Objective To systematically analyze the characteristics of the disease burden of hypertensive heart disease (HHD) in the elderly (≥60 years) globally and in China from 1990 to 2021, and to predict its future trends from 2022 to 2040, with the aim of providing data support for optimizing comprehensive prevention and control strategies for HHD. Methods Based on the Global Burden of Disease (GBD) 2021 database, the number of prevalent cases and disability-adjusted life years (DALYs) of HHD in the elderly were extracted for the world, China, and five regions categorized by sociodemographic index (SDI). Joinpoint regression was used to analyze the temporal trends of age-standardized prevalence rate and age-standardized DALYs rate of HHD in the elderly. A three-factor decomposition method was applied to evaluate the relative contributions of aging, population growth, and epidemiological changes to the variations in the elderly HHD burden. Additionally, a Bayesian age-period-cohort model was used to predict the elderly HHD burden from 2022 to 2040. Results In 2021, the number of prevalent elderly HHD cases reached 10 283 000 globally and 3 412 400 in China, representing increases of 179.20% and 159.20% respectively, compared with 1990. The DALYs of elderly HHD were 18 812 700 person-years globally and 4 731 400 person-years in China, rising by 76.08% and 29.45% respectively from 1990. Meanwhile, the growth rates of the number of prevalent cases and DALYs of elderly HHD varied across different SDI regions. From 1990 to 2021, the age-standardized prevalence rate of elderly HHD in China, as well as the age-standardized DALYs rate of elderly HHD both globally and in China, showed significant downward trends (all average annual percentage changes<0, all P<0.001). In 2021, the 70-74 years age group accounted for the highest proportion of prevalent cases and DALYs of elderly HHD, both globally and in China. Decomposition analysis revealed that population growth was the dominant factor driving the increase in the elderly HHD burden across all regions. The prediction model results indicated that the number of prevalent cases and DALYs of elderly HHD would continue to rise globally and in China from 2022 to 2040, with the growth rate of the elderly HHD burden in China between 2021 and 2040 expected to exceed the global average. Conclusion Over the past 32 years, although the age-standardized disease rates of elderly HHD have mainly shown a downward trend globally and in China, the absolute number of the disease burden has increased substantially. The projection model indicates a continued upward trajectory, with the growth rate in China higher than the global average. Therefore, there is an urgent need to implement precise prevention and control strategies to effectively mitigate the disease burden of elderly HHD.

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.

Objective: To evaluate the feasibility of dynamic contrast-enhanced MRI (DCE-MRI) in differentiating tumor deposits (TDs) from metastatic lymph nodes (MLNs) in rectal cancer. Materials and Methods: A retrospective analysis was conducted on 70 patients with rectal cancer, including 168 lesions (70 TDs and 98 MLNs confirmed by histopathology), who underwent pretreatment MRI and subsequent surgery between March 2019 and December 2022. The morphological characteristics of TDs and MLNs, along with quantitative parameters derived from DCE-MRI (K trans , kep, and v e) and DWI (ADCmin, ADCmax, and ADCmean), were analyzed and compared between the two groups.Multivariable binary logistic regression and receiver operating characteristic (ROC) curve analyses were performed to assess the diagnostic performance of significant individual quantitative parameters and combined parameters in distinguishing TDs from MLNs. Results: All morphological features, including size, shape, border, and signal intensity, as well as all DCE-MRI parameters showed significant differences between TDs and MLNs (all P < 0.05). However, ADC values did not demonstrate significant differences (all P > 0.05). Among the single quantitative parameters, v e had the highest diagnostic accuracy, with an area under the ROC curve (AUC) of 0.772 for distinguishing TDs from MLNs. A multivariable logistic regression model incorporating short axis, border, v e, and ADC mean improved diagnostic performance, achieving an AUC of 0.833 (P = 0.027). Conclusion The combination of morphological features, DCE-MRI parameters, and ADC values can effectively aid in the preoperative differentiation of TDs from MLNs in rectal cancer.

Objective: To evaluate the feasibility of dynamic contrast-enhanced MRI (DCE-MRI) in differentiating tumor deposits (TDs) from metastatic lymph nodes (MLNs) in rectal cancer. Materials and Methods: A retrospective analysis was conducted on 70 patients with rectal cancer, including 168 lesions (70 TDs and 98 MLNs confirmed by histopathology), who underwent pretreatment MRI and subsequent surgery between March 2019 and December 2022. The morphological characteristics of TDs and MLNs, along with quantitative parameters derived from DCE-MRI (K trans , kep, and v e) and DWI (ADCmin, ADCmax, and ADCmean), were analyzed and compared between the two groups.Multivariable binary logistic regression and receiver operating characteristic (ROC) curve analyses were performed to assess the diagnostic performance of significant individual quantitative parameters and combined parameters in distinguishing TDs from MLNs. Results: All morphological features, including size, shape, border, and signal intensity, as well as all DCE-MRI parameters showed significant differences between TDs and MLNs (all P < 0.05). However, ADC values did not demonstrate significant differences (all P > 0.05). Among the single quantitative parameters, v e had the highest diagnostic accuracy, with an area under the ROC curve (AUC) of 0.772 for distinguishing TDs from MLNs. A multivariable logistic regression model incorporating short axis, border, v e, and ADC mean improved diagnostic performance, achieving an AUC of 0.833 (P = 0.027). Conclusion The combination of morphological features, DCE-MRI parameters, and ADC values can effectively aid in the preoperative differentiation of TDs from MLNs in rectal cancer.

The bioactivity-guided isolation of potentially active natural products has been widely utilized in pharmaceutical discovery. In this study, by screening fungal extracts against coxsackievirus B3 (CVB3), three new aspochalasins, templichalasins A‒C (1‒3), along with six known aspochalasins (4‒9) were isolated from an active extract derived from the endophytic fungus Aspergillus templicola LHWf045. Compound 1 features a unique 5/6/5/7/5 pentacyclic ring system, while compounds 2 and 3 possess unusual 5/6/6/7 tetracyclic skeletons. Their structures were characterized through extensive spectroscopic analyses, electronic circular dichroism (ECD) calculations, and single-crystal X-ray diffraction analysis. Additionally, we demonstrated that compound 4 can be readily converted into compounds 1‒3 under mild acidic conditions and proposed a plausible mechanism for this conversion. Bioactivity evaluation of compounds 1‒9 against CVB3 revealed the inhibitory effects of all compounds against the virus. Notably, compound 9 exhibited superior antiviral activity, surpassing the commercial drug ribavirin in selectivity index (SI) value.
Antiviral Agents/isolation & purification* ; Aspergillus/chemistry* ; Molecular Structure ; Enterovirus B, Human/drug effects* ; Endophytes/chemistry* ; Cytochalasins/isolation & purification* ; Drug Discovery ; Humans

OBJECTIVE To provide standardized whole-process guidance on drug traceability codes for medical institutions in Sichuan province， ensuring medication safety and compliance with medical insurance supervision requirements. METHODS Based on evidence-based principles and expert consensus， Expert Consensus on Whole-process Management of Drug Traceability Codes in Medical Institutions of Sichuan Province （hereinafter referred to as the Consensus） was formulated through systematic literature review， field investigations， establishment of a multidisciplinary expert committee and multiple rounds of questionnare consultation via the modified Delphi method， and finalized through consensus meetings. RESULTS & CONCLUSIONS The Consensus clarifies key operating procedures for code verification， code assignment and code return， whole-process operational standards for drug warehouse acceptance and storage， drug warehouse outbound delivery and pharmacy acceptance check， drug distribution and dispensing in pharmacy and intravenous admixture center， medication administration in nursing units and examination departments， as well as drug return process. Key recommendations are proposed such as improving the core functions of the drug traceability system， unifying the hospital-wide traceability code database， strengthening the management of traceability codes for backup medications， establishing a management organization and institutional framework， and optimizing the architectural design and data governance requirements of the drug traceability system. The release of the Consensus will provide scientific， standardized and implementable practical guidelines for medical institutions of Sichuan province， helping to improve closed-loop management of the drug traceability system， strengthen medication safety and fulfil medical insurance fund supervision.

Objective: For patients with elevated hematocrit (Hct), platelet-rich plasma (PRP) apheresis is prone to red blood cell contamination—commonly referred to as “flushing” or erythrocyte carryover—which compromises product quality and therapeutic efficacy. This study reports two clinicaly derived measures to mitigate this issue. Methods: For 21 patients with Hct ≥53%, intravenous 0.9% sodium chloride infusion before apheresis process (replacement method, n=13) or 0.9% sodium chloride fluids hemodilution within the centrifuge bowl during PRP apheresis process (dilution method, n=8) were given, respectively. The collection time, adverse reactions, and the celluar composition of PRP—including white blood cells, red blood cells, and platelet counts—were recorded and compared. Results: Neither method resulted in visible RBC contamination (“flushing”). The red blood cell counts [(0.021±0.014)×10 /L vs (0.019±0.011)×10 /L, P>0.05], white blood cell counts [(2.258±3.288) ×10 /L vs (0.557 5±1.203) ×10 /L, P>0.05], and platelet counts [(1 140±308.2)×10 /L vs (1 105±309.9)×10 /L, P>0.05] in the PRP products obtained by two methods all met the control standards of PRP. There was no significant difference [(2.268±0.927) vs (2.438±0.762) mL/min, P=0.669 2] between the two methods in terms of the speed of PRP collection. One case of adverse reaction occurred with the fluid replacement method, while no adverse reaction occurred with the dilution method. Conclusion: For patients with elevated Hct, both fluid replacement and dilution methods can effectively prevent RBC contamination during PRP collection, yielding products that meet clinical quality standards.

Centrifugal therapeutic plasma exchange (CTPE), as an important therapeutic plasma separation technique, has gained widespread application in clinical treatment in recent years due to its high efficiency, safety, and operational flexibility. By utilizing centrifugal force to separate plasma from cellular blood components, CTPE demonstrates significant advantages over conventional membrane-based therapeutic plasma exchange (MTPE), particularly regarding therapeutic efficacy and procedural rapidity in managing complex diseases. This article provides a systematic review of the principles, operational procedures, and differences between CTPE and MTPE from the professional perspective of transfusion medicine specialists. It focuses on its applications in various clinical conditions and introduces advanced techniques in CTPE. By integrating the latest research findings and clinical practice experience, this article aims to provide theoretical basis and practical guidance for transfusion medicine specialists and related clinical personnel, thereby promoting the standardisation and optimisation of CTPE technology.

Objective To study the influence of imaging parameters on the dosimetric parameters of the lung in intensity modulated radiotherapy(IMRT)for thoracic esophageal cancer,and provide a guideline for dose limitation in IMRT for thoracic esophageal cancer.Methods The imaging and pulmonary dosimetric parameters were collected from 142 patients with thoracic esophageal cancer receiving IMRT at Chongqing University Three Gorges Hospital from February 2017 to January 2019.The linear correlation analysis between the im-aging parameters and pulmonary dosimetric parameters was conducted using bivariate Pearson test.The regression model was established by multiple linear regression.Results The maximum transverse diameter of planning target volume(PTV)was moderately correlated with lung V30 and mean lung dose(MLD,both 0.40,all P<0.05).The volume of PTV intersecting with the lung was moderately positively correlated with lung V20,V30,and MLD(all 0.4

Objective To investigate the classification capability of a deep learning classification model based on biparametric mag-netic resonance imaging(bpMRI)for clinically significant prostate cancer(csPCa)and clinically insignificant prostate cancer(cisPCa).Methods A retrospective analysis was conducted on the data of 565 prostate bpMRI patients.A deep learning classification model was established for csPCa.The patients were randomly divided into training set(452 cases)and internal test set(113 cases)at a ratio of 8︰2.Internal validation was performed,followed by external validation(external validation set)using data from 120 patients across four different hospitals.The area under the curve(AUC)of the receiver operating characteristic(ROC)curve,F1 score,precision,sensi-tivity,specificity,accuracy,and calibration curves were used to evaluate the model.Decision curve analysis(DCA)was also applied to assess the clinical benefit of the model.Results The deep learn-ing classification model for csPCa classification demonstrated the following performance across the training set,internaltest set,and external validation set:sensitivity of 0.986,0.887,and 0.750;specificity of 0.967,0.850,and 0.976;precision of 0.963,0.839,and 0.818;accuracy of 0.974,0.862,and 0.792;F1 score of 0.974,0.862,and 0.783;and AUC of 0.998,0.896,and 0.883,respec-tively.The calibration curves for all three datasets showed high consistency between predicted and actual probabilities.DCA indicated that the highest net benefit threshold probabilities for the training set,internal test set,and external validation set were 0.2-0.7,0.2-0.6,and 0.2-0.5,respectively.Conclusion The deep learning classification model demonstrated excellent performance in classifying csPCa and exhibited good generalizability,which is worhty of clinical application.