Stage-dependent differential impact of network communication on cognitive function across the continuum of cognitive decline in Parkinson's disease.

OBJECTIVE: Our objective was to explore the patterns of resting-state network (RSN) connectivity alterations and investigate how the influences of individual-level network connections on cognition varied across clinical stages without assuming a constant relationship. METHODS: 108 PD patients with continuum of cognitive decline (PD-NC = 46, PD-MCI = 43, PDD = 19) and 34 healthy controls (HCs) underwent resting-state functional MRI and neuropsychological tests. Independent component analysis (ICA) and graph theory analyses (GTA) were employed to explore RSN connection changes. Additionally, stage-dependent differential impact of network communication on cognitive performance were examined using sparse varying coefficient modeling. RESULTS: Compared to HCs, the dorsal attention network (DAN) and dorsal sensorimotor network (dSMN) were central networks with decreased connections in PD-NC and PD-MCI stage, while the lateral visual network (LVN) emerged as a central network in patients with dementia. Additionally, connectivity of the cerebellum network (CBN) increased in the PD-NC and PD-MCI stages. GTA demonstrated decreased nodal metrics for DAN and dSMN, coupled with an increase for CBN. Moreover, the degree centrality (DC) values of DAN and dSMN exhibited a stage-dependent differential impact on cognitive performance across the continuum of cognitive decline. CONCLUSION: Our findings suggest that across the progression of cognitive impairment, the LVN gradually transitions into a core node with reduced connectivity, while the enhancement of connections in CBN diminishes. Furthermore, the non-linear relationship between the DC values of RSNs and cognitive decline indicates the potential for tailored interventions targeting specific stages.

Clipping the Positive Lymph Node in Patients with Clinically Node Positive Breast Cancer Treated with Neoadjuvant Chemotherapy: Impact on Axillary Surgery in the ISPY-2 Clinical Trial.

BACKGROUND: For patients with clinically node-positive (cN+) breast cancer undergoing neoadjuvant chemotherapy (NAC), retrieving previously clipped, biopsy-proven positive lymph nodes during sentinel lymph node biopsy [i.e., targeted axillary dissection (TAD)] may reduce false negative rates. However, the overall utilization and impact of clipping positive nodes remains uncertain. PATIENTS AND METHODS: We retrospectively analyzed cN+ ISPY-2 patients (2011-2022) undergoing axillary surgery after NAC. We evaluated trends in node clipping and associations with type of axillary surgery [sentinel lymph node (SLN) only, SLN and axillary lymph node dissection (ALND), or ALND only] and event-free survival (EFS) in patients that were cN+ on a NAC trial. RESULTS: Among 801 cN+ patients, 161 (20.1%) had pre-NAC clip placement in the positive node. The proportion of patients that were cN+ undergoing clip placement increased from 2.4 to 36.2% between 2011 and 2021. Multivariable logistic regression showed nodal clipping was independently associated with higher odds of SLN-only surgery [odds ratio (OR) 4.3, 95% confidence interval (CI) 2.8-6.8, p < 0.001]. This was also true among patients with residual pathologically node-positive (pN+) disease. Completion ALND rate did not differ based on clip retrieval success. No significant differences in EFS were observed in those with or without clip placement, both with or without successful clip retrieval [hazard ratio (HR) 0.85, 95% CI 0.4-1.7, p = 0.7; HR 1.8, 95% CI 0.5-6.0, p = 0.3, respectively]. CONCLUSION: Clip placement in the positive lymph node before NAC is increasingly common. The significant association between clip placement and omission of axillary dissection, even among patients with pN+ disease, suggests a paradigm shift toward TAD as a definitive surgical management strategy in patients with pN+ disease after NAC.

Matrix crosslinking forces tumor progression by enhancing integrin signaling.

Tumors are characterized by extracellular matrix (ECM) remodeling and stiffening. The importance of ECM remodeling to cancer is appreciated; the relevance of stiffening is less clear. We found that breast tumorigenesis is accompanied by collagen crosslinking, ECM stiffening, and increased focal adhesions. Induction of collagen crosslinking stiffened the ECM, promoted focal adhesions, enhanced PI3 kinase (PI3K) activity, and induced the invasion of an oncogene-initiated epithelium. Inhibition of integrin signaling repressed the invasion of a premalignant epithelium into a stiffened, crosslinked ECM and forced integrin clustering promoted focal adhesions, enhanced PI3K signaling, and induced the invasion of a premalignant epithelium. Consistently, reduction of lysyl oxidase-mediated collagen crosslinking prevented MMTV-Neu-induced fibrosis, decreased focal adhesions and PI3K activity, impeded malignancy, and lowered tumor incidence. These data show how collagen crosslinking can modulate tissue fibrosis and stiffness to force focal adhesions, growth factor signaling and breast malignancy.

The impact of cognitive behavioural therapy-based psychological intervention on emotional improvement in elderly patients with extensive burns.

To evaluate the efficacy of cognitive behavioural therapy (CBT) as a psychological intervention for elderly patients with extensive burns, focusing on its impact on emotional well-being, self-efficacy and quality of life. A prospective, randomized study involving 200 elderly burn patients was conducted from November 2021 to January 2023. The patients were randomly assigned to receive either standard care (control group) or burn care based on cognitive behavioural therapy (CBT-B) (study group), with 100 patients in each group. Outcome measures included the Visual Analog Scale (VAS) for pain assessment, 36-item Short Form Survey (SF-36) for quality of life, General Self-Efficacy Scale (GSES) and Rosenberg Self-Esteem Scale (RSES). The study revealed that CBT-based intervention significantly reduced anxiety and depression scores compared with standard care (p < 0.05). Additionally, patients in the CBT group exhibited improved self-efficacy, self-esteem and quality of life (p < 0.05). CBT proves to be a valuable intervention for elderly burn patients, effectively addressing emotional distress and enhancing their psychological well-being. By modifying negative cognitive patterns, providing coping mechanisms and fostering problem-solving skills, CBT-based care contributes to a more positive recovery experience and improved quality of life.

Breast Conservation Surgery and Mastectomy Have Similar Locoregional Recurrence After Neoadjuvant Chemotherapy: Results From 1462 Patients on the Prospective, Randomized I-SPY2 Trial.

Neoadjuvant chemotherapy (NAC) increases rates of successful breast-conserving surgery (BCS) in patients with breast cancer. However, some studies suggest that BCS after NAC may confer an increased risk of locoregional recurrence (LRR). We assessed LRR rates and locoregional recurrence-free survival (LRFS) in patients enrolled on I-SPY2 (NCT01042379), a prospective NAC trial for patients with clinical stage II to III, molecularly high-risk breast cancer. Cox proportional hazards models were used to evaluate associations between surgical procedure (BCS vs mastectomy) and LRFS adjusted for age, tumor receptor subtype, clinical T category, clinical nodal status, and residual cancer burden (RCB). In 1462 patients, surgical procedure was not associated with LRR or LRFS on either univariate or multivariate analysis. The unadjusted incidence of LRR was 5.4% after BCS and 7.0% after mastectomy, at a median follow-up time of 3.5 years. The strongest predictor of LRR was RCB class, with each increasing RCB class having a significantly higher hazard ratio for LRR compared with RCB 0 on multivariate analysis. Triple-negative receptor subtype was also associated with an increased risk of LRR (hazard ratio: 2.91, 95% CI: 1.8-4.6, P < 0.0001), regardless of the type of operation. In this large multi-institutional prospective trial of patients completing NAC, we found no increased risk of LRR or differences in LRFS after BCS compared with mastectomy. Tumor receptor subtype and extent of residual disease after NAC were significantly associated with recurrence. These data demonstrate that BCS can be an excellent surgical option after NAC for appropriately selected patients.

Functional connectome automatically differentiates multiple system atrophy (parkinsonian type) from idiopathic Parkinson's disease at early stages.

Differentiating the parkinsonian variant of multiple system atrophy (MSA-P) from idiopathic Parkinson's disease (IPD) is challenging, especially in the early stages. This study aimed to investigate differences and similarities in the brain functional connectomes of IPD and MSA-P patients and use machine learning methods to explore the diagnostic utility of these features. Resting-state fMRI data were acquired from 88 healthy controls, 76 MSA-P patients, and 53 IPD patients using a 3.0 T scanner. The whole-brain functional connectome was constructed by thresholding the Pearson correlation matrices of 116 regions, and topological properties were evaluated through graph theory approaches. Connectome measurements were used as features in machine learning models (random forest [RF]/logistic regression [LR]/support vector machine) to distinguish IPD and MSA-P patients. Regarding graph metrics, early IPD and MSA-P patients shared network topological properties. Both patient groups showed functional connectivity disruptions within the cerebellum-basal ganglia-cortical network, but these disconnections were mainly in the cortico-thalamo-cerebellar circuits in MSA-P patients and the basal ganglia-thalamo-cortical circuits in IPD patients. Among the connectome parameters, t tests combined with the RF method identified 15 features, from which the LR classifier achieved the best diagnostic performance on the validation set (accuracy = 92.31%, sensitivity = 90.91%, specificity = 93.33%, area under the receiver operating characteristic curve = 0.89). MSA-P and IPD patients show similar whole-brain network topological alterations. MSA-P primarily affects cerebellar nodes, and IPD primarily affects basal ganglia nodes; both conditions disrupt the cerebellum-basal ganglia-cortical network. Moreover, functional connectome parameters showed outstanding value in the differential diagnosis of early MSA-P and IPD.

Alterations of voxel-wise spontaneous activity and corresponding brain functional networks in multiple system atrophy patients with mild cognitive impairment.

Emerging evidence has indicated that cognitive impairment is an underrecognized feature of multiple system atrophy (MSA). Mild cognitive impairment (MCI) is related to a high risk of dementia. However, the mechanism underlying MCI in MSA remains controversial. In this study, we conducted the amplitude of low-frequency fluctuation (ALFF) and seed-based functional connectivity (FC) analyses to detect the characteristics of local neural activity and corresponding network alterations in MSA patients with MCI (MSA-MCI). We enrolled 80 probable MSA patients classified as cognitively normal (MSA-NC, n = 36) and MSA-MCI (n = 44) and 40 healthy controls. Compared with MSA-NC, MSA-MCI exhibited decreased ALFF in the right dorsal lateral prefrontal cortex (RDLPFC) and increased ALFF in the right cerebellar lobule IX and lobule IV-V. In the secondary FC analyses, decreased FC in the left inferior parietal lobe (IPL) was observed when we set the RDLPFC as the seed region. Decreased FC in the bilateral cuneus, left precuneus, and left IPL and increased FC in the right middle temporal gyrus were shown when we set the right cerebellar lobule IX as the seed region. Furthermore, FC of DLPFC-IPL and cerebello-cerebral circuit, as well as ALFF alterations, were significantly correlated with Montreal Cognitive Assessment scores in MSA patients. We also employed whole-brain voxel-based morphometry analysis, but no gray matter atrophy was detected between the patient subgroups. Our findings indicate that altered spontaneous activity in the DLPFC and the cerebellum and disrupted DLPFC-IPL, cerebello-cerebral networks are possible biomarkers of early cognitive decline in MSA patients.

Detection of mild cognitive impairment in Parkinson's disease using gradient boosting decision tree models based on multilevel DTI indices.

BACKGROUND: Cognitive dysfunction is the most common non-motor symptom in Parkinson's disease (PD), and timely detection of a slight cognitive decline is crucial for early treatment and prevention of dementia. This study aimed to build a machine learning model based on intra- and/or intervoxel metrics extracted from diffusion tensor imaging (DTI) to automatically classify PD patients without dementia into mild cognitive impairment (PD-MCI) and normal cognition (PD-NC) groups. METHODS: We enrolled PD patients without dementia (52 PD-NC and 68 PD-MCI subtypes) who were assigned to the training and test datasets in an 8:2 ratio. Four intravoxel metrics, including fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (AD), and radial diffusivity (RD), and two novel intervoxel metrics, local diffusion homogeneity (LDH) using Spearman's rank correlation coefficient (LDHs) and Kendall's coefficient concordance (LDHk), were extracted from the DTI data. Decision tree, random forest, and eXtreme gradient boosting (XGBoost) models based on individual and combined indices were built for classification, and model performance was assessed and compared via the area under the receiver operating characteristic curve (AUC). Finally, feature importance was evaluated using SHapley Additive exPlanation (SHAP) values. RESULTS: The XGBoost model based on a combination of the intra- and intervoxel indices achieved the best classification performance, with an accuracy of 91.67%, sensitivity of 92.86%, and AUC of 0.94 in the test dataset. SHAP analysis showed that the LDH of the brainstem and MD of the right cingulum (hippocampus) were important features. CONCLUSIONS: More comprehensive information on white matter changes can be obtained by combining intra- and intervoxel DTI indices, improving classification accuracy. Furthermore, machine learning methods based on DTI indices can be used as alternatives for the automatic identification of PD-MCI at the individual level.

Changes in Surgical Management of the Axilla Over 11 Years - Report on More Than 1500 Breast Cancer Patients Treated with Neoadjuvant Chemotherapy on the Prospective I-SPY2 Trial.

BACKGROUND: Axillary surgery after neoadjuvant chemotherapy (NAC) is becoming less extensive. We evaluated the evolution of axillary surgery after NAC on the multi-institutional I-SPY2 prospective trial. METHODS: We examined annual rates of sentinel lymph node (SLN) surgery with resection of clipped node, if present), axillary lymph node dissection (ALND), and SLN and ALND in patients enrolled in I-SPY2 from January 1, 2011 to December 31, 2021 by clinical N status at diagnosis and pathologic N status at surgery. Cochran-Armitage trend tests were calculated to evaluate patterns over time. RESULTS: Of 1578 patients, 973 patients (61.7%) had SLN-only, 136 (8.6%) had SLN and ALND, and 469 (29.7%) had ALND-only. In the cN0 group, ALND-only decreased from 20% in 2011 to 6.25% in 2021 (p = 0.0078) and SLN-only increased from 70.0% to 87.5% (p = 0.0020). This was even more striking in patients with clinically node-positive (cN+) disease at diagnosis, where ALND-only decreased from 70.7% to 29.4% (p < 0.0001) and SLN-only significantly increased from 14.6% to 56.5% (p < 0.0001). This change was significant across subtypes (HR-/HER2-, HR+/HER2-, and HER2+). Among pathologically node-positive (pN+) patients after NAC (n = 525) ALND-only decreased from 69.0% to 39.2% (p < 0.0001) and SLN-only increased from 6.9% to 39.2% (p < 0.0001). CONCLUSIONS: Use of ALND after NAC has significantly decreased over the past decade. This is most pronounced in cN+ disease at diagnosis with an increase in the use of SLN surgery after NAC. Additionally, in pN+ disease after NAC, there has been a decrease in use of completion ALND, a practice pattern change that precedes results from clinical trials.

Fullerene-polysaccharide supramolecular hydrogel displaying antioxidation/antiglycation behavior.

A fullerene-polysaccharide supramolecular hydrogel was constructed by carrying out a co-assembly of fullerene@hydroxypropyl-ß-cyclodextrin, chitosan and bentonite, and displayed good antioxidant and antiglycation properties, and hence showed promising cosmetics applications. Benefitting from the cyclodextrin hydrophobic cavity, hydroxypropyl-ß-cyclodextrin formed a stoichiometric 2 : 1 complex with fullerene, effectively enhancing the water solubility and biological activity of fullerene, and the encapsulation ratio of the prepared fullerene was calculated to be 79%. Results of oxygen radical absorbance capacity and pyrogallol autoxidation experiments showed high antioxidant activity displayed by the fullerene@HP-ß-CD inclusion complex. The supramolecular inclusion was further co-assembled, using multiple hydrogen bonds and electrostatic interactions, with chitosan and bentonite to form a supramolecular hydrogel; this hydrogel was successfully used in antiglycation, with a glycation end products inhibition rate of 43.99% at a 10% sample concentration. Therefore, the fullerene-polysaccharide ternary co-assembly supramolecular hydrogel showed good antioxidant and antiglycation abilities, and the construction of the polysaccharide supramolecular hydrogel provided a new perspective for raw materials to consider in cosmetics applications.

Epidemiological characteristics, spatial clusters and monthly incidence prediction of hand, foot and mouth disease from 2017 to 2022 in Shanxi Province, China.

Hand, foot and mouth disease (HFMD) is a common infection in the world, and its epidemics result in heavy disease burdens. Over the past decade, HFMD has been widespread among children in China, with Shanxi Province being a severely affected northern province. Located in the temperate monsoon climate, Shanxi has a GDP of over 2.5 trillion yuan. It is important to have a comprehensive understanding of the basic features of HFMD in those areas that have similar meteorological and economic backgrounds to northern China. We aimed to investigate epidemiological characteristics, identify spatial clusters and predict monthly incidence of HFMD. All reported HFMD cases were obtained from the Shanxi Center for Disease Control and Prevention. Overall HFMD incidence showed a significant downward trend from 2017 to 2020, increasing again in 2021. Children aged < 5 years were primarily affected, with a high incidence of HFMD in male patients (relative risk: 1.316). The distribution showed a seasonal trend, with major peaks in June and July and secondary peaks in October and November with the exception of 2020. Other enteroviruses were the predominant causative agents of HFMD in most years. Areas with large numbers of HFMD cases were primarily in central Shanxi, and spatial clusters in 2017 and 2018 showed a positive global spatial correlation. Local spatial autocorrelation analysis showed that hot spots and secondary hot spots were concentrated in Jinzhong and Yangquan in 2018. Based on monthly incidence from September 2021 to August 2022, the mean absolute error (MAE), mean absolute percentage error (MAPE), and root mean square error (RMSE) of the long short-term memory (LSTM) and seasonal autoregressive integrated moving average (SARIMA) models were 386.58 vs. 838.25, 2.25 vs. 3.08, and 461.96 vs. 963.13, respectively, indicating that the predictive accuracy of LSTM was better than that of SARIMA. The LSTM model may be useful in predicting monthly incidences of HFMD, which may provide early warnings of HFMD epidemics.

Cognitive status and its risk factors in patients with hypertension and diabetes in a low-income rural area of China: A cross-sectional study.

OBJECTIVES: The proportion of older people with dementia in China is gradually increasing with the increase in the aging population over recent years. Hypertension and diabetes are common non-communicable diseases among rural populations in China. However, it remains unclear whether these conditions affect the occurrence and development of cognitive impairment as there is limited research on cognitive status and its risk factors among residents of rural areas. METHODS: A multi-stage stratified cluster random sampling method was used to select 5400 participants from rural permanent residents. A self-designed structured questionnaire was used to investigate demographic data of the participants. Cognitive function was assessed using the Montreal Cognitive Function Assessment Scale (MoCA). The results were analyzed using chi-square test, ANOVA and multiple linear regression analysis. RESULTS: A total of 5028 participants returned the survey, giving a response rate of 93.1%. Higher education (odds ratio (OR) = 3.2, 95% confidence interval (CI) 2.87-3.54, p < 0.001), higher income (OR = 1.61, 95% CI 1.16-2.07, p < 0.001), and dietary control (OR = 0.66, 95%CI 0.34-0.98, p < 0.001) were protective factors. A visual representation of the relationship between annual income and MoCA score showed an inverted U-curve, the group with an annual income of 6000-7999 RMB had a maximum OR of 1.93 (95%CI 0.12-2.74, p < 0.001). While difficulty in maintaining sleep were risk factors for cognitive impairment (OR = -2.28, 95% CI-4.18-0.39, p = 0.018). CONCLUSIONS: Participants with middle incomes had better cognitive status than those with the highest incomes. Higher education, proper diet control and good sleep are beneficial to the cognitive status of residents in rural areas.

The mediating effects of depression, anxiety, and rapid eye movement sleep behavior disorder on the association between dopaminergic replacement therapy and impulse control disorders in Parkinson's disease.

OBJECTIVES: This study aims to longitudinally explore whether and how rapid eye movement sleep behavior disorder (RBD), depression, and anxiety mediate the association between dopaminergic replacement therapy (DRT) and impulse control disorders (ICDs) in patients with Parkinson's disease (PD). METHODS: Subjects were selected from the Parkinson's Progression Markers Initiative. After excluding missing data, 268, 223, 218, 238, and 219 patients with PD diagnosed at 12, 24, 36, 48, and 60 months prior, respectively, were included. We used the Questionnaire for Impulsive-Compulsive Disorders, RBD Screening Questionnaire, Geriatric Depression Scale, and State-Trait-Anxiety Inventory to assess ICBs, RBD, depression, and anxiety, respectively. We constructed three causal mediation analysis models to infer potential contingent pathways from DRT to ICD mediated by depression, anxiety, and RBD separately. RESULTS: DRT was associated with an increased risk of PD incidence. Aggravation of ICDs was partly explained by improvements in depression (the average causal mediation effect accounted for 8.0% of the total effect) and RBD (the average causal mediation effect of RBD accounted for 16.4% of the total effect). This suggested that anxiety (the average causal mediation effect accounted for 12.7% of the total effect) plays a mediating role. CONCLUSIONS: Focusing on changes in RBD, depression, and anxiety associated with hyperdopaminergic status should be an essential part of strategies to prevent ICDs in patients with Parkinson's disease.

Atrophy in subcortical gray matter in adult patients with moyamoya disease.

BACKGROUND: Acute cerebrovascular accidents, long-term hypoperfusion, and/or remote neuronal degeneration may lead to structural alterations in patients with moyamoya disease (MMD). This study sought to comprehensively investigate the distribution characteristics of subcortical gray matter volume and their correlations with angiographic changes in the intracranial artery in patients with MMD. METHOD: One hundred forty-two patients with MMD and 142 age- and sex-matched healthy controls underwent 3-dimensional high-resolution structural magnetic resonance imaging. Volumes of subcortical gray matter and subregions of the hippocampus and amygdala were calculated, and the degree of stenosis/occlusion of intracranial arteries in patients with MMD was evaluated on MR angiography. RESULTS: Volume reductions in the thalamus, caudate, putamen, hippocampus, amygdala, pallidum, and nucleus accumbens were found in patients with MMD. Hippocampal subfields and amygdala subnuclei in patients with MMD showed distinct vulnerability, and morphological alterations in specific subregions were more obvious than in the whole hippocampus/amygdala. Volume loss in several subcortical areas was related to disease duration and intracranial arterial changes. CONCLUSIONS: Our findings revealed structural alteration patterns of subcortical gray matter in MMD. The specific atrophy in subregions of the hippocampus and the amygdala suggested potential cognitive and affective impairments in MMD, which warrants further investigation. Chronic cerebral hemodynamic alterations in MMD may play a pivotal role in morphological changes in subcortical areas.

Multi-parametric radiomics of conventional T1 weighted and susceptibility-weighted imaging for differential diagnosis of idiopathic Parkinson's disease and multiple system atrophy.

OBJECTIVES: This study aims to investigate the potential of radiomics with multiple parameters from conventional T1 weighted imaging (T1WI) and susceptibility weighted imaging (SWI) in distinguishing between idiopathic Parkinson's disease (PD) and multiple system atrophy (MSA). METHODS: A total of 201 participants, including 57 patients with PD, 74 with MSA, and 70 healthy control (HCs) individuals, underwent T1WI and SWI scans. From the 12 subcortical nuclei (e.g. red nucleus, substantia nigra, subthalamic nucleus, putamen, globus pallidus, and caudate nucleus), 2640 radiomic features were extracted from both T1WI and SWI scans. Three classification models - logistic regression (LR), support vector machine (SVM), and light gradient boosting machine (LGBM) - were used to distinguish between MSA and PD, as well as among MSA, PD, and HC. These classifications were based on features extracted from T1WI, SWI, and a combination of T1WI and SWI. Five-fold cross-validation was used to evaluate the performance of the models with metrics such as sensitivity, specificity, accuracy, and area under the receiver operating curve (AUC). During each fold, the ANOVA and least absolute shrinkage and selection operator (LASSO) methods were used to identify the most relevant subset of features for the model training process. RESULTS: The LGBM model trained by the features combination of T1WI and SWI exhibited the most outstanding differential performance in both the three-class classification task of MSA vs. PD vs. HC and the binary classification task of MSA vs. PD, with an accuracy of 0.814 and 0.854, and an AUC of 0.904 and 0.881, respectively. The texture-based differences (GLCM) of the SN and the shape-based differences of the GP were highly effective in discriminating between the three classes and two classes, respectively. CONCLUSIONS: Radiomic features combining T1WI and SWI can achieve a satisfactory differential diagnosis for PD, MSA, and HC groups, as well as for PD and MSA groups, thus providing a useful tool for clinical decision-making based on routine MRI sequences.

Model-based analysis of the incidence trends and transmission dynamics of COVID-19 associated with the Omicron variant in representative cities in China.

BACKGROUND: In 2022, Omicron outbreaks occurred at multiple sites in China. It is of great importance to track the incidence trends and transmission dynamics of coronavirus disease 2019 (COVID-19) to guide further interventions. METHODS: Given the population size, economic level and transport level similarities, two groups of outbreaks (Shanghai vs. Chengdu and Sanya vs. Beihai) were selected for analysis. We developed the SEAIQRD, ARIMA, and LSTM models to seek optimal modeling techniques for waves associated with the Omicron variant regarding data predictive performance and mechanism transmission dynamics, respectively. In addition, we quantitatively modeled the impacts of different combinations of more stringent interventions on the course of the epidemic through scenario analyses. RESULTS: The best-performing LSTM model showed better prediction accuracy than the best-performing SEAIQRD and ARIMA models in most cases studied. The SEAIQRD model had an absolute advantage in exploring the transmission dynamics of the outbreaks. Regardless of the time to inflection point or the time to Rt curve below 1.0, Shanghai was later than Chengdu (day 46 vs. day 12/day 54 vs. day 14), and Sanya was later than Beihai (day 16 vs. day 12/day 20 vs. day 16). Regardless of the number of peak cases or the cumulative number of infections, Shanghai was higher than Chengdu (34,350 vs. 188/623,870 vs. 2,181), and Sanya was higher than Beihai (1,105 vs. 203/16,289 vs. 3,184). Scenario analyses suggested that upgrading control level in advance, while increasing the index decline rate and quarantine rate, were of great significance for shortening the time to peak and Rt below 1.0, as well as reducing the number of peak cases and final affected population. CONCLUSIONS: The LSTM model has great potential for predicting the prevalence of Omicron outbreaks, whereas the SEAIQRD model is highly effective in revealing their internal transmission mechanisms. We recommended the use of joint interventions to contain the spread of the virus.

Differential Diagnosis of Intracranial Malignant Tumors Using MRI Based on Morphological Features and Signal Intensity Ratio of Lesions.

Objective: To investigate the clinical value of conventional MRI morphological features and signal intensity ratio in the differential diagnosis of intracranial malignant tumors (high-grade glioma (HGG), primary central nervous system Lymphoma (PCNSL) and single brain metastasis (BM). Methods: Retrospective analysis of 92 cases of HGG, 27 cases of PCNSL, and 35 cases of BM. MRI data in The General Hospital of Western Theater Command from August 2014 to December 2021, comparative analysis of morphological characteristics of tumors and lesion/normal brain parenchyma signal ratio (lesiontonormal parenchymaratio, LNR), five indexes were included T1WI signal ratio (LNRT1), T2WI signal intensity ratio (LNRT2), T2WI/T1WI signal ratio (LNRT2/T1), T1WI enhanced signal ratio (LNRT1CE) and contrast enhancement ratio (CER). The differential diagnostic performance was also assessed by subject operating characteristic (ROC) curves. Results: HGG, PCNSL, and BM were all seen more frequently in the supratentorial region, More than 50% of HGG mainly showed irregular morphology, intratumoral necrosis, cystic degeneration, peritumoral severe edema, cyclic uneven enhancement after enhancement, PCNSL significantly enhanced the main uniformity, necrosis cyst became rare, BM group showed uneven enhancement, no obvious specificity, and the differences in tumor morphology, peritumor edema, intratumor hemorrhage, necrotic cystic lesions, and enhancement patterns were statistically significant among the three (P < .05). PCNSL LNRT1 and its LNRT1CE (LNRT1: 0.558 ± 0.050, LNRT1CE: 1.637 ± 0.125) were significantly higher than those of HGG (LNRT1: 0.480 ± 0.077, LNRT1CE: 1.425 ± 0.160) and BM (LNRT1: 0.514 ± 0.120, LNRT1CE: 1.375 ± 0.122), while LNRT2 and LNRT2/T1 (LNRT2: 1.389 ± 0.086, LNRT2/T1: 2.511 ± 0.295) were significantly lower than those of HGG (LNRT2: 1.527 ± 0.191, LNRT2/T1: 3.263 ± 0.657), and BM (LNRT2: 1.504 ± 0.089, LNRT2/T1: 3.103 ± 0.830). There was no significant difference in CER among the three groups (P > .05). ROC curve analysis of LNRT1, LNRT2, LNRT1CE, and LNRT2/T1 could be used to discriminate PCNSL from HGG and BM, with LNRT1CE having the largest area under the curve of 0.873, sensitivity of 0.963 and specificity of 0.669. Conclusion: MRI lesion morphological features and signal intensity ratio are important for discriminating HGG from PCNSL and BM. As a quantitative parameter, tumor signal intensity ratio can provide an important supplement for subjective judgment, to improve the accuracy of tumor qualitative diagnosis and differential diagnosis.

XGBoost-SHAP-based interpretable diagnostic framework for alzheimer's disease.

BACKGROUND: Due to the class imbalance issue faced when Alzheimer's disease (AD) develops from normal cognition (NC) to mild cognitive impairment (MCI), present clinical practice is met with challenges regarding the auxiliary diagnosis of AD using machine learning (ML). This leads to low diagnosis performance. We aimed to construct an interpretable framework, extreme gradient boosting-Shapley additive explanations (XGBoost-SHAP), to handle the imbalance among different AD progression statuses at the algorithmic level. We also sought to achieve multiclassification of NC, MCI, and AD. METHODS: We obtained patient data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database, including clinical information, neuropsychological test results, neuroimaging-derived biomarkers, and APOE-ε4 gene statuses. First, three feature selection algorithms were applied, and they were then included in the XGBoost algorithm. Due to the imbalance among the three classes, we changed the sample weight distribution to achieve multiclassification of NC, MCI, and AD. Then, the SHAP method was linked to XGBoost to form an interpretable framework. This framework utilized attribution ideas that quantified the impacts of model predictions into numerical values and analysed them based on their directions and sizes. Subsequently, the top 10 features (optimal subset) were used to simplify the clinical decision-making process, and their performance was compared with that of a random forest (RF), Bagging, AdaBoost, and a naive Bayes (NB) classifier. Finally, the National Alzheimer's Coordinating Center (NACC) dataset was employed to assess the impact path consistency of the features within the optimal subset. RESULTS: Compared to the RF, Bagging, AdaBoost, NB and XGBoost (unweighted), the interpretable framework had higher classification performance with accuracy improvements of 0.74%, 0.74%, 1.46%, 13.18%, and 0.83%, respectively. The framework achieved high sensitivity (81.21%/74.85%), specificity (92.18%/89.86%), accuracy (87.57%/80.52%), area under the receiver operating characteristic curve (AUC) (0.91/0.88), positive clinical utility index (0.71/0.56), and negative clinical utility index (0.75/0.68) on the ADNI and NACC datasets, respectively. In the ADNI dataset, the top 10 features were found to have varying associations with the risk of AD onset based on their SHAP values. Specifically, the higher SHAP values of CDRSB, ADAS13, ADAS11, ventricle volume, ADASQ4, and FAQ were associated with higher risks of AD onset. Conversely, the higher SHAP values of LDELTOTAL, mPACCdigit, RAVLT_immediate, and MMSE were associated with lower risks of AD onset. Similar results were found for the NACC dataset. CONCLUSIONS: The proposed interpretable framework contributes to achieving excellent performance in imbalanced AD multiclassification tasks and provides scientific guidance (optimal subset) for clinical decision-making, thereby facilitating disease management and offering new research ideas for optimizing AD prevention and treatment programs.

Effect of the Reelin-Dab1 signaling pathway on the abnormal metabolism of Aß protein induced by aluminum.

Aluminum (Al) is a common neurotoxic element that can exacerbate intracellular ß-amyloid (Aß) deposition. Reelin is a highly conserved extracellular glycoprotein that is involved in intracellular Aß deposition. However, the action of Reelin on aluminum-induced Aß deposition is not fully understood. Here, we investigated the effects of the Reelin-Dab1 signaling pathway on Aß deposition in aluminum maltol (Al(mal)3) exposure in rat pheochromocytoma-derived cells (PC12). Our results showed that Al(mal)3 exposure decreased activity of PC12, increased expression of Aß42, and decreased expression of Aß40. Moreover, Al(mal)3 exposure in PC12 induced Reelin-Dab1 signaling pathway-associated proteins changed, decreased expression of Reelin and Dab1, and increased expression of pdab1. Moreover, the expression of Reelin, Dab1, and Aß40 was found to be elevated in PC12 exposed to Al(mal)3 and corticosterone compared to those exposed to Al(mal)3. Also, the expression of Reelin, Dab1, and Aß40 was found to be depressed in PC12 exposed to Al(mal)3 and streptozotocin compared with cells exposed to Al(mal)3 alone. These results suggested that Al(mal)3 inhibits the expression of the Reelin-Dab1 signaling pathway, promoting Aß deposition. Thus, our findings provided important evidence to better understand how the Reelin-Dab1 signaling pathway may be a potential mechanism of Aß deposition induced by aluminum.

Association between long-term exposure to ambient particulate matter and blood pressure, hypertension: an updated systematic review and meta-analysis.

Evidence of more recent studies should be updated to evaluate the effect of long-term exposure to particulate matter (PM) on blood pressure and hypertension.        Studies of long-term effects of PM1, PM2.5 and PM10 on blood pressure (SBP, DBP, MAP), hypertension were searched in Pubmed, Web of Science and Embase before May, 2021. Meta-analysis of 41 studies showed that exposure to PM1, PM2.5 was associated with SBP (1.76 mmHg (95%CI:0.71, 2.80) and 0.63 mmHg (95%CI:0.40, 0.85), per 10 µg/m3 increase in PM), all three air pollutants (PM1, PM2.5, PM10) was associated with DBP (1.16 mmHg (95%CI:0.34, 1.99), 0.31 mmHg (95%CI:0.16, 0.47), 1.17 mmHg (95%CI:0.24, 2.09), respectively. As for hypertension, PM1, PM2.5 and PM10 were all significantly associated with higher risk of hypertension (OR=1.27 (95%CI:1.06, 1.52), 1.15 (95%CI:1.10, 1.20) and 1.11 (95%CI:1.07, 1.16). In conclusion, our study indicated a positive association between long-term exposure to particulate matter and increased blood pressure, hypertension.