BACKGROUND: Parkinson's disease (PD) is a leading cause of death and disability worldwide, and is associated with a significant Global Burden of Disease (GBD). We analyzed the trends in PD incidence, mortality, and disability-adjusted life year (DALY) burden in China, and compared them with global data. METHODS: Estimates and 95% uncertainty intervals (UIs) for incidence, mortality, DALYs, years lived with disability (YLDs), and years of life lost (YLLs) for PD were extracted from the GBD, Injuries, and Risk Factors Study 2021. We describe the epidemiology of PD at global and Chinese levels, analyze trends in incidence and mortality from 1990 to 2021 by joinpoint regression models, and decompose PD burden according to population size, age structure, and epidemiological changes. RESULTS: GBD 2021 estimated 508,378 (95% UI: 430,499-592,748) incident cases of PD, 92,035 (95% UI: 75,908-108,133) deaths, and 2,159,514 (95% UI: 1,826,196-2,521,344) DALYs in China, with the higher age-standardized rate (ASR) in incidence, mortality and DALYs than the global levels. The DALY burden of PD in China increased slightly from 1990 to 2021, consistent with the global upward trend. Joinpoint regression analysis indicated that the ASR of incidence in China increased faster than the global average, while the ASR of mortality decreased, with the fastest decline in 2004-2014. Decomposition analysis revealed that men and the middle sociodemographic index (SDI) quintile (32.82%) were responsible for the most significant DALYs, whose changes were primarily driven by population growth and aging. CONCLUSIONS The burden of PD showed an overall increasing trend from 1990 to 2021, which was primarily driven by population growth and aging. This study highlights the significant challenges in controlling and managing PD, including the increase in cases and gender differences, which may provide guidance for comprehensive strategies to address the changing profiles of PD in China.
Humans ; Parkinson Disease/mortality* ; China/epidemiology* ; Global Burden of Disease ; Male ; Incidence ; Female ; Disability-Adjusted Life Years ; Middle Aged ; Aged ; Adult ; Quality-Adjusted Life Years ; Aged, 80 and over ; Cost of Illness ; Adolescent ; Pattern Analysis, Machine

Oroxylin A (OA) is a natural flavonoid primarily derived from the plants Oroxylum indicum and Scutellaria baicalensis. Currently, OA is obtainable through chemical synthesis and exhibits polypharmacological properties, including anti-cancer, anti-inflammatory, anti-microbial, and multi-organ protective effects. The first-in-class drug OA tablets are presently undergoing phase Ib/IIa clinical trials for hepatocellular carcinoma (HCC) treatment. Substantial evidence suggests that OA demonstrates therapeutic potential against various hepatic and gastrointestinal (GI) disorders, including HCC, hepatic fibrosis, fatty liver disease, hepatitis, liver injury, colitis, and colorectal cancer (CRC). OA exerts its therapeutic effects primarily by modulating several crucial signaling pathways, including those associated with apoptosis, oxidative stress, inflammation, glucolipid metabolism, and fibrosis activation. The oral pharmacokinetics of OA is characterized by phase II metabolism, hydrolysis, and enterohepatic recycling. This review provides a comprehensive overview of the critical stages involved in the development of OA tablets, presenting a holistic perspective on the progression of this first-in-class drug from preclinical to clinical phases. It encompasses the synthesis of active pharmaceutical ingredients, pharmacokinetics, pharmacological efficacy, toxicology, drug delivery, and recent advancements in clinical trials. Importantly, this review examines the potential mechanisms by which OA may influence the gut-liver axis, hypothesizing that these interactions may confer health benefits associated with OA that transcend the limitations posed by its poor bioavailability.
Humans ; Flavonoids/pharmacokinetics* ; Tablets ; Animals ; Gastrointestinal Diseases/drug therapy* ; Liver Diseases/drug therapy* ; Drug Development ; Clinical Trials as Topic ; Scutellaria baicalensis/chemistry*

Objective To investigate the types and incidence of bifid mandibular canal using cone beam computed tomography(CBCT)technology so as to enhance our understanding of these anatomical features and help reduce complications caused by trauma to the neurovascular bundle within them.Methods CBCT data of 803 patients from College of Stomatology,Xi'an Jiaotong University,were collected,and the types and incidence of bifid mandibular canal were statistically analyzed according to the Naitoh classification method.Results The incidence of bifid mandibular canal was 54.05％,with the occurrence rates for the retromolar canal and forward canal being 26.40％ and 32.75％,respectively.Conclusion To ensure the safety of treatments,it is recommended that oral clinical practitioners use CBCT to obtain three-dimensional images for precise assessment of bifid mandibular canal,thereby avoiding potential complications during the perioperative period.

Objective:To investigate and validate the performance of a dosiomics model that utilized 3D dose distribution to forecast radiation-induced temporal lobe injury (RTLI) in nasopharyngeal carcinoma (NPC) patients following intensity-modulated radiotherapy (IMRT).Methods:Clinical data of 3578 patients diagnosed with NPC admitted to Jiangsu Cancer Hospital from January 2011 to December 2021 were retrospectively analyzed. According to the inclusion and exclusion criteria, 97 NPC patients who developed RTLI were assigned into the case group. A 1:1 propensity score matching (PSM) method was used to match 97 NPC patients without RTLI as the control group. Patients were assigned into the training cohort ( n=135) and the validation cohort ( n=59) at a 7:3 ratio by simple random method. Dosiomics features were extracted from the patients' three-dimensional dose distribution maps. Spearman rho and the least absolute shrinkage and selection operator regression were used to select dosiomics features. Clinical features were collected and screened by univariate and multivariate analyses. Eight machine learning classifiers were then trained to build dosiomics models and clinical models, respectively. The area under the ROC curve (AUC), sensitivity, and specificity were calculated to compare the predictive performance of the dosiomics and clinical models. Multivariate analysis was conducted using logistic regression to assess the influencing factors, while comparisons of the ROC curves between two different models were performed using the DeLong test. Results:A total of 1130 dosiomics features were extracted from the three-dimensional dose distribution maps, and 14 features were retained for model building after feature selection. The model based on the support vector machine (SVM) classifier achieved the highest AUC value of 0.977 (95% CI: 0.949-1.000) in the validation cohort, with an AUC of 1.000 (95% CI: 1.000-1.000) in the training cohort. By conducting univariate and multivariate analyses of the patients' clinical features, 2 clinical features were retained to build the clinical model. The model based on the SVM classifier achieved the optimal AUC value of 0.667 (95% CI: 0.523-0.810) in the validation cohort, with an AUC of 0.804 (95% CI: 0.730-0.878) in the training cohort. DeLong test showed that the difference between the dosiomics and clinical models was statistically significant ( P<0.05). Conclusion:The dosiomics model based on 3D dose distribution yields high predictive performance for RTLI in NPC patients after IMRT, which surpasses the clinical feature model, providing a new approach for early clinical prediction of RTLI.

Objective:To explore the dosimetry optimization strategy based on filter thickness and shape selection for the bulb superficial X-ray radiotherapy unit.Methods:Monte Carlo code TOPAS was used to model tubular equipment, and the dose distribution from six X-ray energies (50-150 kV) and five conventional aluminum filters (0.5-3.0 mm) with different thickness were simulated in the water model. The percentage depth dose (PDD) curve along the central axis, the center-axis profile dose at different depths, and the lateral dose distribution were analyzed. The dose distribution of three different designs of aluminum filters (conventional cylindrical, conical and oblique cylindrical filters) was compared to evaluate the effect of dosimetric optimization of different filter shapes.Results:Under the same energy, increasing the thickness of the filter can optimize the superficial skin dose, and the optimization effect of depth dose uniformity can be increased by 26% at a depth of 5.5 mm at 70 kV energy. The raised, flat and inclined dose distribution modes can be achieved by using conventional cylindrical, conical and inclined aluminum filters.Conclusions:By selecting the appropriate X-ray energy and filter thickness, an ideal dose distribution matching the tumor depth can be achieved. The application of personalized filters is also of great significance for diverse target areas.

Identification and functional protection of parathyroid glands are the key to reduce the incidence of postoperative complications after thyroid surgery. In recent years, the development of several fluorescence imaging technology and the application of artificial intelligence based on deep learning in thyroid surgery have brought technical breakthroughs in the identification and blood supply assessment of parathyroid glands during surgery, helping surgeons to identify parathyroid glands quickly and accurately, and improving the prognosis of surgery. Based on this, this article focuses on the new advances in the identification and protection of parathyroid glands in thyroid surgery, especially the research and application progress of fluorescence imaging, lymph node tracers, artificial intelligence and other aspects, and discusses the future development prospects.

Objective:To investigate the characteristics of resting-state mirror homotopic connectivity and the gray matter volume of brain in patients with neuropsychiatric systemic lupus erythematosus (NPSLE).Methods:From June 2020 to March 2023, a total of 35 NPSLE patients (NPSLE group) and 30 non-NPSLE patients (non-NPSLE group) were selected from Taizhou People's Hospital Affiliated to Nanjing Medical University, another 31 healthy volunteers were recruited as the healthy controls(HC group). All participants underwent resting-state functional magnetic resonance imaging (rs-fMRI) and mini-mental state examination (MMSE) assessments. The patients in NPSLE and non-NPSLE groups were additionally assessed using the fatigue scale for motor and cognitive functions (FSMC) and the hospital anxiety and depression scale (HADS).The DPABI V7.0 toolkit based on the MATLAB platform was used to preprocess the rs-fMRI data and calculate the voxel-mirrored homotopic connectivity(VMHC) indexes, and the differences in VMHC between groups were evaluated by covariance analysis in SPM12.0 software, and the VMHC values of brain regions with significant differences were extracted for further comparison between the two groups.Partial correlation analysis was performed to investigate the association between VMHC values and clinical parameters in NPSLE patients.The brain regions with significant differences between NPSLE patients and non-NPSLE patients were used as region of interest (ROI), and gray matter volumes within these ROIs were then calculated by VBM8 toolbox.Results:(1)There were statistically significant differences in the VMHC values of bilateral precentral gyrus, bilateral dorsolateral superior frontal gyrus, bilateral medial and paracingulate gyrus, bilateral parahippocampal gyrus, bilateral middle occipital gyrus, bilateral postcentral gyrus, and bilateral superior temporal gyrus among the 3 groups( F=11.246-14.102, all P<0.05). The NPSLE group exhibited significantly lower VMHC values in these regions compared to both the non-NPSLE group and HC group (all P<0.05), but there were no significant differences in these regions between the non-NPSLE group and HC group (all P>0.05).(2) The gray matter volumes of bilateral dorsolateral superior frontal gyrus(right: (0.57±0.11)mm 3, (0.65±0.08)mm 3, t=-3.409, P=0.001; left: (0.53±0.10)mm 3, (0.60±0.07)mm 3, t=-3.082, P=0.003), bilateral precentral gyrus(right: (0.32±0.06)mm 3, (0.35±0.04)mm 3, t=-2.044, P=0.045; left: (0.39±0.06)mm 3, (0.42±0.04)mm 3, t=-2.505, P=0.015), right medial and paracingulate gyrus((0.66±0.08)mm 3, (0.70±0.07)mm 3, t=-2.491, P=0.015) and left superior temporal gyrus((0.57±0.09)mm 3, (0.61±0.06)mm 3, t=- 2.344, P=0.022) in the NPSLE group were smaller than those of non-NPSLE group.(3)Correlation analysis showed that the VMHC value of dorsolateral superior frontal gyrus was positively correlated with IgA level in NPSLE patients ( r=0.353, P=0.047). Conclusion:Patients with NPSLE generally have decreased mirror homotopy functional connectivity in the cerebral hemispheres, accompanied by a decrease in gray matter volume in some brain regions, which can provide a certain neuroimaging basis for the pathogenesis of brain injury.

The superiority of magnetic resonance(MR)images in soft tissue imaging makes them indispensable for medical diagnosis and radiotherapy,but factors such as acquisition cost and contraindications limit their widespread application.In contrast,computed tomography(CT)scanning has the advantages of fast imaging speed and low cost.Herein,this review summarizes the research progress of generative deep learning models in the field of medical CT to MR image synthesis,and especially analyzes the technical characteristics,performance advantages,and challenges of various MR image synthesis methods from clinical scenarios such as spinal lesions,acute ischemic stroke,and tumor segmentation.Furthermore,the application value and future research prospects of medical image synthesis are discussed.

Identification and functional protection of parathyroid glands are the key to reduce the incidence of postoperative complications after thyroid surgery. In recent years, the development of several fluorescence imaging technology and the application of artificial intelligence based on deep learning in thyroid surgery have brought technical breakthroughs in the identification and blood supply assessment of parathyroid glands during surgery, helping surgeons to identify parathyroid glands quickly and accurately, and improving the prognosis of surgery. Based on this, this article focuses on the new advances in the identification and protection of parathyroid glands in thyroid surgery, especially the research and application progress of fluorescence imaging, lymph node tracers, artificial intelligence and other aspects, and discusses the future development prospects.

Objective:To investigate the characteristics of resting-state mirror homotopic connectivity and the gray matter volume of brain in patients with neuropsychiatric systemic lupus erythematosus (NPSLE).Methods:From June 2020 to March 2023, a total of 35 NPSLE patients (NPSLE group) and 30 non-NPSLE patients (non-NPSLE group) were selected from Taizhou People's Hospital Affiliated to Nanjing Medical University, another 31 healthy volunteers were recruited as the healthy controls(HC group). All participants underwent resting-state functional magnetic resonance imaging (rs-fMRI) and mini-mental state examination (MMSE) assessments. The patients in NPSLE and non-NPSLE groups were additionally assessed using the fatigue scale for motor and cognitive functions (FSMC) and the hospital anxiety and depression scale (HADS).The DPABI V7.0 toolkit based on the MATLAB platform was used to preprocess the rs-fMRI data and calculate the voxel-mirrored homotopic connectivity(VMHC) indexes, and the differences in VMHC between groups were evaluated by covariance analysis in SPM12.0 software, and the VMHC values of brain regions with significant differences were extracted for further comparison between the two groups.Partial correlation analysis was performed to investigate the association between VMHC values and clinical parameters in NPSLE patients.The brain regions with significant differences between NPSLE patients and non-NPSLE patients were used as region of interest (ROI), and gray matter volumes within these ROIs were then calculated by VBM8 toolbox.Results:(1)There were statistically significant differences in the VMHC values of bilateral precentral gyrus, bilateral dorsolateral superior frontal gyrus, bilateral medial and paracingulate gyrus, bilateral parahippocampal gyrus, bilateral middle occipital gyrus, bilateral postcentral gyrus, and bilateral superior temporal gyrus among the 3 groups( F=11.246-14.102, all P<0.05). The NPSLE group exhibited significantly lower VMHC values in these regions compared to both the non-NPSLE group and HC group (all P<0.05), but there were no significant differences in these regions between the non-NPSLE group and HC group (all P>0.05).(2) The gray matter volumes of bilateral dorsolateral superior frontal gyrus(right: (0.57±0.11)mm 3, (0.65±0.08)mm 3, t=-3.409, P=0.001; left: (0.53±0.10)mm 3, (0.60±0.07)mm 3, t=-3.082, P=0.003), bilateral precentral gyrus(right: (0.32±0.06)mm 3, (0.35±0.04)mm 3, t=-2.044, P=0.045; left: (0.39±0.06)mm 3, (0.42±0.04)mm 3, t=-2.505, P=0.015), right medial and paracingulate gyrus((0.66±0.08)mm 3, (0.70±0.07)mm 3, t=-2.491, P=0.015) and left superior temporal gyrus((0.57±0.09)mm 3, (0.61±0.06)mm 3, t=- 2.344, P=0.022) in the NPSLE group were smaller than those of non-NPSLE group.(3)Correlation analysis showed that the VMHC value of dorsolateral superior frontal gyrus was positively correlated with IgA level in NPSLE patients ( r=0.353, P=0.047). Conclusion:Patients with NPSLE generally have decreased mirror homotopy functional connectivity in the cerebral hemispheres, accompanied by a decrease in gray matter volume in some brain regions, which can provide a certain neuroimaging basis for the pathogenesis of brain injury.