Risk of Dementia in Different Types of Cancer Survivors: A Nationwide Cohort Study.

OBJECTIVES: The association between specific types of malignancies and the subsequent risk of dementia remains unknown. DESIGN: A retrospective population-based cohort study based on data from Taiwan National Health Insurance Research Database. SETTING AND PARTICIPANTS: We recruited 32,250 patients who survived malignancies and 322,500 controls between 1998 and 2011 and followed them up until the end of 2013. MEASUREMENTS: Diagnoses of dementia (including Alzheimer's disease (AD), vascular dementia (VaD), and unspecified dementia) was made during the follow-up period. Cox regression analyses were performed after adjusting for potential confounders. A sensitivity analysis was conducted to exclude patients with prodromal dementia. RESULTS: Cancer survivors were more likely to develop AD (hazard ratio [HR]: 1.68, 95% confidence interval [CI]: 1.38-2.06), unspecified dementia (HR: 1.19, 95% CI: 1.07-1.32), and any dementia (HR: 1.26, 95% CI: 1.16-1.37) compared with controls after adjusting for potential confounders. Importantly, cancers of the digestive and genitourinary organs seem to be associated with AD, unspecified dementia, and any dementia, whereas only malignant neoplasms of the brain are more likely to develop into VaD. Sensitivity analyses after exclusion of the first three or five years of observation and after exclusion of case enrollment before 2009 or 2007 showed consistent findings. CONCLUSION: Cancer survivors are at higher risk of subsequent dementia. Different types of cancer survivors may contribute to variable risks of specific dementias. Further studies are necessary to investigate the underlying mechanisms in cancer survivors and patients with dementia.

IP3R1-mediated MAMs formation contributes to mechanical trauma-induced hepatic injury and the protective effect of melatonin.

INTRODUCTION: There is a high morbidity and mortality rate in mechanical trauma (MT)-induced hepatic injury. Currently, the molecular mechanisms underlying liver MT are largely unclear. Exploring the underlying mechanisms and developing safe and effective medicines to alleviate MT-induced hepatic injury is an urgent requirement. The aim of this study was to reveal the role of mitochondria-associated ER membranes (MAMs) in post-traumatic liver injury, and ascertain whether melatonin protects against MT-induced hepatic injury by regulating MAMs. METHODS: Hepatic mechanical injury was established in Sprague-Dawley rats and primary hepatocytes. A variety of experimental methods were employed to assess the effects of melatonin on hepatic injury, apoptosis, MAMs formation, mitochondrial function and signaling pathways. RESULTS: Significant increase of IP3R1 expression and MAMs formation were observed in MT-induced hepatic injury. Melatonin treatment at the dose of 30 mg/kg inhibited IP3R1-mediated MAMs and attenuated MT-induced liver injury in vivo. In vitro, primary hepatocytes cultured in 20% trauma serum (TS) for 12 h showed upregulated IP3R1 expression, increased MAMs formation and cell injury, which were suppressed by melatonin (100 µmol/L) treatment. Consequently, melatonin suppressed mitochondrial calcium overload, increased mitochondrial membrane potential and improved mitochondrial function under traumatic condition. Melatonin's inhibitory effects on MAMs formation and mitochondrial calcium overload were blunted when IP3R1 was overexpressed. Mechanistically, melatonin bound to its receptor (MR) and increased the expression of phosphorylated ERK1/2, which interacted with FoxO1 and inhibited the activation of FoxO1 that bound to the IP3R1 promoter to inhibit MAMs formation. CONCLUSION: Melatonin prevents the formation of MAMs via the MR-ERK1/2-FoxO1-IP3R1 pathway, thereby alleviating the development of MT-induced liver injury. Melatonin-modulated MAMs may be a promising therapeutic therapy for traumatic hepatic injury.

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%.

MLP-based classification of COVID-19 and skin diseases.

Recent years have witnessed a growing interest in neural network-based medical image classification methods, which have demonstrated remarkable performance in this field. Typically, convolutional neural network (CNN) architectures have been commonly employed to extract local features. However, the transformer, a newly emerged architecture, has gained popularity due to its ability to explore the relevance of remote elements in an image through a self-attention mechanism. Despite this, it is crucial to establish not only local connectivity but also remote relationships between lesion features and capture the overall image structure to improve image classification accuracy. Therefore, to tackle the aforementioned issues, this paper proposes a network based on multilayer perceptrons (MLPs) that can learn the local features of medical images on the one hand and capture the overall feature information in both spatial and channel dimensions on the other hand, thus utilizing image features effectively. This paper has been extensively validated on COVID19-CT dataset and ISIC 2018 dataset, and the results show that the method in this paper is more competitive and has higher performance in medical image classification compared with existing methods. This shows that the use of MLP to capture image features and establish connections between lesions is expected to provide novel ideas for medical image classification tasks in the future.

Migraine management: an update for the 2020s.

Migraine is a common malady cutting across socioeconomic and ethnic divides in Australia. It is typically diagnosed late with significant impact on quality of life. Management options have emerged over the past several years that promise simpler treatment regimens with less potential for side-effects. The development of rationally designed migraine preventives is the most significant advance in treatment since the development of the triptans and delivers significant hope to many headache sufferers.

HARNU-Net: Hierarchical Attention Residual Nested U-Net for Change Detection in Remote Sensing Images.

Change detection (CD) is a particularly important task in the field of remote sensing image processing. It is of practical importance for people when making decisions about transitional situations on the Earth's surface. The existing CD methods focus on the design of feature extraction network, ignoring the strategy fusion and attention enhancement of the extracted features, which will lead to the problems of incomplete boundary of changed area and missing detection of small targets in the final output change map. To overcome the above problems, we proposed a hierarchical attention residual nested U-Net (HARNU-Net) for remote sensing image CD. First, the backbone network is composed of a Siamese network and nested U-Net. We remold the convolution block in nested U-Net and proposed ACON-Relu residual convolution block (A-R), which reduces the missed detection rate of the backbone network in small change areas. Second, this paper proposed the adjacent feature fusion module (AFFM). Based on the adjacency fusion strategy, the module effectively integrates the details and semantic information of multi-level features, so as to realize the feature complementarity and spatial mutual enhancement between adjacent features. Finally, the hierarchical attention residual module (HARM) is proposed, which locally filters and enhances the features in a more fine-grained space to output a much better change map. Adequate experiments on three challenging benchmark public datasets, CDD, LEVIR-CD and BCDD, show that our method outperforms several other state-of-the-art methods and performs excellent in F1, IOU and visual image quality.

Image Super-Resolution via Dual-Level Recurrent Residual Networks.

Recently, the feedforward architecture of a super-resolution network based on deep learning was proposed to learn the representation of a low-resolution (LR) input and the non-linear mapping from these inputs to a high-resolution (HR) output, but this method cannot completely solve the interdependence between LR and HR images. In this paper, we retain the feedforward architecture and introduce residuals to a dual-level; therefore, we propose the dual-level recurrent residual network (DLRRN) to generate an HR image with rich details and satisfactory vision. Compared with feedforward networks that operate at a fixed spatial resolution, the dual-level recurrent residual block (DLRRB) in DLRRN utilizes both LR and HR space information. The circular signals in DLRRB enhance spatial details by the mutual guidance between two directions (LR to HR and HR to LR). Specifically, the LR information of the current layer is generated by the HR and LR information of the previous layer. Then, the HR information of the previous layer and LR information of the current layer jointly generate the HR information of the current layer, and so on. The proposed DLRRN has a strong ability for early reconstruction and can gradually restore the final high-resolution image. An extensive quantitative and qualitative evaluation of the benchmark dataset was carried out, and the experimental results proved that our network achieved good results in terms of network parameters, visual effects and objective performance metrics.

Efficient Image Super-Resolution via Self-Calibrated Feature Fuse.

Recently, many super-resolution reconstruction (SR) feedforward networks based on deep learning have been proposed. These networks enable the reconstructed images to achieve convincing results. However, due to a large amount of computation and parameters, SR technology is greatly limited in devices with limited computing power. To trade-off the network performance and network parameters. In this paper, we propose the efficient image super-resolution network via Self-Calibrated Feature Fuse, named SCFFN, by constructing the self-calibrated feature fuse block (SCFFB). Specifically, to recover the high-frequency detail information of the image as much as possible, we propose SCFFB by self-transformation and self-fusion of features. In addition, to accelerate the network training while reducing the computational complexity of the network, we employ an attention mechanism to elaborate the reconstruction part of the network, called U-SCA. Compared with the existing transposed convolution, it can greatly reduce the computation burden of the network without reducing the reconstruction effect. We have conducted full quantitative and qualitative experiments on public datasets, and the experimental results show that the network achieves comparable performance to other networks, while we only need fewer parameters and computational resources.

Comparing the relative and absolute effect of erenumab: is a 50% response enough? Results from the ESTEEMen study.

BACKGROUND: Monoclonal antibodies acting on the calcitonin gene-related peptide (CGRP) or its receptor have changed migraine preventive treatment. Those treatments have led to reconsidering the outcomes of migraine prevention. Available data mostly considered benefits in terms of relative efficacy (percent or absolute decrease in monthly migraine days [MMDs] or headache days compared with baseline). However, not enough attention has been paid to residual MMDs and/or migraine-related disability in treated patients. In the present study, we aimed at comparing the relative and absolute efficacy of erenumab. METHODS: ESTEEMen was a collaborative project among 16 European headache centers which already performed real-life data collections on patients treated with erenumab for at least 12 weeks. For the present study, we performed a subgroup analysis on patients with complete data on MMDs at baseline and at weeks 9-12 of treatment. Starting from efficacy thresholds proposed by previous literature, we classified patients into 0-29%, 30-49%, 50-74%, and ≥75% responders according to MMD decrease from baseline to weeks 9-12 of treatment. For each response category, we reported the median MMDs and Headache Impact test-6 (HIT-6) scores at baseline and at weeks 9-12. We categorized the number of residual MMDs at weeks 9-12 as follows: 0-3, 4-7, 8-14, ≥15. We classified HIT-6 score into four categories: ≤49, 50-55, 56-59, and ≥60. To keep in line with the original scope of the ESTEEMen study, calculations were performed in men and women. RESULTS: Out of 1215 patients, at weeks 9-12, 381 (31.4%) had a 0-29% response, 186 (15.3%) a 30-49% response, 396 (32.6%) a 50-74% response, and 252 (20.7%) a ≥75% response; 246 patients (20.2%) had 0-3 residual MMDs, 443 (36.5%) had 4-7 MMDs, 299 (24.6%) had 8-14 MMDs, and 227 (18.7%) had ≥15 MMDs. Among patients with 50-74% response, 246 (62.1%) had 4-7 and 94 (23.7%) 8-14 residual MMDs, while among patients with ≥75% response 187 (74.2%) had 0-3 and 65 (25.8%) had 4-7 residual MMDs. CONCLUSIONS: The present study shows that even patients with good relative response to erenumab may have a clinically non-negligible residual migraine burden. Relative measures of efficacy cannot be enough to thoroughly consider the efficacy of migraine prevention.

FAC-Net: Feedback Attention Network Based on Context Encoder Network for Skin Lesion Segmentation.

Considerable research and surveys indicate that skin lesions are an early symptom of skin cancer. Segmentation of skin lesions is still a hot research topic. Dermatological datasets in skin lesion segmentation tasks generated a large number of parameters when data augmented, limiting the application of smart assisted medicine in real life. Hence, this paper proposes an effective feedback attention network (FAC-Net). The network is equipped with the feedback fusion block (FFB) and the attention mechanism block (AMB), through the combination of these two modules, we can obtain richer and more specific feature mapping without data enhancement. Numerous experimental tests were given by us on public datasets (ISIC2018, ISBI2017, ISBI2016), and a good deal of metrics like the Jaccard index (JA) and Dice coefficient (DC) were used to evaluate the results of segmentation. On the ISIC2018 dataset, we obtained results for DC equal to 91.19% and JA equal to 83.99%, compared with the based network. The results of these two main metrics were improved by more than 1%. In addition, the metrics were also improved in the other two datasets. It can be demonstrated through experiments that without any enhancements of the datasets, our lightweight model can achieve better segmentation performance than most deep learning architectures.

Dual Branch Attention Network for Person Re-Identification.

As a sub-direction of image retrieval, person re-identification (Re-ID) is usually used to solve the security problem of cross camera tracking and monitoring. A growing number of shopping centers have recently attempted to apply Re-ID technology. One of the development trends of related algorithms is using an attention mechanism to capture global and local features. We notice that these algorithms have apparent limitations. They only focus on the most salient features without considering certain detailed features. People's clothes, bags and even shoes are of great help to distinguish pedestrians. We notice that global features usually cover these important local features. Therefore, we propose a dual branch network based on a multi-scale attention mechanism. This network can capture apparent global features and inconspicuous local features of pedestrian images. Specifically, we design a dual branch attention network (DBA-Net) for better performance. These two branches can optimize the extracted features of different depths at the same time. We also design an effective block (called channel, position and spatial-wise attention (CPSA)), which can capture key fine-grained information, such as bags and shoes. Furthermore, based on ID loss, we use complementary triplet loss and adaptive weighted rank list loss (WRLL) on each branch during the training process. DBA-Net can not only learn semantic context information of the channel, position, and spatial dimensions but can integrate detailed semantic information by learning the dependency relationships between features. Extensive experiments on three widely used open-source datasets proved that DBA-Net clearly yielded overall state-of-the-art performance. Particularly on the CUHK03 dataset, the mean average precision (mAP) of DBA-Net achieved 83.2%.

Erenumab in Chronic Migraine: An Australian Experience.

OBJECTIVE: To determine the effectiveness and safety of erenumab in patients with chronic migraine in the real-world setting of 3 headache centers in Australia. METHODS: Patients with migraine were prescribed erenumab (70 or 140 mg) in the setting of either a product familiarization program or paid access to the medication in 3 headache centers in Australia. We obtained baseline and monthly prospective data on monthly headache days, monthly migraine days, monthly triptan use days, monthly codeine use days, Headache Impact Test-6 scores, and adverse reactions. In this paper, we present our data at 3 and 6 months in our subgroup of patients with chronic migraine with and without medication overuse. RESULTS: A total of 170 patients with chronic migraine were prescribed erenumab in the 3 headache centers. At 3 months, 100/170 (58.8%) had 50% or greater reduction in monthly migraine days. At 6 months, 79/170 (46.5%) had 50% or greater reduction in monthly migraine days. At 6 months, there was a mean reduction in monthly headache days of 9.2 days, a mean reduction in monthly migraine days of 10.2 days. There were few adverse events reported. CONCLUSION: This is the first report from 3 Australian headache centers about erenumab in the real world. Our analysis has supported erenumab as an effective and well-tolerated migraine preventative therapy for patients with chronic migraine who have failed many preventative therapies.

The effectiveness of long-acting injectable antipsychotics versus oral antipsychotics in the maintenance treatment of outpatients with chronic schizophrenia.

OBJECTIVE: To compare the psychiatric service utilization between patients who only received long-acting injectable antipsychotics (LAIAs) and those who only received oral antipsychotics (OAPs) in the maintenance treatment of chronic schizophrenia. METHODS: We constructed a cohort of chronic schizophrenia patients who underwent maintenance treatment from the Taiwan National Health Insurance Research Database in 2011 and followed these patients for 12 months. We included patients who had been diagnosed with schizophrenia for at least 3 years, were not hospitalized in 2011, and had received 1 year of maintenance treatment. Inverse probability of treatment weighting logistic, linear, and negative binomial regression models were used to estimate associated psychiatric services utilization and adjust for covariate imbalances between the LAIAs and OAPs groups. RESULTS: Among 40,194 patients, 948 (2.36%) received only LAIAs and 39,246 (97.64%) received only OAPs. Compared with those who received only OAPs, the sole LAIAs users were associated with a lower percentage of psychiatric hospitalization (8.4% and 5.8%, respectively; odds ratio: 0.63, p < .01), shorter lengths of hospitalization days (82.8 and 65.9, respectively; coefficient [b]: -16.87, p = .03), and fewer emergency room visits (2.3 and 1.8, respectively; b: -0.24, p < .01) per patient. CONCLUSIONS: Chronic schizophrenia patients who received only LAIs had a lower risk of disease relapse and a reduction in psychiatric service utilization than those receiving only OAPs.

A Key Management Scheme Based on Pairing-Free Identity Based Digital Signature Algorithm for Heterogeneous Wireless Sensor Networks.

The secure transmission of data within a network has received great attention. As the core of the security management mechanism, the key management scheme design needs further research. In view of the safety and energy consumption problems in recent papers, we propose a key management scheme based on the pairing-free identity based digital signature (PF-IBS) algorithm for heterogeneous wireless sensor networks (HWSNs). Our scheme uses the PF-IBS algorithm to complete message authentication, which is safer and more energy efficient than some recent schemes. Moreover, we use the base station (BS) as the processing center for the huge data in the network, thereby saving network energy consumption and improving the network life cycle. Finally, we indirectly prevent the attacker from capturing relay nodes that upload data between clusters in the network (some cluster head nodes cannot communicate directly). Through performance evaluation, the scheme we proposed reasonably sacrifices part of the storage space in exchange for entire network security while saving energy consumption.

Perceived social support and depression symptoms in patients with major depressive disorder in Taiwan: An association study.

This study analyzes the association between social support and depression symptoms of inpatients with major depressive disorder. A total of 160 inpatients were recruited from the acute psychiatric ward of a medical center in northern Taiwan between July 1, 2015, and December 31, 2016. Data were analyzed using descriptive statistics, simple linear regression and multiple linear regression. Our results reveal that patient depression level is significantly associated with gender, age, marital status, education, occupation and number of admissions due to depression. Social support is significantly associated with marital status and number of admissions due to depression. The depression symptoms of the patients were significantly and negatively associated with overall perceived social support and perceived social support from family, friends and a significant other. These results could serve as a reference for the clinical practice of clinical specialists and argue for the inclusion of social support as an intervention for patients with depression.

Deep Semantic-Preserving Reconstruction Hashing for Unsupervised Cross-Modal Retrieval.

Deep hashing is the mainstream algorithm for large-scale cross-modal retrieval due to its high retrieval speed and low storage capacity, but the problem of reconstruction of modal semantic information is still very challenging. In order to further solve the problem of unsupervised cross-modal retrieval semantic reconstruction, we propose a novel deep semantic-preserving reconstruction hashing (DSPRH). The algorithm combines spatial and channel semantic information, and mines modal semantic information based on adaptive self-encoding and joint semantic reconstruction loss. The main contributions are as follows: (1) We introduce a new spatial pooling network module based on tensor regular-polymorphic decomposition theory to generate rank-1 tensor to capture high-order context semantics, which can assist the backbone network to capture important contextual modal semantic information. (2) Based on optimization perspective, we use global covariance pooling to capture channel semantic information and accelerate network convergence. In feature reconstruction layer, we use two bottlenecks auto-encoding to achieve visual-text modal interaction. (3) In metric learning, we design a new loss function to optimize model parameters, which can preserve the correlation between image modalities and text modalities. The DSPRH algorithm is tested on MIRFlickr-25K and NUS-WIDE. The experimental results show that DSPRH has achieved better performance on retrieval tasks.

Sleep apnoea and the neurologist.

There is increasing awareness and interest in the complex and extensive inter-relationships between sleep disorders and neurological disorders. This review focuses on the clinical interactions between obstructive sleep apnoea and stroke, headaches, epilepsy, cognition and idiopathic Parkinson's disease. We highlight to the neurologist the importance of taking a sleep history and considering the diagnosis and treatment of obstructive sleep apnoea.

Non-convulsive status epilepticus in the elderly.

Altered mental state is a very common presentation in the elderly admitted to the emergency department. It has been determined that about 16% of patients aged 60 or older with confusion of unknown origin have non-convulsive status epilepticus. The diagnosis of non-convulsive status epilepticus is difficult in the elderly because possible aetiologies of confusion may present with the same clinical picture. Non-convulsive status epilepticus in the elderly carries major morbidity and mortality, attributable primarily to aetiology, and treatment is complex, involving treatment of the aetiology and concomitant medical illnesses, whilst balancing the side effects and drug interactions of antiepileptic drugs.

Mechanism of action of FoxiangSan in diabetic gastroparesis: Gut microbiota and cAMP/PKA pathway.

Diabetic gastroparesis, a common complication of type 2 diabetes (T2DM), presents a significant treatment challenge. FoxiangSan is emerging as a potential therapy. FoxiangSan is a traditional Chinese medicine formula with the potential for treating diabetic gastroparesis by modulating gut microbiota and cAMP/PKA signaling pathways. This study explores the mechanisms behind FoxiangSan's effects on T2DM-induced gastroparesis, focusing on its impact on gut microbiota and the cAMP/PKA pathway. A rat model of type 2 diabetic gastroparesis was established through a high-fat diet and streptozotocin (STZ) injection, and the effects of FoxiangSan were assessed. Additionally, protein expression related to the cAMP/PKA pathway was examined, and FoxiangSan's influence on gut microbiota was studied using 16S rRNA sequencing. FoxiangSan significantly alleviated hyperglycemia, improved gastric pathology in rats with gastroparesis, enhanced the expression of 5-HT4, cAMP, PKA, and pPKA in the gastric antrum, and rebalanced gut microbiota. FoxiangSan demonstrates the therapeutic potential for T2DM-associated gastroparesis by modulating the cAMP/PKA pathway and gut microbiota.

Twelve-month efficacy of CGRP monoclonal antibodies and predictive value of short-term response: results of an Australian multicentre study.

Introduction: Clinical trials show that calcitonin gene-related peptide monoclonal antibodies (CGRP mAbs) are effective preventative treatments for chronic migraine. Their efficacy over longer time periods and in cohorts originally excluded from trials remains uncertain. This study aims to explore the impact of CGRP mAbs in an Australian real-life setting. Methods: A multicentre cohort study was performed in the tertiary headache clinics of the Alfred and Austin Hospitals, Melbourne, Australia. Patients were commenced on a CGRP mAb for chronic migraine and asked to keep a headache diary, recorded at 3 monthly appointments for 12 months. Primary outcome was a ≥50% reduction in monthly headache days (MHD). Results: From a population of 105 patients, 90 patients commenced galcanezumab and 15 commenced fremanezumab. The ≥50% responder rate of the cohort was 52.4% after 3 months. Over 12 months follow-up, 25.7% of the cohort ceased due to a lack of efficacy and 16.2% ceased due to an adverse event. There was no difference in response or cessation between medications. There was poor agreement in 3-month and 12-month response rates (Cohen's κ=0.130; p=0.171). On subgroup analysis, continuous headache at baseline and number of trialled preventative treatments were the only factors associated with efficacy. Conclusion: CGRP mAbs were associated with sustained reductions in MHD over 12-month follow-up in patients with resistant migraine in Australia. Further studies are required to determine treatment options for patients with continuous headache. Poor agreement between outcomes at 3 and 12 months highlights the need to assess some patients at later timepoints.