Longitudinal assessment of plasma biomarkers for early detection of cognitive changes in subjective cognitive decline.

Background: Individuals experiencing subjective cognitive decline (SCD) are at an increased risk of developing mild cognitive impairment and dementia. Early identification of SCD and neurodegenerative diseases using biomarkers may help clinical decision-making and improve prognosis. However, few cross-sectional and longitudinal studies have explored plasma biomarkers in individuals with SCD using immunomagnetic reduction. Objective: To identify plasma biomarkers for SCD. Methods: Fifty-two participants [38 with SCD, 14 healthy controls (HCs)] underwent baseline assessments, including measurements of plasma Aß42, Aß40, t-tau, p-tau, and α-synuclein using immunomagnetic reduction (IMR) assays, cognitive tests and the Mini-Mental State Examination (MMSE). Following initial cross-sectional analysis, 39 individuals (29 with SCD, 10 HCs) entered a longitudinal phase for reassessment of these biomarkers and the MMSE. Biomarker outcomes across different individual categories were primarily assessed using the area under the receiver operating characteristic (ROC) curve. The SCD subgroup with an MMSE decline over one point was compared to those without such a decline. Results: Higher baseline plasma Aß1-42 levels significantly discriminated participants with SCD from HCs, with an acceptable area under the ROC curve (AUC) of 67.5% [95% confidence interval (CI), 52.7-80.0%]. However, follow-up and changes in MMSE and IMR data did not significantly differ between the SCD and HC groups (p > 0.05). Furthermore, lower baseline plasma Aß1-42 levels were able to discriminate SCD subgroups with and without cognitive decline with a satisfied performance (AUC, 75.0%; 95% CI, 55.6-89.1%). At last, the changes in t-tau and Aß42 × t-tau could differentiate between the two SCD subgroups (p < 0.05). Conclusion: Baseline plasma Aß42 may help identify people with SCD and predict SCD progression. The role of plasma Aß42 levels as well as their upward trends from baseline in cases of SCD that progress to mild cognitive impairment and Alzheimer's disease require further investigation.

Comparative effectiveness and tolerability of calcitonin gene-related peptide (CGRP) monoclonal antibodies and onabotulinumtoxinA in chronic migraine: A multicenter, real-world study in Taiwan.

OBJECTIVE: To compare the real-world effectiveness and tolerability of calcitonin gene-related peptide (CGRP) monoclonal antibodies (mAbs) and onabotulinumtoxinA in chronic migraine (CM) patients. METHODS: This multicenter study involved retrospective analysis of prospectively collected data of CM patients treated with CGRP mAbs or onabotulinumtoxinA, including difficult-to-treat (DTT) patients (i.e., ≥3 preventive failures). Treatment outcomes were determined at 6 months based on prospective headache diaries and Migraine Disability Assessment (MIDAS). RESULTS: The study included 316 (55 M/261F, mean age 44.4 ± 13.5 years) and 333 (61 M/272F, mean age 47.9 ± 13.4 years) CM patients treated with CGRP mAbs or onabotulinbumtoxinA, respectively. At 6 months, CGRP mAb treatment was associated with a greater decrease in monthly migraine days (MMDs) (-13.0 vs. -8.7 days/month, p < 0.001) and a higher ≥50% responder rate (RR) (74.7% vs. 50.7%, p < 0.001) compared with onabotulinumtoxinA injections. The findings were consistent in DTT patients (-13.0 vs. -9.1 MMDs, p < 0.001; ≥50% RR: 73.9% vs. 50.3%, p < 0.001) or those with medication-overuse headache (MOH) (-13.3 vs. -9.0 MMDs, p < 0.001; ≥50% RR: 79.0% vs. 51.6%, p < 0.001). Besides, patients receiving CGRP mAbs had greater improvement (-42.2 vs. -11.8, p < 0.001) and a higher ≥50% RR (62.0% vs. 40.0%, p = 0.001) in MIDAS scores and a lower rate of adverse events (AEs) (6.0% vs. 21.0%, p < 0.001). However, none of the patients discontinued treatment due to AEs. CONCLUSIONS: In this multicenter, real-world study, CGRP mAbs were more effective than onabotulinumtoxinA in CM patients, even in DTT or MOH patients. All of these injectables were well tolerated. Further prospective studies are needed to verify these findings.

Influences of Genetic and Environmental Factors on Chronic Migraine: A Narrative Review.

PURPOSE OF REVIEW: In this narrative review, we aim to summarize recent insights into the complex interplay between environmental and genetic factors affecting the etiology, development, and progression of chronic migraine (CM). RECENT FINDINGS: Environmental factors such as stress, sleep dysfunction, fasting, hormonal changes, weather patterns, dietary compounds, and sensory stimuli are critical triggers that can contribute to the evolution of episodic migraine into CM. These triggers are particularly influential in genetically predisposed individuals. Concurrently, genome-wide association studies (GWAS) have revealed over 100 genetic loci linked to migraine, emphasizing a significant genetic basis for migraine susceptibility. In CM, environmental and genetic factors are of equal importance and contribute to the pathophysiology of the condition. Understanding the bidirectional interactions between these elements is crucial for advancing therapeutic approaches and preventive strategies. This balanced perspective encourages continued research into the complex gene-environment nexus to improve our understanding and management of CM.

The association of total pulses with the efficacy of repetitive transcranial magnetic stimulation for treatment-resistant major depression: A dose-response meta-analysis.

AIM: This study aimed to examine dose-effects of total pulses on improvement of depressive symptoms in patients with treatment-resistant depression (TRD) receiving repetitive transcranial magnetic stimulation (rTMS) over the left dorsal lateral prefrontal cortex (DLPFC). MATERIALS AND METHODS: The MEDLINE, Cochrane Central Register of Controlled Trials (CENTRAL), EMBASE, PsycINFO, and ClinicalTrial.gov databases were systematically searched. We included randomized, double-blind, placebo-controlled trials (RCT) that used rTMS over left DLPFC in patients with TRD. Excluded studies were non-TRD, non-RCTs, or combined other brain stimulation interventions. The outcome of interest was the difference between rTMS arms and sham controls in improvement of depressive symptoms in a dose-response manner. A random-effects meta-analysis and dose-response meta-analysis(DRMA) was used to examine antidepressant efficacy of rTMS and association with total pulses. RESULTS: We found that rTMS over left DLPFC is superior to sham controls (reported as standardized mean difference[SMD] with 95% confidence interval: 0.77; 0.56-0.98). The best-fitting model of DRMA was bell-shaped (estimated using restricted cubic spline model; R2 =0.42), indicating that higher doses (>26,660 total pulses) were not associated with increased improvement of depressive symptoms. Stimulation frequency(R2 =0.53) and age(R2 =0.51) were significant moderators for the dose-response curve. Furthermore, 15-20 Hz rTMS was superior to 10 Hz rTMS (0.61, 0.15-1.10) when combining all doses. CONCLUSIONS: Our findings suggest higher doses(total pulses) of rTMS were not always associated with increased improvement of depressive symptoms in patients with TRD, and that the dose-response relationship was moderated by stimulation frequency and age. These associations emphasize the importance of determining dosing parameters to achieve maximum efficacy.

ATIP/ATIP1 regulates prostate cancer metastasis through mitochondrial dynamic-dependent signaling.

Mitochondria play a fundamental role in cell survival and motility. Abnormalities in mitochondria are associated with carcinogenesis, especially with tumor metastasis. In this study, we explore the biological function of ATIP1, which is a mitochondrial-located isoform of angiotensin II AT2 receptor interacting proteins (ATIPs) in prostate cancer cells. The results showed that ATIP is downregulated in prostate cancer tissues and is negatively correlated with the disease-free survival rate of prostate cancer patients. Silencing of ATIP promotes mitochondrial fission and enhances tumor cell migration and invasion. Reconstitution of ATIP1 in ATIP-deficient cells significantly attenuates mitochondrial trafficking and tumor cell movement. Therefore, ATIP1 is a negative regulator of mitochondrial dynamics and tumor cell motility and is also a potential biomarker for predicting prostate cancer malignancy.

Trends and hotspots in research of traumatic brain injury from 2000 to 2022: A bibliometric study.

Traumatic brain injury (TBI) is a major health concern globally, which is characterized by high morbidity and mortality rates. Since the 21st century, TBI has received increasing attention and the number of publications is growing rapidly. This study aimed to characterize the volume and quality of scholarly output on TBI and identify the most impactful literature, research trends, and hotspots from the year 2000-2022. We searched publications on TBI through the Web of Science Core Collection-Science Citation Index Expanded database which were published from 2000 to 2022. Basic information of each paper, including publication year, countries, authors, affiliations, journal, fundings, subject areas, and keywords were collected for further analysis by using Microsoft Excel, VOSviewer, and CiteSpace software. A total of 47231 TBI-related publications were identified through database retrieval. The annual number of publications on TBI has increased steadily over the past twenty years and the number in the year 2022 is sevenfold higher than that in 2000. The United States of America (USA) was the leading country in both numbers of publications and citations, which is consistent with the finding that it had the most funding agencies. Menon DK was the author with the highest influence and the University of California System was the most productive affiliation. Moreover, keywords analysis suggested that the research topics can be mainly divided into six categories: management, rehabilitation, mechanisms, concussion, neuroimaging, and neuroendocrine. This study visualized the trends and focuses of scientific research related to TBI, both quantitatively and qualitatively. The USA had a relatively high academic impact owing to its productive experts and institutions in this field. Neuroinflammation, machine learning, tranexamic acid, and extracellular vesicles are currently hot topics in the field of TBI.

Usefulness of cone-beam computed tomography-reformatted epidurography in percutaneous epidural adhesiolysis: A pilot study.

BACKGROUND: Conventional epidurography (CE) is thought to have insufficient usefulness on percutaneous epidural adhesiolysis (PEA). We aimed to evaluate the association between the outcome of PEA and cone-beam computed tomography-reformatted epidurography (CBCT-RE). METHODS: After ethics board approval and written informed consent were obtained, we performed 30 PEA in 26 participants, and evaluated their post-PEA image findings. Two independent radiologists categorized and recorded the occurrence of contrast in the intracanal ventral and extraforaminal regions on CE, and in the dorsal canal (DC), ventral canal (VC), dorsal foramen (DF), and ventral foramen (VF) on CBCT-RE. Reproducibility was assessed using intraclass correlation coefficients (ICCs). Baseline characteristics along with contrast distribution patterns of CE and CBCT-RE were analyzed in terms of their association with symptom relief at 1 month after PEA. RESULTS: The rate of patients with symptoms relief >50% after PEA was 63.3%. The inter-reader agreement was higher for CBCT-RE (ICC = 0.955) than for CE (ICC = 0.793). Participants with contrast coexisting in VC and DF adjacent to the irritated nerve root on CBCT-RE ( p = 0.015) had a significantly better response after PEA than those without contrast at these locations on CBCT-RE, independent of baseline characteristics (adjusted odds ratio: 11.414 [ p = 0.012]). CONCLUSION: CBCT-RE with identifying contrast distribution patterns is useful for predicting outcome of PEA.

Value of CT in targeted CT-guided epidural blood patching: Predictors for successful epidural punctures.

BACKGROUND AND PURPOSE: Differentiating epidural from intrathecal punctures before computed tomography (CT)-guided epidural blood patching (EBP) is subjective, relying on operator experience. This study aimed to investigate CT findings for epidural and intrathecal punctures and identify reliable predictors for successful epidural punctures before targeted CT-guided EBP. MATERIALS AND METHODS: We included 65 patients with low-cerebrospinal fluid (CSF)-pressure headache receiving targeted CT-guided EBP between January 2021 and October 2022 in this retrospective study. We analyzed clinical data, technical information, and CT features before EBP. Fisher's exact test was used for discrete variables, while Mann-Whitney U test was used for continuous variables. Positive (PLR) and negative likelihood ratios (NLR) were calculated to identify predictors for confirming epidural punctures. RESULTS: We confirmed 43 patients as epidural punctures and 22 patients as intrathecal punctures. Before contrast injection, epidural fat at the needle tip in the epidural group was higher than the intrathecal group (37.2 % [16/43] vs. 4.5 % [1/22], p = 0.006). After contrast injection, the "contrast-needle tip connection" sign was mostly observed in the epidural group than the intrathecal group (95.3 % [41/43] vs. 9.1 % [2/22], p < 0.001). Additionally, the epidural group had significantly higher boomerang-shaped contrast morphology than the intrathecal group (65.1 % [28/43] vs. 9.1 % [2/22], p < 0.001). The "contrast-needle tip connection" sign had the highest PLR (10.49) and lowest NLR (0.05). CONCLUSION: Identifying epidural fat at the needle tip, "contrast-needle tip connection" sign, and boomerang-shaped contrast morphology on CT scans are useful for confirming proper placement of the needle tip within the epidural space.

Comparisons of Quality, Correctness, and Similarity Between ChatGPT-Generated and Human-Written Abstracts for Basic Research: Cross-Sectional Study.

BACKGROUND: ChatGPT may act as a research assistant to help organize the direction of thinking and summarize research findings. However, few studies have examined the quality, similarity (abstracts being similar to the original one), and accuracy of the abstracts generated by ChatGPT when researchers provide full-text basic research papers. OBJECTIVE: We aimed to assess the applicability of an artificial intelligence (AI) model in generating abstracts for basic preclinical research. METHODS: We selected 30 basic research papers from Nature, Genome Biology, and Biological Psychiatry. Excluding abstracts, we inputted the full text into ChatPDF, an application of a language model based on ChatGPT, and we prompted it to generate abstracts with the same style as used in the original papers. A total of 8 experts were invited to evaluate the quality of these abstracts (based on a Likert scale of 0-10) and identify which abstracts were generated by ChatPDF, using a blind approach. These abstracts were also evaluated for their similarity to the original abstracts and the accuracy of the AI content. RESULTS: The quality of ChatGPT-generated abstracts was lower than that of the actual abstracts (10-point Likert scale: mean 4.72, SD 2.09 vs mean 8.09, SD 1.03; P<.001). The difference in quality was significant in the unstructured format (mean difference -4.33; 95% CI -4.79 to -3.86; P<.001) but minimal in the 4-subheading structured format (mean difference -2.33; 95% CI -2.79 to -1.86). Among the 30 ChatGPT-generated abstracts, 3 showed wrong conclusions, and 10 were identified as AI content. The mean percentage of similarity between the original and the generated abstracts was not high (2.10%-4.40%). The blinded reviewers achieved a 93% (224/240) accuracy rate in guessing which abstracts were written using ChatGPT. CONCLUSIONS: Using ChatGPT to generate a scientific abstract may not lead to issues of similarity when using real full texts written by humans. However, the quality of the ChatGPT-generated abstracts was suboptimal, and their accuracy was not 100%.

Development of machine learning model to predict pulmonary function with low-dose CT-derived parameter response mapping in a community-based chest screening cohort.

PURPOSE: To construct and evaluate the performance of a machine learning-based low dose computed tomography (LDCT)-derived parametric response mapping (PRM) model for predicting pulmonary function test (PFT) results. MATERIALS AND METHODS: A total of 615 subjects from a community-based screening population (40-74 years old) with PFT parameters, including the ratio of the first second forced expiratory volume to forced vital capacity (FEV1/FVC), the percentage of forced expiratory volume in the one second predicted (FEV1%), and registered inspiration-to-expiration chest CT scanning were enrolled retrospectively. Subjects were classified into a normal, high risk, and COPD group based on PFT. Data of 72 PRM-derived quantitative parameters were collected, including volume and volume percentage of emphysema, functional-small airways disease, and normal lung tissue. A machine-learning with random forest regression model and a multilayer perceptron (MLP) model were constructed and tested on PFT prediction, which was followed by evaluation of classification performance based on the PFT predictions. RESULTS: The machine-learning model based on PRM parameters showed better performance for predicting PFT than MLP, with a coefficient of determination (R2 ) of 0.749 and 0.792 for FEV1/FVC and FEV1%, respectively. The Mean Squared Errors (MSE) for FEV1/FVC and FEV1% are 0.0030 and 0.0097 for the random forest model, respectively. The Root Mean Squared Errors (RMSE) for FEV1/FVC and FEV1% are 0.055 and 0.098, respectively. The sensitivity, specificity, and accuracy for differentiating between the normal group and high-risk group were 34/40 (85%), 65/72 (90%), and 99/112 (88%), respectively. For differentiating between the non-COPD group and COPD group, the sensitivity, specificity, and accuracy were 8/9 (89%), 112/112 (100%), 120/121 (99%), respectively. CONCLUSIONS: The machine learning-based random forest model predicts PFT results in a community screening population based on PRM, and it identifies high risk COPD from normal populations with high sensitivity and reliably predicts of high-risk COPD.

Multimorbidity Pattern and Risk for Mortality Among Patients With Dementia: A Nationwide Cohort Study Using Latent Class Analysis.

OBJECTIVE: Individuals with dementia are at a substantially elevated risk for mortality; however, few studies have examined multimorbidity patterns and determined the inter-relationship between these comorbidities in predicting mortality risk. METHODS: This is a prospective cohort study. Data from 6,556 patients who were diagnosed with dementia between 1997 and 2012 using the Taiwan National Health Insurance Research Database were analyzed. Latent class analysis was performed using 16 common chronic conditions to identify mortality risk among potentially different latent classes. Logistic regression was performed to determine the adjusted association of the determined latent classes with the 5-year mortality rate. RESULTS: With adjustment for age, a three-class model was identified, with 42.7% of participants classified as "low comorbidity class (cluster 1)", 44.2% as "cardiometabolic multimorbidity class (cluster 2)", and 13.1% as "FRINGED class (cluster 3, characterized by FRacture, Infection, NasoGastric feeding, and bleEDing over upper gastrointestinal tract)." The incidence of 5-year mortality was 17.6% in cluster 1, 26.7% in cluster 2, and 59.6% in cluster 3. Compared with cluster 1, the odds ratio for mortality was 9.828 (95% confidence interval [CI]=6.708-14.401; p<0.001) in cluster 2 and 1.582 (95% CI=1.281-1.953; p<0.001) in cluster 3. CONCLUSION: Among patients with dementia, the risk for 5-year mortality was highest in the subpopulation characterized by fracture, urinary and pulmonary infection, upper gastrointestinal bleeding, and nasogastric intubation, rather than cancer or cardiometabolic comorbidities. These findings may improve decision-making and advance care planning for patients with dementia.

DreamStone: Image as a Stepping Stone for Text-Guided 3D Shape Generation.

This paper presents a new text-guided 3D shape generation approach DreamStone that uses images as a stepping stone to bridge the gap between the text and shape modalities for generating 3D shapes without requiring paired text and 3D data. The core of our approach is a two-stage feature-space alignment strategy that leverages a pre-trained single-view reconstruction (SVR) model to map CLIP features to shapes: to begin with, map the CLIP image feature to the detail-rich 3D shape space of the SVR model, then map the CLIP text feature to the 3D shape space through encouraging the CLIP-consistency between the rendered images and the input text. Besides, to extend beyond the generative capability of the SVR model, we design the text-guided 3D shape stylization module that can enhance the output shapes with novel structures and textures. Further, we exploit pre-trained text-to-image diffusion models to enhance the generative diversity, fidelity, and stylization capability. Our approach is generic, flexible, and scalable. It can be easily integrated with various SVR models to expand the generative space and improve the generative fidelity. Extensive experimental results demonstrate that our approach outperforms the state-of-the-art methods in terms of generative quality and consistency with the input text.

Prevalence and risk factors of difficult mask ventilation: A systematic review and meta-analysis.

STUDY OBJECTIVE: This meta-analysis aimed at identifying the risk factors for and their strengths in predicting difficult mask ventilation (MV) through a systematic approach. DESIGN: Meta-analysis of observational studies. SETTING: Operating room. INTERVENTION: Airway- or patient-related risk factors for difficult MV reported in over 20% of eligible studies identified through literature review. PATIENTS: Adults receiving anesthetic induction with requirement of MV. MEASUREMENTS: Databases including EMBASE, MEDLINE, Google Scholar, and Cochrane Library were searched from inception to July 2022. The primary outcomes were the identification of commonly reported risk factors for MV and a comparison of their strengths in difficult MV prediction, while the secondary outcomes were the prevalence of difficult MV in the general population and those with obesity. MAIN RESULTS: Meta-analysis of 20 observational studies involving 335,846 patients identified 13 risk factors with predictive strengths (all p < 0.05): neck radiation (OR = 5.0, five studies, n = 277,843), increased neck circumference (OR = 4.04, 11 studies, n = 247,871), obstructive sleep apnea (OSA) (OR = 3.61, 12 studies, n = 331,255), presence of beard (OR = 3.35, 12 studies, n = 295,443), snoring (OR = 3.06, 14 studies, n = 296,105), obesity (OR = 2.99, 11 studies, n = 278,297), male gender (OR = 2.76, 16 studies, n = 320,512), Mallampati score III-IV (OR = 2.36, 17 studies, n = 335,016), limited mouth opening (OR = 2.18, six studies, n = 291,795), edentulous (OR = 2.12, 11 studies, n = 249,821), short thyroid-mental distance (OR = 2.12, six studies, n = 328,311), old age (OR = 2, 11 studies, n = 278,750), and limited neck movement (OR = 1.98, nine studies, n = 155,101). The prevalence of difficult MV was 6.1% (16 studies, n = 334,694) and 14.4% (four studies, n = 1152) in the general population and those with obesity, respectively. CONCLUSIONS: Our results demonstrated the strengths of 13 most common risk factors for predicting difficult MV, which may serve as an evidence-based reference for clinicians to incorporate into their daily practice.

Shared alterations in hippocampal structural covariance in subjective cognitive decline and migraine.

Introduction: Subjective cognitive decline (SCD) and migraine are often comorbid. Hippocampal structural abnormalities have been observed in individuals with both SCD and migraine. Given the known structural and functional heterogeneity along the long axis (anterior to posterior) of the hippocampus, we aimed to identify altered patterns of structural covariance within hippocampal subdivisions associated with SCD and migraine comorbidities. Methods: A seed-based structural covariance network analysis was applied to examine large-scale anatomical network changes of the anterior and posterior hippocampus in individuals with SCD, migraine and healthy controls. Conjunction analyses were used to identify shared network-level alterations in the hippocampal subdivisions in individuals with both SCD and migraine. Results: Altered structural covariance integrity of the anterior and posterior hippocampus was observed in the temporal, frontal, occipital, cingulate, precentral, and postcentral areas in individuals with SCD and migraine compared with healthy controls. Conjunction analysis revealed that, in both SCD and migraine, altered structural covariance integrity was shared between the anterior hippocampus and inferior temporal gyri and between the posterior hippocampus and precentral gyrus. Additionally, the structural covariance integrity of the posterior hippocampus-cerebellum axis was associated with the duration of SCD. Conclusion: This study highlighted the specific role of hippocampal subdivisions and specific structural covariance alterations within these subdivisions in the pathophysiology of SCD and migraine. These network-level changes in structural covariance may serve as potential imaging signatures for individuals who have both SCD and migraine.

Generalizability and Diagnostic Performance of AI Models for Thyroid US.

Background Artificial intelligence (AI) models have improved US assessment of thyroid nodules; however, the lack of generalizability limits the application of these models. Purpose To develop AI models for segmentation and classification of thyroid nodules in US using diverse data sets from nationwide hospitals and multiple vendors, and to measure the impact of the AI models on diagnostic performance. Materials and Methods This retrospective study included consecutive patients with pathologically confirmed thyroid nodules who underwent US using equipment from 12 vendors at 208 hospitals across China from November 2017 to January 2019. The detection, segmentation, and classification models were developed based on the subset or complete set of images. Model performance was evaluated by precision and recall, Dice coefficient, and area under the receiver operating characteristic curve (AUC) analyses. Three scenarios (diagnosis without AI assistance, with freestyle AI assistance, and with rule-based AI assistance) were compared with three senior and three junior radiologists to optimize incorporation of AI into clinical practice. Results A total of 10 023 patients (median age, 46 years [IQR 37-55 years]; 7669 female) were included. The detection, segmentation, and classification models had an average precision, Dice coefficient, and AUC of 0.98 (95% CI: 0.96, 0.99), 0.86 (95% CI: 0.86, 0.87), and 0.90 (95% CI: 0.88, 0.92), respectively. The segmentation model trained on the nationwide data and classification model trained on the mixed vendor data exhibited the best performance, with a Dice coefficient of 0.91 (95% CI: 0.90, 0.91) and AUC of 0.98 (95% CI: 0.97, 1.00), respectively. The AI model outperformed all senior and junior radiologists (P < .05 for all comparisons), and the diagnostic accuracies of all radiologists were improved (P < .05 for all comparisons) with rule-based AI assistance. Conclusion Thyroid US AI models developed from diverse data sets had high diagnostic performance among the Chinese population. Rule-based AI assistance improved the performance of radiologists in thyroid cancer diagnosis. © RSNA, 2023 Supplemental material is available for this article.

Risk of neurodegenerative diseases in patients with sleep disorders: A nationwide population-based case-control study.

OBJECTIVE: Our study aimed to explore the associative relationship between neurodegenerative diseases and sleep disorders. PATIENTS: This 15-year retrospective longitudinal nationwide population-based matched case-control study used data extracted from the National Health Insurance Research Database. We evaluated 25,589 patients diagnosed with neurodegenerative diseases between 2000 and 2015 and a matched control of 102,356 patients without neurodegenerative diseases. RESULTS: Sleep disorders were an independent risk factor for the development of neurodegenerative diseases (adjusted odds ratio (OR): 1.794, 95% confidence interval (CI): 1.235-2.268, P < 0.001), with a positive dose-effect relationship (adjusted OR (95% CI): <1 year: 1.638 (1.093-2.872), P < 0.001; 1-5 years: 1.897 (1.260-3.135), P < 0.001; >5 years: 2.381 (1.467-3.681), P < 0.001. Moreover, patients with sleep disorder and comorbid depression had a significantly higher risk of neurodegenerative disorders (adjusted OR: 5.874). Subgroup analysis showed that insomnia was associated with Alzheimer's disease, Pick's disease and essential tremor (adjusted OR (95% CI): 1.555 (1.069-1.965), 1.934 (1.331-2.445) and 2.089 (1.439-2.648), respectively). Obstructive sleep apnea was associated with Parkinson's disease, essential tremor, and primary dystonia (adjusted OR (95% CI): 1.801 (1.239-2.275), 5.523 (3.802-6.977), and 4.892 (3.365-6.178), respectively). Other specific sleep disorders were associated with Pick's disease, Parkinson's disease, essential tremor, and primary dystonia (adjusted OR (95% CI): 8.901 (6.101-11.010), 1.549 (1.075-1.986), 2.791 (1.924-3.531), and 9.114 (6.283-10.506), respectively). CONCLUSION: Sleep disorders are associated with the subsequent development of neurodegenerative disorders. Moreover, sleep disorder patients with comorbid depression have a higher risk of neurodegenerative diseases.

Case Report: Severe rebound after withdrawal of fingolimod in a patient with neuromyelitis optica spectrum disorder.

Purpose: Fingolimod, an oral treatment for relapsing-remitting multiple sclerosis (RRMS), has been associated with a significant rebound in disease activity after therapy cessation. We described a patient with neuromyelitis optica spectrum disorder (NMOSD) who was previously diagnosed with RRMS and experienced fatal rebound syndrome after cessation of fingolimod. Case report: A 54-year-old woman, previously diagnosed with RRMS, experienced relapse after orthopedic surgery. The diagnosis was later revised to NMOSD based on a positive aquaporin-4 antibody. Three weeks after converting the immunomodulator from fingolimod to azathioprine, severe disease reactivation was observed. Considering the multiple new and enlarging magnetic resonance imaging lesions, the temporal relationship between fingolimod cessation and symptom onset, and the relatively low possibility of disease reactivation within a short time, the diagnosis of fingolimod withdrawal syndrome was proposed. Although immediate steroid pulse therapy and plasma exchange were performed, the patient eventually died owing to a fulminant clinical course. Conclusion: Fingolimod withdrawal syndrome is well known in patients with multiple sclerosis (MS). It can also occur in patients with NMOSD. Recognizing patients with NMOSD who present with MS-like manifestations, and avoiding drugs that may be harmful to patients with NMOSD, are important.

Efficacy and safety of gefapixant for chronic cough: a meta-analysis of randomised controlled trials.

BACKGROUND: The efficacy and safety of gefapixant in adults with chronic cough remain unclear. Our objective was to assess the efficacy and safety of gefapixant using updated evidence. METHODS: MEDLINE, Cochrane Central Register of Controlled Trials (CENTRAL) and Embase databases were searched from inception through September 2022. Subgroup analysis based on dose of gefapixant (i.e. ≤20, 45-50 and ≥100 mg twice daily for low, moderate and high doses, respectively) was performed to explore a potential dose-dependent effect. RESULTS: Five studies involving seven trials showed the efficacy of moderate- or high-dose gefapixant for reducing objective 24-h cough frequency (estimated relative reduction 30.9% and 58.5%, respectively) (i.e. primary outcome) and awake cough frequency (estimated relative reduction 47.3% and 62.8%, respectively). Night-time cough frequency was only reduced with high-dose gefapixant. Consistently, the use of moderate- or high-dose gefapixant significantly alleviated cough severity and improved cough-related quality of life, but increased the risk of all-cause adverse events (AEs), treatment-related AEs and ageusia/dysgeusia/hypogeusia. Subgroup analysis showed dose dependency in both efficacy and AEs with a cut-off dose being ≥45 mg twice daily. CONCLUSIONS: This meta-analysis revealed dose-dependent efficacy and adverse effects of gefapixant against chronic cough. Further studies are required to investigate the feasibility of moderate-dose (i.e. 45-50 mg twice daily) gefapixant in clinical practice.

Association of prognostic nutritional index with risk of contrast induced nephropathy: A meta-analysis.

Background: Although prognostic nutritional index (PNI) has been frequently applied in patients with malignancy or those during postoperative recovery, whether it is also an optimal indicator of the risk of contrast-induced nephropathy (CIN) in patients receiving coronary angiography remains uncertain. This meta-analysis aimed at investigating the clinical association of PNI with the risk of CIN in patients receiving coronary angiography or percutaneous coronary intervention. Methods: Embase, Medline, Cochrane Library, and Google scholar were searched for studies until January 2023. The relationship between CIN risk and PNI (i.e., low vs. high) (primary outcome) as well as other variables (secondary outcomes) were analyzed using a random-effects model. Results: Overall, 10 observational studies with 17,590 patients (pooled incidence of CIN: 18%) were eligible for analysis. There was a higher risk of CIN in patients with a low PNI compared to those with a high PNI [odd ratio (OR) = 3.362, 95% confidence interval (CI): 2.054 to 5.505, p < 0.0001, I 2 = 89.6%, seven studies, 12,972 patients, certainty of evidence: very low]. Consistently, a lower PNI was noted in patients with CIN compared to those without (Mean difference = -5.1, 95% CI: -6.87 to -3.33, p < 0.00001, I 2 = 96%, eight studies, 15,516 patients, certainty of evidence: very low). Other risks of CIN included diabetes and hypertension, while male gender and the use of statins were associated with a lower risk of CIN. Patients with CIN were older, had a higher creatinine level, and received a higher contrast volume compared to those without. On the other hand, pre-procedural albumin, estimated glomerular filtration rate, ejection fraction, hemoglobin, lymphocyte ratio were found to be lower in patients with CIN than in those without. Conclusion: This meta-analysis highlighted an inverse association of PNI with the risk of CIN, which required further studies for verification. Systematic review registration: [https://www.crd.york.ac.uk/prospero/], identifier [CRD42023389185].

All-digital transmit beamformer for portable high-frequency ultrasound imaging systems.

To meet the requirements of high-frequency ultrasound imaging systems, a transmit-beamforming integrated circuit with higher delay resolution than conventional transmit-beamforming circuits, which are typically implemented using field-programmable gate array chips, is presented. It also requires smaller volumes, allowing for portable applications. Its proposed design includes two all-digital delay-locked loops providing a specified digital control code for a counter-based beamforming delay chain (CBDC) to generate stable and suitable delays for exciting the array transducer elements without variations in process, voltage, and temperature. Moreover, to maintain the duty cycle of long propagation signals, this novel CBDC requires only a few delay cells, significantly reducing hardware costs and power consumption. Simulations were conducted, revealing a maximum time delay of 451.9 ns with a time resolution of 652 ps and a maximum lateral resolution error of 0.04 mm at 6.8 mm.