ObjectivePrimary liver cancer, predominantly hepatocellular carcinoma (HCC), is a significant global health issue, ranking as the sixth most diagnosed cancer and the third leading cause of cancer-related mortality. Accurate and early diagnosis of HCC is crucial for effective treatment, as HCC and non-HCC malignancies like intrahepatic cholangiocarcinoma (ICC) exhibit different prognoses and treatment responses. Traditional diagnostic methods, including liver biopsy and contrast-enhanced ultrasound (CEUS), face limitations in applicability and objectivity. The primary objective of this study was to develop an advanced, light-weighted classification network capable of distinguishing HCC from other non-HCC malignancies by leveraging the automatic analysis of brightness changes in CEUS images. The ultimate goal was to create a user-friendly and cost-efficient computer-aided diagnostic tool that could assist radiologists in making more accurate and efficient clinical decisions. MethodsThis retrospective study encompassed a total of 161 patients, comprising 131 diagnosed with HCC and 30 with non-HCC malignancies. To achieve accurate tumor detection, the YOLOX network was employed to identify the region of interest (ROI) on both B-mode ultrasound and CEUS images. A custom-developed algorithm was then utilized to extract brightness change curves from the tumor and adjacent liver parenchyma regions within the CEUS images. These curves provided critical data for the subsequent analysis and classification process. To analyze the extracted brightness change curves and classify the malignancies, we developed and compared several models. These included one-dimensional convolutional neural networks (1D-ResNet, 1D-ConvNeXt, and 1D-CNN), as well as traditional machine-learning methods such as support vector machine (SVM), ensemble learning (EL), k-nearest neighbor (KNN), and decision tree (DT). The diagnostic performance of each method in distinguishing HCC from non-HCC malignancies was rigorously evaluated using four key metrics: area under the receiver operating characteristic (AUC), accuracy (ACC), sensitivity (SE), and specificity (SP). ResultsThe evaluation of the machine-learning methods revealed AUC values of 0.70 for SVM, 0.56 for ensemble learning, 0.63 for KNN, and 0.72 for the decision tree. These results indicated moderate to fair performance in classifying the malignancies based on the brightness change curves. In contrast, the deep learning models demonstrated significantly higher AUCs, with 1D-ResNet achieving an AUC of 0.72, 1D-ConvNeXt reaching 0.82, and 1D-CNN obtaining the highest AUC of 0.84. Moreover, under the five-fold cross-validation scheme, the 1D-CNN model outperformed other models in both accuracy and specificity. Specifically, it achieved accuracy improvements of 3.8% to 10.0% and specificity enhancements of 6.6% to 43.3% over competing approaches. The superior performance of the 1D-CNN model highlighted its potential as a powerful tool for accurate classification. ConclusionThe 1D-CNN model proved to be the most effective in differentiating HCC from non-HCC malignancies, surpassing both traditional machine-learning methods and other deep learning models. This study successfully developed a user-friendly and cost-efficient computer-aided diagnostic solution that would significantly enhances radiologists’ diagnostic capabilities. By improving the accuracy and efficiency of clinical decision-making, this tool has the potential to positively impact patient care and outcomes. Future work may focus on further refining the model and exploring its integration with multimodal ultrasound data to maximize its accuracy and applicability.

Objective To predict the lymph node metastasis status of patients with invasive pulmonary adenocarcinoma by constructing machine learning models based on primary tumor radiomics, peritumoral radiomics, and habitat radiomics, and to evaluate the predictive performance and generalization ability of different imaging features. Methods A retrospective analysis was performed on the clinical data of 1 263 patients with invasive pulmonary adenocarcinoma who underwent surgery at the Department of Thoracic Surgery, Jiangsu Province Hospital, from 2016 to 2019. Habitat regions were delineated by applying K-means clustering (average cluster number of 2) to the grayscale values of CT images. The peritumoral region was defined as a uniformly expanded area of 3 mm around the primary tumor. The primary tumor region was automatically segmented using V-net combined with manual correction and annotation. Subsequently, radiomics features were extracted based on these regions, and stacked machine learning models were constructed. Model performance was evaluated on the training, testing, and internal validation sets using the area under the receiver operating characteristic curve (AUC), F1 score, recall, and precision. Results After excluding patients who did not meet the screening criteria, a total of 651 patients were included. The training set consisted of 468 patients (181 males, 287 females) with an average age of (58.39±11.23) years, ranging from 29 to 78 years, the testing set included 140 patients (56 males, 84 females) with an average age of (58.81±10.70) years, ranging from 34 to 82 years, and the internal validation set comprised 43 patients (14 males, 29 females) with an average age of (60.16±10.68) years, ranging from 29 to 78 years. Although the habitat radiomics model did not show the optimal performance in the training set, it exhibited superior performance in the internal validation set, with an AUC of 0.952 [95%CI (0.87, 1.00)], an F1 score of 84.62%, and a precision-recall AUC of 0.892, outperforming the models based on the primary tumor and peritumoral regions. Conclusion The model constructed based on habitat radiomics demonstrated superior performance in the internal validation set, suggesting its potential for better generalization ability and clinical application in predicting lymph node metastasis status in pulmonary adenocarcinoma.

Bacterial biofilms gave rise to persistent infections and multi-organ failure, thereby posing a serious threat to human health. Biofilms were formed by cross-linking of hydrophobic extracellular polymeric substances (EPS), such as proteins, polysaccharides, and eDNA, which were synthesized by bacteria themselves after adhesion and colonization on biological surfaces. They had the characteristics of dense structure, high adhesiveness and low drug permeability, and had been found in many human organs or tissues, such as the brain, heart, liver, spleen, lungs, kidneys, gastrointestinal tract, and skeleton. By releasing pro-inflammatory bacterial metabolites including endotoxins, exotoxins and interleukin, biofilms stimulated the body’s immune system to secrete inflammatory factors. These factors triggered local inflammation and chronic infections. Those were the key reason for the failure of traditional clinical drug therapy for infectious diseases.In order to cope with the increasingly severe drug-resistant infections, it was urgent to develop new therapeutic strategies for bacterial-biofilm eradication and anti-bacterial infections. Based on the nanoscale structure and biocompatible activity, nanobiomaterials had the advantages of specific targeting, intelligent delivery, high drug loading and low toxicity, which could realize efficient intervention and precise treatment of drug-resistant bacterial biofilms. This paper highlighted multiple strategies of biofilms eradication based on nanobiomaterials. For example, nanobiomaterials combined with EPS degrading enzymes could be used for targeted hydrolysis of bacterial biofilms, and effectively increased the drug enrichment within biofilms. By loading quorum sensing inhibitors, nanotechnology was also an effective strategy for eradicating bacterial biofilms and recovering the infectious symptoms. Nanobiomaterials could intervene the bacterial metabolism and break the bacterial survival homeostasis by blocking the uptake of nutrients. Moreover, energy-driven micro-nano robotics had shown excellent performance in active delivery and biofilm eradication. Micro-nano robots could penetrate physiological barriers by exogenous or endogenous driving modes such as by biological or chemical methods, ultrasound, and magnetic field, and deliver drugs to the infection sites accurately. Achieving this using conventional drugs was difficult. Overall, the paper described the biological properties and drug-resistant molecular mechanisms of bacterial biofilms, and highlighted therapeutic strategies from different perspectives by nanobiomaterials, such as dispersing bacterial mature biofilms, blocking quorum sensing, inhibiting bacterial metabolism, and energy driving penetration. In addition, we presented the key challenges still faced by nanobiomaterials in combating bacterial biofilm infections. Firstly, the dense structure of EPS caused biofilms spatial heterogeneity and metabolic heterogeneity, which created exacting requirements for the design, construction and preparation process of nanobiomaterials. Secondly, biofilm disruption carried the risk of spread and infection the pathogenic bacteria, which might lead to other infections. Finally, we emphasized the role of nanobiomaterials in the development trends and translational prospects in biofilm treatment.

Damarane-type triterpene saponins are the main active ingredients in Gynostemma pentaphyllum (Thunb.) Makino. By using D101 macroporous adsorption resin and silica gel open-columns, C18 medium-pressure column chromatography, and preparative high performance liquid chromatography, we isolated four compounds from the leaves of G. pentaphyllum planted in Guangxi Zhuang Autonomous Region. Their structures were determined by comprehensive analyses of MS, NMR data and circular dichroism spectroscopy and identified as (3β,12β)-dihydroxy-25-peroxyhydrohydrodammarane-20,23-diene-3-O-[β-D-glucopyranosyl(1→2)]-β-D-glucopyranoside (1), (3β,12β,24S)-trihydroxydammarane-20,25-diene-3-O-[β-D-glucopyranosyl(1→2)]-β-D-glucopyranoside (2), (3β,20α)-dihydroxy-24-en-12β,22S-epoxydammarane-3-O-[β-D-glucopyranosyl(1→2)]-β-D-glucopyranoside (3), (3β,12β,20S)-trihydroxydammarane-24-en-3-O-[6-O-acetyl-β-D-glucopyranosyl(1→2)-β-D-glucopyranosyl]-20-O-β-D-xylopyranosyl(1→3)-α-L-rhamnopyranosyl (1→6)-β-D-glucopyranoside (4). Compounds 1-4 are new dammarane-type triterpene saponins.

The current status of exoskeletons was introduced in enhancing individual soldier's battlefield rescue capabilities,promoting the integrated use of battlefield rescue equipment,protecting medical personnel on the battlefield and assisting injured soldiers in rehabilitation training.The challenges of exoskeletons faced in human-machine interaction,power supply endurance,heavy overall structure,restricted movement and high cost were analyzed when applied to medical service support,and some suggestions were proposed accordingly including enhancing technology research and development,integrated application,communication and cooperation and personnel training.References were provided for the application of exoskeletons in China's medical service support.[Chinese Medical Equipment Journal,2024,45(3):71-75]

Drug resistance presents a significant challenge to achieving positive clinical outcomes in anti-tumor therapy.Prior research has illuminated reasons behind drug resistance,including increased drug efflux,alterations in drug targets,and abnormal activation of oncogenic pathways.However,there's a need for deeper investigation into the impact of drug-resistant cells on parental tumor cells and intricate crosstalk between tumor cells and the malignant tumor microenvironment(TME).Recent studies on extracellular vesicles(EVs)have provided valuable insights.EVs are membrane-bound particles secreted by all cells,mediating cell-to-cell communication.They contain functional cargoes like DNA,RNA,lipids,proteins,and metabolites from mother cells,delivered to other cells.Notably,EVs are increasingly recognized as regulators in the resistance to anti-cancer drugs.This review aims to summarize the mechanisms of EV-mediated anti-tumor drug resistance,covering therapeutic approaches like chemo-therapy,targeted therapy,immunotherapy and even radiotherapy.Detecting EV-based biomarkers to predict drug resistance assists in bypassing anti-tumor drug resistance.Additionally,targeted inhibition of EV biogenesis and secretion emerges as a promising approach to counter drug resistance.We highlight the importance of conducting in-depth mechanistic research on EVs,their cargoes,and functional ap-proaches specifically focusing on EV subpopulations.These efforts will significantly advance the devel-opment of strategies to overcome drug resistance in anti-tumor therapy.

Objective To explore the clinical efficacies of different surgical methods for elderly patients with lumbar tuberculosis.Methods The clinical data of 289 elderly patients with lumbar tuberculosis admitted to Hebei Chest Hospital from August 2018 to August 2021 were retrospectively analyzed.According to surgical methods,the patients were divided into the posterior group(109 cases),the anterior and posterior combination group(81 cases),and the anterior group(99 cases).The time of bone graft and fusion,operation time,hospital stay,intraoperative blood loss,and complications of the three groups were collected and compared among the three groups.The spine Cobb angle was regularly determined,the correction degree was calculated;the levels of erythrocyte sedimentation rate(ESR),white blood cell count(WBC),and C-reactive protein(CRP)were collected and compared among the three groups;and the Frankel grading and visual analogue scale(VAS)scores of the three groups were compared.Results After a 2-year follow-up,there was no significant difference in the time of bone graft and fusion among the three groups(P>0.05),the anterior group had the shortest operation time,the posterior group had the shortest hospital stay,and the lowest intraoperative blood loss and incidence of complications,with statistically significant differences(P<0.05).The correction degree of the anterior and posterior combination group was better than that of the posterior group and the anterior group(P<0.05),and the Cobb angles after operation and at the last follow-up in the posterior group was better(P<0.05).The anterior and posterior combination group had better improvement effect on CRP and ESR at the last follow-up(P<0.05),the WBC level of the posterior group was lower(P<0.05).The proportions of patients in grade E of Frankel grading at the last follow-up in the three groups were higher than those after surgery(P<0.05);compared with the preoperative period,the VAS scores at the last follow-up of the three groups decreased(P<0.05),and the VAS score of the posterior group was lower(P<0.05).Conclusion The effects of anterior surgery,posterior surgery and anterior and posterior combined surgery in the treatment of elderly lumbar tuberculosis are good,and the approach method can be scientifically and reasonably formulated according to patients' physical condition to improve the clinical treatment effect.

Objective:To analyze the immune reconstitution after BTKi treatment in patients with chronic lymphocytic leukemia(CLL).Methods:The clinical and laboratorial data of 59 CLL patients admitted from January 2017 to March 2022 in Fujian Medical University Union Hospital were collected and analyzed retrospectively.Results:The median age of 59 CLL patients was 60.5(36-78).After one year of BTKi treatment,the CLL clones(CD5+/CD19+)of 51 cases(86.4％)were significantly reduced,in which the number of cloned-B cells decreased significantly from(46±6.1)× 109/L to(2.3±0.4)× 109/L(P=0.0013).But there was no significant change in the number of non-cloned B cells(CD19+minus CD5+/CD19+).After BTKi treatment,IgA increased significantly from(0.75±0.09)g/L to(1.31±0.1)g/L(P＜0.001),while IgG and IgM decreased from(8.1±0.2)g/L and(0.52±0.6)g/L to(7.1±0.1)g/L and(0.47±0.1)g/L,respectively(P＜0.001,P=0.002).BTKi treatment resulted in a significant change in T cell subpopulation of CLL patients,which manifested as both a decrease in total number of T cells from(2.1±0.1)× 109/L to(1.6±0.4)× 109/L and NK/T cells from(0.11±0.1)× 109/L to(0.07±0.01)× 109/L(P=0.042,P=0.038),both an increase in number of CD4+cells from(0.15±6.1)× 109/L to(0.19±0.4)× 109/L and CD8+cells from(0.27±0.01)× 109/L to(0.41±0.08)× 109/L(both P＜0.001).BTKi treatment also up-regulated the expression of interleukin(IL)-2 while down-regulated IL-4 and interferon(IFN)-γ.However,the expression of IL-6,IL-10,and tumor necrosis factor(TNF)-α did not change significantly.BTKi treatment could also restored the diversity of TCR and BCR in CLL patients,especially obviously in those patients with complete remission(CR)than those with partial remission(PR).Before and after BTKi treatment,Shannon index of TCR in patients with CR was 0.02±0.008 and 0.14±0.001(P＜0.001),while in patients with PR was 0.01±0.03 and 0.05±0.02(P＞0.05),respectively.Shannon index of BCR in patients with CR was 0.19±0.003 and 0.33±0.15(P＜0.001),while in patients with PR was 0.15±0.009 and 0.23±0.18(P＜0.05),respectively.Conclusions:BTKi treatment can shrink the clone size in CLL patients,promote the expression of IgA,increase the number of functional T cells,and regulate the secretion of cytokines such as IL-2,IL-4,and IFN-γ.BTKi also promote the recovery of diversity of TCR and BCR.BTKi treatment contributes to the reconstitution of immune function in CLL patients.

Objective:To retrospectively analyze the detection and diagnosis process of two cases with double rare thalassemia genotypes,explore the causes of missed diagnosis and misdiagnosis of rare thalassemia,and improve the diagnosis level of rare thalassemia.Methods:Base on the family history,hematological phenotype and hemoglobin electrophoretic analysis results,the common genotypes of α and β-thalassemia were detected by PCR+diversion hybridization.DNA sequencing technology was used for rare α and β protein genes sequencing.Results:Both subjects were combined with double rare thalassemia genotypes,and both rare thalassemia gene combinations were reported for the first time.One of them was αβ complex thalassemia with αα*53_55 del TCC/αα heterozygous merger βIVS Ⅱ2(-T)/βN heterozygous,the other was ααIVS-Ⅱ-55(T→G)in α1/αα4,2-Q double azygous heterozygous α-thalassemia,among whichαα*53_55 del TCC/αα genotype was also reported for the first time.Conclusion:The reported rare gene type αα*53_55 del TCC/αα and two cases of rare gene combinations enriches the spectrum of gene mutations in the Chinese population,and provides richer molecular information for thalassemia diagnosis and eugenics counseling.

Objective:To investigate expression and significance of mitochondrial ribosomal protein L37(MRPL37)in colorectal cancer.Methods:RNA-seq data and clinical information of colorectal cancer patients were obtained from the TCGA and GEO databases to analyze the differ-ential expression,clinicopathological features,and prognosis of MRPL37 in colorectal cancer.En-richment analysis was performed to explore the biological functions and signaling pathways in-volved in MRPL37,and the TISID database was analyzed for the correlation between MRPL37 and immune-infiltrating cells.Results:MRPL37 was found to be significantly overexpressed in colorectal cancer(P＜0.05)and was significantly correlated with the T-stage(P=0.0041),N-stage(P=0.0053),and TNM-stage(P=0.0159)of colorectal cancer patients.The enrichment analysis indi-cates that MRPL37 is primarily involved in mitochondrial physiological activities,cell cycle,oxida-tive phosphorylation,and immune pathways in colorectal cancer.Regarding tumor immunity,changes in MRPL37 expression were found to be correlated with changes in multiple types of im-mune cells in the tumor microenvironment.MRPL37 was an independent prognostic factor(P=0.011),and its expression level was positively associated with patient prognosis(P=0.0017).Con-clusion:MRPL37 may serve as a new potential biomarker for colorectal cancer,which can be used in the future to predict patient prognosis and guide precise patient treatment.