Objective To explore the diagnostic value of exhaled volatile organic compounds (VOCs) for cystic fibrosis (CF). Methods A systematic search was conducted in PubMed, EMbase, Web of Science, Cochrane Library, CNKI, Wanfang, VIP, and SinoMed databases up to August 7, 2024. Studies that met the inclusion criteria were selected for data extraction and quality assessment. The quality of included studies was assessed by the Newcastle-Ottawa Scale (NOS), and the risk of bias and applicability of included prediction model studies were assessed by the prediction model risk of bias assessment tool (PROBAST). Results A total of 10 studies were included, among which 5 studies only identified specific exhaled VOCs in CF patients, and another 5 developed 7 CF risk prediction models based on the identification of VOCs in CF. The included studies reported a total of 75 exhaled VOCs, most of which belonged to the categories of acylcarnitines, aldehydes, acids, and esters. Most models (n=6, 85.7%) only included exhaled VOCs as predictive factors, and only one model included factors other than VOCs, including forced expiratory flow at 75% of forced vital capacity (FEF75) and modified Medical Research Council scale for the assessment of dyspnea (mMRC). The accuracy of the models ranged from 77% to 100%, and the area under the receiver operating characteristic curve ranged from 0.771 to 0.988. None of the included studies provided information on the calibration of the models. The results of the Prediction Model Risk of Bias Assessment Tool (PROBAST) showed that the overall bias risk of all predictive model studies was high, and the overall applicability was unclear. Conclusion The exhaled VOCs reported in the included studies showed significant heterogeneity, and more research is needed to explore specific compounds for CF. In addition, risk prediction models based on exhaled VOCs have certain value in the diagnosis of CF, but the overall bias risk is relatively high and needs further optimization from aspects such as model construction and validation.

Alzheimer’s disease (AD) is a central neurodegenerative disease characterized by progressive cognitive decline and memory impairment in clinical. Currently, there are no effective treatments for AD. In recent years, a variety of therapeutic approaches from different perspectives have been explored to treat AD. Although the drug therapies targeted at the clearance of amyloid β-protein (Aβ) had made a breakthrough in clinical trials, there were associated with adverse events. Neuroinflammation plays a crucial role in the onset and progression of AD. Continuous neuroinflammatory was considered to be the third major pathological feature of AD, which could promote the formation of extracellular amyloid plaques and intracellular neurofibrillary tangles. At the same time, these toxic substances could accelerate the development of neuroinflammation, form a vicious cycle, and exacerbate disease progression. Reducing neuroinflammation could break the feedback loop pattern between neuroinflammation, Aβ plaque deposition and Tau tangles, which might be an effective therapeutic strategy for treating AD. Traditional Chinese herbs such as Polygonum multiflorum and Curcuma were utilized in the treatment of AD due to their ability to mitigate neuroinflammation. Non-steroidal anti-inflammatory drugs such as ibuprofen and indomethacin had been shown to reduce the level of inflammasomes in the body, and taking these drugs was associated with a low incidence of AD. Biosynthetic nanomaterials loaded with oxytocin were demonstrated to have the capability to anti-inflammatory and penetrate the blood-brain barrier effectively, and they played an anti-inflammatory role via sustained-releasing oxytocin in the brain. Transplantation of mesenchymal stem cells could reduce neuroinflammation and inhibit the activation of microglia. The secretion of mesenchymal stem cells could not only improve neuroinflammation, but also exert a multi-target comprehensive therapeutic effect, making it potentially more suitable for the treatment of AD. Enhancing the level of TREM2 in microglial cells using gene editing technologies, or application of TREM2 antibodies such as Ab-T1, hT2AB could improve microglial cell function and reduce the level of neuroinflammation, which might be a potential treatment for AD. Probiotic therapy, fecal flora transplantation, antibiotic therapy, and dietary intervention could reshape the composition of the gut microbiota and alleviate neuroinflammation through the gut-brain axis. However, the drugs of sodium oligomannose remain controversial. Both exercise intervention and electromagnetic intervention had the potential to attenuate neuroinflammation, thereby delaying AD process. This article focuses on the role of drug therapy, gene therapy, stem cell therapy, gut microbiota therapy, exercise intervention, and brain stimulation in improving neuroinflammation in recent years, aiming to provide a novel insight for the treatment of AD by intervening neuroinflammation in the future.

ObjectiveTo investigate the role of paraventricular nucleus (PVN) corticotropin releasing hormone (CRH) neurons in chronic restraint stress (CRS)-induced anxiety-like behavior. And whether exercise relieves chronic restraint stress-induced anxiety through PVN CRH neurons. MethodsTwenty 8-week-old male C57BL/6J mice were randomly divided into control (Ctrl) group and chronic restraint stress (CRS) group. The open field test (OFT) and elevated plus maze (EPM) were used to evaluate anxiety-like behavior of the mice. Food intake was recorded after CRS. Immunofluorescence staining was used to label the expression of c-Fos expression in PVN and calculate the co-expression of c-Fos and CRH neurons. We used chemogenetic activation of PVN CRH neurons to observed the anxiety-like behavior. 8-week treadmill training (10-16 m/min, 60 min/d, 6 d/week) were used to explore the role of exercise in ameliorating CRS-induced anxiety behavior and how PVN CRH neurons involved in it. ResultsCompared with Ctrl group, CRS group exhibited significant anxiety-like behavior. In OFT, the mice in CRS groups spent less time in center area (P<0.001). In EPM, the time in open arm in CRS group were significantly decreased (P<0.001). Besides, food intake was also suppressed in CRS group compared with Ctrl group (P<0.05). Compared with Ctrl group, CRS significantly increase c-Fos expression in PVN and most of CRH neurons co-express c-Fos (P<0.001). Chemogenetic activation of PVN CRH neurons induced anxiety-like behavior (P<0.05) and inhibited feeding behavior (P<0.01). Exercise relieves chronic restraint stress-induced anxiety (P<0.001) and relieved the anorexia caused by chronic restraint stress (P<0.05). Aerobic exercise inhibited the CRS labeled c-Fos in PVN CRH neurons (P<0.001). Furthermore, ablation of PVN CRH neurons attenuated CRS induced anxiety-like behavior. ConclusionCRS activated PVN CRH neurons, induced anxiety-like behavior and reduced food intake. 8-week exercise attenuated CRS-induced anxiety-like behavior through inhibiting PVN CRH neuron. Ablation of CRH PVN neurons ameliorated CRS-induced anxiety-like behavior. These finding reveals a potential neural mechanism of exercise-relieving CRS-induced anxiety-like behavior. This provides a new idea and theoretical basis for the treatment of anxiety and related mental disorders.

Objective To investigate the current status and experience of scientific research,attitude and demand for research training,and other research situations in military doctors with standardized training,to construct a targeted training system for research capacity of military general practitioners,and to explore the effect of the training system on the comprehensive research ability of military doctors.Methods Eighty-one military general practitioners who participated in the standardized training at The First Affiliated Hospital of Naval Medical University in 2022 were selected as research objects.A questionnaire-based survey was conducted among these doctors.SPSS 25.0 software was used to analyze the data obtained.The cultivation of research capacity included the training of basic research skills,advancement of reseach capacity,and the improvement of the assessment and incentive mechanism.Before and one year after the training,the research status,research attitude and demand,comprehensive research capacity,and mentor's evaluation for the research capacity were investigated.Results After one year of training,basic research skills and comprehensive research capacity of military general practitioners were significantly improved,mentor's evaluation was gradually improved,research achievements were significantly increased,training attitudes became more positive,and training needs changed.Conclusion The research capacity of military general practitioners has been significantly improved through the construction of training system of research capacity and continuous practice.

Objective To explore the anatomical landmarks and segmentation method for the intraoperative identification of the cervical segment of the internal carotid artery by studying cadaveric dissections with an endoscopic transoral medial pterygomandibular fold approach and to investigate its clinical significance.Methods The head specimens of five fresh frozen cadavers were dissected in the anatomical laboratory of the Surgical Treatment Technology Innovation Unit of Nasal Skull Base Tumor in Eye&ENT Hospital of Fudan University.The parapharyngeal space was dissected layer by layer through the endoscopic transoral medial pterygomandibular fold approach,and the location marks of parapharyngeal internal carotid artery(ppICA)and adjacent structures of ppICA were anatomically studied.The anatomical landmarks associated with ppICA were observed and characterized,and the ppICA was segmented anatomically according to its adjacent structures.Then,the length of each ppICA segment was measured.Results Muscle structures were essential anatomical landmarks for an endoscopic transoral pterygoid medial approach that identifies mandibular folds.The first layer of muscles included the superior pharyngeal constrictor,tensor veli palatini,and medial pterygoid muscles.The second layer includes the stylopharyngeus,styloglossus,longus capitis,and levator veli palatini muscles.The stylopharyngeal and levator veli palatini muscles were close to the ppICA and were reliable landmarks for locating the ppICA.Furthermore,the ppICA was divided into three segments according to their positional relationship with the ppICA.The first segment of ppICA(P1 ICA)was located between the greater horn plane of the hyoid bone and the intersection plane between the upper margin of stylopharyngeal muscle and ppICA.The second segment of ppICA(P2 ICA)was between the plane where the upper edge of the stylopharyngeal muscle intersected with the ppICA and the plane where the projection of inferior edge of the levator veli palatini muscle intersected with the ppICA.The third segment of ppICA(P3 ICA)was between the intersection of the lower margin projection of the levator veli palatini muscle and ppICA and the external orifice of the carotid canal.The P2 ICA was within an anatomical region bounded by the levator veli palatini muscle,longus capitis muscle,and stylopharyngeus muscle.This region was termed"ICA window"in this paper measured under the cadaver head specimen,the lengths of P1 ICA,P2 ICA,and P3 ICA were(36.5±7.3)mm,(15.5±1.6)mm,(7.4±1.7)mm respectively.Conclusion The muscular structure refers to the relatively constant anatomical reference landmarks within the endoscopic transoral medial pterygomandibular fold.The stylopharyngeus and levator veli palatini muscles are reliable landmarks for precisely locating and segmenting the ppICA,thus having essential clinical implications.

ObjectiveTo identify the active components in Maimendong decoction (MMDD) against pulmonary fibrosis (PF) and validate their molecular effects in vitro, while focusing on the role of methylophiopogonanone B in regulating fibrosis.MethodsData on MMDD components and targets were gathered from databases including BATMAN-TCM and PubMed, whereas the PF gene data were sourced from GeneCards, OMIM, and TTD. Shared targets were determined using the STRING database, and molecular docking was used to analyze the essential molecules associated with fibrosis. To simulate PF conditions, human embryonic lung fibroblasts (HPF) and A549 cells were exposed to transforming growth factor-β1 (TGF-β1). Various assays were used to determine the effects of MMDD and methylophiopogonanone B on signaling pathways, apoptosis, and epithelial–mesenchymal transition.ResultsWe identified 11 active components from MMDD extracts that targeted 511 shared proteins associated with PF, revealing 10 key targets in network analysis. Gene ontology analysis indicated that processes and pathways such as apoptosis regulation and PI3K/Akt signaling were involved. In vitro experiments revealed that MMDD downregulated the expression of α-smooth muscle actin (α-SMA), collagen type I (COL-I), and collagen type III and regulated Bcl-2/Bax signaling pathways to promote apoptosis. The flow cytometry apoptosis assay revealed that MMDD promoted the TGF-β1-induced apoptosis of myofibroblasts. The primary active ingredient in MMDD, methylophiopogonanone B, reduced α-SMA, COL-I, and PI3K/Akt/mTOR-related protein levels in TGF-β1-treated HPF cells, decreased Bcl-2 and cleaved caspase 3, and increased Bax. Moreover, methylophiopogonanone B increased E-cadherin levels and reduced α-SMA, fibronectin, N-cadherin, vimentin, and snail in TGF-β1-treated A549 cells.ConclusionMethylophiopogonanone B demonstrated the potential to treat PF by inducing myofibroblast apoptosis and inhibiting EMT. However, despite encouraging initial results, further clinical research is warranted to verify the safety and efficacy of methylophiopogonanone B in the management of PF

Objective:To construct and evaluate a radiomics model for predicting perineural invasion in patients with intrahepatic cholangiocarcinoma (ICC).Methods:Clinical data of 144 patients with ICC undergoing surgery in the People’s Hospital of Zhengzhou University ( n=113) and the Affiliated Cancer Hospital of Zhengzhou University ( n=31) from January 2018 to June 2023 were retrospectively analyzed, including 80 males and 64 females, aged (58.8±10.1) years. The patients were randomly divided into a training set ( n=100) and a test set ( n=44) at a ratio of 7: 3. The former set was used to build the model for predicting perineural invasion, and the latter was used to evaluate the model. Enhanced CT images and clinical data of the patients were collected, and features related to perineural invasion were screened. A light gradient boosting machine was used to construct an imaging genomics model. The model was evaluated using the receiver operating characteristic (ROC) curve, calibration curve, and decision curve analysis (DCA). Results:Univariate and multivariate logistic regression analysis showed that none of the clinical features were associated with neural invasion in ICC patients (all P>0.05). Six, 25, 32, and 37 radiomics features were obtained by screening the intratumoral, 2 mm peritumoral, 5 mm peritumoral, and 8 mm peritumoral regions, respectively. The area under the ROC curve for predicting perineural invasion in ICC patients was 0.849 (95% CI: 0.774-0.923) in the training set and 0.745 (95% CI: 0.597-0.894) in the test set for the intratumoral model, 0.966 (95% CI: 0.938-0.995) and 0.750 (95% CI: 0.604-0.896) for the 5mm peritumoral model, 0.936 (95% CI: 0.892-0.980) and 0.792 (95% CI: 0.644-0.939) for the 2mm peritumoral model, and 0.961 (95% CI: 0.929-0.992) and 0.689 (95% CI: 0.526-0.853) for the 8mm peritumoral model. The area under the ROC curve, accuracy, sensitivity, and specificity of the combined intratumoral and 5mm peritumoral model for predicting perineural invasion were 0.927 (95% CI: 0.878-0.976), 88.0%, 84.5%, and 89.8% in the training set, and 0.849 (95% CI: 0.737-0.960), 77.3%, 85.2%, and 72.0% in the test set, respectively. The calibration curve showed a deviation between the calibration curve of the combined intratumoral and 5mm peritumoral model and the ideal line, but it could achieve basic consistency. DCA showed that when the threshold was between 0.18 and 0.70, the combined intratumoral and 5mm peritumoral model could bring clinical net benefit to patients when predicting neural invasion. Conclusion:The intratumoral and 5mm peritumoral imaging genomics model based on enhanced CT features can effectively predict neural invasion and offer clinical benefits in patients with ICC.

Objective In this study,clinical data of Alzheimer's disease(AD)patients,structural magnetic resonance imaging(sMRI)data,and positron emission tomography(PET)data were used to construct an auxiliary diagnostic model with good classification effects,so as to formulate a personalized treatment plan at the early stage of the patients,which is of great significance for the prevention and treatment of AD.Methods In this study,a total of 401 study subjects containing complete sMRI images and PET images were selected from the ADNI-1(Alzheimer's disease neuroimaging initiative-1,ADNI-1)database.We used statistical parameters mapping(SPM)and voxel-based morphometric(VBM)analysis of MATLAB to perform pre-processing operations such as spatial normalization and skull stripping on sMRI images and PET images of the study subjects.With the help of the brain atlas was used to segment the brain tissue structure.After that,the segmented gray matter was extracted from the corresponding brain regions based on anatomical automatic labeling,and the feature values of all brain regions were obtained.Then the extracted brain region feature values are then subjected to fisher score,support vector machine-recursive feature elimination(SVM-RFE)and least absolute shrinkage and selection operator(LASSO),a hybrid filtered-wrapped-embedded feature selection method with three different principles,to realize the dimensionality reduction of high-dimensional image data.Finally,the PAC-Bayesian strategy boosting based multi-view learning(PB-MVBoost)model is constructed based on multi-view decision fusion for clinical,sMRI and PET data.And it is compared with the traditional machine learning models support vector machine(SVM),decision tree(DT),K-nearest neighbor(KNN),random forests(RF),adaptive boosting(AdaBoost),and extreme gradient boosting(XGBoost)which are constructed after concatenating views.It is compared with multi-view multi-kernel learning models(AverageMKL,EasyMKL)and multi-view confusion matrix boosting,which is also the same multi-view decision fusion.Results Among all the multi-view fusion models of AD-MCI,the PB-MVBoost model based on decision fusion has the best performance(accuracy=0.98,F1-score=0.97,precision=0.98,recall=0.96,MSE=0.07).Among all the multi-view fusion models of MCI-NC,the model performance of PB-MVBoost based on decision fusion was the best(accuracy=0.99,F1-score=0.98,precision=0.99,recall=0.98,MSE=0.05).Conclusion In the classification of AD-MCI and MCI-NC,the distinction and calibration degree of PB-MVBoost model were optimized,indicating that the auxiliary diagnosis model of Alzheimer's disease constructed by PB-MVBoost classifier based on decision fusion performed the best,which could improve the recognition of patients with mild cognitive impairment and then assist clinical diagnosis.

AIM To investigate the effects of Wenyang Jiedu Tongluo Recipe(WYJDTLR)on macrophage recruitment and polarization function in a mouse model of diabetic kidney disease(DKD).METHODS 50 db/db mice were randomly divided into the model group,the valsartan group(10.29 mg/kg)and the high-dose,medium-dose and low-dose WYJDTLR groups(26.52,13.26 and 6.63 g/kg),with 10 mice in each group,in contrast to another 10 db/m mice of the blank group.After 8 weeks of administration,the mice had their levels of fasting blood glucose,24-hour urinary protein quantity(24h-UTP),serum creatinine(Scr)and blood urea nitrogen(BUN)observed;their morphological changes of renal tissues observed by HE staining;their degree of renal glycogen deposition observed by PAS staining;their degree of renal fibrosis observed by Masson staining;their levels of MCP-1 and MCF-1 in serum and TNF-α and IL-1 β in renal tissue detected by ELISA;their renal protein expressions of VCAM-1 and ICAM-1 detected by IHC and Western blot;and their renal expressions of CD86 and CD206 detected by IF.RESULTS Compared with the model group,the WYJDTLR groups displayed decreased levels of fasting blood glucose,24h-UTP,Scr and BUN(P＜0.05,P＜0.01);improved degree of glomerular hypertrophy,mild proliferation of mesangial cells,dilatation of renal tubular,vacuolar degeneration of renal tubular epithelial cells,deposition of glomerular glycogen,and fibrosis of renal tissues(P＜0.01);decreased levels of MCP-1 and MCF-1 in serum and TNF-α and IL-1β in renal tissue(P＜0.05,P＜0.01);decreased renal protein expressions of VCAM-1 and ICAM-1(P＜0.05,P＜0.01),thus reduced the recruitment of macrophages to the kidney;decreased renal CD86 protein expression(P＜0.01);and increased CD206 protein expression(P＜0.01),thus inhibited M1-type polarization of macrophages and promoted M2-type polarization of macrophages.CONCLUSION WYJDTLR can delay the DKD progression in mice by reducing the occurrence of inflammatory reactions through reducing the level of macrophage recruitment factor,inhibiting the M1-type polarization,and promoting the M2-type polarization.

Objective In this study,clinical data of Alzheimer's disease(AD)patients,structural magnetic resonance imaging(sMRI)data,and positron emission tomography(PET)data were used to construct an auxiliary diagnostic model with good classification effects,so as to formulate a personalized treatment plan at the early stage of the patients,which is of great significance for the prevention and treatment of AD.Methods In this study,a total of 401 study subjects containing complete sMRI images and PET images were selected from the ADNI-1(Alzheimer's disease neuroimaging initiative-1,ADNI-1)database.We used statistical parameters mapping(SPM)and voxel-based morphometric(VBM)analysis of MATLAB to perform pre-processing operations such as spatial normalization and skull stripping on sMRI images and PET images of the study subjects.With the help of the brain atlas was used to segment the brain tissue structure.After that,the segmented gray matter was extracted from the corresponding brain regions based on anatomical automatic labeling,and the feature values of all brain regions were obtained.Then the extracted brain region feature values are then subjected to fisher score,support vector machine-recursive feature elimination(SVM-RFE)and least absolute shrinkage and selection operator(LASSO),a hybrid filtered-wrapped-embedded feature selection method with three different principles,to realize the dimensionality reduction of high-dimensional image data.Finally,the PAC-Bayesian strategy boosting based multi-view learning(PB-MVBoost)model is constructed based on multi-view decision fusion for clinical,sMRI and PET data.And it is compared with the traditional machine learning models support vector machine(SVM),decision tree(DT),K-nearest neighbor(KNN),random forests(RF),adaptive boosting(AdaBoost),and extreme gradient boosting(XGBoost)which are constructed after concatenating views.It is compared with multi-view multi-kernel learning models(AverageMKL,EasyMKL)and multi-view confusion matrix boosting,which is also the same multi-view decision fusion.Results Among all the multi-view fusion models of AD-MCI,the PB-MVBoost model based on decision fusion has the best performance(accuracy=0.98,F1-score=0.97,precision=0.98,recall=0.96,MSE=0.07).Among all the multi-view fusion models of MCI-NC,the model performance of PB-MVBoost based on decision fusion was the best(accuracy=0.99,F1-score=0.98,precision=0.99,recall=0.98,MSE=0.05).Conclusion In the classification of AD-MCI and MCI-NC,the distinction and calibration degree of PB-MVBoost model were optimized,indicating that the auxiliary diagnosis model of Alzheimer's disease constructed by PB-MVBoost classifier based on decision fusion performed the best,which could improve the recognition of patients with mild cognitive impairment and then assist clinical diagnosis.