Dynamic Mechanism of Cerebral Venous Disruption: Longitudinal Evidence From a Community-Based Cohort.

BACKGROUND: This study aims to investigate the temporal and spatial patterns of structural brain injury related to deep medullary veins (DMVs) damage. METHODS AND RESULTS: This is a longitudinal analysis of the population-based Shunyi cohort study. Baseline DMVs numbers were identified on susceptibility-weighted imaging. We assessed vertex-wise cortex maps and diffusion maps at both baseline and follow-up using FSL software and the longitudinal FreeSurfer analysis suite. We performed statistical analysis of global measurements and voxel/vertex-wise analysis to explore the relationship between DMVs number and brain structural measurements. A total of 977 participants were included in the baseline, of whom 544 completed the follow-up magnetic resonance imaging (age 54.97±7.83 years, 32% men, mean interval 5.56±0.47 years). A lower number of DMVs was associated with a faster disruption of white matter microstructural integrity, presented by increased mean diffusivity and radial diffusion (ß=0.0001 and SE=0.0001 for both, P=0.04 and 0.03, respectively), in extensive deep white matter (threshold-free cluster enhancement P<0.05, adjusted for age and sex). Of particular interest, we found a bidirectional trend association between DMVs number and change in brain volumes. Specifically, participants with mild DMVs disruption showed greater cortical enlargement, whereas those with severe disruption exhibited more significant brain atrophy, primarily involving clusters in the frontal and parietal lobes (multiple comparison corrected P<0.05, adjusted for age, sex, and total intracranial volume). CONCLUSIONS: Our findings posed the dynamic pattern of brain parenchymal lesions related to DMVs injury, shedding light on the interactions and chronological roles of various pathological mechanisms.

Value of conventional ultrasound and shear­wave elastography in the assessment of mesenteric lymphadenitis in a paediatric population.

The present retrospective study was designed to explore the value of conventional ultrasound (US) and Virtual Touch Tissue Imaging and Quantification (VTIQ) in the assessment of mesenteric lymphadenitis (ML) in a paediatric population. A total of 103 patients with ML and 60 healthy paediatric patients were examined. VTIQ was performed to assess mesenteric lymph node (MLN) stiffness via shear-wave velocity (SWV). Univariate and multivariate logistic regression analyses were conducted to reveal independent variables for the identification of ML. The diagnostic performance of US, and US combined with VTIQ, were compared. All the quantitative VTIQ parameters (including the SWVMean, SWVMax and SWVMin) were significantly greater for MLNs in the control group than for MLNs in the ML group (all P<0.001). The SWV values in the control group were nearly 2-fold greater than that in the ML group. According to the multivariate logistic regression analysis, the longest diameter [odds ratio (OR)=6.042; P=0.046] was revealed to be the strongest independent predictor for ML, followed by the CRP level (OR=2.310; P<0.001) and the SWVMean (OR=0.106; P<0.001). According to the receiver operating characteristic analysis, the area under the curve (AUC) for US combined with VTIQ was 0.890 (95% CI: 0.831-0.949) with a greater sensitivity of 91.26% and a greater specificity of 86.67% than that for US alone (AUC: 0.798; 95% CI: 0.724-0.872; sensitivity: 79.61%; specificity: 80.00%). A significant negative correlation between increased VTIQ parameters and ML was observed. Utilizing VTIQ to assess MLN stiffness offers a non-invasive, convenient, reliable and reproducible approach for identifying mesenteric lymphadenopathy.

Association of Rare NOTCH3 Variants With Prevalent and Incident Stroke and Dementia in the General Population.

BACKGROUND: It is uncertain whether rare NOTCH3 variants are associated with stroke and dementia in the general population and whether they lead to alterations in cognitive function. This study aims to determine the associations of rare NOTCH3 variants with prevalent and incident stroke and dementia, as well as cognitive function changes. METHODS AND RESULTS: In the prospective community-based Shunyi Study, a total of 1007 participants were included in the baseline analysis. For the follow-up analysis, 1007 participants were included in the stroke analysis, and 870 participants in the dementia analysis. All participants underwent baseline brain magnetic resonance imaging, carotid ultrasound, and whole exome sequencing. Rare NOTCH3 variants were defined as variants with minor allele frequency <1%. A total of 137 rare NOTCH3 carriers were enrolled in the baseline study. At baseline, rare NOTCH3 variant carriers had higher rates of stroke (8.8% versus 5.6%) and dementia (2.9% versus 0.8%) compared with noncarriers. After adjustment for associated risk factors, the epidermal growth factor-like repeats (EGFr)-involving rare NOTCH3 variants were associated with a higher risk of prevalent stroke (odds ratio [OR], 2.697 [95% CI, 1.266-5.745]; P=0.040) and dementia (OR, 8.498 [95% CI, 1.727-41.812]; P=0.032). After 5 years of follow-up, we did not find that the rare NOTCH3 variants increased the risk of incident stroke and dementia. There was no statistical difference in the change in longitudinal cognitive scale scores. CONCLUSIONS: Rare NOTCH3 EGFr-involving variants are genetic risk factors for stroke and dementia in the general Chinese population.

LncRNA RP11-10E18.7 cooperates with lncRNA RP11-481C4.2 to affect the overall survival of breast cancer patients: a TCGA-based retrospective study.

Background: As either oncogenes or tumor suppressor genes, long non-coding RNAs (lncRNAs) have a major role in both tumorigenesis and progression of human cancers, including breast cancer (BC). However, the statistical correlation between the lncRNA-lncRNA interaction and prognosis of BC remains unclear. Methods: We analyzed the fragments per kilobase per million (FPKM) lncRNA expression data in tumor tissue samples from 890 female patients with BC in The Cancer Genome Atlas (TCGA) between May 2021 and October 2022. The Cox proportional hazards model adjusted for age, race, clinical stage, neoadjuvant therapy, estrogen receptor (ER), and progesterone receptor (PR) was adopted to evaluate the lncRNA-lncRNA interaction regarding overall survival (OS) of BC. The multiple comparison was corrected by Bonferroni method. Results: RP11-10E18.7×RP11-481C4.2 was significantly associated with OS of BC patients [hazard ratio (HR)interaction =1.04, 95% confidence interval (CI): 1.03-1.06, P=3.35×10-9]. Then, gene-gene interaction analysis was performed for genes co-expressed with lncRNAs. FOXA1×U2SURP (HRinteraction =1.49, 95% CI: 1.28-1.73, P=2.16×10-7) was found to have a similar interactive pattern to RP11-10E18.7×RP11-481C4.2. after classifying the patients by intersection (3.47), we observed that the effect of FOXA1 opposite in patients with different U2SURP expression level (HRhigh vs. low =0.58, 95% CI: 0.34-0.99, P=0.046 in low expression of U2SURP; HRhigh vs. low =1.56, 95% CI: 1.18-2.87, P=0.029 in high expression of U2SURP). Conclusions: Our comprehensive study identified RP11-10E18.7×RP11-481C4.2 as a potential biomarker of BC prognosis. The results play an essential role in the impact of lncRNA-lncRNA interaction on BC survival. Our findings elucidated potential molecular mechanisms of BC progression under complex association patterns and provided potential dynamic and reversible therapeutic targets for BC patients.

Identifying underlying patterns in Alzheimer's disease trajectory: a deep learning approach and Mendelian randomization analysis.

Background: Alzheimer's disease (AD) is a heterogeneously progressive neurodegeneration disorder with varied rates of deterioration, either between subjects or within different stages of a certain subject. Estimating the course of AD at early stages has treatment implications. We aimed to analyze disease progression to identify distinct patterns in AD trajectory. Methods: We proposed a deep learning model to identify underlying patterns in the trajectory from cognitively normal (CN) to a state of mild cognitive impairment (MCI) to AD dementia, by jointly predicting time-to-conversion and clustering out distinct subgroups characterized by comprehensive features as well as varied progression rates. We designed and validated our model on the ADNI dataset (1370 participants). Prediction of time-to-conversion in AD trajectory was used to validate the expression of the identified patterns. Causality between patterns and time-to-conversion was further inferred using Mendelian randomization (MR) analysis. External validation was performed on the AIBL dataset (233 participants). Findings: The proposed model clustered out patterns characterized by significantly different biomarkers and varied progression rates. The discovered patterns also showed a strong prediction ability, as indicated by hazard ratio (CN→MCI, HR = 3.51, p < 0.001; MCI→AD, HR = 8.11, p < 0.001), C-Index (CN→MCI, 0.618; MCI→AD, 0.718), and AUC (CN→MCI, 3 years 0.802, 5 years 0.876; MCI→AD, 3 years 0.914, 5 years 0.957). In the external validation cohort, our model demonstrated competitive performance on conversion time prediction (CN→MCI, C-Index = 0.693; MCI→AD, C-Index = 0.752). Moreover, suggestive associations between CN→MCI/MCI→AD patterns with four/three SNPs were mediated and MR analysis indicated a causal link between MCI→AD patterns and time-to-conversion in the first three years. Interpretation: Our proposed model identifies biologically and clinically meaningful patterns from real-world data and provides promising performance on time-to-conversion prediction in AD trajectory, which could promote the understanding of disease progression, facilitate clinical trial design, and provide potential for decision-making. Funding: The National Key Research and Development Program of China, the Key R&D Program of Zhejiang, and the National Nature Science Foundation of China.

Pattern of Brain Parenchymal Damage Related to Cerebral Small Vessel Disease in Carriers of Rare NOTCH3 Variants.

BACKGROUND AND OBJECTIVES: Previous studies reported that carriers of rare NOTCH3 variants comprised more than 10% of the general population and are susceptible to a heavy overall burden of cerebral small vessel disease while the injury patterns remain uncovered. This study aimed to investigate the imaging features in relation to rare NOTCH3 variants and the interaction between cortical atrophy and white matter lesions from a longitudinal view, with respect to spatial and dynamic patterns. METHODS: As part of a community-based cohort, we included participants with complete whole-exome sequencing and brain MRI in the baseline analysis. All participants were invited for a 5-year follow-up MRI, and those who did not complete the follow-up were excluded from the longitudinal analysis. NOTCH3 variants with minor allele frequency <1% in all 4 public population databases were defined as rare variants. We used general linear models to compare the volume of white matter hyperintensity (WMH) volume and brain parenchymal fraction between rare NOTCH3 variant carriers and noncarriers. In addition, we compared the WMH probability map and vertex-wise cortex maps at a voxel/vertex-wise level. RESULTS: A total of 1,054 participants were included in baseline analysis (13.56% carried rare NOTCH3 variants), among whom 661 had a follow-up brain MRI (13.76% carried rare NOTCH3 variants). Rare NOTCH3 variant carriers had a heavier white matter hyperintensity burden (1.65 vs 0.85 mL, p = 0.025) and had more extensive WMH distributed in the periventricular areas. We also found that rare NOTCH3 variant carriers were susceptible to worse cortical atrophy (ß = -0.004, SE = 0.002, p = 0.057, adjusted for age and sex). Cortical atrophy of multiple regions in the frontal and parietal lobes was related to white matter hyperintensity progression. DISCUSSION: Individuals with rare NOTCH3 variants have a distinct pattern of brain parenchymal damage related to CSVD. Our findings uncover the important genetic predisposition in age-related cerebral small vessel disease in the general population.

Extracellular Vesicles Play a Central Role in Cerebral Venous Disease-Associated Brain Atrophy.

Cerebral venous abnormalities, distinct from traditional arterial diseases, have been linked to brain atrophy in a previous community-based cohort study, specifically in relation to the reduction of deep medullary veins (r-DMVs). To better understand the properties and biological functions of serum extracellular vesicles (EVs) in cerebral venous disease-associated brain atrophy, EVs are extracted from the serum of both participants with r-DMV and normal controls and analyzed their proteomic profiles using Tandem Mass Tag label quantitation analysis. Phenotypic experiments showed that EVs from individuals with r-DMVs are able to disrupt the normal functions of neurons, endothelial cells, and smooth muscle cells, and induce A1 reactive astrocytes. Additionally, this study provided a comprehensive characterization of the proteomic profile of DMV EVs and found that the collagen hydroxyproline is upregulated, while complement C3 is downregulated in the r-DMV group, suggesting that r-DMV may not be a simple pathological phenomenon and highlighting the potential involvement of EVs in the progression of brain atrophy in r-DMVs which has implications for the development of future therapeutic strategies.

Basal Ganglia and Brainstem Located Cerebral Microbleeds Contributed to Gait Impairment in Patients with Cerebral Small Vessel Disease.

BACKGROUND: The mechanism of gait disorder in patients with cerebral small vessel disease (CSVD) remains unclear. Limited studies have compared the effect of cerebral microbleeds (CMBs) and lacunes on gait disturbance in CSVD patients in different anatomical locations. OBJECTIVE: To investigate the relationship of quantitative gait parameters with varied anatomically located MRI imaging markers in patients with CSVD. METHODS: Quantitative gait tests were performed on 127 symptomatic CSVD patients all with diffuse distributed white matter hyperintensities (WMHs). CMBs and lacunes in regard to anatomical locations and burdens were measured. The correlation between CSVD imaging markers and gait parameters was evaluated using general linear model analysis. RESULTS: Presence of CMBs was significantly associated with stride length (ß= -0.098, p = 0.0272) and right step length (ß= -0.054, p = 0.0206). Presence of CMBs in basal ganglia (BG) was significantly associated with stride length and step length. Presence of CMBs in brainstem was significantly associated with gait parameters including stride length, step length, step height, and step width. Presence of lacunes in brainstem was significantly associated with gait speed (ß= -0.197, p = 0.0365). However, presence of lacunes in the other areas was not associated with worse gait performances. CONCLUSION: BG and brain stem located CMBs contributed to gait impairment in symptomatic CSVD patients.

White matter and gray matter changes related to cognition in community populations.

Objective: Further studies are needed to improve the understanding of the pathological process underlying cognitive impairments. The purpose of this study is to investigate the global and topographic changes of white matter integrity and cortical structure related to cognitive impairments in a community-based population. Methods: A cross-sectional analysis was performed based on 995 subjects (aged 56.8 ± 9.1 years, 34.8% males) from the Shunyi study, a community-dwelling cohort. Cognitive status was accessed by a series of neurocognitive tests including Mini-Mental State Examination (MMSE), Montreal Cognitive Assessment (MoCA), category Verbal Fluency Test (VFT), Digit Span Test (DST), and Trail Making Tests A and B (TMT-A and TMT-B). Structural and diffusional MRI data were acquired. White matter integrity was assessed using fractional anisotropy (FA), mean diffusivity (MD), and peak width of skeletonized mean diffusivity (PSMD). Cortical surface area, thickness, and volume were measured using Freesurfer. Probabilistic tractography was further conducted to track the white matter fibers connecting to the cortical regions related to cognition. General linear models were used to investigate the association between brain structure and cognition. Results: Global mean FA and MD were significantly associated with performances in VFT (FA, ß 0.119, p < 0.001; MD, ß -0.128, p < 0.001). Global cortical surface area, thickness, and volume were not related to cognitive scores. In tract-based spatial statistics analysis, disruptive white matter integrity was related to cognition impairment, mainly in visuomotor processing speed, semantic memory, and executive function (TMT-A and VFT), rather than verbal short-term memory and working memory (DST). In the whole brain vertex-wise analysis, surface area in the left orbitofrontal cortex, right posterior-dorsal part of the cingulate gyrus, and left central sulcus were positively associated with MMSE and MoCA scores, and the association were independent of the connecting white matter tract. Conclusion: Disrupted white matter integrity and regional cortical surface area were related to cognition in community-dwelling populations. The associations of cortical surface area and cognition were independent of the connecting white matter tract.

Engineering the MoS2 /MXene Heterostructure for Precise and Noninvasive Diagnosis of Prostate Cancer with Clinical Specimens.

High-throughput metabolic fingerprinting has been deemed one of the most promising strategies for addressing the high false positive rate of prostate cancer (PCa) diagnosis in the prostate-specific antigen (PSA) gray zone. However, the current metabolic fingerprinting remains challenging in achieving high-precision metabolite detection in complex biological samples (e.g., serum and urine). Herein, a novel self-assembly MoS2 /MXene heterostructure nanocomposite with a tailored doping ratio of 10% is presented as a matrix for laser desorption ionization mass spectrometry analysis in clinical biosamples. Notably, owing to the two-dimensional architecture and doping effect, MoS2 /MXene demonstrates favorable laser desorption ionization performance with low adsorption energy, which is evidenced by efficient urinary metabolic fingerprinting with an enhanced area under curve (AUC) diagnosis capability of 0.959 relative to that of serum metabolic fingerprinting (AUC = 0.902) for the diagnosis of PCa in the PSA gray zone. Thus, this MoS2 /MXene heterostructure is anticipated to offer a novel strategy to precisely and noninvasively diagnose PCa in the PSA gray zone.

Association between Anatomical Variations of the Circle of Willis and Covert Vascular Brain Injury in the General Population.

BACKGROUND AND PURPOSE: The circle of Willis (COW) is a circulatory anastomosis located at the base of the brain. Little is known about the association between covert vascular brain injury and COW configurations in the general population. We explored this relationship in a community-based Chinese sample. METHODS: A total of 1,055 patients (mean age, 54.8 ± 8.9 years; 36.0% men) without intracranial arterial stenosis were included in the analysis. Magnetic resonance imaging was performed to evaluate the presence of imaging markers of covert vascular brain injury, including white matter hyperintensities (WMHs), lacunes, cerebral microbleeds (CMBs), enlarged perivascular spaces, and brain atrophy. Magnetic resonance angiography was used to classify the COW configurations according to the completeness, symmetry, and presence of the fetal posterior cerebral artery (FTP). The association between vascular lesions and variations in COW was analyzed. RESULTS: Among the 1,055 patients, 104 (9.9%) had a complete COW. Completeness correlated with age (p = 0.001). Incomplete COW was positively associated with WMH severity (OR = 2.071; 95% CI, 1.004-4.270) and CMB presence (OR = 1.542; 95% CI, 1.012-2.348), independent of age and sex. The presence of FTP was associated with lacunes (OR = 1.878; 95% CI, 1.069-3.298), more severe WMHs (OR = 1.739; 95% CI, 1.064-2.842), and less severe enlarged perivascular spaces (OR = 0.562; 95% CI, 0.346-0.915). CONCLUSIONS: COW configuration was significantly related to various covert vascular brain injuries.

Arteriolosclerosis differs from venular collagenosis in relation to cerebrovascular parenchymal damages: an autopsy-based study.

BACKGROUND AND PURPOSE: Cerebrovascular parenchymal damage is prevalent in ageing brains; however, its vascular aetiology has not been fully elucidated. In addition to the underlying role of sclerotic arterioles, the correlation between collagenised venules has not been clarified. Here, we aimed to investigate the associations between microvascular injuries, including arteriolosclerosis and venular collagenosis, and related parenchymal damages in ageing brains, to investigate the underlying correlations. METHODS: We evaluated arteriolosclerosis and venular collagenosis in 7 regions from 27 autopsy cases with no history of stroke or brain tumour. The correlations between the ratio of arteriolosclerosis, venular collagenosis and the severity of cerebrovascular parenchymal damage, including lacunes, microinfarcts, myelin loss, and parenchymal and perivascular haemosiderin deposits, were assessed. RESULTS: Arteriolosclerosis and venular collagenosis became more evident with age. Arteriolosclerosis was associated with lacunes (p=0.004) and brain parenchymal haemosiderin deposits in the superior frontal cortex (p=0.024) but not with leukoaraiosis severity. Venular collagenosis was not associated with the number of lacunes or haemosiderin, while white matter generally became paler with severe venular collagenosis in the periventricular (ß=-0.430, p=0.028) and deep white matter (ß=-0.437, p=0.025). CONCLUSION: Our findings imply an important role for venular lesions in relation to microvessel-related parenchymal damage which is different from that for arteriolosclerosis. Different underlying mechanisms of both cerebral arterioles and venules require further investigation.

Mental health status of infected children between 7 to 12 years old in Fangcang Shelter Hospital during the COVID-19 Shanghai lockdown in 2022: a cross-section study.

Background: There has been an increase in research on the potential adverse effects on children's mental health, especially depression and anxiety, during the coronavirus disease 2019 (COVID-19) pandemic over the past few months. Therefore, the aim of the present study was to investigate depression and anxiety symptoms among children in shelter hospitals during the 2022 Shanghai lockdown. Methods: A total of 98 infected children aged 7-12 years were enrolled in this study between April 19 and June 4, 2022. The Children's Depression Inventory (CDI), Anxiety Scale for Children-Autism Spectrum Disorder (ASC-ASD), and Anxiety Scale or Children-Autism Spectrum Disorder Parent Form (ASC-ADS-P) were used to assess children's depression and anxiety symptoms. Children's guardians completed the survey by verbally asking their child/children the questions. The guardians additionally completed the ASC-ASD-P. Results: The prevalence of depression and anxiety was 12.2% and 13.3%, respectively. A total of 66 respondents reported no physical symptoms. Linear regression showed that myalgia [7.198, 95% confidence interval (CI): 3.163-11.232], headache (7.189, 95% CI: 3.842-10.535) coryza (5.362, 95% CI: 2.654-8.070), and number of quarantine days (4.378, 95% CI: 3.409-5.348) were significantly correlated with higher levels of depression, whereas chills (14.337, 95% CI: 9.799-18.875), coryza (9.309, 95% CI: 6.467-12.152), headache (7.193, 95% CI: 3.182-11.204), myalgia (5.571, 95% CI: 0.684-10.459), number of quarantine days (3.190, 95% CI: 1.796-4.584), and gender (male) (-4.137, 95% CI: -6.609 to 1.665) were associated with anxiety scores. Persistent fever was correlated with depression (P=0.007), whereas physical discomfort, such as persistent fever, cough, sore throat, headache, myalgia, and coryza were correlated with anxiety (all P<0.05). Conclusions: The findings of the present study indicated a moderate prevalence of depression and anxiety among infected children in a shelter hospital during the 2022 Shanghai lockdown. Therefore, the findings of this study could provide scientific basis for the development of targeted psychological intervention. It could be helpful for policy-makers to focus on psychological health among infected children and help to optimize future interventions.

Recurrent headache and visual symptoms in a young man: a rare neuronal intranuclear inclusion disease case report.

BACKGROUND: Neuronal intranuclear inclusion disease (NIID) is a progressive neurodegenerative disease. Patients with NIID may present with heterogeneous clinical symptoms, including episodic encephalopathy, dementia, limb weakness, cerebellar ataxia, and autonomic dysfunction. Among the NIID cases reported in China, patients often have complicated and severe manifestations. Therefore, many clinicians do not consider the disease when the patient presents with relatively minor complaints. CASE PRESENTATION: We present the case of a 39-year-old man showing migraine-aura-like symptoms for the past 3 years. Brain magnetic resonance imaging (MRI) revealed hyperintense signals in the splenium of the corpus callosum and corticomedullary junction on diffusion-weighted imaging (DWI) over time. In addition, brain atrophy that was not concomitant with the patient's age was detected while retrospectively reviewing the patient's imaging results. Genetic analysis and skin biopsy confirmed a diagnosis of NIID. The patient was treated with sibelium, and the symptoms did not recur. DISCUSSION AND CONCLUSIONS: Migraine-aura-like symptoms may be the predominant clinical presentation in young patients with NIID. Persistent high-intensity signals on DWI in the brain and early-onset brain atrophy might be clues for the diagnosis of NIID.

A deep learning-based diagnostic pattern for ultrasound breast imaging: can it reduce unnecessary biopsy?

Background: Early studies have demonstrated the potential of deep learning in bringing revolutionary changes in medical analysis. However, it is unknown which deep learning based diagnostic pattern is more effective for differentiating malignant and benign breast lesions (BLs) and can assist radiologists to reduce unnecessary biopsies. Methods: A total of 506 malignant BLs and 557 benign BLs were enrolled in this study after excluding incomplete ultrasound images. 396 malignant BLs and 447 benign BLs were included in the training cohort while 110 malignant and 110 benign BLs were included in the validation cohort. All BLs in the training and validation cohort were biopsy-proven. The most common convolutional neural networks (VGG-16 and VGG-19) were applied to identify malignant and benign BLs using grey-scale ultrasound images. Two radiologists determined the malignant (suggestion for biopsy) and benign (suggestion for follow-up) BLs with a 2-step reading session. The first step was based on conventional ultrasound (US) images alone to make a biopsy or follow-up decision. The second step was to take deep learning results into account for the decision adjustment. If a deep learning result of a first-classified benign BL was above the cut-off value, then it was re-classified as malignant. Results: In terms of area under the curve (AUC), the VGG-19 model yielded the best diagnostic performance in both training [0.939, 95% confidence interval (CI): 0.924-0.954] and testing dataset (0.959, 95% CI: 0.937-0.982). With the aid of deep learning models, the AUC of radiologists improved from 0.805 (95% CI: 0.744-0.865) to 0.827 (95% CI: 0.771-0.875, VGG-16) and 0.914 (95% CI: 0.871-0.957, VGG-19). The unnecessary biopsies decreased from 10.0% (11/110) to 8.2% (9/110) (assisted by VGG-16) and 0.9% (1/110) (assisted by VGG-19). Conclusions: The application of deep learning patterns in breast US may improve the diagnostic performance of radiologists by offering a second opinion. And thus, the assist of deep learning algorithm can considerably reduce the unnecessary biopsy rate in the clinical management of breast lesions.

Regional Disruption of White Matter Integrity and Network Connectivity Are Related to Cognition.

BACKGROUND: Cognitive impairment is common in the elderly population. Exploring patterns of white matter damage at the microstructural level would give important indications for the underlying mechanisms. OBJECTIVE: To investigate the spatial patterns of white matter microstructure and structural network alternations in relation to different cognition domainsMethods:Participants from the community-based Shunyi Study were included to investigate the association between white matter measurements and cognition cross-sectionally, via both global and local analysis. Cognitive functions were assessed using digit span, trail making test (TMT)-A/B, Fuld object Memory, and 12-Word Philadelphia Verbal Learning Test (PVLT). White matter measurements including fractional anisotropy (FA), mean diffusivity (MD), and structural network parameters were calculated based on diffusion tensor imaging. RESULTS: Of the 943 participants included, the mean (SD) age was 55.8 (9.1) years, and the mean (SD) education level was 6.7 (3.2) years. We found the whole set of cognitive measurements was related to diffused white matter microstructural integrity damage and lower global efficiency. Poor executive functions (TMTA/B complete time) were related to lower FA and higher MD predominantly on the anterior white matter skeleton, while verbal memory loss (PVLT test scores) was related to sub-network dysconnectivity in the midline and the right temporal lobe. CONCLUSION: The anterior brain is dominantly involved in executive dysfunction, while midline and right temporal brain disconnection are more prominent in verbal memory loss. Global and regional disruption of white matter integrity and network connectivity is the anatomical basis of the cognitive impairment in the aging population.

Comparing characteristics and outcomes of in-hospital stroke and community-onset stroke.

BACKGROUND: In-hospital strokes account for 4-17% of all strokes and usually lead to urgent and severe conditions. However, features of in-hospital strokes have been scarcely reported in China, and the management systems of in-hospital strokes are unestablished. The study aims to analyze the characteristics of in-hospital strokes in comparison to community-onset strokes and provides evidence for the development of national in-patient stroke care systems. METHODS: We retrospectively analyzed consecutive patients with in-hospital strokes (IHS group) and community-onset strokes (COS group) hospitalized in our hospital between June 2012, and January 2022. Clinical characteristics, care measures, and outcomes were compared between the two groups. RESULTS: A total of 1162 patients (age 61 ± 16 and 65% male) were included, of whom 193 (16.6%) had an in-hospital stroke and 969 (83.4%) had community-onset stroke. Compared with COS group, patients in IHS group had higher NIHSS at onset (7.25 vs 5.96, P = 0.054), higher use of endovascular therapy (10.4% vs 2.0%, P < 0.001), and lower use of intravascular thrombolysis (1.6% vs 7.2%, P = 0.003). Also, in-hospital strokes were associated with lower rate of mRS0-2 at discharge (OR[95%CI] = 0.674[0.49, 0.926], P = 0.015) and increased in-hospital mobility (OR[95%CI] = 3.621[1.640, 7.996], P = 0.001), after adjusting for age, sex, and cardiovascular risk factors. CONCLUSION: Compared with community-onset strokes, the patients with in-hospital stroke had insufficient urgent treatment and poorer outcomes, reflecting the need for increased awareness of in-patient stroke, and strategies to streamline in-hospital acute stroke care.

Investigating the Genetic Characteristics of Hippocampal Volume and Plasma ß-Amyloid in a Chinese Community-Dwelling Population.

BACKGROUND AND OBJECTIVES: The genetic characteristics and correlations of hippocampal volume and plasm Aß, probable endophenotypes for dementia, remain to be explored in Chinese community cohort. Using whole-exome sequencing and SNP-array genotyping, we sought to identify rare and common variants and genes influencing these two endophenotypes, and calculate their heritability and genetic correlation. METHODS: Association analyses with both whole-exome sequencing and SNP-array genotyping data were performed for hippocampal volumes and plasm Aß with mixed-effect linear regression model adjusted for sex, age, and total intracranial volume or APOE ε4 while considering familial relatedness. We also performed gene-level analysis for common and gene-burden analysis for rare variants. Heritability and genetic correlation were further examined. RESULTS: Totally 1,261 participants from a Chinese community cohort were included and we identified one gene, PTPRT, for hippocampal volume, with the top significant SNPs by whole genome-wide association study. rs6030076 (P=5.48×10-8, ß=-0.092, SE=0.017) from whole-exome sequencing and rs6030088 (P=8.24×10-9, ß=-105.22 SE=18.09) from SNP-array data, both located in this gene. Gene-burden analysis based on rare mutations detected 6 genes to be significantly associated with Aß. The SNP-based heritability was 0.43±0.13 for hippocampal volume and 0.2-0.3 for plasma Aß. The SNP-based genetic correlation between hippocampal volume and plasma Aß were negative. DISCUSSION: In this study, we identified several SNPs and one gene, PTPRT, which were not reported in previous GWASs, associated with hippocampal volume. Besides, the heritability and the genetic correlation gave an overview of hippocampal volume and plasma Aß. Our findings provide insights into the mechanisms behind the individual variances in these endophenotypes.