Multiplex cerebrospinal fluid proteomics identifies biomarkers for diagnosis and prediction of Alzheimer's disease.

Recent expansion of proteomic coverage opens unparalleled avenues to unveil new biomarkers of Alzheimer's disease (AD). Among 6,361 cerebrospinal fluid (CSF) proteins analysed from the ADNI database, YWHAG performed best in diagnosing both biologically (AUC = 0.969) and clinically (AUC = 0.857) defined AD. Four- (YWHAG, SMOC1, PIGR and TMOD2) and five- (ACHE, YWHAG, PCSK1, MMP10 and IRF1) protein panels greatly improved the accuracy to 0.987 and 0.975, respectively. Their superior performance was validated in an independent external cohort and in discriminating autopsy-confirmed AD versus non-AD, rivalling even canonical CSF ATN biomarkers. Moreover, they effectively predicted the clinical progression to AD dementia and were strongly associated with AD core biomarkers and cognitive decline. Synaptic, neurogenic and infectious pathways were enriched in distinct AD stages. Mendelian randomization did not support the significant genetic link between CSF proteins and AD. Our findings revealed promising high-performance biomarkers for AD diagnosis and prediction, with implications for clinical trials targeting different pathomechanisms.

Prediction of Future Parkinson Disease Using Plasma Proteins Combined With Clinical-Demographic Measures.

BACKGROUND AND OBJECTIVES: Identification of individuals at high risk of developing Parkinson disease (PD) several years before diagnosis is crucial for developing treatments to prevent or delay neurodegeneration. This study aimed to develop predictive models for PD risk that combine plasma proteins and easily accessible clinical-demographic variables. METHODS: Using data from the UK Biobank (UKB), which recruited participants across the United Kingdom, we conducted a longitudinal study to identify predictors for incident PD. Participants with baseline plasma proteins and no PD were included. Through machine learning, we narrowed down predictors from a pool of 1,463 plasma proteins and 93 clinical-demographic. These predictors were then externally validated using the Parkinson's Progression Marker Initiative (PPMI) cohort. To further investigate the temporal trends of predictors, a nested case-control study was conducted within the UKB. RESULTS: A total of 52,503 participants without PD (median age 58, 54% female) were included. Over a median follow-up duration of 14.0 years, 751 individuals were diagnosed with PD (median age 65, 37% female). Using a forward selection approach, we selected a panel of 22 plasma proteins for optimal prediction. Using an ensemble tree-based Light Gradient Boosting Machine (LightGBM) algorithm, the model achieved an area under the receiver operating characteristic curve (AUC) of 0.800 (95% CI 0.785-0.815). The LightGBM prediction model integrating both plasma proteins and clinical-demographic variables demonstrated enhanced predictive accuracy, with an AUC of 0.832 (95% CI 0.815-0.849). Key predictors identified included age, years of education, history of traumatic brain injury, and serum creatinine. The incorporation of 11 plasma proteins (neurofilament light, integrin subunit alpha V, hematopoietic PGD synthase, histamine N-methyltransferase, tubulin polymerization promoting protein family member 3, ectodysplasin A2 receptor, Latexin, interleukin-13 receptor subunit alpha-1, BAG family molecular chaperone regulator 3, tryptophanyl-TRNA synthetase, and secretogranin-2) augmented the model's predictive accuracy. External validation in the PPMI cohort confirmed the model's reliability, producing an AUC of 0.810 (95% CI 0.740-0.873). Notably, alterations in these predictors were detectable several years before the diagnosis of PD. DISCUSSION: Our findings support the potential utility of a machine learning-based model integrating clinical-demographic variables with plasma proteins to identify individuals at high risk for PD within the general population. Although these predictors have been validated by PPMI, additional validation in a more diverse population reflective of the general community is essential.

Clinical trajectories preceding incident dementia up to 15 years before diagnosis: a large prospective cohort study.

BACKGROUND: Dementia has a long prodromal stage with various pathophysiological manifestations; however, the progression of pre-diagnostic changes remains unclear. We aimed to determine the evolutional trajectories of multiple-domain clinical assessments and health conditions up to 15 years before the diagnosis of dementia. METHODS: Data was extracted from the UK-Biobank, a longitudinal cohort that recruited over 500,000 participants from March 2006 to October 2010. Each demented subject was matched with 10 healthy controls. We performed logistic regressions on 400 predictors covering a comprehensive range of clinical assessments or health conditions. Their evolutional trajectories were quantified using adjusted odds ratios (ORs) and FDR-corrected p-values under consecutive timeframes preceding the diagnosis of dementia. FINDINGS: During a median follow-up of 13.7 [Interquartile range, IQR 12.9-14.2] years until July 2022, 7620 subjects were diagnosed with dementia. In general, upon approaching the diagnosis, demented subjects witnessed worse functional assessments and a higher prevalence of health conditions. Associations up to 15 years preceding the diagnosis comprised declined physical strength (hand grip strength, OR 0.65 [0.63-0.67]), lung dysfunction (peak expiratory flow, OR 0.78 [0.76-0.81]) and kidney dysfunction (cystatin C, OR 1.13 [1.11-1.16]), comorbidities of coronary heart disease (OR 1.78 [1.67-1.91]), stroke (OR 2.34 [2.1-1.37]), diabetes (OR 2.03 [1.89-2.18]) and a series of mental disorders. Cognitive functions in multiple tests also demonstrate decline over a decade before the diagnosis. Inadequate activity (3-5 year, overall time of activity, OR 0.82 [0.73-0.92]), drowsiness (3-5 year, sleep duration, OR 1.13 [1.04-1.24]) and weight loss (0-5 year, weight, OR 0.9 [0.83-0.98]) only exhibited associations within five years before the diagnosis. In addition, serum biomarkers of enriched endocrine, dysregulations of ketones, deficiency of brand-chain amino acids and polyunsaturated fatty acids were found in a similar prodromal time window and can be witnessed as the last pre-symptomatic conditions before the diagnosis. INTERPRETATION: Our findings present a comprehensive temporal-diagnostic landscape preceding incident dementia, which could improve selection for preventive and early disease-modifying treatment trials.

Identification of Parkinson's disease subtypes with distinct brain atrophy progression and its association with clinical progression.

Background: Parkinson's disease (PD) patients suffer from progressive gray matter volume (GMV) loss, but whether distinct patterns of atrophy progression exist within PD are still unclear. Objective: This study aims to identify PD subtypes with different rates of GMV loss and assess their association with clinical progression. Methods: This study included 107 PD patients (mean age: 60.06 ± 9.98 years, 70.09% male) with baseline and ≥ 3-year follow-up structural MRI scans. A linear mixed-effects model was employed to assess the rates of regional GMV loss. Hierarchical cluster analysis was conducted to explore potential subtypes based on individual rates of GMV loss. Clinical score changes were then compared across these subtypes. Results: Two PD subtypes were identified based on brain atrophy rates. Subtype 1 (n = 63) showed moderate atrophy, notably in the prefrontal and lateral temporal lobes, while Subtype 2 (n = 44) had faster atrophy across the brain, particularly in the lateral temporal region. Furthermore, subtype 2 exhibited faster deterioration in non-motor (MDS-UPDRS-Part Ⅰ, ß = 1.26 ± 0.18, P = 0.016) and motor (MDS-UPDRS-Part Ⅱ, ß = 1.34 ± 0.20, P = 0.017) symptoms, autonomic dysfunction (SCOPA-AUT, ß = 1.15 ± 0.22, P = 0.043), memory (HVLT-Retention, ß = -0.02 ± 0.01, P = 0.016) and depression (GDS, ß = 0.26 ± 0.083, P = 0.019) compared to subtype 1. Conclusion: The study has identified two PD subtypes with distinct patterns of atrophy progression and clinical progression, which may have implications for developing personalized treatment strategies.

Microstructural alterations of the hypothalamus in Parkinson's disease and probable REM sleep behavior disorder.

BACKGROUND: Whether there is hypothalamic degeneration in Parkinson's disease (PD) and its association with clinical symptoms and pathophysiological changes remains controversial. OBJECTIVES: We aimed to quantify microstructural changes in hypothalamus using a novel deep learning-based tool in patients with PD and those with probable rapid-eye-movement sleep behavior disorder (pRBD). We further assessed whether these microstructural changes associated with clinical symptoms and free thyroxine (FT4) levels. METHODS: This study included 186 PD, 67 pRBD, and 179 healthy controls. Multi-shell diffusion MRI were scanned and mean kurtosis (MK) in hypothalamic subunits were calculated. Participants were assessed using Unified Parkinson's Disease Rating Scale (UPDRS), RBD Questionnaire-Hong Kong (RBDQ-HK), Hamilton Depression Rating Scale (HAMD), and Activity of Daily Living (ADL) Scale. Additionally, a subgroup of PD (n = 31) underwent assessment of FT4. RESULTS: PD showed significant decreases of MK in anterior-superior (a-sHyp), anterior-inferior (a-iHyp), superior tubular (supTub), and inferior tubular hypothalamus when compared with healthy controls. Similarly, pRBD exhibited decreases of MK in a-iHyp and supTub. In PD group, MK in above four subunits were significantly correlated with UPDRS-I, HAMD, and ADL. Moreover, MK in a-iHyp and a-sHyp were significantly correlated with FT4 level. In pRBD group, correlations were observed between MK in a-iHyp and UPDRS-I. CONCLUSIONS: Our study reveals that microstructural changes in the hypothalamus are already significant at the early neurodegenerative stage. These changes are associated with emotional alterations, daily activity levels, and thyroid hormone levels.

Incidence and risk factors of lymph node metastasis in breast cancer patients without preoperative chemoradiotherapy and neoadjuvant therapy: analysis of SEER data.

Background: Breast cancer (BC) is the leading cause of death in the female reproductive system, often linked to lymph node involvement, indicating poor prognosis. This study investigated lymph node metastasis incidence and risk factors in M0 stage BC patients who hadn't received preoperative chemoradiotherapy or neoadjuvant therapy. We explored the influence of various factors on lymph node metastasis. Methods: We conducted a retrospective analysis using Surveillance, Epidemiology, and End Results data from BC patients diagnosed between 2010 and 2015. Binary logistic regression and propensity score matching (PSM) assessed significant factors in BC patients without preoperative treatment. We developed predictive nomograms and evaluated model performance using the concordance index, calibration curve, area under the curve, and decision curve analysis. Results: Among 256,504 eligible BC patients, 25.57% had lymph node metastasis. Multivariate logistic regression revealed associations between lymph node metastasis and younger age, African-American ethnicity, central/nipple location, lobular carcinoma, human epidermal growth factor receptor 2 (HER2)-positive status, grade III classification, and T3 stage. PSM confirmed these findings. Interactions were identified between age, race, primary site, histology, breast subtype, grade, and T stage, all influencing lymph node metastasis. Conclusions: This retrospective study identified lymph node metastasis in female BC patients with distinct clinicopathological characteristics who received no preoperative treatment. We constructed valuable nomograms, revealing that: (I) young age (<35 years), African-American race, central/nipple location, infiltrating duct carcinoma, HER2 positivity, high histological grade (grade III), and larger tumor size are risk factors for regional lymph node metastasis; (II) lymph node metastasis may not solely represent the invasive nature of triple-negative BC; (III) patients with different BC subtypes in T1c-T2 stages may benefit from individualized neoadjuvant treatment strategies.

Plasma proteomic profiles predict individual future health risk.

Developing a single-domain assay to identify individuals at high risk of future events is a priority for multi-disease and mortality prevention. By training a neural network, we developed a disease/mortality-specific proteomic risk score (ProRS) based on 1461 Olink plasma proteins measured in 52,006 UK Biobank participants. This integrative score markedly stratified the risk for 45 common conditions, including infectious, hematological, endocrine, psychiatric, neurological, sensory, circulatory, respiratory, digestive, cutaneous, musculoskeletal, and genitourinary diseases, cancers, and mortality. The discriminations witnessed high accuracies achieved by ProRS for 10 endpoints (e.g., cancer, dementia, and death), with C-indexes exceeding 0.80. Notably, ProRS produced much better or equivalent predictive performance than established clinical indicators for almost all endpoints. Incorporating clinical predictors with ProRS enhanced predictive power for most endpoints, but this combination only exhibited limited improvement when compared to ProRS alone. Some proteins, e.g., GDF15, exhibited important discriminative values for various diseases. We also showed that the good discriminative performance observed could be largely translated into practical clinical utility. Taken together, proteomic profiles may serve as a replacement for complex laboratory tests or clinical measures to refine the comprehensive risk assessments of multiple diseases and mortalities simultaneously. Our models were internally validated in the UK Biobank; thus, further independent external validations are necessary to confirm our findings before application in clinical settings.

A retrospective comparative study on the diagnostic efficacy and the complications: between CassiII rotational core biopsy and core needle biopsy.

Accurate pathologic diagnosis and molecular classification of breast mass biopsy tissue is important for determining individualized therapy for (neo)adjuvant systemic therapies for invasive breast cancer. The CassiII rotational core biopsy system is a novel biopsy technique with a guide needle and a "stick-freeze" technology. The comprehensive assessments including the concordance rates of diagnosis and biomarker status between CassiII and core needle biopsy were evaluated in this study. Estrogen receptor (ER), progesterone receptor (PgR), human epidermal growth factor receptor 2 (HER2), and Ki67 were analyzed through immunohistochemistry. In total, 655 patients with breast cancer who underwent surgery after biopsy at Sir Run Run Shaw Hospital between January 2019 to December 2021 were evaluated. The concordance rates (CRs) of malignant surgical specimens with CassiII needle biopsy was significantly high compared with core needle biopsy. Moreover, CassiII needle biopsy had about 20% improvement in sensitivity and about 5% improvement in positive predictive value compared to Core needle biopsy. The characteristics including age and tumor size were identified the risk factors for pathological inconsistencies with core needle biopsies. However, CassiII needle biopsy was associated with tumor diameter only. The CRs of ER, PgR, HER2, and Ki67 using Cassi needle were 98.08% (kappa, 0.941; p<.001), 90.77% (kappa, 0.812; p<.001), 69.62% (kappa, 0.482; p<.001), and 86.92% (kappa, 0.552; p<.001), respectively. Post-biopsy complications with CassiII needle biopsy were also collected. The complications of CassiII needle biopsy including chest stuffiness, pain and subcutaneous ecchymosis are not rare. The underlying mechanism of subcutaneous congestion or hematoma after CassiII needle biopsy might be the larger needle diameter and the effect of temperature on coagulation function. In summary, CassiII needle biopsy is age-independent and has a better accuracy than CNB for distinguishing carcinoma in situ and invasive carcinoma.

Analysis of neoadjuvant chemotherapy for breast cancer: a 20-year retrospective analysis of patients of a single institution.

BACKGROUND: Neoadjuvant chemotherapy (NAC) has been widely applied in operable breast cancer patients. This study aim to identify the predictive factors of overall survival(OS) and recurrence free survival (RFS) in breast cancer patients who received NAC from a single Chinese institution. PATIENTS AND METHODS: There were 646 patients recruited in this study. All the patients were treated at department of Surgical Oncology, Sir Run Run Shaw Hospital between February 25, 1999 and August 22, 2018. The relevant clinicopathological and follow-up data were collected retrospectively. RFS and OS were assessed using the Kaplan-Meier method. Multivariate Cox proportional hazards model was also employed. Multi-variate logistic regression model was simulated to predict pathologic complete response (pCR). RESULTS: In total, 118 patients (18.2%) achieved pCR during NAC. The 5-year OS was 94.6% versus 78.1% in patients with and without pCR, respectively (P < 0.001). The 5-year RFS was 95.3% and 72.7%, respectively (P < 0.001). No difference was detected among molecular subtypes of 5-year RFS in patients obtained pCR. Factors independently predicting RFS were HER2-positive subtype (hazard ratio(HR), 1.906; P = 0.004), triple-negative breast cancer (TNBC) (HR,2.079; P = 0.003), lymph node positive after NAC(HR,2.939; P < 0.001), pCR (HR, 0.396;P = 0.010), and clinical stage III (HR,2.950; P = 0.016). Multi-variate logistic regression model was simulated to predict the pCR rate after NAC, according to clinical stage, molecular subtype, ki-67, LVSI, treatment period and histology. In the ROC curve analysis, the AUC of the nomogram was 0.734 (95%CI,0.867-12.867). CONCLUSIONS: Following NAC, we found that pCR positively correlated with prognosis and the molecular subtype was a prognostic factor.

Two distinct trajectories of clinical and neurodegeneration events in Parkinson's disease.

Increasing evidence suggests that Parkinson's disease (PD) exhibits disparate spatial and temporal patterns of progression. Here we used a machine-learning technique-Subtype and Stage Inference (SuStaIn) - to uncover PD subtypes with distinct trajectories of clinical and neurodegeneration events. We enrolled 228 PD patients and 119 healthy controls with comprehensive assessments of olfactory, autonomic, cognitive, sleep, and emotional function. The integrity of substantia nigra (SN), locus coeruleus (LC), amygdala, hippocampus, entorhinal cortex, and basal forebrain were assessed using diffusion and neuromelanin-sensitive MRI. SuStaIn model with above clinical and neuroimaging variables as input was conducted to identify PD subtypes. An independent dataset consisting of 153 PD patients and 67 healthy controls was utilized to validate our findings. We identified two distinct PD subtypes: subtype 1 with rapid eye movement sleep behavior disorder (RBD), autonomic dysfunction, and degeneration of the SN and LC as early manifestations, and cognitive impairment and limbic degeneration as advanced manifestations, while subtype 2 with hyposmia, cognitive impairment, and limbic degeneration as early manifestations, followed later by RBD and degeneration of the LC in advanced disease. Similar subtypes were shown in the validation dataset. Moreover, we found that subtype 1 had weaker levodopa response, more GBA mutations, and poorer prognosis than subtype 2. These findings provide new insights into the underlying disease biology and might be useful for personalized treatment for patients based on their subtype.

Mechanism of aconitine mediated neuronal apoptosis induced by mitochondrial calcium overload caused by MCU.

Aconitine is a crucial toxic component in Chinese herbal medicines such as Aconitum, Aconitum coreanum, and Aconitum soongaricum. The poisoning symptoms of the central nervous system and cardiovascular system caused by it are relatively common in China, and there are many studies on cardiovascular system diseases caused by aconitine. However, the specific mechanism of neurotoxicity induced by aconitine is still unclear. This study explored the effect and mechanism of mitochondrial calcium uniporter on mitochondrial energy metabolism disorder in aconitine poisoning hippocampal neurons. The results showed that after treatment with 400µmol/L aconitine, mitochondrial energy metabolism was abnormal in rat hippocampal neuron cells, the expression of MCU in mitochondria was up-regulated, calcium overload in mitochondria, ATP production decreased, and mitochondrial membrane potential Changes, increased expression of the apoptosis gene Cleaved-Caspase-3. After treatment with the MCU agonist spermine, mitochondrial energy metabolism was significantly abnormal, and cell apoptosis was increased considerably. However, pretreatment with calcium ion channel inhibitor Ruthenium Red (RR) effectively promoted the generation of ATP, thereby improving mitochondrial energy metabolism disorders and reducing cell apoptosis. These results suggest that aconitine induces mitochondrial energy metabolism dysfunction in hippocampal neurons, which may be related to the increased expression of MCU.

Case Report: A case of Chlamydia psittaci infection in an HIV patient.

Chlamydia psittaci is the pathogen of psittacosis and infects a wide range of birds and even humans. Human infection occurs most commonly in those with a history of contact with birds or poultry. We describe a case of psittacosis in a human immunodeficiency virus infected patient in Zhejiang Province for the first time. C. psittaci infection was confirmed by nested polymerase chain reaction (PCR) and Real-Time PCR. Phylogenetic analysis revealed that the sequences from the patient's samples clustered with genotype A in the same branch. Our study highlights the possibility of diagnosing psittacosis in patients with a chronic disease such as HIV-infected patients, and should increase awareness and surveillance of psittacosis in China.

Keyframe-aided resolution enhancement network for dynamic super-resolution structured illumination microscopy.

Deep learning has been used to reconstruct super-resolution structured illumination microscopy (SR-SIM) images with wide-field or fewer raw images, effectively reducing photobleaching and phototoxicity. However, the dependability of new structures or sample observation is still questioned using these methods. Here, we propose a dynamic SIM imaging strategy: the full raw images are recorded at the beginning to reconstruct the SR image as a keyframe, then only wide-field images are recorded. A deep-learning-based reconstruction algorithm, named KFA-RET, is developed to reconstruct the rest of the SR images for the whole dynamic process. With the structure at the keyframe as a reference and the temporal continuity of biological structures, KFA-RET greatly enhances the quality of reconstructed SR images while reducing photobleaching and phototoxicity. Moreover, KFA-RET has a strong transfer capability for observing new structures that were not included during network training.

Association of Cortical and Subcortical Microstructure With Clinical Progression and Fluid Biomarkers in Patients With Parkinson Disease.

BACKGROUND AND OBJECTIVES: Mean diffusivity (MD) of diffusion MRI (dMRI) has been used to measure cortical and subcortical microstructural properties. This study investigated relationships of cortical and subcortical MD, clinical progression, and fluid biomarkers in Parkinson disease (PD). METHODS: This longitudinal study using data from the Parkinson's Progression Markers Initiative was collected from April 2011 to July 2022. Clinical symptoms were assessed with Movement Disorder Society-sponsored revision of the Unified Parkinson's Disease Rating Scale (UPDRS) and Montreal Cognitive Assessment (MoCA) scores. Clinical assessments were followed up to 5 years. Linear mixed-effects (LME) models were performed to examine associations of MD and the annual rate of changes in clinical scores. Partial correlation analysis was conducted to examine the associations of MD and fluid biomarker levels. RESULTS: A total of 174 patients with PD (age 61.9 ± 9.7 years, 63% male) with baseline dMRI and at least 2 years of clinical follow-up were included. Results of LME models revealed a significant association between MD values, predominantly in subcortical regions, temporal lobe, occipital lobe, and frontal lobe, and annual rate of changes in clinical scores (UPDRS-Part-I, standardized ß > 2.35; UPDRS-Part-II, standardized ß > 2.34; postural instability and gait disorder score, standardized ß > 2.47; MoCA, standardized ß < -2.42; all p < 0.05, false discovery rate [FDR] corrected). In addition, MD was associated with the levels of neurofilament light chain in serum (r > 0.22) and α-synuclein (right putamen r = 0.31), ß-amyloid 1-42 (left hippocampus r = -0.30), phosphorylated tau at 181 threonine position (r > 0.26), and total tau (r > 0.23) in CSF at baseline (all p < 0.05, FDR corrected). Furthermore, the ß coefficients derived from MD and annual rate of changes in the clinical score recapitulated the spatial distribution of dopamine (DAT, D1, and D2), glutamate (mGluR5 and NMDA), serotonin (5-HT1a and 5-HT2a), cannabinoid (CB1), and γ-amino butyric acid A receptor neurotransmitter receptors/transporters (p < 0.05, FDR corrected) derived from PET scans in the brain of healthy volunteers. DISCUSSION: In this cohort study, cortical and subcortical MD values at baseline were associated with clinical progression and baseline fluid biomarkers, suggesting that microstructural properties could be useful for stratification of patients with fast clinical progression.

Efficacy of Remotely Delivered Evidence-Based Psychosocial Treatments for Schizophrenia-Spectrum Disorders: A Series of Systematic Reviews and Meta-Analyses.

BACKGROUND: Schizophrenia is among the most persistent and debilitating mental health conditions worldwide. The American Psychological Association (APA) has identified 10 psychosocial treatments with evidence for treating schizophrenia and these treatments are typically provided in person. However, in-person services can be challenging to access for people living in remote geographic locations. Remote treatment delivery is an important option to increase access to services; however, it is unclear whether evidence-based treatments for schizophrenia are similarly effective when delivered remotely. STUDY DESIGN: The current study consists of a series of systematic reviews and meta-analyses examining the evidence-base for remote-delivery of each of the 10 APA evidence-based treatments for schizophrenia. RESULTS: Of the 10 treatments examined, only cognitive remediation (CR), cognitive-behavioral therapy (CBT), and family psychoeducation had more than 2 studies examining their efficacy for remote delivery. Remote delivery of CBT produced moderate effects on symptoms (g = 0.43) and small effects on functioning (g = 0.26). Remote delivery of CR produced small-moderate effects on neurocognition (g = 0.35) and small effects on functioning (g = 0.21). There were insufficient studies of family psychoeducation with equivalent outcome measures to assess quantitatively, however, studies of remotely delivered family psychoeducation suggested that it is feasible, acceptable, and potentially effective. CONCLUSIONS: Overall, the evidence-base for remotely delivered treatment for schizophrenia is limited. Studies to date suggest that remote adaptations may be effective; however, more rigorous trials are needed to assess efficacy and methods of remote delivery that are most effective.

Robust fusion for skin lesion segmentation of dermoscopic images.

Robust skin lesion segmentation of dermoscopic images is still very difficult. Recent methods often take the combinations of CNN and Transformer for feature abstraction and multi-scale features for further classification. Both types of combination in general rely on some forms of feature fusion. This paper considers these fusions from two novel points of view. For abstraction, Transformer is viewed as the affinity exploration of different patch tokens and can be applied to attend CNN features in multiple scales. Consequently, a new fusion module, the Attention-based Transformer-And-CNN fusion module (ATAC), is proposed. ATAC augments the CNN features with more global contexts. For further classification, adaptively combining the information from multiple scales according to their contributions to object recognition is expected. Accordingly, a new fusion module, the GAting-based Multi-Scale fusion module (GAMS), is also introduced, which adaptively weights the information from multiple scales by the light-weighted gating mechanism. Combining ATAC and GAMS leads to a new encoder-decoder-based framework. In this method, ATAC acts as an encoder block to progressively abstract strong CNN features with rich global contexts attended by long-range relations, while GAMS works as an enhancement of the decoder to generate the discriminative features through adaptive fusion of multi-scale ones. This framework is especially good at lesions of varying sizes and shapes and of low contrasts and its performances are demonstrated with extensive experiments on public skin lesion segmentation datasets.

Three-dimensional cerebral vasculature topological parameter extraction of transgenic zebrafish embryos with a filling-enhancement deep learning network.

Quantitative analysis of zebrafish cerebral vasculature is essential for the study of vascular development and disease. We developed a method to accurately extract the cerebral vasculature topological parameters of transgenic zebrafish embryos. The intermittent and hollow vascular structures of transgenic zebrafish embryos, obtained from 3D light-sheet imaging, were transformed into continuous solid structures with a filling-enhancement deep learning network. The enhancement enables the extraction of 8 vascular topological parameters accurately. Quantitation of the zebrafish cerebral vasculature vessels with the topological parameters show a developmental pattern transition from 2.5 to 5.5 dpf.

Contribution of Alzheimer's disease pathology to biological and clinical progression: A longitudinal study across two cohorts.

INTRODUCTION: Amyloid beta (Aß) deposition, tau accumulation, and brain atrophy occurr in sequence, but the contribution of Alzheimer's disease (AD) pathology to biological and clinical progression remains unclear. METHODS: We included 290 and 70 participants with longitudinal assessment on Aß-positron emission tomography (PET), tau-PET, magnetic resonance imaging, and cognitive function from the Harvard Aging Brain Study (HABS) and Alzheimer's Disease Neuroimaging Initiative (ADNI) datasets, respectively. Partial least squares structural equation modeling (PLS-SEM) was used to determine the contribution of AD pathology to the biological and clinical longitudinal changes. RESULTS: Imaging biomarkers and cognitive function were significantly associated in cross-sectional and longitudinal analyses. At the final time point, the percentage of variance explained by PLS-SEM was 27% for Aß, 30% for tau (Aß accounted for 61%), 29% for brain atrophy (tau accounted for 37%), and 37% for cognitive decline (brain atrophy accounted for 35%). DISCUSSION: This study highlights distinctive contributing proportions of AD pathology to biological and clinical progression. Treatments targeting Aß and tau may partially block AD progression.

Targeting pyroptosis in breast cancer: biological functions and therapeutic potentials on It.

Pyroptosis is a lytic and inflammatory type of programmed cell death that is mediated by Gasdermin proteins (GSDMs). Attractively, recent evidence indicates that pyroptosis involves in the development of tumors and can serve as a new strategy for cancer treatment. Here, we present a basic knowledge of pyroptosis, and an overview of the expression patterns and roles of GSDMs in breast cancer. In addition, we further summarize the available evidence of pyroptosis in breast cancer progression and give insight into the clinical potential of applying pyroptosis in anticancer strategies for breast cancer. This review will deepen our understanding of the relationship between pyroptosis and breast cancer, and provide a novel potential therapeutic avenue for breast cancer.