Background/Aims: Endoscopic ultrasound-guided fine needle biopsy (EUS-FNB) is an essential tool for tissue acquisition in solid pancreatic tumors. Rapid on-site evaluation (ROSE) by cytologists ensures diagnostic accuracy. However, the universal application of the ROSE is limited by its availability. Therefore, we aimed to investigate the feasibility of determining the end of the procedure based on the results of in-room cytological evaluation by trained endosonographers (IRCETE). Methods: A training course focusing on the cytological interpretation of common pancreatic tumors was provided to the three endosonographers. After training, the decision to terminate EUS-FNB was made based on IRCETE results. The diagnostic accuracy, concordance rate of diagnostic categories, and sample adequacy were compared with those determined by board-certified cytologists and macroscopic on-site evaluation (MOSE). Results: We enrolled 65 patients with solid pancreatic tumors, most of whom were malignant (86.2%). The diagnostic accuracy was 90.8% when the end of the procedure was determined based on IRCETE, compared to 87.7% and 98.5% when determined by MOSE and cytologists, respectively (p=0.060). Based on the cytologists’ results, the accuracy of IRCETE in diagnostic category interpretation was 97.3%. Conclusions In the absence of ROSE, IRCETE can serve as a supplementary alternative to MOSE in determining the end of tissue sampling with a high accuracy rate.

Background/Aims: Endoscopic ultrasound-guided fine needle biopsy (EUS-FNB) is an essential tool for tissue acquisition in solid pancreatic tumors. Rapid on-site evaluation (ROSE) by cytologists ensures diagnostic accuracy. However, the universal application of the ROSE is limited by its availability. Therefore, we aimed to investigate the feasibility of determining the end of the procedure based on the results of in-room cytological evaluation by trained endosonographers (IRCETE). Methods: A training course focusing on the cytological interpretation of common pancreatic tumors was provided to the three endosonographers. After training, the decision to terminate EUS-FNB was made based on IRCETE results. The diagnostic accuracy, concordance rate of diagnostic categories, and sample adequacy were compared with those determined by board-certified cytologists and macroscopic on-site evaluation (MOSE). Results: We enrolled 65 patients with solid pancreatic tumors, most of whom were malignant (86.2%). The diagnostic accuracy was 90.8% when the end of the procedure was determined based on IRCETE, compared to 87.7% and 98.5% when determined by MOSE and cytologists, respectively (p=0.060). Based on the cytologists’ results, the accuracy of IRCETE in diagnostic category interpretation was 97.3%. Conclusions In the absence of ROSE, IRCETE can serve as a supplementary alternative to MOSE in determining the end of tissue sampling with a high accuracy rate.

With the widespread use of antibiotics, drug-resistant bacterial infections have become a significant threat to human health. Finding new antibacterial strategies that can effectively control drug-resistant bacterial infections has become an urgent task. Unlike small molecule drugs that target bacterial proteins, antisense oligonucleotide (ASO) can target genes related to bacterial resistance, pathogenesis, growth, reproduction and biofilm formation. By regulating the expression of these genes, ASO can inhibit or kill bacteria, providing a novel approach for the development of antibacterial drugs. To overcome the challenge of delivering antisense oligonucleotide into bacterial cells, various drug delivery systems have been applied in this field, including cell-penetrating peptides, lipid nanoparticles and inorganic nanoparticles, which have injected new momentum into the development of antisense oligonucleotide in the antibacterial realm. This review summarizes the current development of small nucleic acid drugs, the antibacterial mechanisms, targets, sequences and delivery vectors of antisense oligonucleotide, providing a reference for the research and development of antisense oligonucleotide in the treatment of bacterial infections.

Background/Aims: Endoscopic ultrasound-guided fine needle biopsy (EUS-FNB) is an essential tool for tissue acquisition in solid pancreatic tumors. Rapid on-site evaluation (ROSE) by cytologists ensures diagnostic accuracy. However, the universal application of the ROSE is limited by its availability. Therefore, we aimed to investigate the feasibility of determining the end of the procedure based on the results of in-room cytological evaluation by trained endosonographers (IRCETE). Methods: A training course focusing on the cytological interpretation of common pancreatic tumors was provided to the three endosonographers. After training, the decision to terminate EUS-FNB was made based on IRCETE results. The diagnostic accuracy, concordance rate of diagnostic categories, and sample adequacy were compared with those determined by board-certified cytologists and macroscopic on-site evaluation (MOSE). Results: We enrolled 65 patients with solid pancreatic tumors, most of whom were malignant (86.2%). The diagnostic accuracy was 90.8% when the end of the procedure was determined based on IRCETE, compared to 87.7% and 98.5% when determined by MOSE and cytologists, respectively (p=0.060). Based on the cytologists’ results, the accuracy of IRCETE in diagnostic category interpretation was 97.3%. Conclusions In the absence of ROSE, IRCETE can serve as a supplementary alternative to MOSE in determining the end of tissue sampling with a high accuracy rate.

Background::Cardiovascular disease (CVD) has emerged as the leading cause of death from prostate cancer (PCa) in recent decades, bringing a great disease burden worldwide. Men with preexisting CVD have an increased risk for major adverse cardiovascular events when treated with androgen deprivation therapy (ADT). The present study aimed to explore the prevalence and risk evaluation of CVD among people with newly diagnosed PCa in China.Methods::Clinical data of newly diagnosed PCa patients were retrospectively collected from 34 centers in China from 2010 to 2022 through convenience sampling. CVD was defined as myocardial infarction, arrhythmia, heart failure, stroke, ischemic heart disease, and others. CVD risk was estimated by calculating Framingham risk scores (FRS). Patients were accordingly divided into low-, medium-, and high-risk groups. χ2 or Fisher’s exact test was used for comparison of categorical variables. Results::A total of 4253 patients were enrolled in the present study. A total of 27.0% (1147/4253) of patients had comorbid PCa and CVD, and 7.2% (307/4253) had two or more CVDs. The enrolled population was distributed in six regions of China, and approximately 71.0% (3019/4253) of patients lived in urban areas. With imaging and pathological evaluation, most PCa patients were diagnosed at an advanced stage, with 20.5% (871/4253) locally progressing and 20.5% (871/4253) showing metastasis. Most of them initiated prostatectomy (46.6%, 1983/4253) or regimens involving ADT therapy (45.7%, 1944/4253) for prostate cancer. In the present PCa cohort, 43.1% (1832/4253) of patients had hypertension, and half of them had poorly controlled blood pressure. With FRS stratification, as expected, a higher risk of CVD was related to aging and metabolic disturbance. However, we also found that patients with treatment involving ADT presented an originally higher risk of CVD than those without ADT. This was in accordance with clinical practice, i.e., aged patients or patients at advanced oncological stages were inclined to accept systematic integrative therapy instead of surgery. Among patients who underwent medical castration, only 4.0% (45/1118) received gonadotropin releasing hormone antagonists, in stark contrast to the grim situation of CVD prevalence and risk.Conclusions::PCa patients in China are diagnosed at an advanced stage. A heavy CVD burden was present at the initiation of treatment. Patients who accepted ADT-related therapy showed an original higher risk of CVD, but the awareness of cardiovascular protection was far from sufficient.

Aim To investigate the mechanism of Banxia Baizhu Tianma Decoction(BBTD)in interfer-ing methamphetamine(MA)addiction using network pharmacology.Methods The mechanism of BBTD intervention in MA addiction was analyzed using net-work pharmacology,and MA-dependent SH-SY5Y cell model was further constructed to observe the effects of BBTD on cell model and PI3K-Akt pathway.Results A total of 88 active ingredients and 583 potential tar-gets of BBTD were screened.KEGG analysis showed that BBTD might intervene in MA addiction through PI3K-Akt,cAMP and other pathways.The molecular docking results showed that key active ingredients ex-hibited strong binding ability with core targets of PI3K-Akt pathway.In vitro experiments showed that MA-de-pendent model cells had shorter synapses,tended to be elliptical in morphology,had blurred cell boundaries,showed typical cell damage morphology,and had high intracellular expression of cAMP(P＜0.01)and low expression of 5-HT(P＜0.05).BBTD intervention could counteract the above morphology,cAMP,and 5-HT changes,suggesting that it had therapeutic effects on MA-dependent model cells.Western blot showed that MA modeling elevated the p-PI3K/PI3K(P＜0.05)and p-Akt/Akt(P＜0.01);BBTD inter-vention decreased their relative expression.Conclu-sions Gastrodin and other active ingredients in BBTD have therapeutic effects on MA addiction,and the mechanism may be related to regulation of PI3K-Akt pathway relevant targets.

Type Ⅵ secretion system(T6SS)is a lethal weapon that releases effectors in direct contact to kill eukaryotic predators or prokaryotic competitors.T6SS is of great significance in bacterial environmental adaptability,pathogenicity,and gene horizontal transfer.T6SS has been identified in about 25％of Gram-negative bacteria.Because of its widespread existence,T6SS is considered the key factor of ecological competition.T6SS effectors exerting biological functions have high diversity and do not have conserved sequences,and the regulatory mechanisms involved are complex.Therefore,it is a hot and difficult topic in T6SS research.Vibrio fluvialis(V.fluvialis)as a newly emerging foodborne pathogen,has unique characteristics in the quantity,composition,and physiological function of T6SS,which is related to its wide environmental adaptability and pathoge-nicity.This article mainly reviews the research progress of V.fluvialis T6SS,including its composition,structure,functional activity,and regulatory mechanism.

Objective:To construct and validate the best predictive model for 28-day death risk in patients with septic shock based on different supervised machine learning algorithms.Methods:The patients with septic shock meeting the Sepsis-3 criteria were selected from Medical Information Mart for Intensive Care-Ⅳ v2.0 (MIMIC-Ⅳ v2.0). According to the principle of random allocation, 70% of these patients were used as the training set, and 30% as the validation set. Relevant predictive variables were extracted from three aspects: demographic characteristics and basic vital signs, serum indicators within 24 hours of intensive care unit (ICU) admission and complications possibly affecting indicators, functional scoring and advanced life support. The predictive efficacy of models constructed using five mainstream machine learning algorithms including decision tree classification and regression tree (CART), random forest (RF), support vector machine (SVM), linear regression (LR), and super learner [SL; combined CART, RF and extreme gradient boosting (XGBoost)] for 28-day death in patients with septic shock was compared, and the best algorithm model was selected. The optimal predictive variables were determined by intersecting the results from LASSO regression, RF, and XGBoost algorithms, and a predictive model was constructed. The predictive efficacy of the model was validated by drawing receiver operator characteristic curve (ROC curve), the accuracy of the model was assessed using calibration curves, and the practicality of the model was verified through decision curve analysis (DCA).Results:A total of 3?295 patients with septic shock were included, with 2?164 surviving and 1?131 dying within 28 days, resulting in a mortality of 34.32%. Of these, 2?307 were in the training set (with 792 deaths within 28 days, a mortality of 34.33%), and 988 in the validation set (with 339 deaths within 28 days, a mortality of 34.31%). Five machine learning models were established based on the training set data. After including variables at three aspects, the area under the ROC curve (AUC) of RF, SVM, and LR machine learning algorithm models for predicting 28-day death in septic shock patients in the validation set was 0.823 [95% confidence interval (95% CI) was 0.795-0.849], 0.823 (95% CI was 0.796-0.849), and 0.810 (95% CI was 0.782-0.838), respectively, which were higher than that of the CART algorithm model (AUC = 0.750, 95% CI was 0.717-0.782) and SL algorithm model (AUC = 0.756, 95% CI was 0.724-0.789). Thus above three algorithm models were determined to be the best algorithm models. After integrating variables from three aspects, 16 optimal predictive variables were identified through intersection by LASSO regression, RF, and XGBoost algorithms, including the highest pH value, the highest albumin (Alb), the highest body temperature, the lowest lactic acid (Lac), the highest Lac, the highest serum creatinine (SCr), the highest Ca 2+, the lowest hemoglobin (Hb), the lowest white blood cell count (WBC), age, simplified acute physiology score Ⅲ (SAPSⅢ), the highest WBC, acute physiology score Ⅲ (APSⅢ), the lowest Na +, body mass index (BMI), and the shortest activated partial thromboplastin time (APTT) within 24 hours of ICU admission. ROC curve analysis showed that the Logistic regression model constructed with above 16 optimal predictive variables was the best predictive model, with an AUC of 0.806 (95% CI was 0.778-0.835) in the validation set. The calibration curve and DCA curve showed that this model had high accuracy and the highest net benefit could reach 0.3, which was significantly outperforming traditional models based on single functional score [APSⅢ score, SAPSⅢ score, and sequential organ failure assessment (SOFA) score] with AUC (95% CI) of 0.746 (0.715-0.778), 0.765 (0.734-0.796), and 0.625 (0.589-0.661), respectively. Conclusions:The Logistic regression model, constructed using 16 optimal predictive variables including pH value, Alb, body temperature, Lac, SCr, Ca 2+, Hb, WBC, SAPSⅢ score, APSⅢ score, Na +, BMI, and APTT, is identified as the best predictive model for the 28-day death risk in patients with septic shock. Its performance is stable, with high discriminative ability and accuracy.

Objective:To delineate the clinical characteristics and outcomes associated with severe cardiac toxicity during the preconditioning phase of allogeneic hematopoietic stem cell transplantation (allo-HSCT) in patients with aplastic anemia (AA).Methods:This retrospective case series study included 31 patients with severe AA who underwent allo-HSCT and were diagnosed with severe cardiac toxicity at the Hematology Department of Peking University People′s Hospital from August 2012 to June 2022. The clinical manifestations of severe cardiac toxicity observed during the preconditioning process were assessed. Patient survival was assessed using the Kaplan-Meier method.Results:In this cohort of 31 patients, the median follow-up period was 9 days (range: 4-365 days). Severe cardiac toxicity manifested within 6 days after the initial cyclophosphamide (Cy) administration. Twenty patients died within 30 days of initiating Cy preconditioning, of which 16 patients died due to severe cardiac toxicity within 25 days. Patients whose cardiac function improved within 30 days post-preconditioning showed a median survival duration of 222 days ( n=11). Troponin I (TNI) levels in patients who died within 30 days of initiating Cy preconditioning began increasing on day 5 post-Cy, peaking sharply by day 9 after a notable rise on day 8. B-type natriuretic peptide (BNP) levels in patients who died within 30 days of initiating Cy preconditioning started to rise from day 1, stabilized between days 2 and 5, and then doubled daily from days 6 to 8, remaining elevated thereafter. Notably, the initial increases in BNP and TNI correlated with electrocardiogram (ECG) signs of low voltage and T-wave inversion in 83.87% of cases ( n=26). Most patients ( n=28, 90.32%) were administered corticosteroid therapy. In those with restored cardiac function, the ejection fraction returned to >50% within 30 days of initiating Cy preconditioning. Conclusions:Patients with severe cardiac toxicity during the preconditioning phase of allo-HSCT typically exhibit early, sustained, and marked elevations in myocardial damage markers, including BNP and TNI, accompanied by ECG abnormalities following Cy administration, with BNP often increasing first. These indicators are associated with rapid disease progression and high mortality. Prompt initiation of treatment upon clinical diagnosis is critical for improving survival outcomes.

Objective To propose a deep learning-based method for removing Gaussian and pepper noises of the surgical instrument visual images so as to recover the detailed features of the images.Methods A lightweight multi-task progressive network was constructed involving in a multi-feature fusion encoder-decoder network,an attention-guided network and a detail-recovery progressive network,which used the multi-feature fusion encoder-decoder network to predict and eliminate the noise information in the visual images,the attention-guided network to remove the residual noise and the detail-recovery progressive network to restore the underlying detail features of the denoised images.Some of the regular convolutions in the detail recovery progressive network were replaced with depth separable convolutions to realize lightweight design of the network constructed.Denoising experiments were conducted on the publicly available CBSD68 and Kodak24 datasets and the self-constructed surgical instrument noise dataset so as to compare the denoising effects of the network constructed and the traditional methods and the classification accuracies of ResNet-18 model and ResNet-34 model for the denosied images by the network and to analyze computing power and memory usage before and after the lightweight design.Results The network constructed gained better denoising effect than the classical methods for publicly available datasets,and ResNet-18 model and ResNet-34 model had higher accuracies when used to classify the images denoised by the network for the self-constructed surgical instrument noise dataset.Lightweight design had the parameter number and floating point operations(FLOPs)decreased by approximately 27.27％and 29.81％,respectively.Conclusion The proposed lightweight multi-task progressive network behaves well in denoising surgical instrument visual images with reduced computating power consump-tion and memory usage.[Chinese Medical Equipment Journal,2024,45(2):1-7]