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.

Aim To investigate the regulatory role and mechanism of resveratrol in inhibiting autophagy and promoting apoptosis in choroidal melanoma cells. Methods Choroidal melanoma cells (MUM2B) were divided into control and experimental groups, and treated with different concentrations of resveratrol (0, 10, 20,40,60,80 μmol ·L

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.

Social behavior is extremely important for the physical and mental health of individuals, their growth and development, and for social development. Social behavioral disorders have become a typical clinical representation of a variety of psychiatric disorders and have serious adverse effects on the development of individuals. The prefrontal cortex, as one of the key areas responsible for social behavior, involves in many advanced brain functions such as social behavior, emotion, and decision-making. The neural activity of prefrontal cortex has a major impact on the performance of social behavior. Numerous studies demonstrate that neurons and glial cells can regulate certain social behaviors by themselves or the interaction which we called neural microcircuits; and the collaboration with other brain regions also regulates different types of social behaviors. The prefrontal cortex (PFC)-thalamus projections mainly influence social dominance and social preference; the PFC-amygdala projections play a key role in fear behavior, emotional behavior, social exploration, and social identification; and the PFC-nucleus accumbens projections mainly involve social preference, social memory, social cognition, and spatial-social associative learning. Based on the above neural mechanism, many studies have focused on applying the non-invasive neurostimulation to social deficit-related symptoms, including transcranial magnetic stimulation (TMS), transcranial electrical stimulation (TES) and focused ultrasound stimulation (FUS). Our previous study also investigated that repetitive transcranial magnetic stimulation can improve the social behavior of mice and low-intensity focused ultrasound ameliorated the social avoidance behavior of mice by enhancing neuronal activity in the prefrontal cortex. In this review, we summarize the relationship between neurons, glial cells, brain projection and social behavior in the prefrontal cortex, and systematically show the role of the prefrontal cortex in the regulation of social behavior. We hope our summarization will provide a reference for the neural mechanism and effective treatment of social disorders.

Pulmonary fibrosis(PF)is a chronic progressive end-stage lung disease.However,the mechanisms un-derlying the progression of this disease remain elusive.Presently,clinically employed drugs are scarce for the treatment of PF.Hence,there is an urgent need for developing novel drugs to address such diseases.Our study found for the first time that a natural source of Prismatomeris connata Y.Z.Ruan(Huang Gen,HG)ethyl acetate extract(HG-2)had a significant anti-PF effect by inhibiting the expression of the transforming growth factor beta 1/suppressor of mothers against decapentaplegic(TGF-β1/Smad)pathway.Network pharmacological analysis suggested that HG-2 had effects on tyrosine kinase phosphorylation,cellular response to reactive oxygen species,and extracellular matrix(ECM)disassembly.Moreover,mass spec-trometry imaging(MSI)was used to visualize the heterogeneous distribution of endogenous metabolites in lung tissue and reveal the anti-PF metabolic mechanism of HG-2,which was related to arginine biosyn-thesis and alanine,asparate and glutamate metabolism,the downregulation of arachidonic acid meta-bolism,and the upregulation of glycerophospholipid metabolism.In conclusion,we elaborated on the relationship between metabolite distribution and the progression of PF,constructed the regulatory metabolic network of HG-2,and discovered the multi-target therapeutic effect of HG-2,which might be conducive to the development of new drugs for PF.

The tannin content of sorghum seeds had a significant impact on the wine's quality during the brewing process.Additionally,when used as a feed ingredient,the tannin content had a major impact on feed consumption.Thus the tannin content of sorghum has a substantial impact on its quality and application.To quickly and nondestructively determine the tannin content of sorghum,near-infrared spectroscopy was combined with chemometrics in this study,which eliminated the need for time-consuming and costly conventional approaches.Following the spectra's preprocessing,anomalous samples were removed by using a combination of Gaussian process regression(GPR)and Monte Carlo cross-validation(MCCV).The sample set was then randomly divided into a modeling set and a prediction set,with feature wavelength selection carried out using the elimination of uninformative variables(UVE)method.Subsequently,a GPR model was developed,and its performance was compared with partial least squares regression(PLSR)and support vector machine regression(SVR)models.The results indicated that the GPR model outperformed the PLSR and SVR models in all aspects.The optimized GPR model,generated following pre-processing process such as Detrending and Savitzky-Golay smoothing,elimination of anomalous samples,and selection of feature wavelengths,demonstrated superior performance,with model set determination coefficient(Rc2),prediction set determination coefficient(RP2),and relative percent deviation(RPD)values of 0.9979,0.9529,and 4.8453,respectively.These findings validated the effectiveness of the GPR regression model,which integrated near-infrared spectroscopy with chemometrics,for the rapid and non-destructive detection of sorghum tannins.

Objective To compare the efficacies of retrograde tibial nailing(RTN)versus minimally invasive percutaneous plate osteosynthesis(MIPPO)in the treatment of distal tibial fractures.Methods A retrospective analysis was conducted on the clinical data of 55 patients with distal tibial fractures who underwent surgery in our hospital.Patients were divided into two groups based on the different surgical methods,patients in the RTN group(n=25)were treated with RTN,and patients in the MIPPO group(n=30)were treated with MIPPO.The surgical parameters(operation time,intraoperative blood loss,intraoperative fluoroscopy times,and success rate of closed reduction),fracture healing time,ankle dorsiflexion range of motion and American Orthopaedic Foot and Ankle Society(AOFAS)ankle-hindfoot scores 6 months after operation and at the last follow-up,and the incidence of complications during perioperative period and follow-up were compared between the two groups.Results The operation time,intraoperative blood loss,and intraoperative fluoroscopy times in the RTN group were significantly shorter/less than those in the MIPPO group(P＜0.05).The ankle dorsiflexion range of motion and AOFAS ankle-hindfoot score 6 months after operation in the RTN group were significantly greater/higher than those in the MIPPO group(P＜0.05).There was no statistically significant difference in the fracture healing time,or ankle dorsiflexion range of motion and AOFAS ankle-hindfoot score at the last follow-up between the two groups(P＞0.05).The success rate of closed reduction in the RTN group was 72.00％,which was lower than that of 96.67％in the MIPPO group(P＜0.05).The incidence of soft tissue-related complications in the RTN group was signifi-cantly lower than that in the MIPPO group(P＜0.05),while there was no statistically significant difference in the overall incidence of complica-tions between the two groups(P＞0.05).Conclusion RTN is an effective minimally invasive surgical technique for the treatment of distal tibial fractures,characterized by minimal trauma,low incidence of soft tissue complications,and fast recovery of joint function compared with MIPPO.

Alzheimer's disease (AD) is a typical cognitive disorder with an increasing incidence in recent years. AD is also one of the main causes of disability and death of the elderly in current aging society. One of the most common symptoms of AD is spatial memory impairment, which occurs in more than 60% of patients. This memory loss is closely related to the impairment of cognitive maps in the brain. The entorhinal grid cells and the hippocampal place cells are important cellular basis for spatial memory and navigation functions in the brain. Understanding the abnormal firing pattern of these neurons and their impaired coordination to neural oscillations in transgenic rodents is crucial for identifying the therapeutic targets for AD. In this article, we review recent studies on neural activity based on transgenic rodent models of AD, with a focus on the changes in the firing characteristics of neurons and the abnormal electroencephalogram (EEG) rhythm in the entorhinal cortex and hippocampus. We also discuss potential cell-network mechanism of spatial memory disorders caused by AD, so as to provide a scientific basis for the diagnosis and treatment of AD in the future.
Animals ; Mice ; Alzheimer Disease/genetics* ; Animals, Genetically Modified ; Cognition ; Cognitive Dysfunction ; Hippocampus/physiology* ; Memory Disorders ; Mice, Transgenic ; Neurons/physiology*

The latest epidemiological data suggests that the situation of adult diabetes in China is severe, and metabolic diseases have become significant chronic illnesses that have a serious impact on public health and social development. After more than six years of practice, the National Metabolic Management Center(MMC) has developed distinctive approaches to manage metabolic patients and has achieved a series of positive outcomes, continuously advancing the standardized diagnosis and treatment model. In order to further improve the efficiency, based on the first edition, the second edition guideline was composed by incorporating experience of the past six years in conjunction with the latest international and domestic guidelines.

COVID-19 has been prevalent for three years. The virulence of SARS-CoV-2 is weaken as it mutates continuously. However, elderly patients, especially those with underlying diseases, are still at high risk of developing severe infections. With the continuous study of the molecular structure and pathogenic mechanism of SARS-CoV-2, antiviral drugs for COVID-19 have been successively marketed, and these anti-SARS-CoV-2 drugs can effectively reduce the severe rate and mortality of elderly patients. This article reviews the mechanism, clinical medication regimens, drug interactions and adverse reactions of five small molecule antiviral drugs currently approved for marketing in China, so as to provide advice for the clinical rational use of anti-SARS-CoV-2 in the elderly.