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.

Twelve compounds were isolated from the ethyl acetate fraction of the 80% aqueous ethanol extract of the roots and stems of Dalbergia rimosa Roxb. by silica gel, MCI, Sephadex LH-20 column chromatography, and semi-preparative HPLC. Their structures were identified by spectral analysis such as UV, IR, MS, 1D/2D NMR and by comparison with literature information as dalbergiquinol A (1), dalbergiquinol B (2), R-(-)-3′-hydroxy-2,4,5-trimethoxydalbergiquinol (3), neokhriol A (4), mucronulatol (5), (3R)-7,2′,3′-trihydroxy-4′-methoxy-isoflavane (6), isomucronulatol (7), (3S)-violanone (8), 3′-O-methylviolanone (9), eryvarin M (10), (±)-α,3,4,2′,4′-pentahydroxydihydrochalcone (11) and (-)-butin (12). Compound 1 and 2 are new compounds, and compounds 3-12 were isolated from this plant for the first time. Compounds 1, 2, 4, 6, 8, 11, 12 showed good scavenging effect on DPPH free radical.

The analysis of ammonia nitrogen in real water samples is challenging due to matrix interferences and difficulties for rapid on-site analysis.On the basis of the standard method,i.e.water quality-determination of ammonia nitrogen-salicylic acid spectrophotometry(HJ 536-2009),a simple device for online detecting ammonia nitrogen was developed using a sequential injection analysis(SIA)system in this work.The ammonia nitrogen transformation system,color reaction system,and detection system were built in compatible with the SIA system,respectively.In particular,the detection system was assembled by employing light-emitting diode as the light source,photodiode as the detector,and polyvinylchloride tube as the cuvette,thus significantly reducing the volume,energy consumption and fabricating cost of the detection system.As a result,the accurate analysis of ammonia nitrogen in complex water samples was achieved.A quantitative detection of ammonia nitrogen in water sample was obtained in 12 min,along with linear range extending to 1000 μmol/L,precisions(Relative standard deviation,RSD)of 4.3%(C=10 μmol/L,n=7)and 4.2%(C=500 μmol/L,n=7),and limit of detection(LOD)of 0.65 μmol/L(S/N=3,n=7).The results of interfering experiments showed that the detection of ammonia nitrogen by the developed device was not interfered by the common coexisting ions and components,therefore the environmental water could be directly analyzed,such as reservoir water,domestic sewage,sea water and leachate of waste landfill.The analytical results were consistent with those obtained by the environmental protection standard method(Water quality determination of ammonia nitrogen-salicylic acid spectrophotometry,HJ 536-2009).In addition,the spiking recoveries were in the range of 92.3%-98.1%,further confirming the accuracy and practicality of the developed device.

Objective:To study the current status of longitudinal extrauterine growth restriction (EUGR) in extremely preterm infants (EPIs) and to develop a prediction model based on clinical data from multiple NICUs.Methods:From January 2017 to December 2018, EPIs admitted to 32 NICUs in North China were retrospectively studied. Their general conditions, nutritional support, complications during hospitalization and weight changes were reviewed. Weight loss between birth and discharge > 1SD was defined as longitudinal EUGR. The EPIs were assigned into longitudinal EUGR group and non-EUGR group and their nutritional support and weight changes were compared. The EPIs were randomly assigned into the training dataset and the validation dataset with a ratio of 7∶3. Univariate Cox regression analysis and multiple regression analysis were used in the training dataset to select the independent predictive factors. The best-fitting Nomogram model predicting longitudinal EUGR was established based on Akaike Information Criterion. The model was evaluated for discrimination efficacy, calibration and clinical decision curve analysis.Results:A total of 436 EPIs were included in this study, with a mean gestational age of (26.9±0.9) weeks and a birth weight of (989±171) g. The incidence of longitudinal EUGR was 82.3%(359/436). Seven variables (birth weight Z-score, weight loss, weight growth velocity, the proportion of breast milk ≥75% within 3 d before discharge, invasive mechanical ventilation ≥7 d, maternal antenatal corticosteroids use and bronchopulmonary dysplasia) were selected to establish the prediction model. The area under the receiver operating characteristic curve of the training dataset and the validation dataset were 0.870 (95% CI 0.820-0.920) and 0.879 (95% CI 0.815-0.942), suggesting good discrimination efficacy. The calibration curve indicated a good fit of the model ( P>0.05). The decision curve analysis showed positive net benefits at all thresholds. Conclusions:Currently, EPIs have a high incidence of longitudinal EUGR. The prediction model is helpful for early identification and intervention for EPIs with higher risks of longitudinal EUGR. It is necessary to expand the sample size and conduct prospective studies to optimize and validate the prediction model in the future.

Objective To provide references for the individualized medicine of tetracyclines through mining the adverse drug events(ADE)signal.Methods All the ADE data were obtained from the Food and Drug Administration adverse event reporting system.The reports of primary suspect for tigecycline,omadacyeline or eravacycline were extracted and analyzed by using the reporting odds ratio(ROR)method.Results We identified 5 896 reports of ADE related to tetracyclines,and 99,37 and 8 ADE signals of tigecycline,omadacyeline and eravacycline,respectively.The safety profile of tetracyclines varies.The highest signal intensities of tigecycline were observed in thrombin time prolonged and amylase abnormal.Drug dose titration not performed and tooth discolouration were the highest signal intensities of omadacyeline,while blood fibrinogen decreased and pancreatic enzymes increased were found the highest signal intensities of eravacycline.Meanwhile,the main ADE signals in tigecycline were found in hepatobiliary(ROR=7.15)and blood and lymphatic systems(ROR=4.95),while gastrointestinal system in omadacyeline(ROR=3.32),and investigations(ROR=2.79)and vascular systems(ROR=2.72)in eravacycline,respectively.Furthermore,we also found ADE signals not reported in previous studies,such as sleep disorders and methemoglobinemia.Conclusion The safety profiles of tigecycline,omadacyeline and eravacycline varies.Individualized drug selection and monitoring of ADE according to the characteristics of tetracyclines are needed during treatment.

Objective To establish the method for content determination of three lignans of Dendrobium Fimbriatum Hook..Methods The lignans in Dendrobium tasselii were identified by high-performance liquid chromatography/multi-stage mass spectrometry(HPLC-ESI/MSn)coupled with ultraviolet absorption spectrometry(UV)coupled with retention time localization of high-performance liquid chromatography(HPLC).The separation was carried out on a Kromasil 100-5 C18 column(4.6 mm×250 mm,5 μm)using a gradient elution of acetonitrile-0.1%formic acid solution as the mobile phase,the volume flow rate was 0.8 mL·min-1 and the column temperature was 35℃,and the mass spectrometry was performed using an ESI ion source with the data collected in the negative ion mode.The HPLC content was determined on the same column as that of MS analysis,with the mobile phase methanol + acetonitrile(V/V=1∶1)-0.01 mol/L ammonium acetate solution,gradient elution,flow rate of 0.8 mL·min-1,column temperature of 40℃,and detection wavelength of 215 nm.Results Syringaresinol di-O-glucoside and(-)-Syringaresinol 4-O-β-D-glucopyranoside and DL-Syringaresinol were identified from Dendrobium fimbriatum Hook.,and the results of content determination showed that the linear ranges of above three components were respectively 0.1701-3.4020,0.1020-2.0400,0.0403-0.8060 μg(r≥0.9995),the average recoveries were in the range of 97.71%-101.67%,and the relative standard deviations(RSDs)were all less than 3.0%.The contents of Syringaresinol di-O-glucoside and(-)-Syringaresinol 4-O-β-D-glucopyranoside and DL-Syringaresinol in the 10 batches of samples were 0.7779-1.3852,0.0734-0.1966,0.0295-0.1882 mg·g-1.Conclusion This research method can provide a reference basis for the quality evaluation method of Dendrobium fimbriatum Hook..

In recent years, nucleic acid therapy, as a revolutionary therapeutic tool, has shown great potential in the treatment of genetic diseases, infectious diseases and cancer. Lipid nanoparticles (LNPs) are currently the most advanced mRNA delivery carriers, and their emergence is an important reason for the rapid approval and use of COVID-19 mRNA vaccines and the development of mRNA therapy. Currently, mRNA therapeutics using LNP as a carrier have been widely used in protein replacement therapy, vaccines and gene editing. Conventional LNP is composed of four components: ionizable lipids, phospholipids, cholesterol, and polyethylene glycol (PEG) lipids, which can effectively load mRNA to improve the stability of mRNA and promote the delivery of mRNA to the cytoplasm. However, in the face of the complexity and diversity of clinical diseases, the structure, properties and functions of existing LNPs are too homogeneous, and the lack of targeted delivery capability may result in the risk of off-targeting. LNPs are flexibly designed and structurally stable vectors, and the adjustment of the types or proportions of their components can give them additional functions without affecting the ability of LNPs to deliver mRNAs. For example, by replacing and optimizing the basic components of LNP, introducing a fifth component, and modifying its surface, LNP can be made to have more precise targeting ability to reduce the side effects caused by treatment, or be given additional functions to synergistically enhance the efficacy of mRNA therapy to respond to the clinical demand for nucleic acid therapy. It is also possible to further improve the efficiency of LNP delivery of mRNA through machine learning-assisted LNP iteration. This review can provide a reference method for the rational design of engineered lipid nanoparticles delivering mRNA to treat diseases.

This study using maltodextrin as raw material, 1%-5% polyvinylpyrrolidone K30 as template agent, 1%-5% ammonium bicarbonate as pore-forming agent, curcumin and ibuprofen as model drugs. Porous maltodextrin was prepared by template and pore-forming agent methods, respectively. The structure and drug delivery behavior of porous maltodextrin prepared by different technologies were comprehensively characterized. The results showed that the porous maltodextrin prepared by pore-forming agent method had larger specific surface area (6.449 4 m²·g^-1) and pore size (32.804 2 nm), which was significantly better than that by template agent method (3.670 2 m²·g^-1, 15.278 5 nm). The adsorption kinetics between porous maltodextrin prepared by pore-forming agent method and curcumin were suitable for quasi-first order adsorption kinetic model, and that between porous maltodextrin and ibuprofen were suitable for quasi-second order adsorption kinetic model. While the adsorption kinetics between porous maltodextrin prepared by template agent method and two model drugs were both suitable for the quasi-first order adsorption kinetic model. In addition, the dissolution behavior analysis showed that the porous maltodextrin prepared by the two technologies can significantly improve the dissolution behavior of insoluble drugs, and the drug release was both carried out by diffusion mechanism, which suitable for the Peppas kinetic release model, but the porous maltodextrin prepared by template agent method had a faster release rate. The change of nozzle diameter had no significant effect on the adsorption process and drug release behavior of porous maltodextrin. In conclusion, the porous maltodextrins prepared by two different technologies were both beneficial to the delivery of insoluble drugs, and the template agent method was the best for delivery of insoluble drugs. This study can provide theoretical basis for the preparation of porous particles, promote the application of porous particles in insoluble drugs, and improve the bioavailability of insoluble drugs.

Studies on chemical constituents in the rhizome of Dalbergia rimosa Roxb. The chemical constituents from the ethyl acetate part of D. rimosa were isolated and purified by silica gel, MCI gel, Sephadex LH-20 gel and semi-preparative HPLC, and the stuctures were identified by spectral method. Thirteen compounds were isolated from the ethyl acetate part of the rhizome of D. rimosa and identified as dalbergiaisoflavones A, B (1, 2), formononetin (3), 7,4′-dimethoxyisoflavone (4), 4′,6,7-trimethoxyisoflavone (5), biochanin A (6), prunetin (7), 7-O-methyltectorigenin (8), 3′-hydroxydaidzein (9), orobol 7,3′-dimethyl ether (10), 2′,7-dihydroxy-4′,5′- dimethoxyisoflavone (11), pruinosanone E (12), caviunin (13). Compounds 1 and 2 are new compounds, and compounds 3-13 were isolated from this plant for the first time. Compounds 9 and 11 had remarkable scavenging effect on DPPH free radicals.

Objective To evaluate the efficacy of double U-stitch and modified"double U-stitch pancreaticojejunostomy"in pancreaticoduodenectomy.Methods We retrospectively analyzed the clinical information of 150 patients who underwent pancreaticoduodenectomy between January 2022 and September 2023 in Wang Wei's team in department of pancreatic surgery,Fudan University Shanghai Cancer Center.The patients were divided into two groups according to the pancreaticojejunostomy method:the double U-stitch 1.0 group(70 cases before modification)and the double U-stitch 2.0 group(80 cases after modification).The postoperative complications of the two groups were compared.Results The overall postoperative complications were significantly lower in the double U-stitch 2.0 group as compared with the double U-stitch 1.0 group(13.8％vs.38.6％,P=0.001).The incidence of clinically relevant pancreatic fistula was 18.6％in the double U-stitch 1.0 group,while this was significantly decreased in the double U-stitch 2.0 group(6.3％)(P=0.021).Postoperative extraluminal hemorrhage occurred in 5 patients in the double U-stitch 1.0 group,while in the double U-stitch 2.0 group,only one patient experienced postoperative intraluminal hemorrhage,the difference was statistically significant between two groups(P=0.007).The median postoperative length of hospital stay was significantly shorter in the double U-stitch 2.0 group than that in the double U-stitch 1.0 group(11d vs.14 d,P=0.001).Conclusions The modified"double U-stitch pancreaticojejunostomy"can significantly reduce the incidence of clinically relevant pancreatic fistula and other relevant severe complications,which helps improve the safety of the procedure.