BackgroundChronic non-specific neck pain (CNSNP) is the most common type of chronic neck pain encountered in clinical practice. Existing studies have demonstrated that intermediate frequency therapy can effectively alleviate neck pain symptoms. Among other conservative treatment modalities, Tai Chi, a typical mind-body exercise, may improve musculoskeletal function and postural control, but its effect on cervical stability and CNSNP remains unclear. The aim of this study was to compare the clinical efficacy of Tai Chi combined with intermediate frequency therapy vs. intermediate frequency therapy alone in patients with CNSNP.MethodsAccording to the inclusion and exclusion criteria, patients with CNSNP were recruited from the rehabilitation medicine clinic of the Sixth People's Hospital of Kunshan City, resulting in the enrollment of 60 eligible participants. Patients were randomly assigned to either the experimental group (EG) or the control group (CG). The EG received Tai Chi combined with intermediate frequency therapy, while the CG received intermediate frequency therapy alone. The primary outcome was the visual analogue scale (VAS) for pain. Secondary outcomes included the Neck Disability Index (NDI), the D value of cervical physiological curvature measured by x-ray, and the cervical range of motion (ROM) score. The intervention lasted eight weeks, with sessions conducted five times per week, for a total of 40 sessions. Assessments were performed at baseline, at four weeks (mid-intervention), and at the end of eight weeks.ResultsDuring the study, one participant in the EG withdrew after missing one week of Tai Chi intervention. Two participants in the CG discontinued: one due to a change in their treatment plan, and one for personal reasons. Thus, 57 patients with CNSNP completed the study. Both groups showed significant improvements in VAS, NDI, cervical physiological curvature (D value), and ROM scores after treatment compared to baseline. Notably, the improvement in the D value was significantly greater in the EG than in the CG.ConclusionFor patients with CNSNP, the combination of Tai Chi and intermediate frequency therapy appeared to alleviate pain and improve function. Compared to intermediate frequency therapy alone, this combined approach significantly improves the physiological curvature of the cervical spine in individuals with CNSNP. Furthermore, these findings suggest that Tai Chi may be a safe and beneficial adjunctive therapy, and may represent a promising alternative for the management of CNSNP. However, larger-scale long-term studies are still needed.Clinical Trial Registration:www. itmctr.ccebtcm.org.cn, identifier (TTM-CTR-2025000447).
Guldeniz Cetin, Ozlem Totuk, Ozdem Erturk Cetin
et al.
Abstract Objective This study aimed to compare the short-term prophylactic efficacy of greater occipital nerve (GON) blockade in menstrual migraine (MM) subgroups and evaluate the long-term effects on patients’ quality of life. Methods In this prospective study, 33 patients diagnosed with MM (15 with pure menstrual migraine [PMM] and 18 with menstrually related migraine [MRM]) received bilateral GON blockade once a month, one week before menstrual bleeding, for three months. Patients were evaluated before treatment (month 0) and after treatment completion (months 3 and 6) using the Visual Analog Scale (VAS), Headache Impact Test-6 (HIT-6), Migraine Disability Assessment (MIDAS), and Beck Depression Inventory (BDI) scores. Results MRM patients had a lower age of MM onset (p = 0.024), higher headache frequency (p = 0.004), and increased medication overuse (p = 0.027) compared to PMM patients. After GON blockade, significant improvements were observed in VAS, HIT-6, MIDAS, and BDI scores in both subgroups, with no significant differences between them. The improvement persisted during the medication-free follow-up period (months 3–6). Patients with mild or no depression showed a more substantial increase in quality of life. Patients experiencing a 50% reduction in headache days demonstrated significant improvement in BDI scores. Conclusion GON blockade may be an effective option for short-term and long-term prophylaxis in the treatment of MM, reducing the frequency and severity of headaches and improving quality of life and psychological state. Further research with larger patient cohorts and placebo-controlled trials is necessary to validate these findings.
BackgroundEncephalitis associated with antibodies against the N-methyl-D-aspartate receptor (NMDAR) results in a distinctive neuro-psychiatric syndrome. It has been reported that the serum phenylalanine-tyrosine (Phe/Tyr) ratio increases during infection. However, the connection between phenylalanine-tyrosine metabolism and psychiatric symptoms remains unclear.MethodsWe enrolled 24 individuals with anti-NMDAR encephalitis and 18 individuals with non-inflammatory neurological diseases (OND). Chromatography was used to measure serum levels of phenylalanine and tyrosine. Serum and cerebrospinal fluid (CSF) TNF-α levels were obtained from the clinical database. The modified Rankin Scale (mRS) score and Glasgow Coma Scale (GCS) score were recorded during the acute phase. The area under the curve (AUC) of the receiver operating characteristic curve was used to assess prediction efficacy.ResultsIn NMDAR patients, levels of serum Phe and the ratio of serum Phe/Tyr were higher compared to OND patients. The serum Phe/Tyr ratio was also elevated in NMDAR patients with psychiatric syndrome. Furthermore, serum Phe and Tyr levels were correlated with inflammatory indexes.ConclusionThe serum Phe/Tyr ratio is elevated in NMDAR patients with psychiatric syndrome and is associated with severity. Therefore, the serum Phe/Tyr ratio may serve as a potential prognostic biomarker.
Ghazaleh Babanejaddehaki, Aijun An, Manos Papagelis
Infectious diseases occur when pathogens from other individuals or animals infect a person, resulting in harm to both individuals and society as a whole. The outbreak of such diseases can pose a significant threat to human health. However, early detection and tracking of these outbreaks have the potential to reduce the mortality impact. To address these threats, public health authorities have endeavored to establish comprehensive mechanisms for collecting disease data. Many countries have implemented infectious disease surveillance systems, with the detection of epidemics being a primary objective. The clinical healthcare system, local/state health agencies, federal agencies, academic/professional groups, and collaborating governmental entities all play pivotal roles within this system. Moreover, nowadays, search engines and social media platforms can serve as valuable tools for monitoring disease trends. The Internet and social media have become significant platforms where users share information about their preferences and relationships. This real-time information can be harnessed to gauge the influence of ideas and societal opinions, making it highly useful across various domains and research areas, such as marketing campaigns, financial predictions, and public health, among others. This article provides a review of the existing standard methods developed by researchers for detecting outbreaks using time series data. These methods leverage various data sources, including conventional data sources and social media data or Internet data sources. The review particularly concentrates on works published within the timeframe of 2015 to 2022.
Md Aziz Hosen Foysal, Foyez Ahmed, Md Zahurul Haque
Plant diseases significantly impact agricultural productivity, resulting in economic losses and food insecurity. Prompt and accurate detection is crucial for the efficient management and mitigation of plant diseases. This study investigates advanced techniques in plant disease detection, emphasizing the integration of image processing, machine learning, deep learning methods, and mobile technologies. High-resolution images of plant leaves were captured and analyzed using convolutional neural networks (CNNs) to detect symptoms of various diseases, such as blight, mildew, and rust. This study explores 14 classes of plants and diagnoses 26 unique plant diseases. We focus on common diseases affecting various crops. The model was trained on a diverse dataset encompassing multiple crops and disease types, achieving 98.14% accuracy in disease diagnosis. Finally integrated this model into mobile apps for real-time disease diagnosis.
In modern healthcare, addressing the complexities of accurate disease prediction and personalized recommendations is both crucial and challenging. This research introduces MLtoGAI, which integrates Semantic Web technology with Machine Learning (ML) to enhance disease prediction and offer user-friendly explanations through ChatGPT. The system comprises three key components: a reusable disease ontology that incorporates detailed knowledge about various diseases, a diagnostic classification model that uses patient symptoms to detect specific diseases accurately, and the integration of Semantic Web Rule Language (SWRL) with ontology and ChatGPT to generate clear, personalized health advice. This approach significantly improves prediction accuracy and ensures results that are easy to understand, addressing the complexity of diseases and diverse symptoms. The MLtoGAI system demonstrates substantial advancements in accuracy and user satisfaction, contributing to developing more intelligent and accessible healthcare solutions. This innovative approach combines the strengths of ML algorithms with the ability to provide transparent, human-understandable explanations through ChatGPT, achieving significant improvements in prediction accuracy and user comprehension. By leveraging semantic technology and explainable AI, the system enhances the accuracy of disease prediction and ensures that the recommendations are relevant and easily understood by individual patients. Our research highlights the potential of integrating advanced technologies to overcome existing challenges in medical diagnostics, paving the way for future developments in intelligent healthcare systems. Additionally, the system is validated using 200 synthetic patient data records, ensuring robust performance and reliability.
Pumpkin is a vital crop cultivated globally, and its productivity is crucial for food security, especially in developing regions. Accurate and timely detection of pumpkin leaf diseases is essential to mitigate significant losses in yield and quality. Traditional methods of disease identification rely heavily on subjective judgment by farmers or experts, which can lead to inefficiencies and missed opportunities for intervention. Recent advancements in machine learning and deep learning offer promising solutions for automating and improving the accuracy of plant disease detection. This paper presents a comprehensive analysis of state-of-the-art Convolutional Neural Network (CNN) models for classifying diseases in pumpkin plant leaves. Using a publicly available dataset of 2000 highresolution images, we evaluate the performance of several CNN architectures, including ResNet, DenseNet, and EfficientNet, in recognizing five classes: healthy leaves and four common diseases downy mildew, powdery mildew, mosaic disease, and bacterial leaf spot. We fine-tuned these pretrained models and conducted hyperparameter optimization experiments. ResNet-34, DenseNet-121, and EfficientNet-B7 were identified as top-performing models, each excelling in different classes of leaf diseases. Our analysis revealed DenseNet-121 as the optimal model when considering both accuracy and computational complexity achieving an overall accuracy of 86%. This study underscores the potential of CNNs in automating disease diagnosis for pumpkin plants, offering valuable insights that can contribute to enhancing agricultural productivity and minimizing economic losses.
In response to the COVID-19 pandemic, the integration of interpretable machine learning techniques has garnered significant attention, offering transparent and understandable insights crucial for informed clinical decision making. This literature review delves into the applications of interpretable machine learning in predicting the prognosis of respiratory diseases, particularly focusing on COVID-19 and its implications for future research and clinical practice. We reviewed various machine learning models that are not only capable of incorporating existing clinical domain knowledge but also have the learning capability to explore new information from the data. These models and experiences not only aid in managing the current crisis but also hold promise for addressing future disease outbreaks. By harnessing interpretable machine learning, healthcare systems can enhance their preparedness and response capabilities, thereby improving patient outcomes and mitigating the impact of respiratory diseases in the years to come.
The emerging research shows that lncRNAs are associated with a series of complex human diseases. However, most of the existing methods have limitations in identifying nonlinear lncRNA-disease associations (LDAs), and it remains a huge challenge to predict new LDAs. Therefore, the accurate identification of LDAs is very important for the warning and treatment of diseases. In this work, multiple sources of biomedical data are fully utilized to construct characteristics of lncRNAs and diseases, and linear and nonlinear characteristics are effectively integrated. Furthermore, a novel deep learning model based on graph attention automatic encoder is proposed, called HGATELDA. To begin with, the linear characteristics of lncRNAs and diseases are created by the miRNA-lncRNA interaction matrix and miRNA-disease interaction matrix. Following this, the nonlinear features of diseases and lncRNAs are extracted using a graph attention auto-encoder, which largely retains the critical information and effectively aggregates the neighborhood information of nodes. In the end, LDAs can be predicted by fusing the linear and nonlinear characteristics of diseases and lncRNA. The HGATELDA model achieves an impressive AUC value of 0.9692 when evaluated using a 5-fold cross-validation indicating its superior performance in comparison to several recent prediction models. Meanwhile, the effectiveness of HGATELDA in identifying novel LDAs is further demonstrated by case studies. the HGATELDA model appears to be a viable computational model for predicting LDAs.
High-mobility group box-1 (HMGB1) is a nuclear protein associated with early inflammatory changes upon extracellular secretion expressed in various cells, including neurons and microglia. With the progress of research, neuroinflammation is believed to be involved in the pathogenesis of neurological diseases such as Parkinson's, epilepsy, and autism. As a key promoter of neuroinflammation, HMGB1 is thought to be involved in the pathogenesis of Parkinson's disease, stroke, traumatic brain injury, epilepsy, autism, depression, multiple sclerosis, and amyotrophic lateral sclerosis. However, in the clinic, HMGB1 has not been described as a biomarker for the above-mentioned diseases. However, the current preclinical research results show that HMGB1 antagonists have positive significance in the treatment of Parkinson's disease, stroke, traumatic brain injury, epilepsy, and other diseases. This review discusses the possible mechanisms by which HMGB1 mediates Parkinson's disease, stroke, traumatic brain injury, epilepsy, autism, depression, multiple sclerosis, amyotrophic lateral sclerosis, and the potential of HMGB1 as a biomarker for these diseases. Future research needs to further explore the underlying molecular mechanisms and clinical translation.
BackgroundThe growing interest suggests that the widespread application of radiomics has facilitated the development of neurological disease diagnosis, prognosis, and classification. The application of artificial intelligence methods in radiomics has increasingly achieved outstanding prediction results in recent years. However, there are few studies that have systematically analyzed this field through bibliometrics. Our destination is to study the visual relationships of publications to identify the trends and hotspots in radiomics research and encourage more researchers to participate in radiomics studies.MethodsPublications in radiomics in the field of neurological disease research can be retrieved from the Web of Science Core Collection. Analysis of relevant countries, institutions, journals, authors, keywords, and references is conducted using Microsoft Excel 2019, VOSviewer, and CiteSpace V. We analyze the research status and hot trends through burst detection.ResultsOn October 23, 2022, 746 records of studies on the application of radiomics in the diagnosis of neurological disorders were retrieved and published from 2011 to 2023. Approximately half of them were written by scholars in the United States, and most were published in Frontiers in Oncology, European Radiology, Cancer, and SCIENTIFIC REPORTS. Although China ranks first in the number of publications, the United States is the driving force in the field and enjoys a good academic reputation. NORBERT GALLDIKS and JIE TIAN published the most relevant articles, while GILLIES RJ was cited the most. RADIOLOGY is a representative and influential journal in the field. “Glioma” is a current attractive research hotspot. Keywords such as “machine learning,” “brain metastasis,” and “gene mutations” have recently appeared at the research frontier.ConclusionMost of the studies focus on clinical trial outcomes, such as the diagnosis, prediction, and prognosis of neurological disorders. The radiomics biomarkers and multi-omics studies of neurological disorders may soon become a hot topic and should be closely monitored, particularly the relationship between tumor-related non-invasive imaging biomarkers and the intrinsic micro-environment of tumors.
ObjectiveThe consequences and impact of violent behavior in schizophrenia are often serious, and identification of risk factors is of great importance to achieve early identification and effective management.MethodsThis follow-up study sampled adult patients with schizophrenia in primary mental health care in a rural area of southern China, in which 491 participants completed a comprehensive questionnaire at baseline and the 2-year follow-up. Sociodemographic, clinical and psychological assessment data were collected from all participants. Paired sample T-Tests and the McNemar Test were performed to examine changes over the follow-up period. Generalized Estimating Equations (GEE) were used to analyze the risk factors for violent behavior.ResultsThe results showed that about two in five community-dwelling patients with schizophrenia reported violent behavior in the past year. At follow-up, participants were significantly less employed, had more times of hospitalization, more psychotropic medication, and severer depressive symptoms, but had better health-related quality of life than at baseline. Use of clozapine and better insight into medication decreased the possibility of violent behavior, while more severe positive symptoms, insomnia, as well as use of second-generation antipsychotics other than clozapine, antidepressants and mood stabilizers increased the possibility of violent behavior.ConclusionsRisk evaluation, prevention and management of violence in patients with schizophrenia are demanded in primary mental health care.
Kiesler's (1983) Interpersonal Cycle Model has become a powerful tool for conceptualizing, organizing, and evaluating interpersonal tendencies in recent years. Accordingly, the Interpersonal Cyclical Model provides a theoretical basis for the nature of relationships and thus facilitates an understanding of 'self' and 'the others’' relationships. The first of the two main purposes of our study was to create valid and reliable stories about the four themes (failure, abandonment, rejec-tion, and criticism) that we define as challenging life events. The second one offered a theoretical example of these stories in interpersonal relations, based on the model. For this purpose, two different samples were defined. In the first, twelve stories were created, inspired by sample situa-tions of failure, abandonment, rejection, and criticism, received from a total of 40 people aged between 18-35 (age: M = 25.46, SD = 1.66). The level of representation of the target theme of the stories was examined by three judges and ten reviewers who are experts in clinical psychology. To test the effectiveness of the scenarios, five judges with theoretical knowledge on Interpersonal Schemas. Secondly, three separate judges were involved to evaluate their codability in accord-ance with the model. At this stage, the intraclass correlation coefficient of reliability for all stories was found to be high and significant (ICC = .84 to .99, p = .025) according to the results of the pilot study, which was performed on 15 people (age: M = 22.43, SD = 3.87), seven of whom were male. It was decided that the psychometric properties of the Story Completion Inventory in Ro-mantic Relationships were at levels that could be studied within the framework of Cognitive Interpersonal Theory and could be used in the literature.
Jamun leaf diseases pose a significant threat to agricultural productivity, negatively impacting both yield and quality in the jamun industry. The advent of machine learning has opened up new avenues for tackling these diseases effectively. Early detection and diagnosis are essential for successful crop management. While no automated systems have yet been developed specifically for jamun leaf disease detection, various automated systems have been implemented for similar types of disease detection using image processing techniques. This paper presents a comprehensive review of machine learning methodologies employed for diagnosing plant leaf diseases through image classification, which can be adapted for jamun leaf disease detection. It meticulously assesses the strengths and limitations of various Vision Transformer models, including Transfer learning model and vision transformer (TLMViT), SLViT, SE-ViT, IterationViT, Tiny-LeViT, IEM-ViT, GreenViT, and PMViT. Additionally, the paper reviews models such as Dense Convolutional Network (DenseNet), Residual Neural Network (ResNet)-50V2, EfficientNet, Ensemble model, Convolutional Neural Network (CNN), and Locally Reversible Transformer. These machine-learning models have been evaluated on various datasets, demonstrating their real-world applicability. This review not only sheds light on current advancements in the field but also provides valuable insights for future research directions in machine learning-based jamun leaf disease detection and classification.
We introduce Walk4Me, a telehealth community mobility assessment system designed to facilitate early diagnosis, severity, and progression identification. Our system achieves this by 1) enabling early diagnosis, 2) identifying early indicators of clinical severity, and 3) quantifying and tracking the progression of the disease across the ambulatory phase of the disease. To accomplish this, we employ an Artificial Intelligence (AI)-based detection of gait characteristics in patients and typically developing peers. Our system remotely and in real-time collects data from device sensors (e.g., acceleration from a mobile device, etc.) using our novel Walk4Me API. Our web application extracts temporal/spatial gait characteristics and raw data signal characteristics and then employs traditional machine learning and deep learning techniques to identify patterns that can 1) identify patients with gait disturbances associated with disease, 2) describe the degree of mobility limitation, and 3) identify characteristics that change over time with disease progression. We have identified several machine learning techniques that differentiate between patients and typically-developing subjects with 100% accuracy across the age range studied, and we have also identified corresponding temporal/spatial gait characteristics associated with each group. Our work demonstrates the potential of utilizing the latest advances in mobile device and machine learning technology to measure clinical outcomes regardless of the point of care, inform early clinical diagnosis and treatment decision-making, and monitor disease progression.
Detecting dental diseases through panoramic X-rays images is a standard procedure for dentists. Normally, a dentist need to identify diseases and find the infected teeth. While numerous machine learning models adopting this two-step procedure have been developed, there has not been an end-to-end model that can identify teeth and their associated diseases at the same time. To fill the gap, we develop YOLOrtho, a unified framework for teeth enumeration and dental disease detection. We develop our model on Dentex Challenge 2023 data, which consists of three distinct types of annotated data. The first part is labeled with quadrant, and the second part is labeled with quadrant and enumeration and the third part is labeled with quadrant, enumeration and disease. To further improve detection, we make use of Tufts Dental public dataset. To fully utilize the data and learn both teeth detection and disease identification simultaneously, we formulate diseases as attributes attached to their corresponding teeth. Due to the nature of position relation in teeth enumeration, We replace convolution layer with CoordConv in our model to provide more position information for the model. We also adjust the model architecture and insert one more upsampling layer in FPN in favor of large object detection. Finally, we propose a post-process strategy for teeth layout that corrects teeth enumeration based on linear sum assignment. Results from experiments show that our model exceeds large Diffusion-based model.
Fact-checking real-world claims often requires complex, multi-step reasoning due to the absence of direct evidence to support or refute them. However, existing fact-checking systems often lack transparency in their decision-making, making it challenging for users to comprehend their reasoning process. To address this, we propose the Question-guided Multi-hop Fact-Checking (QACHECK) system, which guides the model's reasoning process by asking a series of questions critical for verifying a claim. QACHECK has five key modules: a claim verifier, a question generator, a question-answering module, a QA validator, and a reasoner. Users can input a claim into QACHECK, which then predicts its veracity and provides a comprehensive report detailing its reasoning process, guided by a sequence of (question, answer) pairs. QACHECK also provides the source of evidence supporting each question, fostering a transparent, explainable, and user-friendly fact-checking process. A recorded video of QACHECK is at https://www.youtube.com/watch?v=ju8kxSldM64