OBJECTIVE To evaluate the efficacy and safety of amoxicillin combined with proton pump inhibitor （PPI） or potassium-competitive acid blocker （P-CAB） for Helicobacter pylori （Hp） eradication. METHODS Randomized controlled trial （RCTs） on amoxicillin combined with PPI or P-CAB for Hp eradication were retrieved from PubMed， Embase， the Cochrane Library， Web of Science， CNKI， Wanfang， and VIP data. The search time frame was from database inception to September 5， 2025. After literature screening， data extraction， and quality assessment， a network meta-analysis was performed using Stata 17.0 software. RESULTS A total of 12 RCTs involving 5 515 patients were included， encompassing 8 therapeutic regimens： PPI combined with high-dose amoxicillin for 14 days （TR1）， PPI combined with low-dose amoxicillin for 14 days （TR2）， P-CAB combined with high-dose amoxicillin for 7 days （TR3）， P-CAB combined with high-dose amoxicillin for 14 days （TR4）， P-CAB combined with high-dose amoxicillin for 10 days （TR5）， P-CAB combined with low-dose amoxicillin for 7 days （TR6）， P-CAB combined with low-dose amoxicillin for 14 days （TR7）， and P-CAB combined with low-dose amoxicillin for 10 days （TR8）. The network meta-analysis results showed that， in terms of intention-to-treat Hp eradication rates， the eradication rates of TR5 and TR4 were significantly higher than those of TR3， TR8， TR6 and TR1 （ P ＜0.05）. The surface under the cumulative ranking curve （SUCRA） values from highest to lowest were： TR4 （89.7%）＞TR5 （82.3%）＞TR7 （71.5%）＞ TR2 （48.6%）＞TR1 （43.9%）＞TR8 （28.7%）＞TR3 （22.7%）＞TR6 （12.6%）. Regarding safety， the incidence of adverse reactions in TR3 and TR5 was significantly lower than that in TR1 （ P ＜0.05）. The SUCRA values from highest to lowest were： TR1 （91.3%）＞TR4 （79.8%）＞TR5 （55.0%）＞TR7 （50.9%）＞TR8 （41.3%）＞TR2 （36.4%）＞TR3 （27.6%） ＞TR6 （17.7%）. CONCLUSIONS Although the regimen of P-CAB combined with high-dose amoxicillin for 14 days demonstrates the best efficacy， the combination of P-CAB with high-dose amoxicillin for 10 days exhibits a better balanced profile in terms of both efficacy and safety.

BACKGROUND:Fibromyalgia syndrome,as a common rheumatic disease,is related to central sensitization and immune abnormalities.However,the specific mechanism has not been elucidated,and there is a lack of specific diagnostic markers.Exploring the possible pathogenesis of this disease has important clinical significance. OBJECTIVE:To screen the potential diagnostic marker genes of fibromyalgia syndrome and analyze the possible immune infiltration characteristics based on bioinformatics methods,such as weighted gene co-expression network analysis(WGCNA),and machine learning. METHODS:Gene expression profiles in peripheral serum of fibromyalgia syndrome patients and healthy controls were obtained from the gene expression omnibus(GEO)database.The differentially co-expressed genes were screened in the expression profile by differential analysis and WGCNA analysis.Least absolute shrinkage and selection operator(LASSO)and support vector machine-recursive feature elimination(SVM-RFE)machine learning algorithm were further used to identify hub biomarkers,and draw receiver operating characteristic curve(ROC)to evaluate the accuracy of diagnosing fibromyalgia syndrome.Finally,single sample gene set enrichment analysis(ssGSEA)and gene set enrichment analysis(GSEA)were used to evaluate the immune cell infiltration and pathway enrichment in patients with fibromyalgia syndrome. RESULTS AND CONCLUSION:Eight down-regulated differentially expressed genes(DEGs)were obtained after differential analysis of the GSE67311 dataset according to the conditions of log2|(FC)|>0 and P<0.05.After WGCNA analysis,497 genes were included in the module(MEdarkviolet)with the highest positive correlation(r=0.22,P=0.04),and 19 genes were included in the module(MEsalmon2)with the highest negative correlation(r=-0.41,P=6×10-5).After intersecting DEGs and the module genes of WGCNA,seven genes were obtained.Four genes were screened out by LASSO regression algorithm and five genes were screened out by SVM-RFE machine learning algorithm.After the intersection of the two,three core genes were identified,which were germinal center associated signaling and motility like,integrin beta-8,and carboxypeptidase A3.The areas under the ROC curve of the three core genes were 0.744,0.739,and 0.734,respectively,indicating that they have good diagnostic value and can be used as biomarkers for fibromyalgia syndrome.The results of immune infiltration analysis showed that memory B cells,CD56 bright NK cells,and mast cells were significantly down-regulated in patients with fibromyalgia syndrome compared with the control group(P<0.05),and were significantly positively correlated with the above three biomarkers(P<0.05).The enrichment analysis suggested that there were nine fibromyalgia syndrome enrichment pathways,mainly related to olfactory transduction pathway,neuroactive ligand-receptor interaction,and infection pathway.The above results showed that the occurrence and development of fibromyalgia syndrome are related to the involvement of multiple genes,abnormal immune regulation,and multiple pathways imbalance.However,the interactions between these genes and immune cells,as well as their relationships with various pathways need to be further investigated.

OBJECTIVE To analyze the occurrence characteristics and risk factors of adverse drug reactions （ADR） of gemcitabine for injection in national centralized volume-based procurement （hereinafter referred to as “centralized procurement”）， and provide reference for clinical safe drug use. METHODS A retrospective study was conducted to collect the relevant case reports of gemcitabine for injection reported to the National Adverse Drug Reaction Monitoring System by Inner Mongolia Autonomous Region from January 2022 to December 2023； basic information of patients， drug use status， patient outcomes， rational drug use and other information were collected， and the occurrence characteristics of ADRs with leukopenia， myelosuppression， neutropenia， thrombocytopenia and liver dysfunction were analyzed. Univariate analysis and multivariate Logistic regression were used to analyze the correlation of gender， age， combination of antitumor drugs， original malignant tumor and drug dose with ADR. RESULTS A total of 315 cases reports （315 patients） of gemcitabine-induced ADR were included in this study， with a male-to-female ratio of 1.42∶1 and age of （61.17±9.13） years. The primary malignant tumor was pancreatic cancer （73 cases， 23.17%）. Leukopenia， myelosuppression and nausea were the most common ADR， followed by neutropenia， thrombocytopenia， liver dysfunction and so on. The severity grade of ADR was mainly 1-2， and the outcome of most ADR was good. Multivariate Logistic regression analysis showed that combination of antitumor drugs was a risk factor for myelosuppression and neutropenia （RR＝2.154， 95%CI： 1.218- 3.807， P＝0.008； RR＝3.099， 95%CI： 1.240-7.744， P＝0.016）； gender （female） was a risk factor for leukopenia and liver dysfunction （RR＝0.508， 95%CI： 0.302-0.853， P＝0.010； RR＝0.301， 95%CI： 0.102-0.887， P＝0.029）. In terms of drug use rationality， there were 143 cases （45.40%） of drug 126.com use in accordance with the indications of the label， and 172 cases （54.60%） of off-label drug use. Among them， the primary malignant tumors were bladder cancer， bile duct cancer and ovarian cancer， which ranked the top three off-label drug use. CONCLUSIONS The ADR caused by gemcitabine in Inner Mongolia is mainly in the blood and digestive systems. The severity of ADRs is mainly classified as 1-2 levels， and most ADRs have good outcomes. Gender （female） and combination medication are risk factors for gemcitabine-induced ADR. Appropriate chemotherapy regimen should be selected according to the patient’s condition and physical condition， and ADR monitoring in blood and digestive systems should be strengthened during medication of gemcitabine.

Objective To establish and validate the diagnostic model of ferroptosis genes for acute myocardial infarction (AMI) based on bioinformatics. Methods Five AMI gene expression data were obtained from Gene Expression Omnibus (GEO), namely GSE66360, GSE48060, GSE60993, GSE83500, GSE34198. Among them, GSE66360 was used as the training set to perform differential analysis, and intersection of differential genes and ferroptosis genes was taken to obtain differentially expressed ferroptosis genes in AMI. GO and KEGG enrichment analyses were performed using Metascape website. Subsequently, random forest (RF) algorithm was used to screen out key genes with high classification performance according to the Keeny coefficient score, and artificial neural network (ANN) diagnostic model of AMI ferroptosis feature gene was constructed by model group GSE83500. The area under the receiver operating characteristic curve (AUC) of 10-fold cross-validation was used to evaluate the performance and generalization ability of the model, and 3 external independent datasets were used to verify the diagnostic performance of this model. The single sample gene set enrichment analysis was used to explore the difference in immune cell infiltration between infarcted myocardium and normal myocardium after AMI. In addition, correlation analysis between immune cells and key genes was also conducted. Finally, potential drugs that would prevent and treat AMI by regulating ferroptosis were screened out from the Coremin Medical platform. Results A total of 16 differentially expressed ferroptosis genes were obtained in the training set, GO enrichment analysis showed that they mainly participated in biological functions such as cellular response to biological stimuli and chemical stress, and regulation of interleukin 17. KEGG enrichment analysis showed that these genes were significantly enriched in NOD-like receptor signaling pathway, programmed cell necrosis, Leishmaniasis and other pathways. Four genes with good classification performance were screened out using RF algorithm, namely EPAS1, SLC7A5, FTH1, and ZFP36. The results of 10-fold cross-validation showed that the minimum AUC value was 0.746, the maximum value was 0.906, and the average value was 0.805. The AUC of the ANN model was 0.859, and the AUC values of the three independent validation sets were 0.763 (GSE48060), 0.673 (GSE60993), 0.698 (GSE34198). Immune cell infiltration found that macrophages, mast cells and monocytes were significantly active after AMI. Correlation analysis found that there were positive correlations between 4 key genes and activated dendritic cells, eosinophils and γδT cells. A total of 20 potential western medicines were predicted which could prevent and treat AMI by regulating ferroptosis, and the predicted potential Chinese medicine was mainly heat-clearing and detoxifying and blood-activating and removing blood stasis drugs. Conclusion The identified AMI ferroptosis genes by bioinformatics method have certain diagnostic significance, which provides a reference for disease diagnosis and treatment.

Standardized reporting is a crucial factor affecting the use of patient guidelines （PGs）， particularly in the reporting and presentation of recommendations. This paper introduced the current status of PG reporting， including the research on PG content and presentation formats， and provided comprehensive recommendations for PG reporting from aspects such as overall framework， recommendations， presentation format， and readability. First， the presentation of PG recommendations should include clearly defined clinical questions， recommendations and their rationale， and guidance on how patients should implement the interventions； for specific content in the PG， such as level of evidence， level of recommendation， it is recommended to explain in text the reasons for giving different levels of recommendation， i.e.， to present the logic behind giving the level of recommendation to the patient； additional information needed in the recommendation framework should be supplemented by tracing references or authoritative textbooks and literature that support the recommendations. Subsequently， the PG text should be written based on the Reporting Checklist for Public Versions of Guidelines （RIGHT-PVG） reporting framework. Finally， to enhance readability and comprehension， it is recommended to refer to the Patient Education Materials Assessment Tool （PEMAT） for translating PG content. To enhance the readability of PGs， it is suggested to present the PG content in a persona-lized and layered manner.

Since the concept of patient versions of guidelines （PVGs） was introduced into China， several PVGs have been published in China， but we found that there is a big difference between the concept of PVG at home and abroad， and the reason for this difference has not been reasonably explained， which has led to ambiguity and even misapplication of the PVG concept by guideline developers. By analyzing the background and purpose of PVGs， and the understanding of the PVG concept by domestic scholars， we proposed the term patient guidelines （PGs）. This refers to guidelines developed under the principles of evidence-based medicine， centered on health issues that concern patients， and based on the best available evidence， intended for patient use. Except for the general attribute of providing information or education， which is typical of common health education materials， PGs also provide recommendations and assist in decision-making， so PGs include both the patient versions of guidelines （PVG） as defined by the Guidelines International Network （GIN） and "patient-directed guidelines"， i.e. clinical practice guidelines resulting from the adaptation or reformulation of recommendations through clinical practice guidelines.

At present， the process and methodology of patient guidelines （PGs） development varies greatly and lacks systematic and standardised guidance. In addition to the interviews with PG developers， we have sorted out the relevant methodology for the adaptation and development of existing clinical practice guideline recommendations and facilitated expert deliberations to achieve a consensus， so as to finally put forward a proposal for guidance on the process and methodology for the development of PGs. The development of PGs can be divided into the preparation stage， the construction stage， and the completion stage in general， but the specific steps vary according to the different modes of development of PGs. The development process of Model 1 is basically the same as the patient version of the guideline development process provided by the International Guidelines Network， i.e.， team formation， screening of recommendations， guideline drafing， user testing and feedback， approval and dissemination. The developer should also first determine the need for and scope of translating the clinical practice guideline into a patient version during the preparation phase. Model 2 adds user experience and feedback to the conventional clinical practice guideline development process （forming a team， determining the scope of the PG， searching， evaluating and integrating evidence， forming recommendations， writing the guideline， and expert review）. Based on the different models， we sort out the process and methods of PG development and introduce the specific methods of PG development， including how to identify the clinical problem and how to form recommendations based on the existing clinical practice guidelines， with a view to providing reference for guideline developers and related researchers.

Objective:To investigate and summarize the chest CT imaging features of patients with novel coronavirus pneumonia (COVID-19), bacterial pneumonia and other viral pneumonia.Methods:Chest CT data of 102 patients with pulmonary infection due to different etiologies were retrospectively analyzed, including 36 patients with COVID-19 admitted to Hainan Provincial People's Hospital and the Second Affiliated Hospital of Hainan Medical University from December 2019 to March 2020, 16 patients with other viral pneumonia admitted to Hainan Provincial People's Hospital from January 2018 to February 2020, and 50 patients with bacterial pneumonia admitted to Haikou Affiliated Hospital of Central South University Xiangya School of Medicine from April 2018 to May 2020. Two senior radiologists and two senior intensive care physicians were participated to evaluated the extent of lesions involvement and imaging features of the first chest CT after the onset of the disease.Results:Bilateral pulmonary lesions were more common in patients with COVID-19 and other viral pneumonia, and the incidence was significantly higher than that of bacterial pneumonia (91.6%, 75.0% vs. 26.0%, P < 0.05). Compared with other viral pneumonia and COVID-19, bacterial pneumonia was mainly characterized by single-lung and multi-lobed lesion (62.0% vs. 18.8%, 5.6%, P < 0.05), accompanied by pleural effusion and lymph node enlargement. The proportion of ground-glass opacity in the lung tissues of patients with COVID-19 was 97.2%, that of patients with other viral pneumonia was 56.2%, and that of patients with bacterial pneumonia was only 2.0% ( P < 0.05). The incidence rate of lung tissue consolidation (25.0%, 12.5%), air bronchial sign (13.9%, 6.2%) and pleural effusion (16.7%, 37.5%) in patients with COVID-19 and other viral pneumonia were significantly lower than those in patients with bacterial pneumonia (62.0%, 32.0%, 60.0%, all P < 0.05), paving stone sign (22.2%, 37.5%), fine mesh sign (38.9%, 31.2%), halo sign(11.1%, 25.0%), ground-glass opacity with interlobular septal thickening (30.6%, 37.5%), bilateral patchy pattern/rope shadow (80.6%, 50.0%) etc. were significantly higher than those of bacterial pneumonia (2.0%, 4.0%, 2.0%, 0%, 22.0%, all P < 0.05). The incidence of local patchy shadow in patients with COVID-19 was only 8.3%, significantly lower than that in patients with other viral pneumonia and bacterial pneumonia (8.3% vs. 68.8%, 50.0%, P < 0.05). There was no significant difference in the incidence of peripheral vascular shadow thickening in patients with COVID-19, other viral pneumonia and bacterial pneumonia (27.8%, 12.5%, 30.0%, P > 0.05). Conclusions:The probability of ground-glass opacity, paving stone and grid shadow in chest CT of patients with COVID-19 was significantly higher than those of bacterial pneumonia, and it was more common in the lower lungs and lateral dorsal segment. In other patients with viral pneumonia, ground-glass opacity was distributed in both upper and lower lungs. Bacterial pneumonia is usually characterized by single lung consolidation, distributed in lobules or large lobes and accompanied by pleural effusion.

Aluminum is a light metal which is rich in the earth's crust and widely used. Recently, the adverse health effects of environmental and occupational aluminum exposure on human have attracted more and more attention. Aluminum exposure has toxic effects on the central nervous system and is believed to be closely related to the development and progression of Alzheimer's disease. The neurotoxic mechanism of aluminum is complex, especially the role of regulated cell death (RCD) in aluminum-induced neuronal death remains to be further studied. RCD refers to all modes of cell death regulated by multiple intracellular signal transduction pathways under physiological and pathological conditions, including apoptosis, necroptosis, autophagy, pyroptosis, and ferroptosis. This review summarized the morphological characteristics and mechanisms of each RCD mode in the process of aluminum-induced neuronal death, and discussed the relationship and transformation between different RCD modes, providing a new scientific basis for future studies on the treatment and intervention of neurotoxicity induced by aluminum exposure.

Objective To rapidly explore the chemical components of Xiaotan Tongfu formula, and to provide scientific basis for the basic research and clinical treatment of the formula. Methods Analysis was performed on an Agilent 1290 ultra-performance liquid chromatography system coupled with an Agilent 6530 accurate quality Q-TOF/MS system, by using a Waters ACQUITY UPLC BEH C₁₈ column (2.1 mm × 100 mm, 1.7 μm), with a gradient elution applying 0.1% aqueous formic acid solution and acetonitrile as a mobile phase. The flow rate was 0.3 ml/min. The column temperature was 30°C. The injection volume was 1 μl, and the detection wavelength was 254 nm. Mass spectrometry (MS) data were collected in both positive and negative ESI ion modes. Components in the formula were identified by using the in-house compound database, and comparing the retention time (tR), MS1 and MS2 data with the standard compounds, and the online compound MS database. Results A total of 55 compounds were identified from Coptis coptidis, Pseudomonas solani, Rhubarb, Araceae artemisiae and Pinellia chinensis. Conclusion The established UHPLC-Q-TOF/MS method could systematically and accurately identify the chemical components from Xiaotan Tongfu formula, and provided a reference for the quality marker selection and the research on the active ingredient.