Osteoporosis is a common bone disease， whose incidence is still on the rise， posing great challenges to patients and society. This review mainly studies the pathogenesis of osteoporosis from the aspects of oxidative stress， inflammatory response， and glucolipotoxicity-induced injury and clarifies the efficacy and mechanism of some active ingredients of traditional Chinese medicine against osteoporosis through the integration of in vitro and in vivo experiments. The experimental results suggest that some active ingredients can improve bone resorption markers and maintain bone homeostasis by modulating inflammation， oxidative stress， etc. These active ingredients regulate osteoporosis through the receptor activator of nuclear transcription factor-κB （NF-κB） ligand （RANKL） pathway， osteoprotegerin （OPG） pathway， Wnt/β-catenin pathway， NF-κB pathway， mitogen-activated protein kinase （MAPK） pathway， adenosine monophosphate （AMP）-activated protein kinase （AMPK）/mammalian target of rapamycin （mTOR） pathway， and oxidative stress pathway. This review provides ideas for the progress of the prevention and treatment of osteoporosis with the active ingredients of traditional Chinese medicine， aiming to provide new potential lead compounds and reference for the development of innovative drugs and clinical therapy for the treatment of osteoporosis.

Purpose To explore the application value of combining 18F-FDG PET images with interpretable deep learning radiomics(IDLR)models in the differential diagnosis of primary Parkinson's disease(IPD)and atypical Parkinson's syndrome.Materials and Methods This cross-sectional study was conducted using the Parkinson's Disease PET Imaging Benchmark Database from Huashan Hospital,Fudan University from March 2015 to February 2023.A total of 330 Parkinson's disease patients underwent 18F-FDG PET imaging,both 18F-FDG PET imaging and clinical scale information were collected for all subjects.The study included two cohorts,a training group(n=270)and a testing group(n=60),with a total of 211 cases in the IPD group,59 cases in the progressive supranuclear palsy(PSP)group,and a group of 60 patients with multiple system atrophy(MSA).The clinical information between different groups were compared.An IDLR model was developed to extract feature indicators.Under the supervision of radiomics features,IDLR features were selected from the features collected by neural network extractors,and a binary support vector machine model was constructed for the selected features in images of in testing group.The constructed IDLR model,traditional radiomics model and standard uptake ratio model were separately used to calculate the performance metrics and area under curve values of deep learning models for pairwise classification between IPD/PSP/MSA groups.The study conducted independent classification and testing in two cohorts using 100 10-fold cross-validation tests.Brain-related regions of interest were displayed through feature mapping,using gradient weighted class activation maps to highlight and visualize the most relevant information in the brain.The output heatmaps of different disease groups were examined and compared with clinical diagnostic locations.Results The IDLR model showed promising results for differentiating between Parkinson's syndrome patients.It achieved the best classification performance and had the highest area under the curve values compared to other comparative models such as the standard uptake ratio model(Z=1.22-3.23,all P<0.05),and radiomics model(Z=1.31-2.96,all P<0.05).The area under the curve values for the IDLR model in differentiating MSA and IPD were 0.935 7,for MSA and PSP were 0.975 4,for IPD and PSP were 0.982 5 in the test set.The IDLR model also showed consistency between its filtered feature maps and the visualization of gradient-weighted class activation mapping slice thermal maps in the radiomics regions of interest.Conclusion The IDLR model has the potential for differential diagnosis between IPD and atypical Parkinson's syndrome in 18F-FDG PET images.

Purpose To establish glucose metabolism patterns of Parkinson's disease(PD)at different periods,and to study the changing pattern of target region of interest(ROI)with the period of time,and then explore the relationship between ROIs and cognitive or motor in different periods.Materials and Methods A total of 42 patients with early-stage PD collected from June 2010 to September 2022 in online data from the markers of Parkinson's progression study which included clinical data,and FDG PET imaging was performed at baseline,12,24,36 and 48 months.The data of 8 healthy volunteers were also obtained from the database,and the time range was the same as that of the above-mentioned PD patients.The longitudinal changes of cerebral glucose metabolism in PD patients and the relationship between PD-associated ROI and movement disorder society-sponsored revision of the unified Parkinson's disease rating scale(MDS-UPDRS)score were evaluated.Results PD was relatively reduced activity located in frontal and parietal association areas and relatively increased activity in the cerebellum,the putamen and the cingulate gyrus.In our study of target ROIs over time,FDG uptake in the caudate nucleus,putamen,pallidum,and cerebellum of patients with PD was initially higher than in the normal group,and subsequently decreased.In contrast,the ROI of PD in the anterior cingulate gyrus,posterior cingulate gyrus,the substantia nigra pars compacta and substantia nigra pars reticulata was initially lower than that in healthy controls and subsequently increased.The putamen,pallidum and caudate nucleus metabolic activity showed a positive correlation in 36 month and MDS-UPDRS scores(r=0.659 5,0.678 7,0.716 7,all P<0.05).The caudate nucleus,putamen and pallidum metabolic activity showed a negative correlation in 24 month and baseline(r=-0.541 8,-0.878 9,-0.887 6,all P<0.05).Conclusion We provide 5-year longitudinal data on changes in 18F-FDG imaging outcomes in early PD.In addition,the glucose metabolic activity of caudate nucleus,putamen and globus pallidus are correlated with MDS-UPDRS scores.

Objective:A voxel-level quantification method based on the tau IQ algorithm and Braak staging, excluding β-amyloid (Aβ) imaging, was developed to achieve specific tau quantification. Methods:This cross-sectional study included 92 subjects (35 males, 57 females; age (62.9±10.4) years) from the Nuclear Medicine/PET Center of Huashan Hospital, Fudan University between November 2018 and July 2020. The cohort comprised 28 cognitively normal (CN) individuals, 20 patients with mild cognitive impairment (MCI), and 44 patients with Alzheimer′s disease (AD). All participants underwent 18F-florzolotau PET imaging, Mini-Mental State Examination (MMSE), and Clinical Dementia Rating (CDR) scoring. A longitudinal tau dataset was constructed based on Braak staging. Voxel-level logistic regression fitting provided a baseline matrix, decomposed via least squares to yield the Tau load coefficient. One-way analysis of variance (with post hoc Tukey) was used to compare Tau load and SUV ratio (SUVR) among groups. ROC curve analysis was used to evaluate classification between CN, MCI and AD. Spearman rank correlation was used to assess the relationships between Tau load, SUVR, and MMSE scores or CDR scores. Results:The Tau load in the CN group was close to 0 and significantly lower than that in the MCI and AD groups ( F=55.03, P<0.001; post hoc tests all P<0.001). Significant differences were also observed in the SUVR across all ROIs ( F values: 36.46-55.38, all P<0.001). Compared to SUVR, Tau load demonstrated greater intergroup differences. In ROC curve analyses between each pair of CN, MCI, and AD groups, Tau load consistently achieved the highest AUC (0.754-1.000). Both Tau load and SUVR for each ROI were negatively correlated with MMSE scores ( rs values: from -0.698 to -0.583, all P<0.05) and positively correlated with CDR scores ( rs values: 0.648-0.783, all P<0.05), with Tau load showing the highest absolute correlation coefficients. Conclusion:Compared to the traditional semi-quantitative SUVR method, the Braak-tau IQ algorithm does not require a specific reference brain region to achieve specific tau quantification.

Objective:To investigate the effect of inferior vena cava variability (IVCV) combined with difference of central venous-to-arterial partial pressure of carbon dioxide (Pcv-aCO 2) on guiding fluid resuscitation in septic shock. Methods:Patients with septic shock admitted to the department of critical care medicine of Jiangxi Provincial People's Hospital from January 1, 2018 to December 31, 2020 were enrolled, and they were divided into control group and observation group according to random number table method. Patients in both groups were given fluid resuscitation according to septic shock fluid resuscitation guidelines. The patients in the control group received fluid resuscitation strictly according to the early goal-directed therapy (EGDT) strategy. Resuscitation target: central venous pressure (CVP) 12-15 cmH 2O (1 cmH 2O≈0.098 kPa), mean arterial pressure (MAP) > 65 mmHg (1 mmHg≈0.133 kPa), mean urine volume (UO) > 0.5 mL·kg -1·h -1, central venous oxygen saturation (ScvO 2) > 0.70. In the observation group, the endpoint of resuscitation was evaluated by IVCV dynamically monitored by bedside ultrasound and Pcv-aCO 2. Resuscitation target: fixed filling of inferior vena cava with diameter > 2 cm, IVCV < 18%, and Pcv-aCO 2 < 6 mmHg. The changes in recovery indexes before and 6 hours and 24 hours of resuscitation of the two groups were recorded, and the 6-hour efficiency of fluid resuscitation, 6-hour lactate clearance rate (LCR) and 6-hour and 24-hour total volume of resuscitation were also recorded; at the same time, the duration of mechanical ventilation, length of intensive care unit (ICU) stay, 28-day mortality and the incidence of acute renal failure and acute pulmonary edema between the two groups were compared. Results:A total of 80 patients were enrolled in the analysis, with 40 in the control group and 40 in the observation group. The MAP, CVP and ScvO 2 at 6 hours and 24 hours of resuscitation in the two groups were significantly higher than those before resuscitation, while Pcv-aCO 2 and blood lactic acid (Lac) were significantly decreased, and UO was increased gradually with the extension of resuscitation time, indicating that both resuscitation endpoint evaluation schemes could alleviate the shock state of patients. Compared with before resuscitation, IVCV at 6 hours and 24 hours of resuscitation in the observation group were decreased significantly [(17.54±4.52)%, (18.32±3.64)% vs. (27.49±10.56)%, both P < 0.05]. Compared with the control group, MAP and ScvO 2 at 6 hours of resuscitation in the observation group were significantly increased [MAP (mmHg): 69.09±4.64 vs. 66.37±4.32, ScvO 2: 0.666±0.033 vs. 0.645±0.035, both P < 0.05], 24-hour MAP was increased significantly (mmHg: 75.16±3.28 vs. 70.12±2.18, P < 0.05), but CVP was relatively lowered (cmH 2O: 9.25±1.49 vs. 10.25±1.05, P < 0.05), indicating that the fluid resuscitation efficiency was higher in the observation group. Compared with the control group, 6-hour LCR in the observation group was significantly increased [(55.64±6.23)% vs. (52.45±4.52)%, P < 0.05], 6-hour and 24-hour total volume of resuscitation was significantly decreased (mL: 2 860.73±658.32 vs. 3 568.54±856.43, 4 768.65±1 085.65 vs. 5 385.34±1 354.83, both P < 0.05), the duration of mechanical ventilation was significantly shortened (days: 6.78±3.45 vs. 8.45±2.85, P < 0.05), while the incidence of acute pulmonary edema was significantly decreased [2.5% (1/40) vs. 20.0% (8/40), P < 0.05]. There was no significant difference in the length of ICU stay, 28-day mortality or incidence of acute renal failure between the two groups. Conclusions:Dynamic monitoring of IVCV and Pcv-aCO 2 can effectively guide the early fluid resuscitation of patients with septic shock, and compared with EGDT, it can significantly shorten the duration of mechanical ventilation, reduce the amount of fluid resuscitation, and reduce the incidence of acute pulmonary edema. Combined with its non-invasive characteristics, it has certain clinical advantages.

Objective:To explore the potential application of combining 18F-FDG PET imaging and radiomics in the diagnosis of Parkinson′s disease (PD) and atypical parkinsonian syndromes (APS). Methods:A total of 154 subjects of two cohorts (training set and validation set) were enrolled from Huashan Hospital, Fudan University from March 2015 to August 2020 in this cross-sectional study, including 40 normal controls (NC; 23 males and 17 females, age: (60.2±10.5) years), 40 PD patients (20 males and 20 females, age: (64.7±6.3) years), 40 progressive supranuclear palsy (PSP) patients (20 males and 20 females, age: (64.1±5.9) years), and 34 multiple system atrophy (MSA) patients (19 males and 15 females, age: (65.0±9.2) years). 18F-FDG PET images and clinical scale were selected, and one-way analysis of variance was used to compare differences of clinical scale among groups. Radiomic features extraction and feature selection were carried out. Two and three classification models were constructed based on logistic regression, and the ROC curves of clinical model, radiomics model and combined model were calculated. Independent classification tests were conducted 100 times with 5-fold cross validation in two cohorts. Results:There were significant differences in the scores of unified PD Rating Scale (UPDRS) and Hoehn-Yahr rating scale (H&Y) among different groups in cohort 1 and cohort 2 respectively ( F values: 4.83-17.95, all P<0.05). A total of 2 444 imaging features were extracted from each subject, and after features selection, 15 features for classification were obtained. In the two classification experiment, the AUCs of the three models in binary classification of PD/MSA/PSP/NC group were 0.56-0.68, 0.74-0.93 and 0.72-0.93, respectively. The classification effects of the radiomics model were significantly better than those of the clinical model ( z values: 1.71-2.85, all P<0.05). In the three classification experiment, the sensitivity of the radiomics model reached 80%, 80% and 77% for PD, MSA and PSP, respectively. Conclusion:18F-FDG imaging combined with radiomics has potential in the diagnosis of PD and APS.

Due to the availability of 18F-FDG in PET centers, this article aims to advocate and promote the standardization of 18F-FDG PET brain imaging in dementia in order to improve the reliability, repeatability and comparison of the imaging process and results. It is also provided to guide the PET imaging operation standard and to give suggestions on image interpretation.

ObjectiveTo analyze the drop-out rate of participants in antidepressant clinical trials and to explore the related influencing factors. MethodsA retrospective analysis was carried out on the participants of 9 antidepressant clinical trials conducted at the Affiliated Brain Hospital of Guangzhou Medical University from 2013 to 2020. A self-compiled questionnaire was used to collect the subjects' demographic data， disease characteristics and the final completion of the trial， thereafter， the participant drop-out rate and related influencing factors were discussed. ResultsA total of 157 cases were enrolled， including 120 cases completed and 37 cases dropped out the trail. The causes of drop-out were poor efficacy in 13 cases （35.14%）， presence of adverse reactions in 12 cases （32.43%）， withdrawal of informed consent in 8 cases （21.62%） and loss of follow-up in 4 cases （10.81%）. Correlation analysis showed that participant drop-out was positively correlated with the level of anxiety （r=0.224， P<0.01） and presence of adverse events （r=0.158， P<0.05）， meantime， negatively correlated with the level of education （r=-0.209， P<0.01） and overall efficacy （r=-0.545， P<0.01）. Binary Logistic regression analysis showed that education level （β=-0.611， OR=0.543， P<0.05）， number of visits （β=-1.831， OR=0.160， P<0.01） and overall efficacy （β=-2.286， OR=0.102， P<0.01） were the influencing factors of participant drop-out. ConclusionLow education level， first visit， poor outcome， high level of anxiety， and adverse events are the factors affecting participant drop-out in antidepressant clinical trials.

Objective: To investigate the value of statistical parametric mapping (SPM) analysis of ¹⁸F-fluorodeoxyglucose (FDG) PET imaging in the differential diagnosis of Parkinsonism in single-case level. Methods: SPM software was used to retrospectively analyze the ¹⁸F-FDG PET images of 160 patients (104 males, 56 females, age: 30-82 years) who were suspected with Parkinsonism at baseline and were clinical confirmed by follow-up from April 2010 to December 2017. ¹⁸F-FDG PET images of patients was compared with those of age-matched healthy controls in single-case level using two-sample t test in SPM software to obtain the imaging diagnosis. By comparing imaging diagnosis with the final clinical diagnosis, the diagnostic accuracy of SPM in the overall cohort as well as the early subcohort (duration of disease less than 2 years (56 males, 22 females, age: 50-82 years)) were calculated respectively. Results: Among 160 patients with Parkinsonism, 146(91.2%) had the same ¹⁸F-FDG PET diagnosis as their final clinical diagnosis. The diagnostic sensitivity for Parkinson′s disease (PD), multiple system atrophy (MSA), progressive supranuclear palsy (PSP) and cortical basal ganglia degeneration (CBD) were 93.5%(86/92), 92.3%(24/26), 84.0%(21/25) and 15/17, respectively. The specificity were 95.6%(65/68), 95.5%(128/134), 96.3%(130/135) and 100%(143/143), respectively. In the early subcohort, the analysis also achieved similar differential diagnosis effectiveness(92.3%). Conclusion The single-case ¹⁸F-FDG PET imaging SPM analysis can be helpful in the early differential diagnosis of Parkinsonism effectively.

Objective To investigate the value of statistical parametric mapping (SPM) analysis of 18F-fluorodeoxyglucose (FDG) PET imaging in the differential diagnosis of Parkinsonism in single-case level.Methods SPM software was used to retrospectively analyze the 18F-FDG PET images of 160 patients (104males,56 females,age:30-82 years) who were suspected with Parkinsonism at baseline and were clinical confirmed by follow-up from April 2010 to December 2017.18F-FDG PET images of patients was compared with those of age-matched healthy controls in single-case level using two-sample t test in SPM software to obtain the imaging diagnosis.By comparing imaging diagnosis with the final clinical diagnosis,the diagnostic accuracy of SPM in the overall cohort as well as the early subcohort (duration of disease less than 2 years (56 males,22 females,age:50-82 years)) were calculated respectively.Results Among 160 patients with Parkinsonism,146(91.2％) had the same 18F-FDG PET diagnosis as their final clinical diagnosis.The diagnostic sensitivity for Parkinson's disease (PD),multiple system atrophy (MSA),progressive supranuclear palsy (PSP) and cortical basal ganglia degeneration (CBD) were 93.5％ (86/92),92.3％ (24/26),84.0％(21/25) and 15/17,respectively.The specificity were 95.6％(65/68),95.5％(128/134),96.3％ (130/135) and 100％(143/143),respectively.In the early subcohort,the analysis also achieved similar differential diagnosis effectiveness(92.3％).Conclmion The single-case 18F-FDG PET imaging SPM analysis can be helpful in the early differential diagnosis of Parkinsonism effectively.