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 investigate the effects of ischemia time and storage periods on RNA quality in fresh-frozen breast cancer(BC)and esophageal cancer(EC)tissue samples in order to establish evidence-based protocols for biobank sample management.Methods The tumor(T)and paired normal(N)tissue samples from 6 cases of BC and 6 cases of EC were collected and cryopreserved in Biobank,Shantou Central Hospital.Mirror paraffin-embedded tissues were simultaneously prepared into sections for morphological analysis.The samples were divided into two groups of<15 min and 15-30 min according to ischemia time,and RNA quality was analyzed at 4 storage periods of 8-10 months(T1),14-16 months(T2),26-28 months(T3)and 38-40 months(T4).Results In 96 analyzed samples,93.8%(90/96)exhibited high quality(RIN≥6),with 89.6%(43/48)in BC and 97.9%(47/48)in EC.Significant differences in RIN were observed between BC group and EC group(8.050 vs.8.600,P=0.009).In EC group,RIN value was significantly negatively correlated with RNA yield(P<0.001).Moreover,RIN values of tumor-normal pairs exhibited markedly significant differences(7.550 vs.9.000,P<0.001).In contrast,no significant difference was detected in BC group(8.200 vs.7.700,P=0.348).Statistical analysis showed that RIN value was positively correlated with 28S/18S(P<0.001),but had no correlation with tumor content(P=0.676)and necrotic content(P=0.055).Neither ischemia time(<15 min vs.15-30 min:8.200 vs.8.300,P=0.932)nor storage periods(T1-T4:8.400,7.700,8.450,8.600,P=0.163)compromised RNA quality.Conclusion Organ origin and tissue type could influence RNA quality of fresh-frozen tissue samples.However,limited ischemia time(≤30 min)and long-term storage period(38-40 months)do not adversely affect RNA quality in fresh-frozen breast cancer and esophageal cancer tissue samples.

Objective:To investigate the completion rate of tumor evaluation at initial assessment and after neoadjuvant therapy for mid and low rectal cancer patients in the national multicenter real-world database.Methods:The prospective real-world study was conducted. The clinicopathological data of 1 074 patients who underwent surgical treatment for mid and low rectal cancer in 47 national medical institutions, including Peking Union Medical College Hospital et al, from May 12,2023 to May 11,2024 were collected. Observation indicators: (1) clinical characteristics of patients with mid and low rectal cancer; (2) initial colonoscopy and pathologic evaluation of tumors in patients with mid and low rectal cancer; (3) initial imaging evaluation of patients with mid and low rectal cancer; (4) imaging evaluation after neoadjuvant therapy for patients with mid and low rectal cancer. Measurement data with normal distribution were represented as Mean± SD, and measurement data with skewed distribution were represented as M( Q1, Q3). Count data were described as absoluter numbers and/or percentages. Results:(1) Clinical characteristics of patients with mid and low rectal cancer. Of the 1 074 patients, there were 713 males and 361 females, aged 63(56,70)years. The body mass index of 1 074 patients was 24(21,26)kg/m 2.For American Society of Anesthesiologists classification, there were 147 cases of stage Ⅰ, 641 cases of stage Ⅱ, 157 cases of stage Ⅲ, 2 cases of stage Ⅳ, and there were 127 cases missing data. (2) Initial colonoscopy and pathologic evaluation of tumors in patients with mid and low rectal cancer. Of the 1 074 patients, there were 787 cases (73.28%) undergoing complete colonoscopy, and there were only 197 cases (18.34%) undergoing immunohistochemical evaluation of all four mismatch repair proteins. (3) Initial imaging evaluation of patients with mid and low rectal cancer. Of the 1 074 patients, there were 842(78.40%) patients completing magnetic resonance imaging (MRI) or ultrasound evaluation, and there were 914(85.10%) patients completing chest, abdomen, and pelvis enhanced computed tomography (CT) evaluation. In the 149 patients completing rectal ultrasound evaluation, there were 122 cases (81.88%) comple-ting T staging evaluation, and there were 81 cases (54.36%) completing N staging evaluation. In the 808 patients completing rectal MRI evaluation, there were 708 cases (87.62%) completing T staging evaluation, and there were 590 cases (73.02%) completing N staging evaluation. (4) Imaging evalua-tion after neoadjuvant therapy for patients with mid and low rectal cancer. Of the 388 patients with neoadjuvant therapy, there were 332 patients (85.57%) completing MRI or ultrasound evaluation, and there were 327 patients (84.28%) completing chest, abdomen, and pelvis enhanced CT evalua-tion. In the 70 patients completing rectal ultrasound evaluation, there were 65 cases (92.86%) com-pleting T staging evaluation, and there were 49 cases (70.00%) completing N staging evaluation. In the 327 patients completing rectal MRI evaluation, there were 246 cases (75.23%) completing T staging, and there were 228 cases (69.72%) completing N staging evaluation. Conclusion:The com-pletion rate of tumor imaging evaluation at initial assessment and after neoadjuvant therapy for mid and low rectal cancer patients on a national scale is relatively good.

Breast cancer is the leading cause of cancer-related mortality among women worldwide and has drawn extensive research attention.Owing to its molecular heterogeneity,drug resistance,and low therapeutic response,single-modality treatments often fail to achieve satisfactory efficacy or broad applicability.Combination therapy,designed based on the pathophysiological characteristics,related signaling pathways,and biomarkers of breast cancer,has emerged as a promising approach for improving therapeutic outcomes.With the advancement of research on combination strategies,the understanding of their molecular mechanisms—particularly key signaling pathways and biomarkers—has become increasingly important.However,comprehensive reviews addressing these molecular mechanisms and synergistic strategies remain scarce.This article summarizes recent advances in combination therapy for breast cancer,providing a comprehensive review of recent combination therapies for breast cancer and their underlying molecular mechanisms,and focusing on key signaling pathways involved in combination therapy and synergistic strategies,thereby providing theoretical insights and reference for researchers,graduate students,and clinicians engaged in the development of novel combination therapeutic strategies for breast cancer and related malignancies.

Breast cancer is the leading cause of cancer-related mortality among women worldwide and has drawn extensive research attention.Owing to its molecular heterogeneity,drug resistance,and low therapeutic response,single-modality treatments often fail to achieve satisfactory efficacy or broad applicability.Combination therapy,designed based on the pathophysiological characteristics,related signaling pathways,and biomarkers of breast cancer,has emerged as a promising approach for improving therapeutic outcomes.With the advancement of research on combination strategies,the understanding of their molecular mechanisms—particularly key signaling pathways and biomarkers—has become increasingly important.However,comprehensive reviews addressing these molecular mechanisms and synergistic strategies remain scarce.This article summarizes recent advances in combination therapy for breast cancer,providing a comprehensive review of recent combination therapies for breast cancer and their underlying molecular mechanisms,and focusing on key signaling pathways involved in combination therapy and synergistic strategies,thereby providing theoretical insights and reference for researchers,graduate students,and clinicians engaged in the development of novel combination therapeutic strategies for breast cancer and related malignancies.

Objective:To observe the efficacy of plasma beam combined with intense pulsed light on facial burn scars and its impact on scar score and pain level.Method:This study was a prospective study,and a random number table method was used to divided 92 patients with facial burn scars who were admitted to Maoming People's Hospital from March 2024 to August 2024 into control group(received intense pulsed light treatment,46 cases)and study group(received plasma beam treatment in addition to the control group,46 cases).The efficacy,related scale scores,scar recovery and incidence of adverse reaction rates between two groups were compaerd.Result:Compared with control group after treatment,the clinical total effective rate and the chinese version of the simplified burn health scale(BSHS-B)score of the study group were higher,while vancouver scar scale(VSS),visual analogue scale(VAS)scores,scar thickness,and scar blood flow perfusion were lower(P＜0.05).There was no significant difference in the incidence of adverse reaction rates between the two groups(P＞0.05).Conclusion:Plasma beam combined with intense pulsed light on facial burn scars,can improve clinical efficacy,improve scar thickness and blood flow perfusion,alleviate scar itching and pain,and has good safety in treating facial burn scar patients.

Objective To predict the poorly differentiated subtype of stage T1 invasive lung adenocarcinoma based on the morphological and quantitative characteristics of preoperative CT images.Methods The CT images,clinical information and pathological report of 333 cases with stage T1 lung adenocarcinoma in the Affiliated Traditional Chinese Medicine Hospital of Southwest Medical University and the Second Affiliated Hospital of Naval Medical University were retrospectively analyzed.All data were divided into histological subtypes according to the WHO Classification of Chest Tumors(5th Edition).CT signs include the maximum diameter,minimum diameter,location,nodule type,CT value,proportion of solid components,burr sign,lobular sign,vacuole sign,air bronchial sign,halo sign,bronchial truncation sign,vascular cluster sign,pleural depression sign.Clinical factors included gender,age,smoking history,tumor markers,and spread through air spaces.Independent predictors of poorly differentiated subtypes of lung adenocarcinoma were obtained via univariate and multivariate logistic regression analysis,and the clinical model,CT model and combined model were constructed based on the analysis results.Results In univariate logistic regression analysis,gender,smoking history,tumor markers,spread through air spaces,maximum diameter,minimum diameter,location,nodule type,CT value,proportion of solid components,burr sign,vacuole sign and vascular cluster sign were significantly related to poorly differentiated subtypes.Multivariate logistic regression analysis showed that gender,tumor markers,vacuole sign,minimum diameter,and nodule type were independent influencing factors of poorly differentiated subtypes of lung adenocarcinoma.Clinical model,CT model and combined model were constructed based on the analysis results.Area under the curve(AUC)of the clinical model,CT model and combined model were 0.772[95％confidence interval(CI)0.712-0.831],0.776(95％CI 0.714-0.837)and 0.825(95％CI 0.776-0.874)for the poorly differentiated subtypes,respectively.The combined model had a higher AUC,with the better prediction.There was significant difference in predicting lung adenocarcinoma poorly differentiated subtypes between the clinical model and combined model(P=0.019).Conclusion Logistic regression model based on CT signs has good diagnostic value in predicting poorly differentiated lung adenocarcinoma in stage Ⅰ.

Persistent and maladaptive drug-related memories represent a key component in drug addiction. Converging evidence from both preclinical and clinical studies has demonstrated the potential efficacy of the memory reconsolidation updating procedure (MRUP), a non-pharmacological strategy intertwining two distinct memory processes: reconsolidation and extinction-alternatively termed "the memory retrieval-extinction procedure". This procedure presents a promising approach to attenuate, if not erase, entrenched drug memories and prevent relapse. The present review delineates the applications, molecular underpinnings, and operational boundaries of MRUP in the context of various forms of substance dependence. Furthermore, we critically examine the methodological limitations of MRUP, postulating potential refinement to optimize its therapeutic efficacy. In addition, we also look at the potential integration of MRUP and neurostimulation treatments in the domain of substance addiction. Overall, existing studies underscore the significant potential of MRUP, suggesting that interventions predicated on it could herald a promising avenue to enhance clinical outcomes in substance addiction therapy.
Humans ; Substance-Related Disorders/psychology* ; Memory Consolidation/physiology* ; Animals ; Extinction, Psychological/physiology*

The anterior cingulate cortex (ACC) has recently been proposed as a key player in the representation of itch stimuli. However, to date, little is known about the contribution of specific ACC interneuron populations to itch processing. Using c-Fos immunolabeling and in vivo Ca²⁺ imaging, we reported that both histamine and chloroquine stimuli-induced acute itch caused a marked enhancement of vasoactive intestinal peptide (VIP)-expressing interneuron activity in the ACC. Behavioral data indicated that optogenetic and chemogenetic activation of these neurons reduced scratching responses related to histaminergic and non-histaminergic acute itch. Similar neural activity and modulatory role of these neurons were seen in mice with chronic itch induced by contact dermatitis. Together, this study highlights the importance of ACC VIP⁺ neurons in modulating itch-related affect and behavior, which may help us to develop novel mechanism-based strategies to treat refractory chronic itch in the clinic.
Animals ; Pruritus/physiopathology* ; Vasoactive Intestinal Peptide/metabolism* ; Interneurons/metabolism* ; Gyrus Cinguli/metabolism* ; Mice ; Male ; Mice, Inbred C57BL ; Histamine ; Chloroquine ; Optogenetics ; Mice, Transgenic

The Baihe Dihuang Decoction(BDD) is a representative traditional Chinese medicine formula that has been used to treat depression. This study employed metabolomics and network pharmacology to investigate the mechanism of BDD in the treatment of depression. Fifty male Sprague-Dawley(SD) rats were randomly assigned to the normal control group, model group, fluoxetine group, and high-and low-dose BDD groups. A rat model of depression was established through chronic unpredictable mild stress(CUMS), and the behavioral changes were detected by forced swimming test and open field test. Metabolomics technology was used to analyze the metabolic profiles of serum and hippocampal tissue to screen differential metabolites and related metabolic pathways. Additionally, network pharmacology and molecular docking techniques were used to investigate the key targets and core active ingredients of BDD in improving metabolic abnormalities of depression. A "component-target-metabolite-pathway" regulatory network was constructed. BDD could significantly improve depressive-like behavior in CUMS rats and regulate 12 differential metabolites in serum and 27 differential metabolites in the hippocampus, involving tryptophan metabolism, phenylalanine, tyrosine, and tryptophan biosynthesis, alanine, aspartate, and glutamate metabolism, tyrosine metabolism, and purine metabolism. Verbascoside, isorbascoside, and regaloside B were the key active ingredients for improving metabolic abnormalities in depression. Epidermal growth factor receptor(EGFR), protooncogene tyrosine-protein kinase(SRC), glycogen synthase kinase 3β(GSK3β), and androgen receptor(AR) were the key core targets for improving metabolic abnormalities of depression. This study offered a preliminary insight into the mechanism of BDD in alleviating metabolic abnormalities of depression through network regulation, providing valuable guidance for its clinical use and subsequent research.
Animals ; Drugs, Chinese Herbal/administration & dosage* ; Male ; Rats, Sprague-Dawley ; Rats ; Metabolomics ; Depression/genetics* ; Antidepressive Agents/chemistry* ; Network Pharmacology ; Hippocampus/drug effects* ; Humans ; Molecular Docking Simulation ; Behavior, Animal/drug effects* ; Disease Models, Animal