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.

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 evaluate the predictive ability and clinical application value of artificial intelligence (AI) systems in the benign and malignant differentiation and pathological type of pulmonary nodules, and to summarize clinical application experience. Methods A retrospective analysis was conducted on the clinical data of patients with pulmonary nodules admitted to the Department of Thoracic Surgery, Second Hospital of Lanzhou University, from February 2016 to February 2025. Firstly, pulmonary nodules were divided into benign and non-benign groups, and the discriminative abilities of AI systems and clinicians were compared. Subsequently, lung nodules reported as precursor glandular lesions (PGL), microinvasive adenocarcinoma (MIA), and invasive adenocarcinoma (IAC) in postoperative pathological results were analyzed, comparing the efficacy of AI systems and clinicians in predicting the pathological type of pulmonary nodules. Results In the analysis of benign/non-benign pulmonary nodules, clinical data from a total of 638 patients with pulmonary nodules were included, of which there were 257 males (10 patients and 1 patient of double and triple primary lesions, respectively) and 381 females (18 patients and 1 patient of double and triple primary lesions, respectively), with a median age of 55.0 (47.0, 61.0) years. Different lesions in the same patient were analyzed as independent samples. Univariate analysis of the two groups of variables showed that, except for nodule location, the differences in the remaining variables were statistically significant (P<0.05). Multivariate logistic regression analysis showed that age, nodule type (subsolid pulmonary nodule), average density, spicule sign, and vascular convergence sign were independent influencing factors for non-benign pulmonary nodules, among which age, nodule type (subsolid pulmonary nodule), spicule sign, and vascular convergence sign were positively correlated with non-benign pulmonary nodules, while average density was negatively correlated with the occurrence of non-benign pulmonary nodules. The area under the receiver operating characteristic curve (AUC) of the malignancy risk value given by the AI system in predicting non-benign pulmonary nodules was 0.811, slightly lower than the 0.898 predicted by clinicians. In the PGL/MIA/IAC analysis, clinical data from a total of 411 patients with pulmonary nodules were included, of which there were 149 males (8 patients of double primary lesions) and 262 females (17 patients of double primary lesions), with a median age of 56.0 (50.0, 61.0) years. Different lesions in the same patient were analyzed as independent samples. Univariate analysis results showed that, except for gender, nodule location, and vascular convergence sign, the differences in the remaining variables among the three groups of PGL, MIA, and IAC patients were statistically significant (P<0.05). Multinomial multivariate logistic regression analysis showed that the differences between the parameters in the PGL group and the MIA group were not statistically significant (P>0.05), and the maximum diameter and average density of the nodules were statistically different between the PGL and IAC groups (P<0.05), and were positively correlated with the occurrence of IAC as independent risk factors. The average AUC value, accuracy, recall rate, and F1 score of the AI system in predicting lung nodule pathological type were 0.807, 74.3%, 73.2%, and 68.5%, respectively, all better than the clinical physicians’ prediction of lung nodule pathological type indicators (0.782, 70.9%, 66.2%, and 63.7% respectively). The AUC value of the AI system in predicting IAC was 0.853, and the sensitivity, specificity, and optimal cutoff value were 0.643, 0.943, and 50.0%, respectively. Conclusion This AI system has demonstrated high clinical value in predicting the benign and malignant nature and pathological type of lung nodules, especially in predicting lung nodule pathological type, its ability has surpassed that of clinical physicians. With the optimization of algorithms and the adequate integration of multimodal data, it can better assist clinical physicians in formulating individualized diagnostic and treatment plans for patients with lung nodules.

OBJECTIVE: To illustrate the role of dehydrocorydaline (DHC) in chronic constriction injury (CCI)-induced neuropathic pain and the underlying mechanism. METHODS: C57BL/6J mice were randomly divided into 3 groups by using a random number table, including sham group (sham operation), CCI group [intrathecal injection of 10% dimethyl sulfoxide (DMSO)], and CCI+DHC group (intrathecal injection of DHC), 8 mice in each group. A CCI mouse model was conducted to induce neuropathic pain through ligating the right common sciatic nerve. On day 14 after CCI modeling or sham operation, mice were intrathecal injected with 5 µL of 10% DMSO or 10 mg/kg DHC (5 µL) into the 5th to 6th lumbar intervertebral space (L5-L6). Pregnant ICR mice were sacrificed for isolating primary spinal neurons on day 14 of embryo development for in vitro experiment. Pain behaviors were evaluated by measuring the paw withdrawal mechanical threshold (PWMT) of mice. Immunofluorescence was used to observe the activation of astrocytes and microglia in mouse spinal cord. Protein expressions of inducible nitric oxide synthase (iNOS), tumor necrosis factor alpha (TNF-α), interleukin 6 (IL-6), phosphorylation of N-methyl-D-aspartate receptor subunit 2B (p-NR2B), and NR2B in the spinal cord or primary spinal neurons were detected by Western blot. RESULTS: In CCI-induced neuropathic pain model, mice presented significantly decreased PWMT, activation of glial cells, overexpressions of iNOS, TNF-α, IL-6, and higher p-NR2B/NR2B ratio in the spinal cord (P<0.05 or P<0.01), which were all reversed by a single intrathecal injection of DHC (P<0.05 or P<0.01). The p-NR2B/NR2B ratio in primary spinal neurons were also inhibited after DHC treatment (P<0.05). CONCLUSION An intrathecal injection of DHC relieved CCI-induced neuropathic pain in mice by inhibiting the neuroinflammation and neuron hyperactivity.
Animals ; Neuralgia/etiology* ; Mice, Inbred C57BL ; Analgesics/pharmacology* ; Neuroinflammatory Diseases/pathology* ; Constriction ; Male ; Receptors, N-Methyl-D-Aspartate/metabolism* ; Nitric Oxide Synthase Type II/metabolism* ; Mice, Inbred ICR ; Microglia/pathology* ; Spinal Cord/drug effects* ; Female ; Mice ; Tumor Necrosis Factor-alpha/metabolism* ; Disease Models, Animal ; Constriction, Pathologic/complications* ; Interleukin-6/metabolism* ; Astrocytes/metabolism* ; Chronic Disease ; Neurons/metabolism*

Cemental tear is a rare and indetectable condition unless obvious clinical signs present with the involvement of surrounding periodontal and periapical tissues. Due to its clinical manifestations similar to common dental issues, such as vertical root fracture, primary endodontic diseases, and periodontal diseases, as well as the low awareness of cemental tear for clinicians, misdiagnosis often occurs. The critical principle for cemental tear treatment is to remove torn fragments, and overlooking fragments leads to futile therapy, which could deteriorate the conditions of the affected teeth. Therefore, accurate diagnosis and subsequent appropriate interventions are vital for managing cemental tear. Novel diagnostic tools, including cone-beam computed tomography (CBCT), microscopes, and enamel matrix derivatives, have improved early detection and management, enhancing tooth retention. The implementation of standardized diagnostic criteria and treatment protocols, combined with improved clinical awareness among dental professionals, serves to mitigate risks of diagnostic errors and suboptimal therapeutic interventions. This expert consensus reviewed the epidemiology, pathogenesis, potential predisposing factors, clinical manifestations, diagnosis, differential diagnosis, treatment, and prognosis of cemental tear, aiming to provide a clinical guideline and facilitate clinicians to have a better understanding of cemental tear.
Humans ; Dental Cementum/injuries* ; Consensus ; Diagnosis, Differential ; Cone-Beam Computed Tomography ; Tooth Fractures/therapy*

BACKGROUND: There is a gap in understanding the effects of different acupoints and treatment methods (acupuncture and moxibustion) on microcirculatory changes in the lumbar region. OBJECTIVE: This study aimed to assess the thermal effects of acupuncture at Weizhong (BL40), with acupuncture at Chize (LU5) and moxibustion at both acupoints as control interventions. DESIGN, SETTING, PARTICIPANTS AND INTERVENTIONS: In this randomized controlled trial, 140 healthy participants were equally divided into four groups: acupuncture at BL40 (Acu-BL40), acupuncture at LU5 (Acu-LU5), moxibustion at BL40 (Mox-BL40) and moxibustion at LU5 (Mox-LU5). Participants underwent a 30-minute session of their assigned treatment. Infrared thermal imaging was used to collect temperature data on the areas of interest for analysis. MAIN OUTCOME MEASURES: The primary measure was the change in average temperature of the observed area after the intervention. The secondary measures included periodic temperature changes every 5 min and the temperature changes of the Governor Vessel and Bladder Meridian in the observed area after the intervention. RESULTS: Significant interactions were observed between treatments and acupoints affecting temperature (P < 0.001). The Acu-BL40 group showed a notably higher increase in mean temperature after 30 min compared to the Acu-LU5 and Mox-BL40 groups, with increases of 0.29 (95% confidence interval [CI] = 0.17 to 0.41) and 0.24 (95% CI = 0.08 to 0.41) °C, respectively. CONCLUSION: Acupuncture at BL40 acupoint can significantly increase the mean temperature in the observed area, highlighting the specific thermal effect of acupuncture compared to moxibustion in the lumbar area. This suggests a potential therapeutic benefit of acupuncture at BL40 for managing lumbar conditions. TRIAL REGISTRATION ClinicalTrials.gov (NCT05665426). Please cite this article as: Zheng SY, Wang XY, Lin LN, Liu S, Huang XX, Liu YY, Yu XS, Pan W, Fang JQ, Liang Y. Lumbar temperature change after acupuncture or moxibustion at Weizhong (BL40) or Chize (LU5) in healthy adults: A randomized controlled trial. J Integr Med. 2025; 23(2): 145-151.
Adult ; Female ; Humans ; Male ; Young Adult ; Acupuncture Points ; Acupuncture Therapy ; Body Temperature ; Healthy Volunteers ; Lumbosacral Region/physiology* ; Moxibustion ; Adolescent

Objective To analyze the epidemiological characteristics and related occupational diagnosis situations of occupational heat illness (OHI) diagnosed by Guangdong Province Hospital for Occupational Disease Prevention and Treatment (GDHOD) from 2011 to 2022. Methods A total of 203 patients who applied for OHI diagnosed in GDHOD from 2011 to 2022 were recruited as research subjects using the retrospective analysis method. Clinical data of OHI diagnosed cases was collected from "Occupational Diseases and Hazards Monitoring Information System" under "China Disease Prevention and Control Information System", and data of cases that diagnosed as non-OHI were collected from the occupational disease diagnosis file of GDHOD for retrospective analysis. Resultsi) The number of OHI diagnosed patients was 174 among 203 cases, with a rate of 85.7%. ii) The heat stroke accounted for 72.4% with a mortality rate of 7.5%, male patients accounted for 86.2% among the 174 OHI diagnosed cases. The median age of onset was 47 years, and patients aged 45-60 years accounting for 56.3%. The median working period of onset of illness was 80 days, with 20.1%, 40.8%, 53.4% and 70.1% of cases occurring within 1, 30, 90 and 365 days of working, respectively. The patients were concentrated in the Pearl River Delta region, accounting for 93.1%. The peak incidence of OHI occurred from May to September, accounted for 96.6%, with the highest incidence occurred in July. About 67.3% of cases occurred on days when the maximum temperature was ≥35.0 ℃. Cases occurred between 12:00 and 18:00 accounting for 74.1%, and cases occurred within 8 hours of work accounting for 75.2%. OHI of patients occurred during operations in non-heat-source workshops accounted for 43.7% of cases, while outdoor operations accounted for 39.7%. OHI patients in manufacturing and small private enterprises accounted for 55.2% and 62.6%, and the main occupations were workshop operators, loaders/unloaders, and sanitation workers, comprising 63.2% of cases. iii) Among the 29 cases diagnosed as non-OHI, accounting for 86.2% patients with high-temperature work history who did not meet clinical diagnostic criteria, most of them were diagnosed as OHI precursor. Conclusion OHI patients in Guangdong Province predominantly occur in summer and autumn, with heat stroke being the primary condition. Middle-aged males, workers in non-heat-source workshops and outdoor settings have higher risk of OHI. OHI cases are concentrated in specific region and enterprise. The OHI prevention should be enhanced on high-risk workers, who work in the Pearl River Delta region, manufacturing, and small private enterprises.

Objective:To investigate the correlation of the emm genotypes and virulence genes with the isolation sites of Group A Streptococcus (GAS). Methods:It was a retrospective study.The specimens were collected from children with impetigo in Beijing Children′s Hospital, Capital Medical University from 2006 to 2008 for GAS isolation and identification.A total of 24 GAS strains were isolated from 16 children with impetigo, among which 7 pairs of strains were isolated from the throat and skin of 7 children, and 1 pair of strains was isolated from the vulva and skin of one child, and the remaining 8 GAS strains were isolated from the skin pus samples of 8 children.Polymerase chain reaction was applied to detect the emm genotypes and 13 virulence genes ( speA, speB, speC, speF, speG, speH, speI, speJ, speK, speL, speM, smeZ and ssa). The correlation of the emm genotypes and virulence genes with the isolation sites of GAS strains was analyzed. Results:In this study, four emm genotypes were detected, including emm1.0 (15/24), emm12.0 (4/24), emm22.0 (2/24) and emm160.0 (1/24), and one subtype emm12.19 (2/24) was detected as well.The carrying rates of 13 virulence genes speA, speB, speC, speF, speG, speH, speI, speJ, speK, speL, speM, smeZ and ssa were 58.3%, 100%, 91.7%, 100%, 50.0%, 12.5%, 54.2%, 66.7%, 16.7%, 25.0%, 12.5%, 100% and 91.7%, respectively.All strains carried 5 to 11 virulence genes and they all carried speB, speF and smeZ.There were significant differences in the carrying rate of speA and speJ among the strains with different emm genotypes (all P<0.05). There was no significant difference in the distribution of virulence genes between skin isolates and pharyngeal isolates, including the 5 pairs of strains carrying the emm1.0 genotype (all P>0.05). Conclusions:The distribution of virulence gene of GAS in children with impetigo is significantly correlated with the emm genotype, rather than the isolation site.

The acupoint sensitization theory,a breakthrough in acupuncture and moxibustion over the past two decades,has greatly enhanced the understanding of meridians and acupoints while increasing the effectiveness of clinical diagnosis and treatment in acupuncture and moxibustion practices.The number of studies on surface sensitization site detection for different diseases is increasing.However,systematic sorting and a summary of the detection mode of surface sensitization points are lacking.Therefore,this study categorizes the method of surface sensitization point detection into two modes:pan-scanning and focused scanning.The classification is based on the characteristics of the openness of the research purpose,the precision of the target range,and the degree of variation during detection.The two modes have considerable differences in the exploration efficiency and the presentation of result.The pan-scanning mode can be further subdivided into holistic and localized pan-scanning modes,whereas the focused-scanning mode can be subdivided into fixed-and variable-focused scanning modes.This study analyzes the application scenarios,characteristics,advantages,and limitations of each detection mode and presents opinions on mode selection,mode innovation,and future development directions.This study aims to provide valuable insights and guidance for the follow-up research on surface sensitization site detection of various diseases.

Objective To investigate the key elements for model establishment and determine the evaluation indicators of animal models for pelvic inflammatory disease(PID),providing a reference for improving modelling methods and optimizing the application of PID animal models.Methods The search query"Pelvic Inflammatory Disease"AND"Animal Model"OR"Rat"OR"Mouse"OR"Guinea Pig"OR"Rabbit"OR"Dog"OR"Pig"was used to retrieve relevant literature on PID animal models published from 2013 to 2023 in China Knowledge Network Infrastructure(CNKI),Wanfang,and PubMed databases.The studies were analyzed and categorized based on experimental animal types,modelling methods,modelling cycles,detection indicators,positive control drugs,and administration duration.A database was established for statistical analysis.Results A total of 214 research articles on PID animal models meeting the inclusion criteria were identified.The most commonly used model animals are Sprague Dawley(SD)rats,followed by Wistar rats.The most frequently employed modelling method is a combination of mechanical injury and bacterial infection,followed by the phenol mucilage method.The most common modelling cycles for acute pelvic inflammatory disease(APID)and chronic pelvic inflammatory disease(CPID)/sequelae of pelvic inflammatory disease(SPID)are 8 to 14 days,while for PID models without specific staging,the cycles are 7 days.High-frequency detection methods and indicators include histopathological observation using hematoxylin-eosin staining,enzyme-linked immunosorbent assay(ELISA)for serum-related indicators,morphological changes of tissues observed with the naked eye,and immunohistochemical detection of related protein expression in uterine tissues,and pathological scoring.The most frequently used positive control drugs are Fuke Qianjin Tablets,followed by Jingangteng Capsules.The most common administration duration for APID is 7 days,and for CPID/SPID models,it ranges from 15 to 21 days.Conclusion Currently,SD rats and Wistar rats are commonly used as experimental animals for PID models.The dual modelling method of mechanical injury combined with mixed bacterial infection aligns closely with clinical pathogenesis and can be used to establish a PID model that simulates postoperative uterine cavity infection.Depending on the research objectives,different positive drugs and detection indicators should be selected for comprehensive evaluation.Most existing PID animal model studies are based on western medical diagnosis,with fewer studies focusing on Traditional Chinese Medicine(TCM)syndromes.There is a need to integrate TCM theories of etiology and pathogenesis to construct PID animal models that are more in line with TCM clinical symptoms.