BACKGROUND:Previous research has illustrated links between dental care and Alzheimer's disease, the aim of this study was to examine whether dental care variables can enhance Alzheimer's risk prediction using two models including Long Short-Term Memory (LSTM) and Temporal Convolutional Network (TCN) models with health and retirement study data. METHODS:9,979 HRS participants without cognitive impairment were analysed in this study (mean age 67 years; and 59.8% female, waves 11-15: 2006/2008-2016). Analysis included 52 predictors including demographic, genetic (APOE ε4), health (including dental visit frequency), and psychosocial domains. The outcomes as cognitive impairment and dementia were defined by Langa-Kabeto-Weir criteria. Class imbalance was addressed by SMOTE and missing data were mean imputed. We standardized and reshaped features into five-wave temporal sequences. Data were split into 3 sets: 70% training, 10% validation, and 20% test. LSTM (single layer, 64 units) and TCN (two dilated convolutional layers, 32 channels) models were trained for up to 50 epochs using binary cross-entropy loss with Adam optimizer, learning-rate reduction on plateau, and early stopping based on validation F1. The accuracy, precision, recall, F1, and AUC-ROC were evaluated via Five-fold stratified cross-validation; the optimization of classification thresholds was done by maximizing F1. McNemar's test compared final predictions. RESULTS:It was evident that LSTM consistently outperformed TCN as demonstrated by the following results: test accuracy 99.78% vs 79.01%; AUC-ROC 99.95% vs 92.74%; F1-score 99.67% vs 75.27%. Cross-validation consistency (LSTM F1 ~99.6%±0.1% vs TCN ~76.6%±1.5%) and stable validation-test metrics (final validation loss 0.0136 vs 0.3312) showed robust LSTM performance without overfitting. moreover, McNemar's test showed a significant difference (statistic = 2605.36; P<0.001). models with dental care variables showed high sensitivity (~99.8%). CONCLUSIONS:Enhancement in Alzheimer's disease risk prediction in older adults was evident when incorporating dental care variables in LSTM-based sequential modeling which suggest potential for early detection. However, further studies in diverse populations and assessment of feature importance is necessary because of reliance on self-reported dental care data and the study's HRS-specific sample.
BACKGROUND:This study aims to determine if self-reported issues with managing medication could be an early sign of cognitive decline, possibly pointing to a future diagnosis of Alzheimer's Disease and Related Dementias (ADRD). There is currently very little information available about the link between issues with medication management and the later diagnosis of an ADRD, despite the potential clinical importance of such a link. METHODS:We analyzed Health and Retirement Study participants present and interviewed at Wave 11 (2012) and followed them through Wave 16 (2020). Medication-taking difficulty (R11MEDS) was classified as no difficulty/don't do vs. difficulty/can't do. Separate cohorts excluded prevalent dementia (R11DEMENE) or AD (R11ALZHEE). Incident outcomes were first respondent-reported physician diagnoses (RwDEMENE, RwALZHEE). Kaplan-Meier curves and multivariable Cox models estimated associations. RESULTS:Of 18,878 dementia-free and 19,348 AD-free HRS respondents at Wave 11, 3.0-3.9% reported difficulty/could not take medications. Kaplan-Meier curves diverged early between exposure groups. In fully adjusted Cox models, medication-taking difficulty predicted higher hazards of incident all-cause dementia (HR 1.59, 95% CI 1.23-2.06) and AD (HR 1.57, 95% CI 1.09-2.27), with corroborating pooled logistic estimates. CONCLUSION:Difficulty in Managing Medications is considered to be a robust prodromal indicator of ADRD, regardless of demographic influences. Therefore, the evaluation of an individual's ability to manage medications within routine assessment may represent a practical, costeffective means to identify cognitive decline and develop appropriate management strategies.
Brucellosis is a prevalent zoonotic disease that is associated with consuming produce from animals infected with Brucella species, usually in the form of unpasteurized milk and milk products. The involvement of the central nervous system (CNS) is an uncommon but dangerous indication of neurobrucellosis. Neurobrucellosis can cause specific imaging findings on magnetic resonance imaging (MRI). In this study, we report a very rare case of neurobrucellosis presenting with stroke-like symptoms that were treated using antibiotics. A 38-year-old male was referred with a presenting complaint of headache, ataxia, and dysarthria. He was admitted one month prior for possible cerebral vascular accident (CVA), which was ruled out. On neurological examination, left-central facial paralysis and bilateral positive Babinski sign were observed. The brain magnetic resonance imaging (MRI) performed with and without contrast demonstrated an acute ischemic stroke in the right middle cerebral artery (MCA) territory and extra-axial heterogeneous ring-enhancing lesions, respectively. Brucellosis was confirmed on serological assessment. In rare instances, neurobrucellosis can cause stroke-like symptoms and brain abscesses. Neurobrucellosis should be considered in such patients when other neurological disorders cannot explain neuroimaging abnormalities.
BACKGROUND:Focal cortical dysplasia (FCD) is a congenital deformity caused by FCD maturation, differentiation, and neuronal migration. Magnetic resonance imaging (MRI) is one of the most popular and consistent procedures for diagnosing FCD. Limited research has evaluated the relationship between the FCD and thalamic volume. Therefore, we conducted the current study to compare thalamic volumes between patients with FCD and healthy individuals. METHODS:The current study was a cross-sectional study of patients with FCD referred to Kashani and Milad Hospitals in Isfahan City in 2019-2021. All patients who met the inclusion criteria were enrolled in the study using the census method. The study population was divided into two groups: patients with FCD and healthy controls. MRI was performed on patients with FCD using a Siemens 1.5 or 3 Tesla MRI device. The data were analyzed using SPSS Statistics for Windows (IBM SPSS Statistics for Windows, Version 18.0). RESULTS:Among the 60 patients, 30 had FCD with a mean age of 22.6 ± 9.3 years, and 30 were healthy with a mean age of 26.3 ± 3.6 years. The only significant difference observed was between the right and left thalamic volumes in the FCD group (P = 0.042). The thalamus on the involved side was significantly smaller than that on the non-involved side in patients (P-value < 0.001). However, no significant differences were observed in the absolute value of the difference between the left and right thalamic volumes when comparing all patients with FCD to those in the non-FCD group (P = 0.054). CONCLUSION:Our study showed that in patients with FCD, the thalamus on the involved side was significantly smaller than that on the noninvolved side.
BACKGROUND:Congenital myasthenic syndromes (CMS) are rare inherited disorders of neuromuscular transmission caused by mutations in presynaptic, synaptic, or postsynaptic components. They usually manifest in childhood with fatigability, ptosis, ophthalmoplegia, and generalized weakness, but late presentations also occur. CASE SUMMARY:We report a 20-year-old male presenting with heart failure and respiratory failure who was found to have a heterozygous AGRN gene mutation (c.4319>T; p. Pro1440Leu). Clinical features included muscle wasting, weakness, restricted gaze, and respiratory compromise requiring ICU care. Genetic sequencing confirmed AGRN-related CMS. Management included ICU support, pyridostigmine trial, heart failure therapy, salbutamol, and fluoxetine with improvement. DISCUSSION:Diagnosis of CMS requires clinical suspicion, characteristic electrophysiology, and genetic confirmation. Treatment varies with subtype; AGRN-related CMS responds variably to salbutamol and ephedrine, while cholinesterase inhibitors may be ineffective. Prognosis depends on timely diagnosis and management.
BACKGROUND:Determining fetal sex during the early stages can help identify potential x-linked disorders and predict pregnancy complications and outcomes related to fetal sex. Few studies have evaluated the use of anogenital distance (AGD) and fetal heart rate (FHR) as sonographic markers for predicting fetal sex in the first trimester. Therefore, this study aimed to predict fetal sex by measuring AGD and FHR using ultrasound in the first trimester. METHODS:This cross-sectional study was conducted at Shahid Beheshti Hospital, Isfahan City, in 2022-2023. Ultrasound scans of 143 singleton pregnancies between 11 and 13 plus 6 gestational weeks and their fetal sex at birth were collected. The exact age of pregnancy was determined by measuring crown-rump length (CRL). The diagnostic value of AGD and FHR in predicting fetal sex was evaluated using receiver operating characteristic (ROC) curve analysis, and indicators such as sensitivity, specificity, positive and negative predictive value, and the area under the curve (AUC) were reported. RESULTS:A total of 143 pregnant women with the mean age of 31.08 ± 5.26 years were entered to our study. The mean CRL and FHR in male and female fetuses were not significantly associated with fetal sex (P > 0.001). However, AGD was significantly higher in male fetuses than in female fetuses (P < 0.001). Moreover, we found that AGD at the cut-off point of 4.2 mm had a significant diagnostic value in predicting male sex (AUC = 0.792; P < 0.001). CONCLUSION:Our study demonstrated that AGD measurement, unlike FHR and CRL, could be a valuable procedure for predicting fetal sex.
BACKGROUND:Hearing impairments are manifestations of Parkinson's disease (PD). We aimed to assess central auditory processing (CAP) functions with PD and their predictors. METHODS:This was a cross-sectional study. It included 35 patients (male = 21; female = 14). The severity of PD was assessed using modified Hoehn and Yahr Scale. The severities of depression and cognitive manifestations were assessed using Beck Depression Inventory II (BDI-II) and Mini Mental State Examination (MMSE) and Montreal Cognitive Assessment (MoCA). Participants underwent audiometry and testing of CAP using dichotic digit (DDT), duration pattern (DPT) and speech in noise (SPIN) tests. RESULTS:Patients had mean age at presentation of 56.66 ± 11.05 yrs and mean duration of PD of 4.77 ± 2.73 yrs. Among were ~69% of patients were in early stages of the disease. Compared to controls (n = 25), patients had poor cognition [MMSE: 20.98 ± 2.36, P = 0.001; MoCA: 18.41 ± 3.00, P = 0.001], hearing impairment at high frequencies (4000 HZ), higher speech reception threshold (SRT) (P = 0.001) and worse performance in DDT (P = 0.0001), DPT (P = 0.0001) and SPIN (P = 0.001). These impairments were independently correlated with cognitive deficits (DDT: P = 0.036; DPT: P = 0.050, SPIN: P = 0.023). CONCLUSIONS:CAP dysfunctions occur in early stages of PD. They include impairments in auditory discrimination, spatial perception, binaural integration, temporal ordering or sequencing, and selective attention. The DDT, DPT and SPIN are useful battery measures for testing CAP with PD. Dopamine deficiencies in PD at different auditory pathway levels including the brainstem and cortico-subcortical levels and neurodegenerative diffuse PD pathology can be the causes of CAP impairments.
Parkinson's disease (PD) is a progressive neurodegenerative disorder that primarily affects motor function. However, PD may also result in substantial cognitive impairments, including spatial memory deficits. Spatial memory, defined as the ability to encode, store, and retrieve information about environmental spatial orientation, is a critical component of daily functioning. A comprehensive understanding of the neural mechanisms underlying these deficits is imperative for the development of targeted interventions. This narrative review explores the neural basis of spatial memory deficits in PD, summarizing evidence from neuroimaging and neurophysiological studies. In addition, it examines current assessment methods and their clinical applications. Spatial memory is primarily governed by the hippocampus and interconnected cortical and subcortical structures, including the basal ganglia, the prefrontal cortex, and the anterior cingulate cortex. In PD, dopaminergic degeneration in the substantia nigra leads to functional disruptions in these networks. The basal ganglia, particularly the striatum, play a crucial role in procedural aspects of spatial navigation, while the hippocampus is essential for allocentric mapping. The utilization of functional neuroimaging techniques has yielded evidence of altered activity in these regions, which is concomitant with spatial memory deficits. Traditional neuropsychological assessments, laboratory-based tasks, and recent advancements, including virtual reality-based tasks, have been employed in the evaluation of spatial memory. The identification of spatial memory deficits in PD is of significant diagnostic and therapeutic importance. Future research should focus on integrating multimodal assessment tools to enhance diagnostic accuracy and explore novel therapeutic approaches targeting spatial memory dysfunction. The cause of spatial memory deficits in PD is multifactorial, arising from complex interactions between dopaminergic depletion and dysfunction in hippocampal-cortical networks. Advancements in assessment methodologies and targeted interventions hold considerable potential for enhancing spatial cognitive outcomes in patients diagnosed with PD. However, further research is required to refine diagnostic tools and develop effective rehabilitation strategies that are targeted at spatial memory impairments in PD.
STUDY DESIGN:A retrospective cohort study. BACKGROUND AND OBJECTIVE:There are no data on changes in cervical sagittal alignment and curvature after second and third surgeries in patients with multilevel cervical degenerative diseases (CDD). This study aimed to explore these changes following multiple decompression and reconstruction surgeries. METHODS:145 patients with multilevel CDD were enrolled based on medical records extracted from 2015 to 2023. They were divided into three groups according to the number of surgeries. 63 patients underwent first decompression and reconstruction surgery (Group 1), 53 patients underwent second surgery (Group 2) and 29 patients underwent third surgery (Group 3). Clinical parameters (Japanese Orthopedic Association (JOA) score for neural functional recovery, visual analogue scale (VAS) and neck disability index (NDI) for neck pain) and radiologic parameters (T1 slope (T1S), cervical lordosis (C2-7CL), C2-7 sagittal vertical axis (C2-7SVA)) were reviewed and analyzed. RESULTS:The mean period between final surgery and last follow-up was more than 12 months. There were significant differences among 3 groups in terms of operation time, blood loss and hospital stay (P < 0.001). Functional scores changed significantly after decompression surgeries (P < 0.001) in 3 groups. Radiographic parameters increased after surgery in group 1 (P < 0.001), while C2-7CL and T1S decreased after second and third surgery in group 2 and group 3 (P < 0.001). Comparing with group 1, there were significant differences showed in terms of C2-7CL, T1S, NDI and VAS in group 2 and group 3 (P < 0.05), NDI and VAS were significantly larger in group3 compare with group 2 (P < 0.05). CONCLUSION:Multiple surgeries may exacerbate cervical lordosis loss and increase axial pain, necessitating cautious surgical planning for multilevel CDD.
Neurodegenerative diseases, including Alzheimer's, Parkinson's, and multiple sclerosis, are a growing healthcare challenge due to their impact on quality of life and the difficulty in treating them. These disorders are associated with brain lesions and barriers, such as the blood-brain barrier (BBB), that impede effective treatment. Nanotechnology, especially functionalized nanoparticles (NPs), is emerging as a promising tool for overcoming these barriers. Nanoparticles, such as liposomes, polymeric micelles, and gold nanoparticles (AuNPs), show potential for targeted drug and gene delivery to the brain, enhancing bioavailability, circulation time, and treatment efficacy. Nanocarrier-based systems have demonstrated success in protecting nucleic acids from degradation, improving BBB penetration, and delivering genetic material to target specific brain areas. Exosomes and artificial vesicles also hold promise for their size and biocompatibility. Gold nanoparticles are gaining attention for their neuroprotective and anti-inflammatory properties, particularly in treating Alzheimer's, Parkinson's, and stroke. These systems can modify gene expression and address the underlying mechanisms of these diseases. In addition to drug delivery, noninvasive strategies like intranasal administration are being explored to enhance patient adherence. However, challenges remain, including regulatory hurdles and the need for further research to optimize these technologies. As research advances, the synergy between materials science, bioengineering, and medicine will pave the way for more effective treatments for neurodegenerative diseases. The aim of this study is to explore the potential of functionalized NPs in overcoming the BBB and improving targeted drug delivery for the treatment of neurodegenerative diseases.
BACKGROUND:Understanding the morphological changes and dimensions of the pituitary gland is crucial for accurate diagnosis and personalized treatment in pediatric patients. Advanced imaging techniques, such as 3D magnetic resonance imaging (MRI), enhance our ability to address knowledge gaps and improve clinical practices in pediatric endocrinology. This study aims to determine normative pituitary gland dimensions and volumes in pediatric patients at Imam Hossein Hospital in Isfahan using advanced 3D MRI protocols. METHODS:Conducted as a prospective cross-sectional study, this research focused on children under 15 years without specific conditions. A total of 412 participants were selected through simple random sampling, and data were analyzed using SPSS version 20 to rigorously assess measurements and extract insights beneficial for pediatric endocrinology. RESULTS:The study included participants aged 0 to 15 years, with a higher representation of boys (63.83%) compared to girls (36.17%). Significant differences were observed in height and volume based on gender and age group. Scatterplots illustrated variations in the pituitary gland's volume, width, height, and anterior-posterior diameter according to age and gender. CONCLUSION:This research provides valuable insights into pediatric endocrinology, facilitating accurate diagnosis and treatment of pituitary disorders in children.
Neurodegenerative diseases present complex challenges that demand advanced analytical techniques to decode intricate brain structures and their changes over time. Curvature estimation within datasets has emerged as a critical tool in areas like neuroimaging and pattern recognition, with significant applications in diagnosing and understanding neurodegenerative diseases. This systematic review assesses state-of-the-art curvature estimation methodologies, covering classical mathematical techniques, machine learning, deep learning, and hybrid methods. Analysing 105 research papers from 2010 to 2023, we explore how each approach enhances our understanding of structural variations in neurodegenerative pathology. Our findings highlight a shift from classical methods to machine learning and deep learning, with neural network regression and convolutional neural networks gaining traction due to their precision in handling complex geometries and data-driven modelling. Hybrid methods further demonstrate the potential to merge classical and modern techniques for robust curvature estimation. This comprehensive review aims to equip researchers and clinicians with insights into effective curvature estimation methods, supporting the development of enhanced diagnostic tools and interventions for neurodegenerative diseases.
Neurodegenerative diseases, such as Alzheimer's, Parkinson's, and Lewy body dementia, are associated with the accumulation of brain proteins, leading to neuroinflammation, disruption of cellular clearance mechanisms, and neuronal death. Nuclear medicine, utilizing technologies like PET and SPECT, plays a crucial role in diagnosing and managing these disorders. Recent advancements in nuclear medicine have enhanced the understanding of disease pathophysiology and facilitated the development of tailored therapeutics. This study aims to address gaps in understanding nuclear medicine's potential to improve early diagnosis, monitor disease progression, and evaluate therapeutic effectiveness. In this review, we analyzed 28 papers and summarized their findings. PET radioligands have revolutionized the in vivo measurement of pathological targets in neurological diseases, offering new insights into the pathophysiology of neurodegenerative conditions. Amyloid PET has emerged as a reliable diagnostic imaging tool, accurately identifying cerebral amyloid-beta accumulation and enabling early differential diagnosis in clinical settings. Furthermore, radiopharmaceuticals such as [18F]Flortaucipir, [18F]FDOPA, and TSPO ligands provide significant advancements in the diagnosis and treatment of neurodegenerative disorders.
OBJECTIVES:This study aims to explore the capabilities of dendritic learning within feedforward tree networks (FFTN) in comparison to traditional synaptic plasticity models, particularly in the context of digit recognition tasks using the MNIST dataset. METHODS:We employed FFTNs with nonlinear dendritic segment amplification and Hebbian learning rules to enhance computational efficiency. The MNIST dataset, consisting of 70,000 images of handwritten digits, was used for training and testing. Key performance metrics, including accuracy, precision, recall, and F1-score, were analysed. RESULTS:The dendritic models significantly outperformed synaptic plasticity-based models across all metrics. Specifically, the dendritic learning framework achieved a test accuracy of 91%, compared to 88% for synaptic models, demonstrating superior performance in digit classification. CONCLUSIONS:Dendritic learning offers a more powerful computational framework by closely mimicking biological neural processes, providing enhanced learning efficiency and scalability. These findings have important implications for advancing both artificial intelligence systems and computational neuroscience.
This study explores the concept of neural reshaping and the mechanisms through which both human and artificial intelligence adapt and learn. OBJECTIVES:To investigate the parallels and distinctions between human brain plasticity and artificial neural network plasticity, with a focus on their learning processes. METHODS:A comparative analysis was conducted using literature reviews and machine learning experiments, specifically employing a multi-layer perceptron neural network to examine regression and classification problems. RESULTS:Experimental findings demonstrate that machine learning models, similar to human neuroplasticity, enhance performance through iterative learning and optimization, drawing parallels in strengthening and adjusting connections. CONCLUSIONS:Understanding the shared principles and limitations of neural and artificial plasticity can drive advancements in AI design and cognitive neuroscience, paving the way for future interdisciplinary innovations.
OBJECTIVES:The aim of this study is to evaluate the impact of various dimensionality reduction methods, including principal component analysis (PCA), Laplacian score, and Chi-square feature selection, on the classification performance of an electroencephalogram (EEG) dataset. METHODS:We applied dimensionality reduction techniques, including PCA, Laplacian score, and Chi-square feature selection, and assessed their impact on the classification performance of EEG data using linear regression, K-nearest neighbour (KNN), and Naive Bayes classifiers. The models were evaluated in terms of their classification accuracy and computational efficiency. RESULTS:Our findings suggest that all dimensionality reduction strategies generally improved or maintained classification accuracy while reducing the computational load. Notably, PCA and Autofeat techniques led to increased accuracy for the models. CONCLUSIONS:The use of dimensionality reduction techniques can enhance EEG data classification by reducing computational demands without compromising accuracy. These results demonstrate the potential for these techniques to be applied in scenarios where both computational efficiency and high accuracy are desired. The code used in this study is available at https://github.com/movahedso/Emotion-analysis.
Chordoma is a rare malignant tumour with an incidence of 0.1 case per 1 lakh population per year. The sacrococcygeal region is the most common site to be involved. Herein, we are reporting a case of sacral chordoma, who is a 32-year-old male patient, a known case of post-polio residual paralysis on the left lower limb, who presented with complaint of pain in the lower back and gluteal region for 2 years with swelling in the gluteal region for 1 year, which was gradually increasing in size for 1 year with associated weight loss. MRI revealed an ill-defined lytic expansile altered signal intensity lesion involving S3 to S5 and coccygeal vertebral bodies measuring 13.2 × 16.2 × 14 cm (ap × tr × cc) with adjacent large lobulated heterogeneous soft tissue component and showed multiple coarse calcifications. The lesion anteriorly displaced and abutted the rectum and was deriving its blood supply from branches of bilateral internal iliac arteries. The patient was planned and underwent wide-margin resection (middle sacrectomy with R0 margins with preservation of both S2 and right S3 nerve roots). Histologic Grade was reported to be G2, moderately differentiated, high grade. Pathologic stage classification was reported as pT3a. Postoperatively patient had the same neurological status and was discharged on advice to do full weight bearing walking and self-intermittent catheterisation and laxatives. He was on routine follow up and improved well symptomatically.
INTRODUCTION:Assessing vaccine willingness and understanding sources of vaccine hesitancy in individuals with multiple sclerosis (MS) helps healthcare providers approach patients more effectively while respecting their autonomy to encourage coronavirus disease 2019 (COVID-19) vaccination. MATERIALS AND METHODS:A descriptive-analytical cross-sectional study using a researcher-made checklist was conducted on MS patients referred to Neshat Clinic of Hamadan during the years 2020-2021. The checklist contained questions about demographic information, MS phenotype, duration of illness, expanded disability status scale (EDSS) score, and COVID-19 vaccination status. The expanded disability status scale (EDSS) is the most commonly used instrument for measuring disability in patients with multiple sclerosis (MS). The EDSS scale ranges from 0 to 10 in increments of 0.5 units, denoting advanced points of disability. RESULTS:Based on the results, 20 individuals (10%) were in the vaccine non-acceptance group, while 181 individuals (90%) were in the vaccine acceptance group. A significant number of relapsing and remitting (RR) type MS patients (90.7%) and all primary progressive (PP) type MS patients (100%) accepted the vaccine. In comparison, vaccine non-acceptance in the secondary progressive (SP) group was relatively higher (20.7%) compared to other types of MS, and this difference was significant (P < 0.05). Additionally, there was a statistically significant relationship between the history of COVID-19 and vaccine acceptance (P < 0.05). CONCLUSION:The study results demonstrated a high rate of COVID-19 vaccine acceptance among MS patients. MS phenotype, previous infection experiences, and other influences allow for COVID-19 vaccine acceptance among MS patients. This information can improve health programs and communication strategies for COVID-19 and future possible infectious disease vaccination in individuals with MS.
INTRODUCTION:Unstable thoracolumbar burst fractures are routinely encountered in orthopedic practice. Recently, short-segment fixation with pedicle screw augmentation of the fractured vertebra for unstable thoracolumbar burst fractures has gained popularity. Nonetheless, the maintenance of the kyphotic correction during the follow-up period remains controversial. This study aimed to examine the clinical-radiological outcomes, complications, and functional outcomes of fractured vertebrae augmentation with intermediate pedicle screws in short-segment instrumentation in acute thoracolumbar spine fractures.METHODS:This retrospective study was conducted in the Department of Orthopedics, All India Institute of Medical Sciences, Jodhpur, using medical records from January 2021 to October 2022. Parameters such as local kyphosis correction, loss of kyphotic correction at final follow-up, anterior body height correction (%), and loss of correction (%) at final follow-up were measured as primary outcomes. Various other parameters such as operative time, blood loss, length of hospital stay, and visual analog scale were measured as secondary outcomes.RESULTS:The mean correction obtained via surgery in the immediate postoperative period was 13.7±2.3 degrees. The mean loss of correction at the final follow-up was 4.1±2.0 degrees, and the mean final local kyphotic angle was 7.2±2.4 degrees (P<0.05). The mean correction obtained via surgery in the immediate postoperative period was 37.2%±9.0%. The mean loss of correction at the final follow-up was 10.5%±5.3%, and the mean final anterior vertebral body height maintained was 72%±11.0% (P<0.05).CONCLUSION:Short-segment posterior fixation with pedicle screw augmentation achieves good correction of local kyphotic angle and anterior vertebral height in the immediate postoperative period, but some loss of correction at final follow-up is common. In our study, the loss of correction corresponded directly to the load-sharing score.
Alzheimer's disease (AD) is a devastative disease, the 1st most frequent neurodegenerative disease worldwide. Its prevalence is increasing and early detection methods as well as potential genomic based therapeutics are urgently needed.To better characterize recent seq studies of AD and site recent relevant literature. Using single-cell RNA sequencing, the characteristics of neuronal cell populations in Alzheimer's disease (AD) have not been completely elucidated.We conducted a dynamic and longitudinal bibliometric analysis to investigate existing studies on Single-cell RNA sequencing analysis and Alzheimer's Disease and identify data gaps and possible new research avenues.All AD papers concentrating on Single-cell RNA sequencing analysis were found using the search terms "Alzheimer's Disease", and "Single-cell RNA sequencing analysis" in the PubMed/MEDLINE database. Only English publications published between 2015 and 2023 were chosen using filters.Original English-language research publications disclosing Single-cell RNA sequencing analysis and Alzheimer's Disease were examined for inclusion. Two sets of independent reviewers discovered and extracted pertinent data. The bibliometric study was carried out using the R software packages Bibliometrix and Biblioshiny. The narrowed search yielded 158 publications, all published between 2015 and 2023. Yet, after applying filters and considering the inclusion requirements, the search results comprise just 51 original articles out of 158 articles. There were 107 articles eliminated. The importance of the discovery of Single-cell RNA sequencing analysis and Alzheimer's Disease a decade ago only grows with time. Our results have important implications for future studies of AD and may help researchers across the world better understand the global context of the Single-cell RNA sequencing analysis and Alzheimer's Disease link. This study puts emphasis on the critical need for more diverse participant demographics in Alzheimer's disease investigations.
