Hasil untuk "Neurology. Diseases of the nervous system"

Qiheng He, Yuhan Shang, Yijun Dong et al.

ObjectiveThis study aimed to enhance the Coma Recovery Scale-Revised (CRS-R) for disorders of consciousness (DoC) by developing a two-dimensional model differentiating cognition and motor function.MethodsWe analyzed 124 DoC patients retrospectively and validated findings using five multicenter datasets (n = 420). CRS-R subscores were decomposed into Consciousness_x (awareness) and Consciousness_y (arousal/motor function) using Projective Non-negative Matrix Factorization. Logistic regression established diagnostic thresholds, evaluated by accuracy, precision, recall, and F1-score.ResultsThe model achieved high accuracy (0.94), precision (0.92), and recall (0.99). Patients with minimally conscious state (MCS) or emerged MCS showed significantly higher scores than vegetative state (VS) patients (p < 0.05). The four-quadrant framework revealed distinct clinical profiles: Quadrant I (high awareness/arousal) identified patients for cognitive rehabilitation; Quadrant II (low awareness/high arousal) suggested arousal-enhancing therapies; Quadrant III (low awareness/arousal) indicated VS requiring basic support; Quadrant IV (high awareness/low arousal) highlighted needs for sensorimotor integration.ConclusionsThe two-dimensionally reduced representation of CRS-R scores maintains diagnostic accuracy while improving DoC classification. The four-quadrant model enables personalized interventions.Trial registrationOur study has been verified by the Chinese Clinical Trial Registry with the registration number: ChiCTR2400085855, and the registration date is June 19, 2024.

Sara Venturini, Saniya Mediratta, Tobias J. Adams et al.

Objective: For patients undergoing decompressive craniectomy for TBI, cranioplasty facilitates rehabilitation and reintegration into society. Cranioplasty practice is poorly documented globally. Challenges including lack of materials and technical skills disproportionately affect LMICs. This study evaluates global cranioplasty practice and the extent to which barriers preclude patients from accessing cranial reconstruction. Methods: An international survey was disseminated to centres performing cranioplasty for TBI. Survey questions addressed baseline hospital information, cranioplasty indications, barriers and techniques, follow-up. Centres in HICs and LMICs were compared. Results: 101 responses were received (86 individual institutions, 39 countries). Variation in practice was seen globally, and between HICs and LMICs. Autologous bone was the most common material used. Titanium and polymethylmethacrylate were the most used artificial implants. LMIC sites were more likely to store bone flaps in the patient's abdomen, while HICs had more access to 3D printing. Lack of infection and good neurological recovery were the commonest eligibility criteria. Cost of materials/operation and unavailable materials were the commonest barriers. Responders suggested easier access to cheaper materials would significantly improve access. Cranioplasty associated costs were higher than the country's GNI per capita in 7 cases. Less than 50 % of patients without cranioplasty had access to protective equipment. Less than a quarter of respondents stated patients had access to brain injury charities, and over 50 % believed stigma affects their patients. Conclusions: Variation in cranioplasty practice was confirmed. Barriers limiting access were identified, specifically, availability of materials and operation-related costs. These findings can inform context-specific interventions to overcome current challenges.

Deng-Pan Wu, Yan-Su Wei, Li-Xiang Hou et al.

Abstract Background Abnormal microglial polarization phenotypes contribute to the pathogenesis of Alzheimer’s disease (AD). Circular RNAs (circRNAs) have garnered increasing attention due to their significant roles in human diseases. Although research has demonstrated differential expression of circRNAs in AD, their specific functions in AD pathogenesis remain largely unexplored. Methods CircRNA microarray was performed to identify differentially expressed circRNAs in the hippocampus of APP/PS1 and WT mice. The stability of circAPP was assessed via RNase R treatment assay. CircAPP downstream targets miR-1906 and chloride intracellular channel 1 (CLIC1) were identified using bioinformatics and proteomics, respectively. RT-PCR assay was conducted to detect the expression of circAPP, miR-1906 and CLIC1. Morris water maze (MWM) test, passive avoidance test and novel object recognition task were used to detect cognitive function of APP/PS1 mice. Microglial M1/M2 polarization and AD pathology were assessed using Western blot, flow cytometry and Golgi staining assays. CLIC1 expression and channel activity were evaluated using Western blot and functional chloride channel assays, respectively. The subcellular location of circAPP was assessed via FISH and RT-PCR assays. RNA pull-down assay was performed to detect the interaction of miR-1906 with circAPP and 3’ untranslated region (3’UTR) of CLIC1 mRNA. Results In this study, we identified a novel circRNA, named circAPP, that is encoded by amyloid precursor protein (APP) and is implicated in AD. CircAPP is a stable circRNA that was upregulated in Aβ-treated microglial cells and the hippocampus of APP/PS1 mice. Downregulation of circAPP or CLIC1, or overexpression of miR-1906 in microglia modulated microglial M1/M2 polarization in Aβ-treated microglial cells and the hippocampus of APP/PS1 mice, and improved AD pathology and the cognitive function of APP/PS1 mice. Further results revealed that circAPP was mainly distributed in the cytoplasm, and circAPP could regulate CLIC1 expression and channel activity by interacting with miR-1906 and affecting miR-1906 expression, thereby regulating microglial polarization in AD. Conclusions Taken together, our study elucidates the regulatory role of circAPP in AD microglial polarization via miR-1906/CLIC1 axis, and suggests that circAPP may act as a critical player in AD pathogenesis and represent a promising therapeutic target for AD.

Elliot Schumacher, Dhruv Naik, Anitha Kannan

Large language models (LLMs) have demonstrated impressive capabilities in disease diagnosis. However, their effectiveness in identifying rarer diseases, which are inherently more challenging to diagnose, remains an open question. Rare disease performance is critical with the increasing use of LLMs in healthcare settings. This is especially true if a primary care physician needs to make a rarer prognosis from only a patient conversation so that they can take the appropriate next step. To that end, several clinical decision support systems are designed to support providers in rare disease identification. Yet their utility is limited due to their lack of knowledge of common disorders and difficulty of use. In this paper, we propose RareScale to combine the knowledge LLMs with expert systems. We use jointly use an expert system and LLM to simulate rare disease chats. This data is used to train a rare disease candidate predictor model. Candidates from this smaller model are then used as additional inputs to black-box LLM to make the final differential diagnosis. Thus, RareScale allows for a balance between rare and common diagnoses. We present results on over 575 rare diseases, beginning with Abdominal Actinomycosis and ending with Wilson's Disease. Our approach significantly improves the baseline performance of black-box LLMs by over 17% in Top-5 accuracy. We also find that our candidate generation performance is high (e.g. 88.8% on gpt-4o generated chats).

Zhi Zhang, Yan Liu, Mengxia Gao et al.

Psychological resilience, defined as the ability to rebound from adversity, is crucial for mental health. Compared with traditional resilience assessments through self-reported questionnaires, resilience assessments based on neurological data offer more objective results with biological markers, hence significantly enhancing credibility. This paper proposes a novel data-efficient model to address the scarcity of neurological data. We employ Neuro Kolmogorov-Arnold Networks as the structure of the prediction model. In the training stage, a new trait-informed multimodal representation algorithm with a smart chunk technique is proposed to learn the shared latent space with limited data. In the test stage, a new noise-informed inference algorithm is proposed to address the low signal-to-noise ratio of the neurological data. The proposed model not only shows impressive performance on both public datasets and self-constructed datasets but also provides some valuable psychological hypotheses for future research.

Vivek Yadav, Anugrah Jain

India, as a predominantly agrarian economy, faces significant challenges in agriculture, including substantial crop losses caused by diseases, pests, and environmental stress. Early detection and accurate identification of diseases across different crops are critical for improving yield and ensuring food security. This paper proposes a deep learning based solution for detecting multiple diseases in multiple crops, aimed to cover India's diverse agricultural landscape. We first create a unified dataset encompassing images of 17 different crops and 34 different diseases from various available repositories. Proposed deep learning model is trained on this dataset and outperforms the state-of-the-art in terms of accuracy and the number of crops, diseases covered. We achieve a significant detection accuracy, i.e., 99 percent for our unified dataset which is 7 percent more when compared to state-of-the-art handling 14 crops and 26 different diseases only. By improving the number of crops and types of diseases that can be detected, proposed solution aims to provide a better product for Indian farmers.

Renato de Filippis, Maiko Fukasawa, Mohammadreza Shalbafan

Jakaria Rabbi, Johannes Kiechle, Christian Beaulieu et al.

This paper presents a comprehensive study focused on disentangling hippocampal shape variations from diffusion tensor imaging (DTI) datasets within the context of neurological disorders. Leveraging a Mesh Variational Autoencoder (VAE) enhanced with Supervised Contrastive Learning, our approach aims to improve interpretability by disentangling two distinct latent variables corresponding to age and the presence of diseases. In our ablation study, we investigate a range of VAE architectures and contrastive loss functions, showcasing the enhanced disentanglement capabilities of our approach. This evaluation uses synthetic 3D torus mesh data and real 3D hippocampal mesh datasets derived from the DTI hippocampal dataset. Our supervised disentanglement model outperforms several state-of-the-art (SOTA) methods like attribute and guided VAEs in terms of disentanglement scores. Our model distinguishes between age groups and disease status in patients with Multiple Sclerosis (MS) using the hippocampus data. Our Mesh VAE with Supervised Contrastive Learning shows the volume changes of the hippocampus of MS populations at different ages, and the result is consistent with the current neuroimaging literature. This research provides valuable insights into the relationship between neurological disorder and hippocampal shape changes in different age groups of MS populations using a Mesh VAE with Supervised Contrastive loss. Our code is available at https://github.com/Jakaria08/Explaining_Shape_Variability

Olga Winkler, Lisa Burback, Andrew J. Greenshaw et al.

Caring for patients with personality disorders can be challenging due to risks associated with suicidal ideation, homicidal threats, splitting, and acting out with problematic behavior in psychiatric inpatient units. Limited resources on inpatient units further add to the stress and burden on staff. This case summarizes how trauma-informed care was implemented in an inpatient setting to produce marked improvement in a patient’s treatment outcomes as well as better staff engagement and satisfaction. This culture change in the approach to care was not an easy process, as effortful planning and resources were required for key elements such as ongoing coaching, education, and regular staff debriefings. This case report signals the need for service providers to enable health systems to examine rules and exceptions from a cultural perspective of considering equity, diversity, and inclusion (EDI)—to allow openness to rational exceptions, even if they are unconventional.

Martina Sebök, Lara Höbner, Jorn Fierstra et al.

Tian Bian, Yuli Jiang, Jia Li et al.

Many patients with chronic diseases resort to multiple medications to relieve various symptoms, which raises concerns about the safety of multiple medication use, as severe drug-drug antagonism can lead to serious adverse effects or even death. This paper presents a Decision Support System, called DSSDDI, based on drug-drug interactions to support doctors prescribing decisions. DSSDDI contains three modules, Drug-Drug Interaction (DDI) module, Medical Decision (MD) module and Medical Support (MS) module. The DDI module learns safer and more effective drug representations from the drug-drug interactions. To capture the potential causal relationship between DDI and medication use, the MD module considers the representations of patients and drugs as context, DDI and patients' similarity as treatment, and medication use as outcome to construct counterfactual links for the representation learning. Furthermore, the MS module provides drug candidates to doctors with explanations. Experiments on the chronic data collected from the Hong Kong Chronic Disease Study Project and a public diagnostic data MIMIC-III demonstrate that DSSDDI can be a reliable reference for doctors in terms of safety and efficiency of clinical diagnosis, with significant improvements compared to baseline methods.

Hayder A. Albaqer, Kadhum J. Al-Jibouri, John Martin et al.

The COVID-19 pandemic has brought to light a concerning aspect of long-term neurological complications in post-recovery patients. This study delved into the investigation of such neurological sequelae in a cohort of 500 post-COVID-19 patients, encompassing individuals with varying illness severity. The primary aim was to predict outcomes using a machine learning approach based on diverse clinical data and neuroimaging parameters. The results revealed that 68% of the post-COVID-19 patients reported experiencing neurological symptoms, with fatigue, headache, and anosmia being the most common manifestations. Moreover, 22% of the patients exhibited more severe neurological complications, including encephalopathy and stroke. The application of machine learning models showed promising results in predicting long-term neurological outcomes. Notably, the Random Forest model achieved an accuracy of 85%, sensitivity of 80%, and specificity of 90% in identifying patients at risk of developing neurological sequelae. These findings underscore the importance of continuous monitoring and follow-up care for post-COVID-19 patients, particularly in relation to potential neurological complications. The integration of machine learning-based outcome prediction offers a valuable tool for early intervention and personalized treatment strategies, aiming to improve patient care and clinical decision-making. In conclusion, this study sheds light on the prevalence of long-term neurological complications in post-COVID-19 patients and demonstrates the potential of machine learning in predicting outcomes, thereby contributing to enhanced patient management and better health outcomes. Further research and larger studies are warranted to validate and refine these predictive models and to gain deeper insights into the underlying mechanisms of post-COVID-19 neurological sequelae.

Rajendra Singh Jain, P V Sripadma, Shankar Tejwani

Background: Recanalization rates in cerebral venous thrombosis (CVT) and its effect on neurological outcome have been debated worldwide and are inadequately addressed in studies from India. Our objective was to study the clinical profile of CVT and determine recanalization rates with its predictors and its effect on outcome. Methods: A prospective single centre cohort study on 101 patients with radiologically confirmed acute CVT between October 2018 and June 2021 was conducted. Anticoagulation was given for 3-12 months or lifelong for thrombophilias. Recanalization status of vessels was assessed between 3-6 months and at 12 months after ictus. Outcome was defined as favorable (mRS 0-1) or unfavorable. Patients with atleast one CT/MR venogram on follow up were included. Results: Of the 101 enrolled patients, 83 completed study protocol. Mean age of patients was 34.2 ± 11.7 years. Clinical characteristics included headache (75.9%),seizure (66.2%), altered mentation(20.4%) with clustering of cases during summers. Transverse- sigmoid sinuses were predominantly involved (66.2 %) followed by superior sagittal sinus (SSS,65.0%). Commonest etiologies were thrombophilia (27.7%) and postpartum state (15.6%). Complete recanalization was achieved in 67.4%, partial in 26.5% and no recanalization in 6.02% at end of 12 months. Recanalization rates improved from 83.09% between 3-6 months to 93.9 % at 12 months. Median time to last follow-up was 12months and at last follow up 95.1% had favorable mRS with recurrence in two patients with raised factor VIII levels. Conclusion: Recanalization occurred in more than 90% of CVT patients. Isolated superior sagittal sinus thrombosis and age <50 years were predictors of complete recanalization. Most patients, except few achieved a favorable mRS.

Asaf Marco

Ting Wang, Gui-Yun Li, Xin-Hui Li et al.

The main aim to build models capable of simulating the spreading of infectious diseases is to control them. And along this way, the key to find the optimal strategy for disease control is to obtain a large number of simulations of disease transitions under different scenarios. Therefore, the models that can simulate the spreading of diseases under scenarios closer to the reality and are with high efficiency are preferred. In the realistic social networks, the random contact, including contacts between people in the public places and the public transits, becomes the important access for the spreading of infectious diseases. In this paper, a model can efficiently simulate the spreading of infectious diseases under random contacts is proposed. In this approach, the random contact between people is characterized by the random matrix with elements randomly generated and the spread of the diseases is simulated by the Markov process. We report an interesting property of the proposed model: the main indicators of the spreading of the diseases such as the death rate are invariant of the size of the population. Therefore, representative simulations can be conducted on models consist of small number of populations. The main advantage of this model is that it can easily simulate the spreading of diseases under more realistic scenarios and thus is able to give a large number of simulations needed for the searching of the optimal control strategy. Based on this work, the reinforcement learning will be introduced to give the optimal control strategy in the following work.

Kush Vora, Dishant Padalia

Apple diseases, if not diagnosed early, can lead to massive resource loss and pose a serious threat to humans and animals who consume the infected apples. Hence, it is critical to diagnose these diseases early in order to manage plant health and minimize the risks associated with them. However, the conventional approach of monitoring plant diseases entails manual scouting and analyzing the features, texture, color, and shape of the plant leaves, resulting in delayed diagnosis and misjudgments. Our work proposes an ensembled system of Xception, InceptionResNet, and MobileNet architectures to detect 5 different types of apple plant diseases. The model has been trained on the publicly available Plant Pathology 2021 dataset and can classify multiple diseases in a given plant leaf. The system has achieved outstanding results in multi-class and multi-label classification and can be used in a real-time setting to monitor large apple plantations to aid the farmers manage their yields effectively.

Shaiban Ahmed, David Le, Taeyoon Son et al.

Chromatic dispersion is a common problem to degrade the system resolution in optical coherence tomography (OCT). This study is to develop a deep learning network for automated dispersion compensation (ADC-Net) in OCT. The ADC-Net is based on a redesigned UNet architecture which employs an encoder-decoder pipeline. The input section encompasses partially compensated OCT B-scans with individual retinal layers optimized. Corresponding output is a fully compensated OCT B-scans with all retinal layers optimized. Two numeric parameters, i.e., peak signal to noise ratio (PSNR) and structural similarity index metric computed at multiple scales (MS-SSIM), were used for objective assessment of the ADC-Net performance. Comparative analysis of training models, including single, three, five, seven and nine input channels were implemented. The five-input channels implementation was observed as the optimal mode for ADC-Net training to achieve robust dispersion compensation in OCT

Valeria Cardellini, Emiliano Casalicchio, Stefano Iannucci et al.

Intrusion Response is a relatively new field of research. Recent approaches for the creation of Intrusion Response Systems (IRSs) use Reinforcement Learning (RL) as a primary technique for the optimal or near-optimal selection of the proper countermeasure to take in order to stop or mitigate an ongoing attack. However, most of them do not consider the fact that systems can change over time or, in other words, that systems exhibit a non-stationary behavior. Furthermore, stateful approaches, such as those based on RL, suffer the curse of dimensionality, due to a state space growing exponentially with the size of the protected system. In this paper, we introduce and develop an IRS software prototype, named irs-partition. It leverages the partitioning of the protected system and Deep Q-Networks to address the curse of dimensionality by supporting a multi-agent formulation. Furthermore, it exploits transfer learning to follow the evolution of non-stationary systems.

Ximing Lu, Sean Welleck, Peter West et al.

The dominant paradigm for neural text generation is left-to-right decoding from autoregressive language models. Constrained or controllable generation under complex lexical constraints, however, requires foresight to plan ahead feasible future paths. Drawing inspiration from the A* search algorithm, we propose NeuroLogic A*esque, a decoding algorithm that incorporates heuristic estimates of future cost. We develop efficient lookahead heuristics that are efficient for large-scale language models, making our method a drop-in replacement for common techniques such as beam search and top-k sampling. To enable constrained generation, we build on NeuroLogic decoding (Lu et al., 2021), combining its flexibility in incorporating logical constraints with A*esque estimates of future constraint satisfaction. Our approach outperforms competitive baselines on five generation tasks, and achieves new state-of-the-art performance on table-to-text generation, constrained machine translation, and keyword-constrained generation. The improvements are particularly notable on tasks that require complex constraint satisfaction or in few-shot or zero-shot settings. NeuroLogic A*esque illustrates the power of decoding for improving and enabling new capabilities of large-scale language models.

Henk S. Temmingh, Sumaya Mall, Fleur M. Howells et al.

Background: Substance use disorders (SUDs) occur frequently in patients with psychotic disorders and have been associated with various demographic and clinical correlates. There is an absence of research on the prevalence and clinical correlates of SUDs in psychotic disorders in low-and-middle-income countries (LMICs). Aim: We aimed to determine the prevalence and correlates of SUDs in psychotic disorders. Setting: Patients attending a large secondary-level psychiatric hospital in Cape Town South Africa. Methods: We used the Structured Clinical Interview for DSM-IV (SCID-I) to determine psychiatric and substance use diagnoses, depressive, anxiety, obsessive-compulsive and post-traumatic symptoms. We used logistic regression models to determine significant predictors of SUDs. Results: In total sample (N = 248), 55.6% of participants had any SUD, 34.3% had cannabis use disorders, 30.6% alcohol use disorders, 27.4% methamphetamine use disorders, 10.4% methaqualone use disorders and 4.8% had other SUDs. There were significant associations with male sex for most SUDs, with younger age and Coloured ethnicity for methamphetamine use disorders, and with lower educational attainment for cannabis use disorders. Anxiety symptoms and suicide attempts were significantly associated with alcohol use disorders; a diagnosis of a substance induced psychosis with cannabis and methamphetamine use disorders. Across most SUDs legal problems and criminal involvement were significantly increased. Conclusion: This study found a high prevalence and wide distribution of SUDs in patients with psychotic disorders, consistent with previous work from high income countries. Given clinical correlates, in individuals with psychotic disorders and SUDs it is important to assess anxiety symptoms, suicidality and criminal involvement.