bluuebunny/crossref_metadata_2025 huggingface dataset:
Association Between Common Variants of APOE, ABCA7, A2M, BACE1, and Cerebrospinal Fluid Biomarkers in Alzheimer’s Disease: Data from the PUMCH Dementia Cohort
Authors: Dong Liling , Mao Chenhui , Liu Caiyan , Li Jie , Huang Xinying , Wang Jie , Lei Dan , Chu Shanshan , Sha Longze , Xu Qi , Peng Bin , Cui Liying , Gao Jing
Published: December 2021

Background:

The previous studies have identified several genes in relation to Alzheimer’s disease (AD), such as ABCA7, CR1, etc. A few studies have explored the association between the common variants, mainly in the non-coding regions of these genes, and cerebrospinal fluid (CSF) biomarkers. Fewer studies target the variants in the coding regions.

Objective:

To illustrate the association between the common variants within or adjacent to the coding regions of AD susceptible genes and CSF biomarkers in AD patients.

Methods:

75 sporadic probable AD patients were extracted from the dementia cohort of Peking Union Medical College Hospital. They all had history inquiry, physical examination, blood test, cognitive assessment, brain MRI, CSF testing of Aβ42, 181p-tau, and t-tau, and next-generation DNA sequencing. Sixty-nine common single nucleotide polymorphisms (SNPs) (minor allele frequency > 0.01) within or near the coding region of 13 AD susceptible genes were included in the analysis.

Results:

The rs7412-CC ( APOE) genotype showed lower CSF Aβ42 level and higher p-tau/Aβ42 ratio than the rs7412-CT genotype. The rs3752246-C ( ABCA7) allele correlated with lower CSF Aβ42 level. The alternate alleles of six ABCA7 SNPs were related to lower CSF p-tau, including rs3745842, rs3764648, rs3764652, rs4147930, rs4147934 and rs881768. The rs11609582-TT ( A2M) genotype showed higher CSF p-tau than the rs11609582-TA genotype. The p-tau/Aβ42 ratio was higher in the rs490460-TT ( BACE1) genotype relative to the rs490460-GT genotype.

Conclusion:

Some common variants within or near the coding regions of APOE, ABCA7, A2M, and BACE1 are associated with CSF Aβ42, p-tau. or p-tau/Aβ42.
Human APOE ɛ3 and APOE ɛ4 Alleles Have Differential Effects on Mouse Olfactory Epithelium
Authors: Khan Naazneen , Alimova Yelena , Clark Sophie J. , Vekaria Hemendra J. , Walsh Adeline E. , Williams Holden C. , Hawk Gregory S. , Sullivan Patrick G. , Johnson Lance A. , McClintock Timothy S.
Published: December 2021

Background:

Alzheimer’s disease (AD) is a progressive age-dependent disorder whose risk is affected by genetic factors. Better models for investigating early effects of risk factors such as apolipoprotein E ( APOE) genotype are needed.

Objective:

To determine whether APOE genotype produces neuropathologies in an AD-susceptible neural system, we compared effects of human APOE ɛ3 (E3) and APOE ɛ4 (E4) alleles on the mouse olfactory epithelium.

Methods:

RNA-Seq using the STAR aligner and DESeq2, immunohistochemistry for activated caspase-3 and phosphorylated histone H3, glucose uptake after oral gavage of 2-[1,2-3H (N)]-deoxy-D-glucose, and Seahorse Mito Stress tests on dissociated olfactory mucosal cells.

Results:

E3 and E4 olfactory mucosae show 121 differentially abundant mRNAs at age 6 months. These do not indicate differences in cell type proportions, but effects on 17 odorant receptor mRNAs suggest small differences in tissue development. Ten oxidoreductases mRNAs important for cellular metabolism and mitochondria are less abundant in E4 olfactory mucosae but this does not translate into differences in cellular respiration. E4 olfactory mucosae show lower glucose uptake, characteristic of AD susceptibility and consistent with greater expression of the glucose-sensitive gene, Asns. Olfactory sensory neuron apoptosis is unaffected at age 6 months but is greater in E4 mice at 10 months.

Conclusion:

Effects of human APOE alleles on mouse olfactory epithelium phenotype are apparent in early adulthood, and neuronal loss begins to increase by middle age (10 months). The olfactory epithelium is an appropriate model for the ability of human APOE alleles to modulate age-dependent effects associated with the progression of AD.
Depression and Dementia: The Role of Cortisol and Vascular Brain Lesions. AGES-Reykjavik Study
Authors: Gerritsen Lotte , Twait Emma L. , Jonsson Palmi V. , Gudnason Vilmundur , Launer Lenore J. , Geerlings Mirjam I.
Published: December 2021

Background:

Late-life depression (LLD) is related to an increased risk of developing dementia; however, the biological mechanisms explaining this relationship remain unclear.

Objective:

To determine whether the relationship between LLD and dementia can be best explained by the glucocorticoid cascade or vascular hypothesis.

Methods:

Data are from 4,354 persons (mean age 76±5 years) without dementia at baseline from the AGES-Reykjavik Study. LLD was assessed with the MINI diagnostic interview (current and remitted major depressive disorder [MDD]) and the Geriatric Depression Scale-15. Morning and evening salivary cortisol were collected (glucocorticoid cascade hypothesis). White matter hyperintensities (WMH; vascular hypothesis) volume was assessed using 1.5T brain MRI. Using Cox proportional hazard models, we estimated the associations of LLD, cortisol levels, and WMH volume with incident all-cause dementia, AD, and non-AD dementia.

Results:

During 8.8±3.2 years of follow-up, 843 persons developed dementia, including 397 with AD. Current MDD was associated with an increased risk of developing all-cause dementia (HR = 2.17; 95% CI 1.66–2.67), with risks similar for AD and non-AD, while remitted MDD was not (HR = 1.02; 95% CI 0.55–1.49). Depressive symptoms were also associated with increased risk of dementia, in particular non-AD dementias. Higher levels of evening cortisol increased risk of dementia, but this was independent of MDD. WMH partially explained the relation between current MDD and dementia risk but remained increased (HR = 1.71; 95% CI 1.34–2.08).

Conclusion:

The current study highlights the importance of LLD in developing dementia. However, neither the glucocorticoid cascade nor the vascular hypotheses fully explained the relation between depression and dementia.
Functional Neural Correlates of Semantic Fluency Task Performance in Mild Cognitive Impairment and Alzheimer’s Disease: An FDG-PET Study
Authors: Ahn Hyejin , Yi Dahyun , Chu Kyungjin , Joung Haejung , Lee Younghwa , Jung Gijung , Sung Kiyoung , Han Dongkyun , Lee Jun Ho , Byun Min Soo , Lee Dong Young
Published: December 2021

Background:

Total score (TS) of semantic verbal fluency test (SVFT) is generally used to interpret results, but it is ambiguous as to specific neural functions it reflects. Different SVFT strategy scores reflecting qualitative aspects are proposed to identify specific cognitive functions to overcome limitations of using the TS.

Objective:

Functional neural correlates of the TS as well as the other strategy scores in subjects with mild cognitive impairment (MCI) and Alzheimer’s disease (AD) dementia using Fluorine-18-Fluorodeoxyglucose positron emission tomography (FDG-PET).

Methods:

Correlations between various SVFT scores (i.e., TS, mean cluster size, switching (SW), hard switching, cluster switching (CSW)) and cerebral glucose metabolism were explored using voxelwise whole-brain approach. Subgroup analyses were also performed based on the diagnosis and investigated the effects of disease severity on the associations.

Results:

Significant positive correlation between TS and cerebral glucose metabolism was found in prefrontal, parietal, cingulate, temporal cortex, and subcortical regions. Significantly increased glucose metabolism associated with the SW were found in similar but smaller regions, mainly in the fronto-parieto-temporal regions. CSW was only correlated with the caudate. In the subgroup analysis conducted to assess different contribution of clinical severity, differential associations between the strategy scores and regional glucose metabolism were found.

Conclusion:

SW and CSW may reflect specific language and executive functions better than the TS. The SVFT is influenced by brain dysfunction due to the progression of AD, as demonstrated by the SW with larger involvement of temporal lobe for the AD, and CSW with significant association only for the MCI.
The Trajectory of Cerebrospinal Fluid Growth-Associated Protein 43 in the Alzheimer’s Disease Continuum: A Longitudinal Study
Authors: Zhang Heng , Lyu Diyang , Jia Jianping ,   for the Alzheimer’s Disease Neuroimaging Initiative
Published: December 2021

Background:

Synaptic degeneration has been suggested as an early pathological event that strongly correlates with severity of dementia in Alzheimer’s disease (AD). However, changes in longitudinal cerebrospinal fluid (CSF) growth-associated protein 43 (GAP-43) as a synaptic biomarker in the AD continuum remain unclear.

Objective:

To assess the trajectory of CSF GAP-43 with AD progression and its association with other AD hallmarks.

Methods:

CSF GAP-43 was analyzed in 788 participants from the Alzheimer’s Disease Neuroimaging Initiative (ADNI), including 246 cognitively normal (CN) individuals, 415 individuals with mild cognitive impairment (MCI), and 127 with AD dementia based on cognitive assessments. The associations between a multimodal classification scheme with amyloid-β (Aβ), tau, and neurodegeneration, and changes in CSF GAP-43 over time were also analyzed.

Results:

CSF GAP-43 levels were increased at baseline in MCI and dementia patients, and increased significantly over time in the preclinical (Aβ-positive CN), prodromal (Aβ-positive MCI), and dementia (Aβ-positive dementia) stages of AD. Higher levels of CSF GAP-43 were also associated with higher CSF phosphorylated tau (p-tau) and total tau (t-tau), cerebral amyloid deposition and hypometabolism on positron emission tomography, the hippocampus and middle temporal atrophy, and cognitive performance deterioration at baseline and follow-up. Furthermore, CSF GAP-43 may assist in effectively predicting the probability of dementia onset at 2- or 4-year follow-up.

Conclusion:

CSF GAP-43 can be used as a potential biomarker associated with synaptic degeneration in subjects with AD; it may also be useful for tracking the disease progression and for monitoring the effects of clinical trials.
Role of bone morphogenetic proteins in periodontal tissue engineering: Relatively unexplored horizon
Authors: Kale Preeti Prakash , Mani Amit , Anarthe Raju , Mustilwar Rachita
Published: December 2021

Tissue engineering aims to reconstruct the natural target tissue by a combination of three key elements stem/progenitor cells (that will create the new tissue), signaling molecules (that instruct the cells to form the desired tissue) scaffold/extracellular matrix (to hold the cells). Regeneration of the periodontal tissues following destructive episodes of various forms of periodontitis is a formidable challenge to periodontologists. Bone morphogenic proteins have been considered as the most potent growth factors that can promote the bone regeneration. This review will emphasize on the unique nature of the tissue engineered bone morphogenic proteins molecules regarding their structure, classification, signaling mechanism, etc. which will further help in understanding their role and potential advances necessary to facilitate the process of regeneration in the field of periodontics.
Impact of Climate Change on Rural Livelihood: A Case Study of Central Rajasthan
Authors: Soz Saifuddin , Mankar Dhananjay
Published: December 2021

Climate change is already bringing tremendous influence on people’s lives, particularly the underprivileged. It’s already visible in a variety of ways. In recent decades, Asia and the Pacific have seen consistent warming trends as well as more frequent and powerful extreme weather events such as droughts, cyclones, floods, and hailstorms. This study was done in Ajmer District of Rajasthan, to find out the climate variation in the last 10 years. The study describes the effects due to climate change on the livelihoods of the people, so a descriptive research design was used for the study to find out the impact of climate change on rural livelihood in central Rajasthan. The study is based on a large representative of sample, quantitative data was collected to gain an idea of the impact on the livelihoods due to climate change at the household level. It shows the negative impact of climate change on rural livelihood which forced the people to change their livelihood directly or indirectly. It was found that climate change had an impact on people’s lives and people do understand the variation in climate change in terms of changes in the weather, unseasonal rain, and drought.
Climate Change Science in School Curriculum: Solution to Ignorance of Commons in Developing Countries
Authors: Thrivikramji K.P. , Sajinkumar K.S. , Rani V.R.
Published: December 2021

In developing countries and to a certain degree in developed countries too, either climate change (CC) consequences or drivers of CC are alien to the mindset of commoners, who after High School had with/without vocational skills entered the workforce. This deficit or ignorance can be rectified only by adding CC education in the school system. We present a school appropriate outline of CC learning content spanning Kinder Garten to High School. Adding CC content in school education is immensely warranted, as a large chunk of high schoolers annually joins the work force with or without vocational or skill training. Further, such a knowledge deficit among the generations of working class can be remedied only by providing appropriate and sufficient knowledge on CC consequences, etc., only through a structured adult education campaign.
The prediction algorithm of credit risk of science and technology finance based on cloud computing
Authors: Li Guiping
Published: December 2021

In order to effectively guarantee the effect of credit risk prediction of science and technology finance and improve the ability of risk prediction, a credit risk prediction algorithm of science and technology finance based on cloud computing is proposed. The logistic regression model is used to predict, and the financial indicators of science and technology credit are selected as the model covariates. According to the characteristics and strong correlation of many financial indicators of science and technology credit, this paper constructs the final index system of online supply chain technology credit risk evaluation based on SMEs. Then the principal component analysis method is used to select the principal component. Combined with the penalty method, the data space dimension of financial indicators is further reduced, and the unrelated principal components are obtained. On this basis, a logistic regression model is established to predict the credit risk by taking the selected main components as covariates. The experimental results show that the algorithm has a good fit to the credit risk of 16 science and technology credit enterprises, and the risk prediction ability is significantly improved, which can effectively guarantee the effect of science and technology credit risk prediction.
Evaluation model of teaching effectiveness of sports characteristic course based on intelligent fuzzy system
Authors: Chen Hanying
Published: December 2021

In order to improve the accuracy of the teaching effect evaluation, a teaching effect evaluation model based on the intelligent fuzzy system is designed. The evaluation index are selected based on the teaching situation of physical education courses, relevant national policy documents, subject textbooks, intelligent fuzzy system to modify the index system through expert interview, determine the weight coefficient of each index by hierarchical analysis method (AHP), and calculate the single layer and total ranking of the index matrix to realize the evaluation of physical education courses. The test results show that the fuzzy evaluation accuracy of the proposed model is above 95.63%, with high evaluation performance and strong utility.
Random walk autonomous groups of particles for particle swarm optimization
Authors: Xu Xinliang , Yan Fu
Published: December 2021

Autonomous groups of particles swarm optimization (AGPSO), inspired by individual diversity in biological swarms such as insects or birds, is a modified particle swarm optimization (PSO) variant. The AGPSO method is simple to understand and easy to implement on a computer. It has achieved an impressive performance on high-dimensional optimization tasks. However, AGPSO also struggles with premature convergence, low solution accuracy and easily falls into local optimum solutions. To overcome these drawbacks, random-walk autonomous group particle swarm optimization (RW-AGPSO) is proposed. In the RW-AGPSO algorithm, Levy flights and dynamically changing weight strategies are introduced to balance exploration and exploitation. The search accuracy and optimization performance of the RW-AGPSO algorithm are verified on 23 well-known benchmark test functions. The experimental results reveal that, for almost all low- and high-dimensional unimodal and multimodal functions, the RW-AGPSO technique has superior optimization performance when compared with three AGPSO variants, four PSO approaches and other recently proposed algorithms. In addition, the performance of the RW-AGPSO has also been tested on the CEC’14 test suite and three real-world engineering problems. The results show that the RW-AGPSO is effective for solving high complexity problems.
A robust hybrid fusion segmentation approach for automated tumor diagnosis and classification in brain MR images
Authors: Sindhiya Devi R. , Perumal B. , Pallikonda Rajasekaran M.
Published: December 2021

In today’s world, Brain Tumor diagnosis plays a significant role in the field of Oncology. The earlier identification of brain tumors increases the compatibility of treatment of patients and offers an efficient diagnostic recommendation from medical practitioners. Nevertheless, accurate segmentation and feature extraction are the vital challenges in brain tumor diagnosis where the handling of higher resolution images increases the processing time of existing classifiers. In this paper, a new robust weighted hybrid fusion classifier has been proposed to identify and classify the tumefaction in the brain which is of the hybridized form of SVM, NB, and KNN (SNK) classifiers. Primarily, the proposed methodology initiates the preprocessing technique such as adaptive fuzzy filtration and skull stripping in order to remove the noises as well as unwanted regions. Subsequently, an automated hybrid segmentation strategy can be carried out to acquire the initial segmentation results, and then their outcomes are compiled together using fusion rules to accurately localize the tumor region. Finally, a Hybrid SNK classifier is implemented in the proposed methodology for categorizing the type of tumefaction in the brain. The hybrid classifier has been compared with the existing state-of-the-art classifier which shows a higher accuracy result of 99.18% while distinguishing the benign and malignant tumors from brain Magnetic Resonance (MR) images.
MFNet algorithm based on indoor scene segmentation
Authors: Cao Rui , Jiang Feng , Wu Zhao , Ren Jia
Published: December 2021

With the advancement of computer performance, deep learning is playing a vital role on hardware platforms. Indoor scene segmentation is a challenging deep learning task because indoor objects tend to obscure each other, and the dense layout increases the difficulty of segmentation. Still, current networks pursue accuracy improvement, sacrifice speed, and augment memory resource usage. To solve this problem, achieve a compromise between accuracy, speed, and model size. This paper proposes Multichannel Fusion Network (MFNet) for indoor scene segmentation, which mainly consists of Dense Residual Module(DRM) and Multi-scale Feature Extraction Module(MFEM). MFEM uses depthwise separable convolution to cut the number of parameters, matches different sizes of convolution kernels and dilation rates to achieve optimal receptive field; DRM fuses feature maps at several levels of resolution to optimize segmentation details. Experimental results on the NYU V2 dataset show that the proposed method achieves very competitive results compared with other advanced algorithms, with a segmentation speed of 38.47 fps, nearly twice that of Deeplab v3+, but only 1/5 of the number of parameters of Deeplab v3 + . Its segmentation results were close to those of advanced segmentation networks, making it beneficial for the real-time processing of images.
Unsupervised authorship attribution using feature selection and weighted cosine similarity
Authors: Martín-del-Campo-Rodríguez Carolina , Sidorov Grigori , Batyrshin Ildar
Published: December 2021

This paper presents a computational model for the unsupervised authorship attribution task based on a traditional machine learning scheme. An improvement over the state of the art is achieved by comparing different feature selection methods on the PAN17 author clustering dataset. To achieve this improvement, specific pre-processing and features extraction methods were proposed, such as a method to separate tokens by type to assign them to only one category. Similarly, special characters are used as part of the punctuation marks to improve the result obtained when applying typed character n-grams. The Weighted cosine similarity measure is applied to improve the B3 F-score by reducing the vector values where attributes are exclusive. This measure is used to define distances between documents, which later are occupied by the clustering algorithm to perform authorship attribution.
A statistical evaluation of the oral vaccine S3pvac papaya against cysticercosis of taenia psiformis
Authors: Loranca Maria Beatriz Bernábe , Rosales José Espinosa , Orea Mirna Huerta , Cardiff John
Published: December 2021

The objective of this paper is to compare and evaluate statistically the behavior of two vaccines against cysticercus in a sample of female rabbits. The two vaccines under discussion are 1) S3Pvac-Papaya12 mg and 2) Wild Type (WT) or S3P Wild and also 3) Saline Solution. The challenge is to show that the developed vaccine, S3Pvac-Papaya, produces more antibodies and with better stability than the other vaccine and saline solution. With the aim of proving this conjecture, an analysis of variance (ANOVA) and multiple Fisher comparisons at 95% confidence were performed. The vaccine of interest, S3Pvac-Papaya, revealed in the box diagram at T2 that the development of antibodies was high and showed little dispersion, which implies that the vaccine S3Pvac Papaya is statistically efficient in the production of antibodies. Finally, the mathematical contribution centers on highlighting the low use of inferential statistical techniques, comparing means of generated antibodies by a set of vaccines in order to determine which one is more efficient and reliable. Tacitly, a methodology both statistical and procedural has been proposed along this work, to apply when contrasting other kinds of vaccines in both animals and humans for diverse conditions.
Fake news spreaders profiling using N-grams of various types and SHAP-based feature selection
Authors: Balouchzahi Fazlourrahman , Sidorov Grigori , Shashirekha Hosahalli Lakshmaiah
Published: December 2021

Complex learning approaches along with complicated and expensive features are not always the best or the only solution for Natural Language Processing (NLP) tasks. Despite huge progress and advancements in learning approaches such as Deep Learning (DL) and Transfer Learning (TL), there are many NLP tasks such as Text Classification (TC), for which basic Machine Learning (ML) classifiers perform superior to DL or TL approaches. Added to this, an efficient feature engineering step can significantly improve the performance of ML based systems. To check the efficacy of ML based systems and feature engineering on TC, this paper explores char, character sequences, syllables, word n-grams as well as syntactic n-grams as features and SHapley Additive exPlanations (SHAP) values to select the important features from the collection of extracted features. Voting Classifiers (VC) with soft and hard voting of four ML classifiers, namely: Support Vector Machine (SVM) with Linear and Radial Basis Function (RBF) kernel, Logistic Regression (LR), and Random Forest (RF) was trained and evaluated on Fake News Spreaders Profiling (FNSP) shared task dataset in PAN 2020. This shared task consists of profiling fake news spreaders in English and Spanish languages. The proposed models exhibited an average accuracy of 0.785 for both languages in this shared task and outperformed the best models submitted to this task.
Detection of the level of attention in children with ADHD through brain waves and corporal posture1
Authors: García Alfredo , González Juan M. , Palomino Amparo D.
Published: December 2021

In the current world, the need to know instantaneous information that helps people to know their current physical and intellectual conditions has become paramount, each time new systems that provide information to the user in real time are incorporated in portable devices. This information indicates different health parameters of the user, it can be obtained through their physiological variables such as: number of steps, heart rate, oxygenation level in the blood and other ones. One of the most requested intellectual conditions to be known by the user is: the level of attention reached when the user executes a task. This work describes a methodology and the experimentation to know the level of attention of people through a test to identify colors also are shown the development and the application of a system (hardware and software) to measure the level of attention of people using two input signals: corporal posture and brain waves. The mathematical analysis to find the correlation between the corporal posture and the level of attention is shown in this paper. The results obtained indicate that the corporal posture influences on the level of attention of people directly.
Deep active reinforcement learning for privacy preserve data mining in 5G environments
Authors: Ahmed Usman , Lin Jerry Chun-Wei , Srivastava Gautam , Chen Hsing-Chung
Published: December 2021

Frequent pattern mining (FIM) identifies the most important patterns in data sets. However, due to the huge and high-dimensional nature of transactional data, classical pattern mining techniques suffer from the limitations of dimensions and data annotations. Recently, data mining while preserving privacy is considered as an important research area. Information privacy is a tradeoff that must be considered when using data. Through many years, privacy-preserving data mining (PPDM) made use of methods that are mostly based on heuristics. The operation of deletion was used to hide the sensitive information in PPDM. In this study, we used deep active learning to protect private and sensitive information. This paper combines entropy-based active learning with an attention-based approach to effectively hide sensitive patterns. The constructed models are then validated using high-dimensional transactional data with attention-based and active learning methods in a reinforcement environment. The results show that the proposed model can support and improve the effectiveness of decision-making by increasing the number of training instances through the use of a pooling technique and an entropy uncertainty measure. The proposed paradigm can achieve data sanitization by the hiding sensitive items and avoiding to hide the non-sensitive items. The model outperforms greedy, genetic, and particle swarm optimization approaches.
Parkinson’s Disease Motor Subtypes Change with the Progression of the Disease: Results from the COPPADIS Cohort at 2-Year Follow-Up
Authors: Santos García Diego , Canfield Hector , de Deus Fonticoba Teresa , Cores Bartolomé Carlos , Naya Ríos Lucía , García Roca Lucía , Martínez Miró Cristina , Jesús Silvia , Aguilar Miquel , Pastor Pau , Cosgaya Marina , García Caldentey Juan , Caballol Nuria , Legarda Inés , Hernández Vara Jorge , Cabo Iria , López Manzanares Lydia , González Aramburu Isabel , Ávila Rivera María A. , Gómez Mayordomo Víctor , Nogueira Víctor , Puente Víctor , Dotor Julio , Borrué Carmen , Solano Vila Berta , Álvarez Sauco María , Vela Lydia , Escalante Sonia , Cubo Esther , Carrillo Padilla Francisco , Martínez Castrillo Juan C. , Sánchez Alonso Pilar , Alonso Losada Maria G. , López Ariztegui Nuria , Gastón Itziar , Kulisevsky Jaime , Blázquez Estrada Marta , Seijo Manuel , Rúiz Martínez Javier , Valero Caridad , Kurtis Mónica , de Fábregues Oriol , González Ardura Jessica , Alonso Redondo Ruben , Ordás Carlos , López Díaz Luis M. , McAfee Darrian , Martinez-Martin Pablo , Mir Pablo ,   COPPADIS Study Group
Published: December 2021

Background:

Motor phenotype (MP) can be associated with a different prognosis in Parkinson’s disease (PD), but it is not fixed and can change over time.

Objective:

Our aim was to analyze how the MP changed over time and to identify factors associated with the changes in PD patients from a multicenter Spanish PD cohort.

Methods:

PD patients who were recruited from January-2016 to November-2017 (baseline visit; V0) and evaluated again at a 2-year±30 days follow-up (V2) from 35 centers of Spain from the COPPADIS cohort, were included in this study.MP was calculated at both visits based on Jankovic classification in TD (tremor dominant), IND (indeterminate), or PIGD (postural instability and gait difficulty). Sociodemographic and clinical data were collected, including serum biomarkers.

Results:

Five hundred eleven patients (62.57±8.59 years old; 59.2%males) were included in the study. At V0, MP was: 47.4%(242/511) TD; 36.6%(187/511) PIGD; 16%(82/511) IND. Up to 38%(194/511) of the patients changed their phenotype from V0 to V2, being the most frequent from TD to IND (8.4%) and from TD to PIGD (6.7%). A worse cognitive status (OR = 0.966) and less autonomy for activities of daily living (OR  =  0.937) at V0 and a greater increase in the globalNMS burden (OR  =  1.011) from V0 to V2 were associated with changing from TD to another phenotype after 2-year follow-up.

Conclusion:

The MP in PD can change over time. With disease progression, the percentage of cases with non-tremoric MP increases. PD patients who changed from TD to postural instability and gait difficulty increased NMS burden significantly.
Intraperitoneal Administration of Forskolin Reverses Motor Symptoms and Loss of Midbrain Dopamine Neurons in PINK1 Knockout Rats
Authors: Vazquez-Mayorga Emmanuel , Grigoruta Mariana , Dagda Raul , Martinez Bridget , Dagda Ruben K.
Published: December 2021

Background:

Parkinson’s disease (PD) is a relentless, chronic neurodegenerative disease characterized by the progressive loss of substantia nigra (SN) neurons that leads to the onset of motor and non-motor symptoms. Standard of care for PD consists of replenishing the loss of dopamine through oral administration of Levodopa; however, this treatment is not disease-modifying and often induces intolerable side effects. While the etiology that contributes to PD is largely unknown, emerging evidence in animal models suggests that a significant reduction in neuroprotective Protein Kinase A (PKA) signaling in the SN contributes to PD pathogenesis, suggesting that restoring PKA signaling in the midbrain may be a new anti-PD therapeutic alternative.

Objective:

We surmised that pharmacological activation of PKA via intraperitoneal administration of Forskolin exerts anti-PD effects in symptomatic PTEN-induced kinase 1 knockout (PINK1-KO), a bona fide in vivo model of PD.

Methods:

By using a beam balance and a grip strength analyzer, we show that Forskolin reverses motor symptoms and loss of hindlimb strength with long-lasting therapeutic effects (> 5 weeks) following the last dose.

Results:

In comparison, intraperitoneal treatment with Levodopa temporarily (24 h) reduces motor symptoms but unable to restore hindlimb strength in PINK1-KO rats. By using immunohistochemistry and an XF24e BioAnalyzer, Forskolin treatment reverses SN neurons loss, elevates brain energy production and restores PKA activity in SN in symptomatic PINK1-KO rats.

Conclusion:

Overall, our collective in vivo data suggest that Forskolin is a promising disease-modifying therapeutic alternative for PD and is superior to Levodopa because it confers long-lasting therapeutic effects.
Cognitive and psychosocial outcomes following stereotactic radiosurgery for acoustic neuroma
Authors: Brownlee Naomi , Wilson Colin , Curran David B. , Wright Gavin , Flannery Tom , Caldwell Sheena B.
Published: December 2021

BACKGROUND: Acoustic Neuroma (AN) is a benign tumour of the eighth cranial nerve. Stereotactic Radiosurgery (SRS) is a common treatment approach. Studies have explored the primary effects of SRS and documented equivalent efficacy for tumour control compared to neurosurgery. OBJECTIVE: Examine the longer term cognitive and psychosocial outcomes of SRS in non-Neurofibromatosis Type II patients utilising both objective and subjective cognitive outcomes associated with quality of life and health related distress. METHODS: Nineteen individuals treated via SRS were assessed using a battery of standardised psychometric tests as well as measures of quality of life and psychological distress. RESULTS: Participants had largely preserved cognitive function except for processing speed, aspects of attention and visual memory relative to age norms. Self-reported quality of life was better than in other AN population studies. Level of psychological distress was equivalent to general population norms. More than half of participants reported subjective cognitive decline though this was not fully supported by objective testing. Subjective cognitive complaints may be associated with lower reported quality of life. CONCLUSIONS: Results are largely consistent with previous findings on the effects of SRS in other clinical groups, which supports SRS as a targeted radiation treatment for AN.
Predictors of fatigue self-management behaviors in adults with multiple sclerosis
Authors: Wang Emily , Chang Julia H.C. , Plow Matthew
Published: December 2021

BACKGROUND: Fatigue is one of the most common and disabling symptoms in people with multiple sclerosis (MS). Fatigue self-management behaviors may be effective in reducing the impact of fatigue in people with MS. However, few studies have examined the factors that influence engagement in fatigue self-management behaviors. OBJECTIVE: Identify factors that directly and indirectly influence fatigue self-management behaviors. METHODS: Participants with MS (n = 287) completed online questionnaires at baseline and 6-weeks. Guided by the Self- and Family Management Framework, we examined the influence of health status, resources and environment, healthcare utilization, and self-management processes on fatigue self-management behaviors at 6-weeks. Multiple regression and path analyses were conducted. RESULTS: The final regression model variables accounted for 41.58% of the variance in fatigue self-management behaviors, which included outcome expectations (β= 0.287), disability (β= 0.265), environmental barriers (β= 0.188), self-efficacy (β= 0.153), symptom severity (β= 0.113), living in an urban community (β= –0.108), and living alone (β= 0.103). Path analysis indicated that outcome expectations may mediate the relationship between disability levels and fatigue self-management behavior. CONCLUSIONS: Health status (i.e., disability and symptom severity), environmental factors (e.g., living situation), and self-management processes (i.e., self-efficacy and outcome expectations) may play an important role in influencing engagement in fatigue self-management behaviors.
Application of myofascial induction therapy in the rehabilitation of patients with acute facial palsy: A nonrandomized controlled trial
Authors: Bi Ranran , Zhang Yahui , Liu Xiaochen , Zhang Shun , Wang Rui , Liang Bingyin , Cui Fang
Published: December 2021

BACKGROUND: In the healthy body, the fascial system maintains elasticity and coordination of movements. If these functions are destroyed, facial paraly appears. Myofascial induction therapy (MIT), a manual physical therapy method that focuses on restoring altered fascial tissue, is prevalently and widely used in clinical treatment. OBJECTIVE: The study aimed to observe the application of MIT in the rehabilitation of patients with acute facial palsy. METHODS: Sixty-eight patients with acute facial palsy were divided into control group and manual treatment group. The control group received drug treatments, such as prednisone, methylcobalamin, and vitamin B1, and instrumental physical therapy, such as semiconductor laser, shortwave therapy, and facial muscle training. In addition to these treatments, the manual treatment group received MIT. Both groups were treated for 4 weeks. The patients were assessed using the following methods: the House-Brackmann facial nerve function evaluation, Sunnybrook facial grading system, facial nerve electrophysiological examination compound muscle action potential (CMAP) amplitude, and blink reflex (BR) R1 latency. RESULTS: House-Brackmann and Sunnybrook scores and CMAP amplitude and BRR1 latencies were significantly different between the two groups (p < 0.05). Furthermore, the manual treatment group showed greater improvement than the control group (p < 0.05). CONCLUSIONS: Treatment with MIT promoted better recovery of acute facial palsy and thus may be considered a valid rehabilitation intervention that is worthy of clinical application.
Comparing the effectiveness of bimanual and unimanual mirror therapy in unilateral neglect after stroke: A pilot study
Authors: Sim Tae Yong , Kwon Jae Sung
Published: December 2021

BACKGROUND: Unilateral neglect in stroke patients is a major obstacle to rehabilitation, which is a great challenge for therapists. OBJECTIVE: This study aimed to compare the effectiveness of bimanual mirror therapy (BMT) and unimanual mirror therapy (UMT), the two protocols of mirror therapy, for the reduction of the symptoms of unilateral neglect in stroke patients. METHODS: Twenty-eight individuals were randomly assigned to the BMT or UMT groups. Both groups received mirror therapy for 30 minutes per day, 5 days a week, for a period of 4 weeks. The Star Cancelation Test (SCT), Line Bisection Test (LBT), Picture Scanning Test (PST), and Korean Catherine Bergego Scale (K-CBS) were used to measure the change in unilateral neglect, and the Korean version of the Modified Barthel Index (K-MBI) was used to evaluate activities of daily living (ADL). RESULTS: The results of SCT, LBT, PST, and K-CBS showed significant decreases in unilateral neglect in both groups (p < 0.05). K-MBI improved significantly in both groups (p < 0.05). There were significant differences between the two groups in the unilateral neglect tests (p < 0.05), but no significant difference in ADL evaluation (p > 0.05). CONCLUSIONS: Mirror therapy protocols can be applied to treat unilateral neglect in stroke patients. However, BMT may be more beneficial for reducing the symptoms of unilateral neglect.
Do chronic pain syndromes in lower limb amputees have an effect on the use of prostheses?
Authors: Donati Danilo , Brunelli Stefano , De Santis Letizia , Mariani Giorgio , Mariani Elisabetta , Perrone Mariada , Benedetti Maria Grazia
Published: December 2021

BACKGROUND: The use of a prosthesis is critical to regain the ability to walk in lower limb amputees but the relationship between the use of a prosthesis and chronic pain syndromes (PLS, PLP, RLP), common in amputees patients, is still poorly understood. OBJECTIVE: This long-term follow-up study investigates the possible correlation between prosthesis use and the presence of PLP, PLS and RLP in lower limb amputees. METHODS: Patients undergoing transtibial, transfemoral or hemipelvectomy amputation of any aetiology at the Rizzoli Orthopaedic Institute from 2008 to 2018 were included. The Houghton scale was used to assess functional use of the prosthesis. The PEQ was used for pain assessment. RESULTS: The results show that, in lower limb amputees, prosthesis use is greater in individuals with below-the-knee amputation and in those who were younger at the time of amputation. No significant correlation between the presence of pain syndromes (PLS, PLP, RLP) and the various items on the Houghton scale was found. CONCLUSIONS: The study found no significant correlation indicating that phantom limb pain syndromes affect amputee use of a prosthesis in the long term follow up.
A quality framework for statistical algorithms
Authors: Yung Wesley , Tam Siu-Ming , Buelens Bart , Chipman Hugh , Dumpert Florian , Ascari Gabriele , Rocci Fabiana , Burger Joep , Choi InKyung
Published: December 2021

As national statistical offices (NSOs) modernize, interest in integrating machine learning (ML) into official statisticians’ toolbox is growing. Two challenges to such an integration are the potential loss of transparency from using “black-boxes” and the need to develop a quality framework. In 2019, the High-Level Group for the Modernisation of Official Statistics (HLG-MOS) launched a project on machine learning with one of the objectives being to address these two challenges. One of the outputs of the HLG-MOS project is a Quality Framework for Statistical Algorithms (QF4SA). While many quality frameworks exist, they have been conceived with traditional methods in mind, and they tend to target statistical outputs. Currently, machine learning methods are being looked at for use in processes producing intermediate outputs, which lead to a final statistical output. Therefore, the QF4SA does not replace existing quality frameworks; it complements them. As the QF4SA targets intermediate outputs and not necessarily the final statistical output, it should be used in conjunction with existing quality frameworks to ensure that high-quality outputs are produced. This paper presents the QF4SA, as well as some recommendations for NSOs considering the use of machine learning in the production of official statistics.
Image classification of vaginal microecology detection based on gabor texture and LSTM model
Authors: Yuan Gaoteng , Dong Yinping , Zhou Xiaofeng
Published: December 2021

BACKGROUND: Gynecological diseases threaten women’s health, and vaginal microecological testing is a common method for detecting gynecological diseases. Efficient and accurate microecological testing methods have always been the goal pursued by gynecologists. OBJECTIVE: In order to automatically identify different types of microbial images in vaginal micromorphology detection, this paper proposes a vaginal microecological image recognition method based on Gabor texture analysis combined with long and short-term memory network (LSTM) model. METHOD: Firstly, we denoise the microecological morphological im-ages, which selects the area of interest and sets the label of the microorganism according to the doctors label. Secondly, texture analysis is carried out for the region of interest, which uses Gabor filters with 8 directions and 5 scales to filter the region of interest to extract the texture features on the image. Comparing the differences between different microbial image features, and screening suitable features to reduce the number of features. Then, we design an LSTM model to analyze the relationship of image features in different categories of microorganisms. Finally, we use the full connection layer and Softmax function to realize the automatic recognition of different microbial images. RESULTS: The experimental results show that the image classification accuracy of 8 common microorganisms is 81.26%. CONCLUSION: Texture analysis combined with LSTM network strategy can identify different kinds of vaginal micro ecological images. Gabor-LSTM model has better classification effect on imbalanced data sets.
The clinical application of an innovative tracheotomy fixation belt in critically ill patients
Authors: Zhang Gui-Li , Shi Zhi-Jing , Hao Ping , Shi Yun-Mei , Gui Cheng-Ying , Xu Lei
Published: December 2021

BACKGROUND: Tracheotomy is one of the first-aid measures for rescuing critically ill patients. OBJECTIVE: This study was designed to observe the clinical effect of using an innovative tracheotomy fixation belt in critically ill patients and to explore the feasibility of using this fixation method. METHODS: Eighty critically ill patients requiring a tracheotomy in Putuo District Central Hospital of Shanghai were enrolled in this study and divided into the observation group and control group (n= 40, each). In the control group, fixation was performed with conventional cotton twill tape, while in the observation group the fixation was carried out using a tracheotomy fixation belt. The differences in neck skin injury, the occurrence of eczema, and the rate of detachment of the tracheotomy tube in the two groups were compared. RESULTS: The rate of neck skin injury was 7.5% lower in the observation group than in the control group (30%), and the difference was statistically significant (p< 0.05). The incidence of skin eczema was 5% lower in the observation group than in the control group (22.5%), and the difference was statistically significant (p< 0.05). The detachment rate of the tracheotomy tube was 5% lower in the observation group than in the control group (20%), and the difference was statistically significant (p< 0.05). CONCLUSION: The use of an innovative tracheotomy fixation belt can effectively reduce skin damage to the patient’s neck, the incidence of eczema, and the incidence of detachment of the tracheotomy tube.
Effects of in vitro short- and long-term treatment with telomerase inhibitor in U-251 glioma cells
Authors: Andrade da Mota Tales Henrique , Reis Guimarães Ana Flávia , Silva de Carvalho Amandda Évelin , Saldanha- de Araujo Felipe , Pinto de Faria Lopes Giselle , Pittella-Silva Fábio , do Amaral Rabello Doralina , Madureira de Oliveira Diêgo
Published: December 2021

BACKGROUND:

The inhibition of the enzyme telomerase (TERT) has been widely investigated as a new pharmacological approach for cancer treatment, but its real potential and the biochemical consequences are not totally understood.

OBJECTIVE:

Here, we investigated the effects of the telomerase inhibitor MST-312 on a human glioma cell line after both short- and long-term (290 days) treatments.

METHODS:

Effects on cell growth, viability, cell cycle, morphology, cell death and genes expression were assessed.

RESULTS:

We found that short-term treatment promoted cell cycle arrest followed by apoptosis. Importantly, cells with telomerase knock-down revealed that the toxic effects of MST-312 are partially TERT dependent. In contrast, although the long-term treatment decreased cell proliferation at first, it also caused adaptations potentially related to treatment resistance and tumor aggressiveness after long time of exposition.

CONCLUSIONS:

Despite the short-term effects of telomerase inhibition not being due to telomere erosion, they are at least partially related to the enzyme inhibition, which may represent an important strategy to pave the way for tumor growth control, especially through modulation of the non-canonical functions of telomerase. On the other hand, long-term exposure to the inhibitor had the potential to induce cell adaptations with possible negative clinical implications.