Immunotherapy for Renal Cell Carcinoma

Despite the rapid development of therapeutic modalities for advanced or metastatic renal cell carcinoma (mRCC) over the past decade to include traditional immunotherapy, such as high-dose interleukin-2 and interferon-α, as well as a number of targeted antiangiogenic therapies, mRCC continues to be associated with poor prognosis. Currently, immunotherapy has seen tremendous development in the form of immune checkpoint inhibition and vaccines at a dizzying pace, which are being studied in mRCC and are showing promise as important steps in the management of this disease. With so many drugs available to clinicians and patients, properly integrating immunotherapy especially immune checkpoint blockade (ICB) into the treatment paradigm is challenging. Emerging research with additional ICB agents and novel combination strategies is likely to further impact clinical decision-making. The further development of biomarkers for predicting a response is required to achieve optimal efficacy with these therapeutic interventions. This chapter summarizes the current landscape of standard and emerging immune therapeutics and other modalities for mRCC

SelfOdom: Self-supervised Egomotion and Depth Learning via Bi-directional Coarse-to-Fine Scale Recovery

Accurately perceiving location and scene is crucial for autonomous driving and mobile robots. Recent advances in deep learning have made it possible to learn egomotion and depth from monocular images in a self-supervised manner, without requiring highly precise labels to train the networks. However, monocular vision methods suffer from a limitation known as scale-ambiguity, which restricts their application when absolute-scale is necessary. To address this, we propose SelfOdom, a self-supervised dual-network framework that can robustly and consistently learn and generate pose and depth estimates in global scale from monocular images. In particular, we introduce a novel coarse-to-fine training strategy that enables the metric scale to be recovered in a two-stage process. Furthermore, SelfOdom is flexible and can incorporate inertial data with images, which improves its robustness in challenging scenarios, using an attention-based fusion module. Our model excels in both normal and challenging lighting conditions, including difficult night scenes. Extensive experiments on public datasets have demonstrated that SelfOdom outperforms representative traditional and learning-based VO and VIO models.Comment: 14 pages, 8 figures, in submissio

NMR Spectra Denoising with Vandermonde Constraints

Nuclear magnetic resonance (NMR) spectroscopy serves as an important tool to analyze chemicals and proteins in bioengineering. However, NMR signals are easily contaminated by noise during the data acquisition, which can affect subsequent quantitative analysis. Therefore, denoising NMR signals has been a long-time concern. In this work, we propose an optimization model-based iterative denoising method, CHORD-V, by treating the time-domain NMR signal as damped exponentials and maintaining the exponential signal form with a Vandermonde factorization. Results on both synthetic and realistic NMR data show that CHORD-V has a superior denoising performance over typical Cadzow and rQRd methods, and the state-of-the-art CHORD method. CHORD-V restores low-intensity spectral peaks more accurately, especially when the noise is relatively high.Comment: 10 pages, 9 figure

Care burden on family caregivers of patients with dementia and affecting factors in China: A systematic review

BackgroundDementia is a chronic and progressive illness characterized by severe impairment and high dependencies. Under the influence of Chinese traditional culture, 80% of patients with dementia are watched over at home by family caregivers as primary caregivers. However, long-term care brings formidable burdens to them and reduces the quality of their life. It is necessary to find out the influencing factors of caregivers’ burden.MethodsA scoping search was conducted on eight electronic databases from 1 January 2010 to 14 June 2022: PubMed, Embase, the Cochrane Library, Web of Science, China National Knowledge Infrastructure, China VIP Database, China Biomedical Literature Database, and Wanfang Data Knowledge Service Platform. Research articles included in this review discussed the factors affecting Chinese dementia family caregivers’ care burden or stress, and the level of care burden was evaluated by a standardized care burden scale.ResultsA total of 1,888 related articles were found and 23 cross-sectional studies were eventually included. After quality assessment, 12 were of good quality and 11 were of fair quality. A total of 32 factors were identified that were associated with caregiver burden, and the results were grouped into three categories: patient, caregiver, and society. The severity of disease, poor self-care ability, neuropsychiatric symptoms, care time, number of helpers, poor health status, economic stress, poor psychological status, social support, and age were reported in many previous studies.ConclusionIn this review, the factors that affect the caregiver burden for people with dementia were clarified. By identifying these factors, hospitals, decision-makers, and communities can carry out special projects for these populations, provide appropriate assistance, or design corresponding intervention measures to reduce the caregiver burden and improve the quality of care for patients with dementia.Systematic review registration[https://www.crd.york.ac.uk/PROSPERO/], identifier [CRD42022347816]

Morphological Redescription and SSU rDNA-based Phylogeny of Two Freshwater Ciliates, Uronema nigricans and Lembadion lucens (Ciliophora, Oligohymenophorea), with Discussion on the Taxonomic Status of Uronemita sinensis

Liu, Mingjian, Li, Lifang, Qu, Zhishuai, Luo, Xiaotian, Al-Farraj, Saleh A., Lin, Xiaofeng, Hu, Xiaozhong (2017): Morphological Redescription and SSU rDNA-based Phylogeny of Two Freshwater Ciliates, Uronema nigricans and Lembadion lucens (Ciliophora, Oligohymenophorea), with Discussion on the Taxonomic Status of Uronemita sinensis. Acta Protozoologica 56 (1): 17-37, DOI: 10.4467/16890027AP.17.003.6967, URL: https://www.mendeley.com/catalogue/cb3bc4f7-739f-32f8-92cd-7da31a838cb6

The efficacy and safety of anti-Aβ agents for delaying cognitive decline in Alzheimer’s disease: a meta-analysis

BackgroundThis meta-analysis evaluates the efficacy and safety of amyloid-β (Aβ) targeted therapies for delaying cognitive deterioration in Alzheimer’s disease (AD).MethodsPubMed, EMBASE, the Cochrane Library, and ClinicalTrials.gov were systematically searched to identify relevant studies published before January 18, 2023.ResultsWe pooled 33,689 participants from 42 studies. The meta-analysis showed no difference between anti-Aβ drugs and placebo in the Alzheimer’s Disease Assessment Scale–Cognitive Subscale (ADAS-Cog), and anti-Aβ drugs were associated with a high risk of adverse events [ADAS-Cog: MDs = −0.08 (−0.32 to 0.15), p = 0.4785; AEs: RR = 1.07 (1.02 to 1.11), p = 0.0014]. Monoclonal antibodies outperformed the placebo in delaying cognitive deterioration as measured by ADAS-Cog, Clinical Dementia Rating–Sum of Boxes (CDR-SB), Mini-Mental State Examination (MMSE) and Alzheimer’s Disease Cooperative Study–Activities of Daily Living (ADCS-ADL), without increasing the risk of adverse events [ADAS-Cog: MDs = −0.55 (−0.89 to 0.21), p = 0.001; CDR-SB: MDs = −0.19 (−0.29 to −0.10), p < 0.0001; MMSE: MDs = 0.19 (0.00 to 0.39), p = 0.05; ADCS-ADL: MDs = 1.26 (0.84 to 1.68), p < 0.00001]. Intravenous immunoglobulin and γ-secretase modulators (GSM) increased cognitive decline in CDR-SB [MDs = 0.45 (0.17 to 0.74), p = 0.002], but had acceptable safety profiles in AD patients. γ-secretase inhibitors (GSI) increased cognitive decline in ADAS-Cog, and also in MMSE and ADCS-ADL. BACE-1 inhibitors aggravated cognitive deterioration in the outcome of the Neuropsychiatric Inventory (NPI). GSI and BACE-1 inhibitors caused safety concerns. No evidence indicates active Aβ immunotherapy, MPAC, or tramiprosate have effects on cognitive function and tramiprosate is associated with serious adverse events.ConclusionCurrent evidence does not show that anti-Aβ drugs have an effect on cognitive performance in AD patients. However, monoclonal antibodies can delay cognitive decline in AD. Development of other types of anti-Aβ drugs should be cautious.Systematic Review RegistrationPROSPERO (https://www.crd.york.ac.uk/prospero/), identifier CRD42023391596

Convolutional Neural Networks-Based MRI Image Analysis for the Alzheimer’s Disease Prediction From Mild Cognitive Impairment

Mild cognitive impairment (MCI) is the prodromal stage of Alzheimer’s disease (AD). Identifying MCI subjects who are at high risk of converting to AD is crucial for effective treatments. In this study, a deep learning approach based on convolutional neural networks (CNN), is designed to accurately predict MCI-to-AD conversion with magnetic resonance imaging (MRI) data. First, MRI images are prepared with age-correction and other processing. Second, local patches, which are assembled into 2.5 dimensions, are extracted from these images. Then, the patches from AD and normal controls (NC) are used to train a CNN to identify deep learning features of MCI subjects. After that, structural brain image features are mined with FreeSurfer to assist CNN. Finally, both types of features are fed into an extreme learning machine classifier to predict the AD conversion. The proposed approach is validated on the standardized MRI datasets from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) project. This approach achieves an accuracy of 79.9% and an area under the receiver operating characteristic curve (AUC) of 86.1% in leave-one-out cross validations. Compared with other state-of-the-art methods, the proposed one outperforms others with higher accuracy and AUC, while keeping a good balance between the sensitivity and specificity. Results demonstrate great potentials of the proposed CNN-based approach for the prediction of MCI-to-AD conversion with solely MRI data. Age correction and assisted structural brain image features can boost the prediction performance of CNN

Molecular Evidence of Bartonella melophagi in Ticks in Border Areas of Xinjiang, China

Bartonella are gram-negative intracellular bacteria; certain species of Bartonella can cause diseases in mammals and humans. Ticks play a major role in the transmission of Bartonella. Xinjiang is the largest province in China according to land area and has one-third of the tick species in China; the infection rate of Bartonella in ticks in the Xinjiang border areas has not been studied in detail. Therefore, this study investigated tick infections by Bartonella in Xinjiang border areas, and the purpose of the study was to fill in gaps in information regarding the genetic diversity of tick infections by Bartonella in Xinjiang. We tested 1,549 tick samples from domestic animals (sheep and cattle) for Bartonella using ribC-PCR. Positive samples from the ribC-PCR assay for Bartonella spp. were further subjected to PCR assays targeting the ITS, rpoB and gltA genes followed by phylogenetic analyses. Bartonella DNA was detected in 2.19% (34/1,549) of tick samples, and the ITS, rpoB and gltA genes of ribC gene-positive samples were amplified to identify nine samples of Bartonella melophagi. In this study, molecular analysis was used to assess the presence and genetic diversity of B. melophagi in ticks collected from sheep and cattle from Xinjiang, China. This study provides new information on the presence and identity of B. melophagi in ticks from sheep and cattle