviae Miltiorrhizae Radix et Rhizoma（SMRR）. MethodsSet the slicing angles of SMRR at 30°， 45°， and 90°. Cut the fresh samples， 1/3 dehydrated samples， and 2/3 dehydrated samples， dry them in an oven at 40 ℃ and take samples at the set time points. Low-field nuclear magnetic resonance（LF-NMR） and magnetic resonance imaging（MRI） were used to analyze the changes in transverse relaxation time（T₂） of SMRR samples in 9 treatment groups at specific times， as well as the distribution and migration of water in the samples. The contents of tanshinone Ⅱ_A， tanshinone Ⅰ， cryptotanshinone， and salvianolic acid B in samples from 9 different treatment groups were determined by high performance liquid chromatography（HPLC）， and the best processing technology of SMRR was screened by combining with One-way ANOVA， Duncan multiple comparison and principal component analysis（PCA）. ResultsThe moisture content of dry basis of SMRR in each treatment group decreased with the extension of drying time. The drying rate of fresh cut group decreased slowly at first， while the drying rate of water loss group showed a trend of increasing at first and then decreasing. The internal water of SMRR could be divided into three states， including bound water， non flowing water and free water. During the drying process， the water migration law showed that the free water of fresh cut group disappeared after drying for 12 h， the content of bound water gradually decreased， and the overall fluidity deteriorated. In the water loss group， part of the free water was transformed into more cohesive and non flowing water after drying for 3 h， and the three kinds of water basically disappeared after drying for 12 h. The MRI results showed that the entire dehydration process slowly moved from the outer side to the center， and the internal water eventually dissipated. In terms of the contents of active ingredients， the order of the effect of slicing angle on the total content of active ingredients in SMRR was 30°>45°>90°. The content of tanshinones was ranked as 1/3 dehydrated group>2/3 dehydrated group>fresh cut group， and the content of salvianolic acid B was ranked as 1/3 dehydrated group>fresh cut group>2/3 dehydrated group. Combined with the results of PCA and comprehensive scoring results， the overall level of effective component content in SMRR was the highest when cut at 30° after 1/3 of water loss. ConclusionAfter comprehensive evaluation， SMRR can be sliced at 30° after 1/3 of water loss. It is not only easy to cut， but also the surface and cross-sectional colors remain basically unchanged after drying， which is similar to the color under traditional processing， and the effective ingredients are preserved the highest. This study can provide a basis for the optimization of processing technology of SMRR.

A 71-year-old male presented with esophageal cancer and severe aortic valve regurgitation. Treatment strategies for such patients are controversial. Considering the risks of cardiopulmonary bypass and potential esophageal cancer metastasis, we successfully performed transcatheter aortic valve implantation and minimally invasive three-incision thoracolaparoscopy combined with radical resection of esophageal cancer (McKeown) simultaneously in the elderly patient who did not require neoadjuvant treatment. This dual minimally invasive procedure took 6 hours and the patient recovered smoothly without any surgical complications.

Objective To analyze the influencing factors of malaria infection of labor service exported to overseas in Langfang City, in order to establish a visualization tool to assist clinicians in predicting the risk of malaria. Methods A total of 4 774 expatriate employees of the Nibei Pipeline Project of the Pipeline Bureau from October 2021 to August 2023 were taken as the subjects, and the gender, age, overseas residence area and Knowledge of malaria controlscores of the study subjects were investigated by questionnaire survey, and the possible risk factors of malaria were screened by logistic regression model. At the same time, the nomogram prediction model was established, and the subjects were divided into the training group and the validation group at a ratio of 2:1, and the area under the curve (ROC) and the decision curve were plotted to evaluate the prediction ability and practicability of the prediction model in this study. Results Among the 4 774 study subjects, 96 cases of malaria occurred, and the detection rate was 2.01%. Junior school （OR=1.723，95% CI:1.361-2.173）， and residence in rural areas（OR=2.091，95%CI:1.760 -3.100）were risk factors (OR>1), while protective measures（OR=0.826，95% CI : 0.781 - 0.901） and high malaria education scores （OR=0.872，95% CI : 0.621 - 0.899）were protective factors.The nomogram prediction model results showed that the area under the curve of the nomogram prediction model in the training group was 0.94 (95% CI : 0.85 - 1.00), while the validation group was 0.93 (95% CI : 0.80 - 1.00). The results of the decision curve showed that when the threshold probability of the population was 0-0.9, the nomogram model was used to predict the risk of malaria occurrence with the highest net income. Conclusion The nomogram prediction model (including gender, education, region, protection and malaria education score) established and validated in this study is of great value for clinicians to screen high-risk patients with malaria.

BACKGROUND:Rheumatoid arthritis is a chronic autoimmune disease.Early diagnosis is crucial for preventing disease progression and for effective treatment.Therefore,it is of significance to investigate the diagnostic characteristics and immune cell infiltration of rheumatoid arthritis. OBJECTIVE:Based on the Gene Expression Omnibus(GEO)database,to screen potential diagnostic markers of rheumatoid arthritis using machine learning algorithms and to investigate the relationship between the diagnostic characteristics of rheumatoid arthritis and immune cell infiltration in this pathology. METHODS:The gene expression datasets of synovial tissues related to rheumatoid arthritis were obtained from the GEO database.The data sets were merged using a batch effect removal method.Differential expression analysis and functional correlation analysis of genes were performed using R software.Bioinformatics analysis and three machine learning algorithms were used for the extraction of disease signature genes,and key genes related to rheumatoid arthritis were screened.Furthermore,we analyzed immune cell infiltration on all differentially expressed genes to examine the inflammatory state of rheumatoid arthritis and investigate the correlation between their diagnostic characteristics and infiltrating immune cells. RESULTS AND CONCLUSION:In both rheumatoid arthritis and normal synovial tissues,we identified 179 differentially expressed genes,with 124 genes up-regulated and 55 genes down-regulated.Enrichment analysis revealed a significant correlation between rheumatoid arthritis and immune response.Three machine learning algorithms identified LRRC15 and MICB as potential biomarkers of rheumatoid arthritis.LRRC15(area under the curve=0.964,95%confidence interval:0.924-0.992)and MICB(area under the curve=0.961,95%confidence interval:0.923-0.990)demonstrated strong diagnostic performance on the validation dataset.The infiltration of 13 types of immune cells was altered,with macrophages being the most affected.In rheumatoid arthritis,the majority of proinflammatory pathways in immune cell function were activated.Immunocorrelation analysis revealed that LRRC15 and MICB had the strongest correlation with M1 macrophages.To conclude,this study identified LRRC15 and MICB as potential diagnostic markers for rheumatoid arthritis,with strong diagnostic performance and significant correlation with immune cell infiltration.Machine learning and bioinformatics analysis deepened the understanding of immune infiltration in rheumatoid arthritis and provided new ideas for the diagnosis and treatment of rheumatoid arthritis.

Objective: : Hemifacial spasm (HFS) is treated by a surgical procedure called microvascular decompression (MVD). However, HFS re-appearing phenomenon after surgery, presenting as early recurrence, is experienced by some patients after MVD. Dynamic susceptibility contrast (DSC) perfusion magnetic resonance imaging (MRI) and two analytical methods : receiver operating characteristic (ROC) curve and machine learning, were used to predict early recurrence in this study. Methods: : This study enrolled 60 patients who underwent MVD for HFS. They were divided into two groups : group A consisted of 32 patients who had early recurrence and group B consisted of 28 patients who had no early recurrence of HFS. DSC perfusion MRI was undergone by all patients before the surgery to obtain the several parameters. ROC curve and machine learning methods were used to predict early recurrence using these parameters. Results: : Group A had significantly lower relative cerebral blood flow than group B in most of the selected brain regions, as shown by the region-of-interest-based analysis. By combining three extraction fraction (EF) values at middle temporal gyrus, posterior cingulate, and brainstem, with age, using naive Bayes machine learning method, the best prediction model for early recurrence was obtained. This model had an area under the curve value of 0.845. Conclusion : By combining EF values with age or sex using machine learning methods, DSC perfusion MRI can be used to predict early recurrence before MVD surgery. This may help neurosurgeons to identify patients who are at risk of HFS recurrence and provide appropriate postoperative care.

Objective: : Hemifacial spasm (HFS) is treated by a surgical procedure called microvascular decompression (MVD). However, HFS re-appearing phenomenon after surgery, presenting as early recurrence, is experienced by some patients after MVD. Dynamic susceptibility contrast (DSC) perfusion magnetic resonance imaging (MRI) and two analytical methods : receiver operating characteristic (ROC) curve and machine learning, were used to predict early recurrence in this study. Methods: : This study enrolled 60 patients who underwent MVD for HFS. They were divided into two groups : group A consisted of 32 patients who had early recurrence and group B consisted of 28 patients who had no early recurrence of HFS. DSC perfusion MRI was undergone by all patients before the surgery to obtain the several parameters. ROC curve and machine learning methods were used to predict early recurrence using these parameters. Results: : Group A had significantly lower relative cerebral blood flow than group B in most of the selected brain regions, as shown by the region-of-interest-based analysis. By combining three extraction fraction (EF) values at middle temporal gyrus, posterior cingulate, and brainstem, with age, using naive Bayes machine learning method, the best prediction model for early recurrence was obtained. This model had an area under the curve value of 0.845. Conclusion : By combining EF values with age or sex using machine learning methods, DSC perfusion MRI can be used to predict early recurrence before MVD surgery. This may help neurosurgeons to identify patients who are at risk of HFS recurrence and provide appropriate postoperative care.

Staphylococcus aureus is a Gram-positive bacterium with strong pathogenicity. With the widespread use of antibiotics， its multi-drug resistance has gradually increased. Among them， methicillin-resistant S. aureus （MRSA） is one of the main pathogens of hospital and community infections. Antimicrobial peptides are short-chain peptides with good antibacterial effects and low drug resistance， which have been widely studied in recent years. This study summarizes the mechanism of action of antimicrobial peptides and related study on antimicrobial peptides against MRSA from different sources. It is found that the mechanisms of action of antimicrobial peptides include targeting bacterial cell membranes， bacterial cells， and bacterial cell walls， etc. Besides isolating antimicrobial peptides with anti-MRSA activity from animals， plants， and microorganisms， antimicrobial peptides can also be obtained through synthetic methods. Among them， GHa-derived peptides from animal sources， Ib-AMP4 from plant sources， Ph-SA from microbial sources， the synthetic peptide LLKLLLKLL-NH2， and so on， due to their effective antibacterial activity， rapid bactericidal speed， and low toxicity， are promising candidates for anti-MRSA drugs.

Objective To analyze the effect of residual cholesterol (RC) on hearing loss in noise-exposed workers. Methods A total of 3 412 workers engaged in noise operation work in an underground railway enterprise were selected as the research subjects using the judgment sampling method. Their occupational health examination data were collected to analyze the relationship between RC and hearing loss. Results The noise intensity of workplace in the underground rail enterprise was 80.0-85.0 (81.4±3.2) dB(A). The detection rate of hearing loss was 20.2% (691/3 412). The rates of abnormal total cholesterol, triacylglycerol, high density lipoprotein cholesterol (HDL-C) and low density lipoprotein cholesterol were 35.6%, 25.7%, 9.5% and 42.4%, respectively. The median and the 25th and 75th percentiles [M(P₂₅,P₇₅)] of RC level were 0.24 (0.15, 0.37) mmol/L. The levels of total cholesterol, triacylglycerol and RC of workers in hearing loss group were higher than those in normal hearing group [M(P₂₅,P₇₅): 4.91(4.37, 5.58) vs 4.84(4.30, 5.46) mmol/L, 1.29(0.91, 1.93) vs 1.16(0.82, 1.67) mmol/L, 0.26(0.16, 0.41) vs 0.24(0.14, 0.37) mmol/L, all P<0.05]. The detection rate of hearing loss in abnormal triglyceride group was higher than that in normal triglyceride group (24.8% vs 18.7%, P<0.01), and the detection rate of hearing loss in abnormal HDL-C group was higher than that in normal HDL-C group (25.0% vs 19.8%, P<0.05). The higher the serum RC level, the higher the detection rate of hearing loss (P<0.01). Multivariate logistic regression result showed that individual with older age, longer work time and higher serum RC level had higher risk of hearing abnormality (all P<0.05), and the risk of hearing abnormality was higher in patients with abnormal fasting blood glucose than patients with normal faseing blood glucose (P<0.05) after controlling for confounding factors such as gender, alcohol consumption, body mass index, and elevated blood pressure. However, abnormal triacylglycerol and HDL-C levels were not significantly related to the risk of hearing abnormality (both P>0.05). Conclusion Serum RC levels are an independent risk factor for hearing loss among noise-exposed workers exposed to noise level of 80.0-85.0 dB(A) in the workplace.

Objective To evaluate the role of distortion product otoacoustic emissions (DPOAE) testing in evaluating early hearing loss among noise-exposed workers. Methods A total of 174 noise-exposed workers in a metal shipbuilding enterprise were selected as the research subjects by the convenience sampling method. Pure tone audiometry (PTA), DPOAE and the level of noise exposure were conducted on the workers. The rank correlation analysis was used to analyze the correlation between DPOAE amplitude and PTA threshold. The multilevel model was used to analyze the effects of gender, age, noise exposure intensity, cumulative noise exposure (CNE), hearing loss classification and PTA threshold on DPOAE results. Results At the frequencies of 0.50, 1.00, 2.00, 3.00, 4.00, 6.00 and 8.00 kHz, the DPOAE amplitude was negatively correlated with the PTA threshold (rank correlation coefficients were -0.12, -0.48, -0.47, -0.18, -0.23, -0.44, -0.19, respectively, all P<0.01). At the most frequencies, DPOAE amplitude was negatively correlated with age and CNE (all P<0.05). The results of multilevel model analysis showed that there were significant differences in DPOAE amplitudes at certain frequencies across gender, age, noise intensity, CNE, and hearing loss classification (all P<0.05). Significant differences in DPOAE responses were found among different CNE and hearing loss groups (all P<0.01). Conclusion DPOAE testing can objectively reflect the hearing status of noise-exposed workers and could be considered for inclusion in routine hearing monitoring to facilitate early detection of noise-induced hearing loss.

Objective: To investigate the role and mechanism of the heat-clearing and detoxifying drug Laggerae Herba in regulating the nuclear factor-erythroid 2-related factor-2(Nrf2)/solute carrier family 7 member 11 (SLC7A11)/glutathione peroxidase 4 (GPX4) signaling pathway to inhibit ferroptosis and improve chronic heart failure induced by transverse aortic arch constriction in mice. Methods: Twenty-four male ICR mice were divided into the sham (n=6) and transverse aortic arch constriction groups (n=18) according to the random number table method. The transverse aortic arch constriction group underwent transverse aortic constriction surgery to establish models. After modeling, the transverse aortic arch constriction group was further divided into the model, captopril, and Laggerae Herba groups according to the random number table method, with six mice per group. The captopril (15 mg/kg) and Laggerae Herba groups (1.95 g/kg) received the corresponding drugs by gavage, whereas the sham operation and model groups were administered the same volume of ultrapure water by gavage once a day for four consecutive weeks. After treatment, the cardiac function indexes of mice in each group were detected using ultrasound. The heart mass and tibia length were measured to calculate the ratio of heart weight to tibia length. Hematoxylin and eosin staining were used to observe the pathological changes in myocardial tissue. Masson staining was used to observe the degree of myocardial fibrosis. Wheat germ agglutinin staining was used to observe the degree of myocardial cell hypertrophy. Prussian blue staining was used to observe the iron deposition in myocardial tissue. An enzyme-linked immunosorbent assay was used to detect the amino-terminal pro-brain natriuretic peptide (NT-proBNP) and glutathione (GSH) contents in mice serum. Colorimetry was used to detect the malondialdehyde (MDA) content in mice serum. Western blotting was used to detect the Nrf2, GPX4, SLC7A11, and ferritin heavy chain 1 (FTH1) protein expressions in mice cardiac tissue. Results: Compared with the sham group, in the model group, the ejection fraction (EF) and fractional shortening (FS) of mice decreased, the left ventricular end-systolic volume (LVESV) and left ventricular end-systolic diameter (LVESD) increased, the left ventricular anterior wall end-systolic thickness (LVAWs) and left ventricular posterior wall end-systolic thickness (LVPWs) decreased, the ratio of heart weight to tibia length increased, the myocardial tissue morphology changed, myocardial fibrosis increased, the cross-sectional area of myocardial cells increased, iron deposition appeared in myocardial tissue, the serum NT-proBNP and MDA levels increased, the GSH level decreased, and Nrf2, GPX4, SLC7A11, and FTH1 protein expressions in cardiac tissue decreased (P<0.05). Compared with the model group, in the captopril and Laggerae Herba groups, the EF, FS, and LVAWs increased, the LVESV and LVESD decreased, the ratio of heart weight to tibia length decreased, the myocardial cells were arranged neatly, the degree of myocardial fibrosis decreased, the cross-sectional area of myocardial cells decreased, the serum NT-proBNP level decreased, and the GSH level increased. Compared with the model group, the LVPWs increased, the iron deposition in myocardial tissue decreased, the serum MDA level decreased, and Nrf2, GPX4, SLC7A11, and FTH1 protein expressions in cardiac tissue increased (P<0.05) in the Laggerae Herba group. Conclusion Laggerae Herba improves the cardiac function of mice with chronic heart failure caused by transverse aortic arch constriction, reduces the pathological remodeling of the heart, and reduces fibrosis. Its mechanism may be related to Nrf2/SLC7A11/GPX4 pathway-mediated ferroptosis.