OBJECTIVE: To study the neurological manifestations of patients with coronavirus disease 2019 (COVID-19). DESIGN: Retrospective case series SETTING: Three designated COVID-19 care hospitals of the Union Hospital of Huazhong University of Science and Technology in Wuhan, China. PARTICIPANTS: Two hundred fourteen hospitalized patients with laboratory confirmed diagnosis of severe acute respiratory syndrome from coronavirus 2 (SARS-CoV-2) infection. Data were collected from 16 January 2020 to 19 February 2020. MAIN OUTCOME MEASURES: Clinical data were extracted from electronic medical records and reviewed by a trained team of physicians. Neurological symptoms fall into three categories: central nervous system (CNS) symptoms or diseases (headache, dizziness, impaired consciousness, ataxia, acute cerebrovascular disease, and epilepsy), peripheral nervous system (PNS) symptoms (hypogeusia, hyposmia, hypopsia, and neuralgia), and skeletal muscular symptoms. Data of all neurological symptoms were checked by two trained neurologists. RESULTS: Of 214 patients studied, 88 (41.1%) were severe and 126 (58.9%) were non-severe patients. Compared with non-severe patients, severe patients were older (58.7 ± 15.0 years vs 48.9 ± 14.7 years), had more underlying disorders (42 [47.7%] vs 41 [32.5%]), especially hypertension (32 [36.4%] vs 19 [15.1%]), and showed less typical symptoms such as fever (40 [45.5%] vs 92 [73%]) and cough (30 [34.1%] vs 77 [61.1%]). Seventy-eight (36.4%) patients had neurologic manifestations. More severe patients were likely to have neurologic symptoms (40 [45.5%] vs 38 [30.2%]), such as acute cerebrovascular diseases (5 [5.7%] vs 1 [0.8%]), impaired consciousness (13 [14.8%] vs 3 [2.4%]) and skeletal muscle injury (17 [19.3%] vs 6 [4.8%]). CONCLUSION: Compared with non-severe patients with COVID-19, severe patients commonly had neurologic symptoms manifested as acute cerebrovascular diseases, consciousness impairment and skeletal muscle symptoms.
Vittorio Nicoletta, Angel Ruiz, Valérie Bélanger
et al.
One major obstacle to advancing research and treatment for major psychiatric disorders is their substantial within-diagnosis heterogeneity in patient lifetime trajectories. Adapted research methods such as cluster analysis to define subgroups of patients are currently used. However few studies have included service delivery descriptors in cluster analysis to investigate the determinants of heterogeneity in long-term trajectories. The aim of this study was to test whether patterns of service delivery could help in defining subgroups in terms of trajectories and clinical profiles in schizophrenia, bipolar disorder or major depressive disorder patients. Hierarchical Agglomerative Clustering (HAC) algorithms were used on a sample extracted from a Quebec government (Canada) transactional database to group and classify patients according to their interactions with the service delivery system. The resulting clusters were analyzed using statistical tools to characterize service trajectories. We observed three distinct trajectories that were not specific to any one of the three lifetime psychiatric diagnoses. Clusters were particularly affected by varying rates of clinician changes across the trajectory and changes of diagnoses. Results suggest that incorporating service delivery characteristics in future longitudinal studies of heterogeneity might be useful as a complement to studies that solely examine patients' clinical features. The inclusion of service delivery elements may also be a useful tool for acquiring knowledge to adapt services to patients' needs in public mental health and mental health economics research.
Faika Fairuj Preotee, Shuvashis Sarker, Shamim Rahim Refat
et al.
Leaf diseases are harmful conditions that affect the health, appearance and productivity of plants, leading to significant plant loss and negatively impacting farmers' livelihoods. These diseases cause visible symptoms such as lesions, color changes, and texture variations, making it difficult for farmers to manage plant health, especially in large or remote farms where expert knowledge is limited. The main motivation of this study is to provide an efficient and accessible solution for identifying plant leaf diseases in Bangladesh, where agriculture plays a critical role in food security. The objective of our research is to classify 21 distinct leaf diseases across six plants using deep learning models, improving disease detection accuracy while reducing the need for expert involvement. Deep Learning (DL) techniques, including CNN and Transfer Learning (TL) models like VGG16, VGG19, MobileNetV2, InceptionV3, ResNet50V2 and Xception are used. VGG19 and Xception achieve the highest accuracies, with 98.90% and 98.66% respectively. Additionally, Explainable AI (XAI) techniques such as GradCAM, GradCAM++, LayerCAM, ScoreCAM and FasterScoreCAM are used to enhance transparency by highlighting the regions of the models focused on during disease classification. This transparency ensures that farmers can understand the model's predictions and take necessary action. This approach not only improves disease management but also supports farmers in making informed decisions, leading to better plant protection and increased agricultural productivity.
Fahud Ahmmed, Md. Zaheer Raihan, Kamnur Nahar
et al.
Skin diseases can arise from infections, allergies, genetic factors, autoimmune disorders, hormonal imbalances, or environmental triggers such as sun damage and pollution. Some skin diseases, such as Actinic Keratosis and Psoriasis, can be fatal if not treated in time. Early identification is crucial, but the diagnostic methods for these conditions are often expensive and not widely accessible. In this study, we propose a novel and efficient method for diagnosing skin diseases using deep learning techniques. This approach employs a modified VGG16 Convolutional Neural Network (CNN) model. The model includes several convolutional layers and utilizes ImageNet weights with modified top layers. The top layer is updated with fully connected layers and a final softmax activation layer to classify skin diseases. The dataset used, titled "Skin Disease Dataset," is publicly available. While the VGG16 architecture does not include data augmentation by default, preprocessing techniques such as rotation, shifting, and zooming were applied to augment the data prior to model training. The proposed methodology achieved 90.67% accuracy using the modified VGG16 model, demonstrating its reliability in classifying skin diseases. The promising results highlight the potential of this approach for real-world applications.
Symptom Checkers (SCs) provide medical information tailored to user symptoms. A critical challenge in SC development is preventing unexpected performance degradation for individual diseases, especially rare diseases, when updating algorithms. This risk stems from the lack of practical pre-deployment evaluation methods. For rare diseases, obtaining sufficient evaluation data from user feedback is difficult. To evaluate the impact of algorithm updates on the diagnostic performance for individual rare diseases before deployment, this study proposes and validates a novel Synthetic Vignette Simulation Approach. This approach aims to enable this essential evaluation efficiently and at a low cost. To estimate the impact of algorithm updates, we generated synthetic vignettes from disease-phenotype annotations in the Human Phenotype Ontology (HPO), a publicly available knowledge base for rare diseases curated by experts. Using these vignettes, we simulated SC interviews to predict changes in diagnostic performance. The effectiveness of this approach was validated retrospectively by comparing the predicted changes with actual performance metrics using the R-squared ($R^2$) coefficient. Our experiment, covering eight past algorithm updates for rare diseases, showed that the proposed method accurately predicted performance changes for diseases with phenotype frequency information in HPO (n=5). For these updates, we found a strong correlation for both Recall@8 change ($R^2$ = 0.83,$p$ = 0.031) and Precision@8 change ($R^2$ = 0.78,$p$ = 0.047). Our proposed method enables the pre-deployment evaluation of SC algorithm changes for individual rare diseases. This evaluation is based on a publicly available medical knowledge database created by experts, ensuring transparency and explainability for stakeholders. Additionally, SC developers can efficiently improve diagnostic performance at a low cost.
Y. Broche-Pérez, R.M. Jiménez-Morales, L.O. Monasterio-Ramos
et al.
Introduction: Relapses are a hallmark of multiple sclerosis, being a characteristic feature of relapsing-remitting multiple sclerosis (RRMS). The occurrence of a relapse constitutes a source of significant discomfort that impacts all domains of daily life of patients with multiple sclerosis (PwMS). In this study we first explored the psychometric properties of the Spanish version of the Fear of Relapse Scale (FoR) in a sample of patients with RRMS. Besides, we explored the relationship between the Fear of Relapse Scale with fatigue and cognitive perceived deficits in our PwMS sample. Methods: An online cross-sectional survey was conducted on 173 MS patients from 12 Spanish-speaking countries (Argentina, Mexico, Uruguay, Dominican Republic, Spain, Cuba, Colombia, Guatemala, Chile, Paraguay, Peru, and El Salvador). Confirmatory factor analysis (CFA) was performed to assess the factor structure of the scale. Multiple linear regression was used to evaluate the effects of health self-perception, fatigue, and perceived cognitive deficits over fear of relapse. Results: The three-factor model in the CFA yielded a good model fit (χ2/df = 2.25, P < .001, RMSEA = .078, CFI = .91). McDonalds’ Omega of the FoR (Spanish version) was .91. There was a statistically significant inverse correlation between FoR and health self-perception, and a positive correlation between FoR, fatigue, and perceived cognitive deficits. Finally, level of fatigue was a predictor of fear of relapse. Conclusions: The Spanish version of the Fear of Relapse Scale is a valid and reliable instrument to explore the experience of fear of relapse in patients with RRMS. Resumen: Introducción: Las recaídas son un sello distintivo de la esclerosis múltiple, siendo un rasgo característico de la esclerosis múltiple remitente-recurrente (EMRR). La ocurrencia de una recaída constituye una fuente de malestar significativo que impacta todos los dominios de la vida diaria de los pacientes con esclerosis múltiple (PcEM). En este estudio primero exploramos las propiedades psicométricas de la versión en español de la Escala de Miedo a la Recaída (FoR) en una muestra de pacientes con EMRR. Además, exploramos la relación entre la Escala de Miedo a la Recaída con la fatiga y los déficits cognitivos percibidos en nuestra muestra de PcEM. Métodos: Se realizó una encuesta transversal en línea a 173 pacientes con EM de 12 países de habla hispana (Argentina, México, Uruguay, República Dominicana, España, Cuba, Colombia, Guatemala, Chile, Paraguay, Perú y El Salvador). Se realizó un análisis factorial confirmatorio (AFC) para evaluar la estructura factorial de la escala. Se utilizó regresión lineal múltiple para evaluar los efectos de la autopercepción de la salud, la fatiga y los déficits cognitivos percibidos sobre el miedo a la recaída. Resultados: El modelo de tres factores en el CFA arrojó un buen ajuste del modelo (χ2/df = 2.25, P < .001, RMSEA = .078, CFI = .91). El valor de Omega de la escala (versión en español) fue de .91. Hubo una correlación estadísticamente significativa e inversa entre la FoR y la autopercepción de salud, y una correlación positiva entre la FoR, la fatiga y los déficits cognitivos percibidos. Finalmente, el nivel de fatiga fue predictores del miedo a la recaída. Conclusiones: La versión española de la Escala de Miedo a la Recaída es un instrumento válido y fiable para explorar la experiencia de miedo a la recaída en pacientes con EMRR.
Introduction
Acupuncture has long been used in treating anxiety, and a literature exists on its effectiveness. However, acupuncture is rarely covered by government insurance (Medicaid or Medicare) or even by many commercial insurance carriers in the United States, making it inaccessible to those who cannot pay separately.
Objectives
We asked if adding acupuncture to an anxiety group would improve outcome.
Methods
We provided acupuncture during group psychotherapy for anxiety as a non-billable service. This was feasible since patients were already being billed for group psychotherapy. A physician and a social work intern led the group. At the start of the group, the physician went around the circle of group members and inserted acupuncture needles, using points in the ears, head, hands, feet, and, in the summer, arms and lower legs). The size of the group ranged from 4 to 12 people. We used Battlefield auricular points, the four gates (Large Intestine 4 and Liver 3, bilaterally), and GV24, GV29, Ht7, and Sp6. Sometimes, other points were added for other symptoms (back pain, neck pain, etc.) People sometimes joined the group without anxiety as a core problem in getting access to acupuncture. A core group of patients formed who came weekly while others came and went. The Hamilton Anxiety Scale measured anxiety after treatments 4, 8, and 12. The group lasted 90 minutes and consisted of mindfulness training, guided imagery, and CBT for anxiety. All patients met the criteria for generalized anxiety disorder. The t-test procedure was used to compare the differences between the means for the two groups.
Results
Thirty-five patients received acupuncture, while another 55 patients attended the group and did not elect to receive acupuncture. All patients were covered by MaineCare health insurance, Maine’s version of Medicaid. All patients had multiple other medical problems, which was why they were referred to the group. Seventy percent of the patients were women, and 30% were men. The average age was 40.1 years. Anxiety ratings on the Hamilton Anxiety Scale decreased by the last time measured for those not receiving acupuncture by an average of 5.17 points (S.D. 2.9; n = 55). Anxiety ratings for those receiving acupuncture decreased by an average of 7.19 points (S.D. 2.5, n = 35). The difference of the means was -2.02 (S.E. 0.595; 95% CI = -2.203 to -0.837; t = -3.394; p = 0.001). Headaches, shoulder pains, and upper back pain also decreased. Patients reported high levels of benefit from the acupuncture and encouraged other patients to continue to come and try the acupuncture. Usually, the needles could be placed within the first third of the group.
Conclusions
Acupuncture improved anxiety ratings for people in group psychotherapy for anxiety over group alone, though the possibility of a placebo effect cannot be eliminated. Patients chose acupuncture, which could also present a potential bias.
Disclosure of Interest
None Declared
IntroductionThis study examined the moderating effects of childhood abuse histories on the associations between low frequency (LF) and high frequency (HF) components of heart rate variability (HRV) and the development of post-traumatic stress disorder (PTSD).MethodsParticipants with physical injuries were recruited from a trauma center and followed for two years. Baseline assessments included LF, HF, and childhood abuse histories, assessed using the Nemesis Childhood Trauma Interview. Socio-demographic and clinical covariates were obtained. PTSD diagnoses were made at 3, 6, 12, and 24 months post-injury using the Clinician-Administered PTSD Scale for DSM-5. Logistic regression analyses assessed the associations.ResultsAmong 538 participants, 58 (10.8%) developed PTSD during the study period. A significant interaction was found: lower LF/HF were significantly associated with PTSD in patients with childhood abuse histories, but not in those without.ConclusionChildhood abuse history significantly moderates the relationship between LF-HF HRV components and PTSD development, suggesting that childhood adversities amplify the risk. These findings support the importance of screening for childhood abuse histories and monitoring HRV in physically injured patients as part of the assessment process.
Giuseppe Maccarrone, Gennaro Saporito, Patrizia Sucapane
et al.
BackgroundGender differences in the access to advanced therapies for Parkinson’s disease (PD) are poorly investigated.ObjectiveThe objective of this study was to investigate the presence of any gender disparity in the access to advanced therapies for PD.DesignRetrospective study.MethodsData from patients with consistent access to the Parkinson’s and Movement Disorder Center of L’Aquila over the last 10-year period were screened. Patients selected for advanced therapies were included.ResultsOut of 1,252 patients, 200 (mean age ± SD 71.02 ± 9.70; 72% males; median Hoen Yahr level: 3, minimum 1 maximum 5) were selected for advanced therapies: 133 for Magnetic Resonance guided Focused Ultrasound (MRgFUS) thalamotomy (mean age ± SD 70.0 ± 8.9; 77% males), 49 for Levodopa/Carbidopa Intestinal Gel (LCIG) infusion (mean age ± SD 74.3 ± 11.4; 59% males), 12 for Deep Brain Stimulation (DBS) (mean age ± SD 71.2 ± 6.3; 75% males), and 7 for Continuous Subcutaneous Apomorphine Infusion (CSAI) (mean age ± SD 69.7 ± 5.5; 43% males). No sex differences were found in relation to age (MRgFUS group: males vs. females 70.2 ± 8.9 vs. 70.8 ± 8.9, p-value = 0.809; LCIG group: males vs. females 73.5 ± 13.0 vs. 75.5 ± 8.5, p-value = 0.557; DBS group: males vs. females 77.2 ± 8.1 vs. 67.3 ± 8.6, p-value = 0.843; CSAI group: males vs. females 73.3 ± 4.0 vs. 67.0 ± 5.2, p-value = 0.144) and disease duration (MRgFUS group: males vs. females 8.3 ± 4.4 vs. 9.6 ± 6.7, p-value = 0.419; LCIG group: males vs. females 14.5 ± 5.81 vs. 17.3 ± 5.5; p-value = 0.205; DBS group: males vs. females 15.0 ± 9.6 vs. 15.5 ± 7.7, p-value = 0.796; CSAI group: males vs. females 11.7 ± 3.7 vs. 10.3 ± 3.7, p-value = 0.505).ConclusionThe predominance of males is higher than that expected based on the higher prevalence of PD in men. Women are less confident in selecting advanced therapies during the natural progression of their disease. Factors accounting for this discrepancy deserve further investigation.
Darlyn Buenaño Vera, Byron Oviedo, Washington Chiriboga Casanova
et al.
The early identification of diseases in cocoa pods is an important task to guarantee the production of high-quality cocoa. The use of artificial intelligence techniques such as machine learning, computer vision and deep learning are promising solutions to help identify and classify diseases in cocoa pods. In this paper we introduce the development and evaluation of a deep learning computational model applied to the identification of diseases in cocoa pods, focusing on "monilia" and "black pod" diseases. An exhaustive review of state-of-the-art of computational models was carried out, based on scientific articles related to the identification of plant diseases using computer vision and deep learning techniques. As a result of the search, EfficientDet-Lite4, an efficient and lightweight model for object detection, was selected. A dataset, including images of both healthy and diseased cocoa pods, has been utilized to train the model to detect and pinpoint disease manifestations with considerable accuracy. Significant enhancements in the model training and evaluation demonstrate the capability of recognizing and classifying diseases through image analysis. Furthermore, the functionalities of the model were integrated into an Android native mobile with an user-friendly interface, allowing to younger or inexperienced farmers a fast and accuracy identification of health status of cocoa pods
Hypomimia is a non-motor symptom of Parkinson's disease that manifests as delayed facial movements and expressions, along with challenges in articulation and emotion. Currently, subjective evaluation by neurologists is the primary method for hypomimia detection, and conventional rehabilitation approaches heavily rely on verbal prompts from rehabilitation physicians. There remains a deficiency in accessible, user-friendly and scientifically rigorous assistive tools for hypomimia treatments. To investigate this, we developed HypomimaCoach, an Action Unit (AU)-based digital therapy system for hypomimia detection and rehabilitation in Parkinson's disease. The HypomimaCoach system was designed to facilitate engagement through the incorporation of both relaxed and controlled rehabilitation exercises, while also stimulating initiative through the integration of digital therapies that incorporated traditional face training methods. We extract action unit(AU) features and their relationship for hypomimia detection. In order to facilitate rehabilitation, a series of training programmes have been devised based on the Action Units (AUs) and patients are provided with real-time feedback through an additional AU recognition model, which guides them through their training routines. A pilot study was conducted with seven participants in China, all of whom exhibited symptoms of Parkinson's disease hypomimia. The results of the pilot study demonstrated a positive impact on participants' self-efficacy, with favourable feedback received. Furthermore, physician evaluations validated the system's applicability in a therapeutic setting for patients with Parkinson's disease, as well as its potential value in clinical applications.
Branko Mitic, Philipp Seeböck, Jennifer Straub
et al.
Fast detection of emerging diseases is important for containing their spread and treating patients effectively. Local anomalies are relevant, but often novel diseases involve familiar disease patterns in new spatial distributions. Therefore, established local anomaly detection approaches may fail to identify them as new. Here, we present a novel approach to detect the emergence of new disease phenotypes exhibiting distinct patterns of the spatial distribution of lesions. We first identify anomalies in lung CT data, and then compare their distribution in a continually acquired new patient cohorts with historic patient population observed over a long prior period. We evaluate how accumulated evidence collected in the stream of patients is able to detect the onset of an emerging disease. In a gram-matrix based representation derived from the intermediate layers of a three-dimensional convolutional neural network, newly emerging clusters indicate emerging diseases.
María Alías-Ferri, María Alías-Ferri, Manuela Pellegrini
et al.
Cannabis is the most widely consumed illegal drug in the world and synthetic cannabinoids are increasingly gaining popularity and replacing traditional cannabis. These substances are a type of new psychoactive substance that mimics the cannabis effects but often are more severe. Since, people with opioids use disorder use widely cannabis, they are a population vulnerable to use synthetic cannabinoids. In addition, these substances are not detected by the standard test used in the clinical practice and drug-checking is more common in recreational settings. A cross-sectional study with samples of 301 opioid use disorder individuals was carried out at the addiction care services from Barcelona and Badalona. Urinalysis was performed by high-sensitivity gas chromatography-mass spectrometry (GC-MS) and ultra-high-performance liquid chromatography-high –resolution mass spectrometry (UHPLC-HRMS). Any synthetic cannabinoid was detected in 4.3% of the individuals and in 23% of these samples two or more synthetic cannabinoids were detected. Among the 8 different synthetic cannabinoids detected, most common were JWH-032 and JWH-122. Natural cannabis was detected in the 18.6% of the samples and only in the 0.7% of them THC was identified. Several different synthetic cannabinoids were detected and a non-negligible percentage of natural cannabis was detected among our sample. Our results suggest that the use of synthetic cannabinoids may be related to the avoidance of detection. In the absence of methods for the detection of these substances in clinical practice, there are insufficient data and knowledge making difficult to understand about this phenomenon among opioid use disorder population.
ObjectiveVoltage-gated sodium channels (VGSCs) play an important role in neuronal excitability and epilepsies. In addition to the brain, VGSCs are also abundant enriched in cardiac tissues and are responsible for normal cardiac rhythm. Theoretically, sodium channel blocking antiseizure medications (SCB-ASMs) may have unwanted cardiac side effects. Lacosamide (LCM) is increasingly used in patients with status epilepticus (SE) due to the availability of intravenous formula. The concerns about the proarrhythmic effect are even higher due to the need for rapid administration of LCM. There were limited data on the cardiac safety of intravenous LCM. Hereby, we performed a study to observe the effect of intravenous loading of LCM in patients with seizures in our Neurological Intensive Care Unit (NICU).MethodsWe retrospectively reviewed the patients using parenteral LCM for seizures in NICU. A routine infusion time of 30 min was performed. The electrocardiogram (ECG) and blood pressure were recorded before and after LCM injection.ResultsWe retrospectively reviewed the clinical data of 38 patients using LCM for treating seizures. Two patients had cardiac side effects after LCM loading, one (3.0%) with new-onset first-degree AV block and the other (3.0%) with atrial premature complex. For the quantitative changes of ECG parameter analysis, there was no change in QRS complex, corrected QT intervals, and heart rate except that the PR interval was mildly increased. A mild decrease in the diastolic blood pressure and mean arterial pressure were also observed. None of the above-mentioned parameter alterations required clinical intervention.ConclusionWe evaluated the cardiac safety concern in real-world epilepsy patients requiring intravenous LCM. Near half of this cohort responded to LCM therapy and there was no life-threatening cardiac adverse effect. Intravenous LCM does have some effects on the ECG parameters and blood pressure but without clinical relevance. Despite the theoretical concern of cardiac adverse effects of LCM, the benefit of seizure control outweighed the risk in patients with status epilepticus or seizure clusters, such as hyperthermia, pulmonary edema, cardiac arrhythmias, or cardiovascular collapse.