ICD-11: an international classification of diseases for the twenty-first century

A Korean field trial of ICD-11 classification under practical clinical coding rules to clarify the reasons for inconsistencies

Background: The World Health Organization (WHO) announced the release of the 11th edition of the International Classification of Diseases (ICD) in May 2019. Although Statistics Korea has been involved in the ongoing research on ICD-11 since 2017, we have been unable to achieve agreement on the gold standards for case scenario clinical coding in previous studies due to high levels of variance in the coding results of participants. Objective: The purpose of this study was to enhance clinical coding accuracy and consistency in ICD-11 by identifying and clarifying the reasons for these inconsistencies through the use of clear clinical coding rules. Method: A pre-experimental design was applied. Two clinical coding field trials (FTs) were conducted in 'ICD-11 for Mortality and Morbidity Statistics (2022 Mar)' targeting diagnostic terms and case scenarios. In the first FT, clinical coding rules were derived by analysing the results, while the second FT was performed under the clinical coding rules set by the first FT. Results: Across the two FTs, accuracy rates for diagnostic terms (75.8% and 71.8%, respectively) were higher than for case scenarios (62.5% and 71.9%). The main reason for the low accuracy levels was post-coordination. Conclusion: For case scenario clinical coding, low accuracy could be explained by variance in clustering methods between participants. This suggests that the accuracy of ICD-11 clinical coding could be increased if the variance between clustering methods can be reduced through the use of a clear coding guide. A guide for various ambiguous cases in each institution and the provision of a proper post-coordination list in the stem code could also be effective.

A Comprehensive Review on Utilizing Human Brain Organoids to Study Neuroinflammation in Neurological Disorders

Most current information about neurological disorders and diseases is derived from direct patient and animal studies. However, patient studies in many cases do not allow replication of the early stages of the disease and, therefore, offer limited opportunities to understand disease progression. On the other hand, although the use of animal models allows us to study the mechanisms of the disease, they present significant limitations in developing drugs for humans. Recently, 3D-cultured in vitro models derived from human pluripotent stem cells have surfaced as a promising system. They offer the potential to connect findings from patient studies with those from animal models. In this comprehensive review, we discuss their application in modeling neurodevelopmental conditions such as Down Syndrome or Autism, neurodegenerative diseases such as Alzheimer's or Parkinson's, and viral diseases like Zika virus or HIV. Furthermore, we will discuss the different models used to study prenatal exposure to drugs of abuse, as well as the limitations and challenges that must be met to transform the landscape of research on human brain disorders.

Smart Textile Technology for the Monitoring of Mental Health

In recent years, smart devices have proven their effectiveness in monitoring mental health issues and have played a crucial role in providing therapy. The ability to embed sensors in fabrics opens new horizons for mental healthcare, addressing the growing demand for innovative solutions in monitoring and therapy. The objective of this review is to understand mental health, its impact on the human body, and the latest advancements in the field of smart textiles (sensors, electrodes, and smart garments) for monitoring physiological signals such as respiration rate (RR), electroencephalogram (EEG), electrodermal activity (EDA), electrocardiogram (ECG), and cortisol, all of which are associated with mental health disorders. Databases such as Web of Science (WoS) and Scopus were used to identify studies that utilized smart textiles to monitor specific physiological parameters. Research indicates that smart textiles provide promising results compared to traditional methods, offering enhanced comfort for long-term monitoring.

Management of alcohol use disorder: a gastroenterology and hepatology-focused perspective

Alcohol use disorder is a prevalent and major but preventable cause of morbidity and mortality worldwide, causing several important health consequences, including chronic liver disease. Despite its substantial effects, most clinicians do not adequately assess alcohol intake in clinical practice, and there are several barriers to providing integrated management to patients with alcohol use disorder. Standardised questionnaires, such as the Alcohol Use Identification Test (AUDIT), can facilitate the identification of individuals at risk of alcohol use disorder, and alcohol biomarkers such as phosphatidylethanol aid in quantifying levels of alcohol consumption. Non-pharmacological interventions-including brief interventions, twelve-step facilitation, motivational enhancement therapy, contingency management, and cognitive behavioural therapy-are effective for patients with alcohol use disorder, regardless of the presence of advanced liver disease. Pharmacological treatments should be considered according to the severity of liver disease and other comorbidities, safety profile, and local availability. The management of patients with alcohol use disorder and associated liver disease should ideally be performed in the setting of integrated multidisciplinary teams.

Advancing cancer data collection: ICD-11 implementation in European oncology and cancer registries

BACKGROUND

The International Classification of Diseases (ICD) is a cornerstone in medical data standardisation worldwide. The newly released ICD-11, with its updated chapter on neoplasms, is expected to revolutionise cancer data collection, particularly in European healthcare system. However, significant challenges to implementation remain, especially when transitioning from ICD-10, which has long underpinned cancer registration. This study aimed to assess the feasibility of implementing ICD-11 in European cancer registries and healthcare system by examining its coding structure for neoplasms, particularly its ability to replace ICD-10 in existing cancer data collection processes.

METHODS

The analysis focused on ICD-11 Chapter 2 "Neoplasms", including pre-coordination and post-coordination coding structures. A comprehensive mapping of ICD-10 to ICD-11 codes was performed for cancer site, morphology, grade, stage, laterality and prognostic factors. Additionally, tools for coding validation and their applications in registries and hospital IT systems were reviewed. Results were assessed for accuracy, consistency, and ease of implementation.

RESULTS

ICD-11 allows for more detailed coding of neoplasms, especially through post-coordination, which enhances precision in capturing tumour subtypes and prognostic factors. Nevertheless, inconsistencies were observed in grading and staging systems, allowing for invalid combinations. Mapping between ICD-10 and ICD-11 also revealed gaps, particularly in representing complex cancer diagnoses.

CONCLUSION

While ICD-11 presents advanced coding options for cancer data, its complexity and the lack of validation mechanisms present challenges for immediate use in cancer registries. The transition from ICD-10 will necessitate extensive training, improved mapping tools, and the introduction of validation systems to ensure accurate data collection. Cancer registries are well-suited to support this transition, but further refinements to ICD-11 are essential before full adoption.

Carbohydrate Structure Database: current state and recent developments

Carbohydrate Structure Database (CSDB) is a curated glycan data collection and a glycoinformatic platform. In this report, its database, analytical, and other components that have appeared for the recent years are reviewed. The major improvements were achieving close-to-full coverage on glycans from microorganisms, launching modules for glycosyltransferases and saccharide conformations, online glycan builder and 3D modeler, NMR simulator, NMR-based structure predictor, and other tools.

Systematic literature review on Calcium Pyrophosphate Deposition (CPPD) nomenclature: condition elements and clinical states- A Gout, Hyperuricaemia and Crystal-Associated Disease Network (G-CAN) consensus project

OBJECTIVES

The Gout, Hyperuricaemia and Crystal-Associated Disease Network (G-CAN) has developed a calcium pyrophosphate deposition (CPPD) nomenclature project. This systematic literature review constituted its first step and aimed to provide a state-of-the-art analysis of the medical literature of the last 20 years.

METHODS

A systematic literature search was undertaken in the PubMed, Cochrane and Embase databases between 2000 and 2022, restricted to studies on humans and in the English language. Eight reviewers independently and manually extracted labels related to CPPD concepts, according to an a priori list generated by the authors: pathogenic conditions and pathogenic crystal labels, elementary imaging condition elements and asymptomatic and symptomatic condition states. For each concept, labels were analysed to determine their frequency.

RESULTS

Among the 2375 articles identified, 886 articles were included, of which 394 (44.5%) were case reports and 169 (19.0%) were scoping reviews. Overall, the most common labels used to designate the pathogenic condition were 'pseudogout' in 365/783 (46.6%), 'chondrocalcinosis' in 207/783 (26.4%) and 'calcium pyrophosphate deposition disease' in 181/783 (23.1%) occurrences. The most common abbreviation was 'CPPD' in 312/390 (80.0%), but with different meanings. CPPD clinical phenotypes were often described as 'pseudo-form' labels.

CONCLUSION

Those results demonstrate the heterogeneity of labels used to describe CPPD condition concepts, with wide variation in condition labels in the medical literature. This work provides the rationale and basis to achieve agreement about CPPD technical nomenclature.

Strategies to overcome barriers to the statistical representation of femicide data-a technical note

Mortality data systems are upstream determinants of health, providing critical information on causes of death and population health trends and influencing health outcomes by shaping policies, research, and resource allocation. Moreover, the gender-related deaths of women and girls are significantly underrepresented or underrecognized in mortality data across many countries. This paper seeks to identify potential barriers and facilitators to improving the representation of femicide data. The primary barriers affecting data representation of femicide are related to definitions, data collection, coding, comparability, access, and systemic challenges. Key recommendations include establishing a nationwide consensus on the definition of femicide, updating training modules for medicolegal professionals, improving pathology reporting processes, ensuring quality assurance in documentation, refining coding practices, developing new analytic methods, and providing deidentified access to cases still under investigation.

Head or skull injury? Consequences of using mistranslated ICD diagnosis category: Multicenter, blinded, randomized controlled analysis

Emergency care units in Hungary treat approximately 140-180 thousand head injuries of varying se'verity each year. These head injuries are mainly caused by traffic accidents, assaults, or domestic accidents. The outpatient care record contains details about the circumstances and underlying mechanisms of the head injury, the results of physical and imaging examinations, and therapeutic recommendations. The record also contains standardized codes for the diagnoses and the interventions performed, using the BNO (Betegségek Nemzetközi Osztályozása) classification, the Hungarian version of the international diagnostic classification system ICD (International Classification of Diseases). These records are important for financial reasons and for statistical purposes. The injury diagnoses consist of the ICD codes with the related diagnostic categories and in most cases also the corresponding Latin diagnoses describing the injuries sustained. The ICD categories often appear as Hungarian translations of the Latin diagnoses in the detailed Medical Diagnostic Reports on Injuries (MDRI). In Hungary, MDRI reports are prepared by physicians at the request of criminal prosecutors for forensic evaluation. In practice, however, MDRIs are usually based on the primary outpatient records. Head injuries are diagnosed using the codes in ICD Chapter 19, S00-S09. Translation errors may occur when ICD is adapted for different languages, resulting in different applications of certain diagnostic codes. The present study investigates the use and frequency of the ICD-10 code S07.1 in Hungarian MDRIs issued for forensic evaluation. The results of our study show that, due to an inaccurate translation, superficial head injuries (specifically, bruises) are incorrectly coded in Hungary with S07.1, which in ICD-10 is reserved for severe head injuries (crushing injury of the skull).

TarIKGC: A Target Identification Tool Using Semantics-Enhanced Knowledge Graph Completion with Application to CDK2 Inhibitor Discovery

Target identification is a critical stage in the drug discovery pipeline. Various computational methodologies have been dedicated to enhancing the classification performance of compound-target interactions, yet significant room remains for improving the recommendation performance. To address this challenge, we developed TarIKGC, a tool for target prioritization that leverages semantics enhanced knowledge graph (KG) completion. This method harnesses knowledge representation learning within a heterogeneous compound-target-disease network. Specifically, TarIKGC combines an attention-based aggregation graph neural network with a multimodal feature extractor network to simultaneously learn internal semantic features from biomedical entities and topological features from the KG. Furthermore, a KG embedding model is employed to identify missing relationships among compounds and targets. In silico evaluations highlighted the superior performance of TarIKGC in drug repositioning tasks. In addition, TarIKGC successfully identified two potential cyclin-dependent kinase 2 (CDK2) inhibitors with novel scaffolds through reverse target fishing. Both compounds exhibited antiproliferative activities across multiple therapeutic indications targeting CDK2.

Mortality and morbidity patterns in Yaoundé, Cameroon: an ICD-11 classification-based analysis

BACKGROUND

In Cameroon, like in many other resource-limited countries, data generated by health settings including morbidity and mortality parameters are not always uniform. In the absence of a national guideline necessary for the standardization and harmonization of data, precision of data required for effective decision-making is therefore not guaranteed. The objective of the present study was to assess the reporting style of morbidity and mortality data in healthcare settings.

METHODS

An institutional-based cross-sectional study was carried out from May to June 2022 at the Yaoundé Central Hospital. A questionnaire was used to assess the need to set up a standard tool to improve the reporting system. Medical records were used to collect mortality and morbidity data which were then compared to the International Statistical Classification of Diseases and Related Health Problems-11 (ICD-11) codification. Data were analyzed using IBM-SPSS version 26.

RESULTS

Out of 200 patients' morbidity causes recorded, nearly three-quarter (74.0%) were heterogeneous, and two over five (41.0%) of mortality causes reported were also heterogeneous. Most of respondents stated the need to set up a standard tool for collecting mortality and morbidity data (83.6%). Less than one-fifth (18.2%) of health care providers were able to understand data flow, correctly archived data (36.6%) and used electronic tools for data quality control (40.0%).

CONCLUSION

There were high levels of heterogeneities of morbidity and mortality causes among patients admitted to the Yaoundé Central Hospital in 2021. It is therefore urgent that Cameroon national health authorities implement the ICD-11 to allow the systematic recording, analysis, interpretation and comparison of mortality and morbidity data collected in Yaoundé Central Hospital at different times; and ensure interoperability and reusability of recorded data for medical decision support.

An examination of ambulatory care code specificity utilization in ICD-10-CM compared to ICD-9-CM: implications for ICD-11 implementation

OBJECTIVE

The ICD-10-CM classification system contains more specificity than its predecessor ICD-9-CM. A stated reason for transitioning to ICD-10-CM was to increase the availability of detailed data. This study aims to determine whether the increased specificity contained in ICD-10-CM is utilized in the ambulatory care setting and inform an evidence-based approach to evaluate ICD-11 content for implementation planning in the United States.

MATERIALS AND METHODS

Diagnosis codes and text descriptions were extracted from a 25% random sample of the IQVIA Ambulatory EMR-US database for 2014 (ICD-9-CM, n = 14 327 155) and 2019 (ICD-10-CM, n = 13 062 900). Code utilization data was analyzed for the total and unique number of codes. Frequencies and tests of significance determined the percentage of available codes utilized and the unspecified code rates for both code sets in each year.

RESULTS

Only 44.6% of available ICD-10-CM codes were used compared to 91.5% of available ICD-9-CM codes. Of the total codes used, 14.5% ICD-9-CM codes were unspecified, while 33.3% ICD-10-CM codes were unspecified.

DISCUSSION

Even though greater detail is available, a 108.5% increase in using unspecified codes with ICD-10-CM was found. The utilization data analyzed in this study does not support a rationale for the large increase in the number of codes in ICD-10-CM. New technologies and methods are likely needed to fully utilize detailed classification systems.

CONCLUSION

These results help evaluate the content needed in the United States national ICD standard. This analysis of codes in the current ICD standard is important for ICD-11 evaluation, implementation, and use.

Impact of cancer-related fatigue on quality of life in patients with cancer: multiple mediating roles of psychological coherence and stigma

OBJECTIVE

The goal of this current research was to explore the impact of cancer-related fatigue on the quality of life among patients with cancer, as well as the multiple mediating roles of psychological coherence and stigma.

METHODS

This study utilized a cross-sectional design. A questionnaire was administered between November 2022 and May 2023 to 364 patients with cancer in two tertiary hospitals in Jinzhou City, Liaoning Province, China. The questionnaires included the General Information Questionnaire, Cancer-Related Fatigue Questionnaire, Psychological Coherence Scale, Stigma Scale, and Quality of Life Questionnaire. SPSS 25.0 and PROCESS 3.5 macros were used for descriptive statistics and correlation analysis of the data, as well as multiple mediation effect tests.

RESULTS

Cancer-related fatigue directly affects quality of life (β = -0.950, 95% CI = -1.138 to -0.763) and indirectly through three mediators: psychological coherence (β = -0.172, 12.58% of total effect), stigma (β = -0.193, 14.12% of total effect), and both psychological coherence and stigma (β = -0.052, 3.80% of total effect), totaling a 30.50% mediating effect.

CONCLUSION

Overall, psychological coherence and stigma may play an important mediating role between cancer-related fatigue and quality of life in patients with cancer. This suggests that alleviating cancer-related fatigue, while enhancing psychological coherence and reducing stigma, could be effective strategies for improving patients' quality of life. Therefore, healthcare professionals and related professionals should pay attention to and adopt effective interventions to alleviate cancer-related fatigue, enhance psychological coherence, and reduce stigma, thereby contributing to the overall well-being and quality of life of patients with cancer.

Frequency of emergency medical service contacts after hospital admissions

Identifying frequent users of Emergency Medical Services (EMS) in the post-discharge period can potentially direct interventions to prevent deterioration at home. This study aimed to describe the frequency of post-discharge emergency phone calls within 30 days after common medical and surgical categories of hospital admission. A retrospective cohort study retrieved data from the electronic medical record and the EMS Capital Region Denmark database after approval by the Danish Health Data Authority. The study investigated the number of 30-day EMS calls per 1000 days alive outside hospital in patients hospitalized due to acute exacerbation of chronic obstructive pulmonary disease (AECOPD), colorectal surgery, and 18 other disease categories. We included 16,338 patients with a discharge from hospital between August 2021 and August 2022.The overall number of EMS calls was 4,263 with 9.1 (95% confidence interval (95% CI)): 8.8-9.4) calls per 1000 patient days within 30 days. Patients discharged after medical hospitalization due to AECOPD contacted EMS 15 (95% CI: 13-16) times per 1000 patient days only surpassed by sepsis with 19 calls per 1000 patient days (95% CI: 17-21). Patients undergoing colorectal surgery had an EMS call frequency of 7.5 (95% CI: 6.4-8.7) and highest among types of surgery was hip- and knee replacements with 12 (95% CI: 11-13) calls per 1000 patient days. Patients discharged after hospitalization due to AECOPD and sepsis had a higher 30-day EMS call frequency compared with other medical cohorts, whereas major orthopedic surgery was followed by more EMS calls than admissions for colorectal surgery.

Limited Changes in Red Blood Cell Parameters After Probiotic Supplementation in Depressive Individuals: Insights from a Secondary Analysis of the PRO-DEMET Randomized Controlled Trial

Background: Depression often coexists with anemia, potentially sharing common pathways, highlighting the need for treatments addressing both conditions simultaneously. This study evaluated the effect of probiotics on red blood cell (RBC) parameters in adults with depressive disorder. We hypothesized that probiotics would positively influence RBC parameters, potentially modulated by baseline inflammation or dietary intake, with improved RBC function correlating with better antidepressant outcomes. Methods: This secondary analysis of a two-arm, randomized, double-blind, controlled trial involved 116 adults with depressive disorder. Participants received a probiotic formulation containing Lactobacillus helveticus Rosell®-52 and Bifidobacterium longum Rosell®-175 or a placebo for 60 days. Data from 97 subjects were analyzed for RBC parameters, including hemoglobin (HGB), RBC count, hematocrit (HCT), mean corpuscular volume (MCV), mean hemoglobin concentration (MCH), mean corpuscular hemoglobin concentration (MCHC), and RBC distribution width (RDW). Results: Probiotic supplementation did not result in significant changes in RBC parameters compared to the placebo. However, probiotics may help stabilize HGB, HCT, MCH, and MCHC levels, potentially preventing fluctuations observed in the placebo group. Conclusions: While probiotics showed potential benefits for depressive symptoms, significant changes in RBC parameters were not observed. Larger studies are needed to clarify the mechanisms and clinical implications.

Selected plant extracts and female fertility: role in the regulation of the hypothalamo-pituitary-ovarian axis in normal and pathological conditions

Female infertility, which affects 10-20% of couples worldwide, is a growing health concern in developing countries. It can be caused by multiple factors, including reproductive disorders, hormonal dysfunctions, congenital malformations and infections. In vitro and in vivo studies have shown that plant extracts regulate gonadotropin-releasing hormone, kisspeptin, and gonadotropin expression and/or secretion at the hypothalamic-pituitary level and modulate somatic and germ cells, such as steroidogenesis, proliferation, apoptosis, and oxidative stress at the ovarian level. In this review, we report evidence for the role of certain plant extracts or plant bioactive compounds in the regulation of the hypothalamic-pituitary-ovary axis and, consequently, for the treatment of female infertility. We will also summarize their possible involvement in ovarian disorders such as polycystic ovary syndrome (PCOS), premature ovarian failure (POF) and ovarian cancers.

Barriers and facilitators for using administrative data for surveillance purpose: a narrative overview

Although administrative data are not originally intended for surveillance purposes, they are frequently used for monitoring public health and patient safety. This article provides a narrative overview of the barriers and facilitators for the use of administrative data for surveillance, with a focus on healthcare-associated infection (HAI) in Canada. In this case, only articles on administrative data in general or related to HAI were included. Validation study and meta-analyses on administrative data accuracy were excluded. Medline, Embase and Google Scholar were searched as well as references list of all included articles, for a total of 90 articles included. Our analysis identified 78 barriers at the individual, organizational and systemic levels and outlined 75 facilitators and solutions to improve administrative data utilization and quality. This narrative overview will help to understand barriers, facilitators and offer practical recommendations for optimizing the use of administrative data.

Comparison of the accuracy of inpatient morbidity coding with ICD-11 and ICD-10

BACKGROUND

One of the challenges when transitioning from International Statistical Classification of Diseases and Related Health Problems, 10th Revision (ICD-10) to International Classification of Diseases, 11th Revision (ICD-11) is to ensure clinical coding accuracy.

OBJECTIVE

To determine the accuracy of clinical coding with ICD-11 in comparison with ICD-10 and identify causes of coding errors in real clinical coding environments.

METHOD

The study was conducted prospectively in two general hospitals. Medical records of discharged inpatients were coded by hospital clinical coders with both ICD-11 and ICD-10 on different days. These medical records were recoded by five mentors. Codes assigned by mentors were used as the gold standard for the evaluation of accuracy.

RESULTS

The accuracy of ICD-10 and ICD-11 coding for 1578 and 2168 codes was evaluated. Coding accuracy was 89.1% and 74.2% for ICD-10 and ICD-11. In ICD-11, the lowest accuracy was observed in chapters 22 (injuries), 10 (ear) and 11 (circulatory) (51.1%, 53.8% and 62.7%, respectively). In both ICD-10 and ICD-11, the most important cause of the coding errors was clinical coders' mistakes (79.5% and 81.8% for ICD-10 and ICD-11, respectively).

CONCLUSION

Accuracy of clinical coding with ICD-11 was lower relative to ICD-10. Hence, it is essential to carry out initial preparations, particularly the training of clinical coders based on their needs, as well as the necessary interventions to enhance the documentation of medical records according to ICD-11 before or simultaneous with the country-wide implementation.

IMPLICATIONS

Clinical coders need complete training, especially in using extension codes and post-coordination coding. Local ICD-11 guidelines based on the needs of local users and reporting policies should be developed. Furthermore, documentation guidelines based on ICD-11 requirements should be developed.

Development of an AI model for predicting hypoxia status and prognosis in non-small cell lung cancer using multi-modal data

Background

Prognosis prediction is crucial for non-small cell lung cancer (NSCLC) treatment planning. While tumor hypoxia significantly impacts patient outcomes, identifying hypoxic genomic markers remains challenging. This study sought to identify hypoxic computed tomography (CT) radiomic features and create an artificial intelligence (AI) model for NSCLC through the integration of multi-modal data.

Methods

In total, 452 NSCLC patients were enrolled in this study, including patients from The Second Affiliated Hospital of Soochow University (SC, n=112), The Cancer Genome Atlas (TCGA)-NSCLC dataset (n=74), the radiogenomics dataset (n=130), and the Gene Expression Omnibus (GEO) datasets (GSE19188: n=82, and GSE87340: n=54). Hypoxia status was classified using optimized cut-off values of hypoxia enrichment scores, which were calculated through single-sample gene set enrichment analysis (ssGSEA) of hypoxic genes. Radiomic features were extracted using three-dimensional (3D)-Slicer software. The least absolute shrinkage and selection operator (LASSO) algorithm was used to identify hypoxic CT radiomic features. A model named ssuBERT (semantic structured unit embedded in Bidirectional Encoder Representations from Transformers) was developed to analyze electronic health records (EHRs). An AI model for overall survival prediction was constructed by integrating CT radiomic features, ssuBERT features, and clinical data, and evaluated using five-fold cross-validation.

Results

Higher hypoxia levels were correlated with worse survival outcomes. Twenty-eight radiomic features showed significant discriminatory power in detecting hypoxia status with an area under the curve (AUC) of 0.8295. The ssuBERT model achieved a weighted accuracy of 0.945 in recognizing semantic structured units in EHRs. The EHR model exhibited superior predictive performance among the single-modal models with an AUC of 0.7662. However, the multi-modal AI model had the highest average AUC of 0.8449 and an F1 score of 0.7557.

Conclusions

The AI model demonstrated potential in predicting NSCLC patient prognosis through multi-modal data integration, warranting further validation.

Comparative Evaluation of Temporomandibular Disorders and Dental Wear in Video Game Players

Background/Objectives: The increasing prevalence of video gaming has raised concerns about its potential impact on musculoskeletal health, particularly temporomandibular disorders (TMDs). This study aims to compare TMD symptoms, mandibular function, and dental wear between gamers and non-gamers among university students. Methods: An observational study included 108 students aged 20 to 23 years, divided into gamers (n = 48) and non-gamers (n = 60). Participants completed questionnaires assessing TMD symptoms, gaming habits, and screen time. Clinical examinations measured mandibular movements, palpation-induced pain, and dental wear using the Smith and Knight Tooth Wear Index. Statistical analyses included independent t-tests, chi-square tests, Pearson's correlations, and logistic regression. Seven comprehensive tables present the findings with p-values. Results: Gamers reported significantly higher screen time (Mean = 6.5 h/day) compared to non-gamers (Mean = 4.0 h/day; p < 0.001). Maximum unassisted mouth opening was greater in gamers (Mean = 48.31 mm) than in non-gamers (Mean = 46.33 mm; p = 0.04). Gamers exhibited a higher prevalence of pain on palpation of the masseter muscle (45.8% vs. 30.0%; p = 0.05). Dental wear scores were significantly higher in gamers for teeth 2.3 (upper left canine) and 3.3 (lower left canine) (p < 0.05). Positive correlations were found between hours spent gaming and maximum mouth opening (r = 0.25; p = 0.01) and dental wear (r = 0.30; p = 0.002). Logistic regression showed that gaming status significantly predicted the presence of TMD symptoms (Odds Ratio = 2.5; p = 0.03). Conclusions: Gamers exhibit greater mandibular opening, increased dental wear, and a higher prevalence of masticatory muscle pain compared to non-gamers. Prolonged gaming may contribute to altered mandibular function and increased risk of TMD symptoms. Further research is needed to explore underlying mechanisms and develop preventive strategies.

Effectiveness of the SYNCHRONIZE + Brief Intervention in Improving Mediterranean Diet Adherence, Nutritional Quality and Intake Pattern in Persons with Fibromyalgia and Chronic Fatigue Syndrome

BACKGROUND

Multidisciplinary lifestyle interventions are being researched to treat fibromyalgia. However, the impact of nutrition as a key treatment component is little studied. This study aimed to evaluate the effectiveness of the SYNCHRONIZE + lifestyle multidisciplinary intervention in improving adherence to the Mediterranean diet, nutrition quality and dietary intake pattern in persons with fibromyalgia and chronic fatigue syndrome.

METHODS

A pragmatic randomized clinical trial was conducted in primary care. Data were collected using the 17-item energy-restricted Mediterranean Adherence Screener (er-MEDAS), the food frequency questionnaire (sFFQ) and the 24 h recall questionnaire (24 HR), in addition to chrono-nutritional, anthropometric, and body composition data, at baseline and 3-, 6-, and 12- month follow-up visits, and statistically analyzed.

RESULTS

A total of 158 participants were evaluated. Results showed the effectiveness of the intervention in improving adherence to the Mediterranean diet. The adherence depended on the group-time interaction being positive and significant at 3 and 6 months post-intervention in the INT group and on the participant age and educational level. Specifically, the intake of legumes, fruits, vegetables, nuts and blue fish was increased, while the intake of sweets and pastries, butter and cream and red and processed meat was reduced. Furthermore, the intake of chips and candies was also reduced, and the consumption of fermented food (yogurts, cheese, kefir) increased. Thus, general diet quality improved. Interestingly, the intake of key nutrients such as protein and iron increased. Furthermore, the number of night eaters was decreased significantly. Muscle mass index was also improved in the intervention group. These results were maintained in the medium to long term.

CONCLUSION

SYNCHRONIZE + is a brief, low-cost, multidisciplinary intervention effective in improving adherence to the Mediterranean diet and improving nutritional and dietary intake patterns in persons with fibromyalgia and chronic fatigue syndrome. Further evaluation of the effect on quality of life and symptoms is needed.

[Referral Profile of Adult Patients Attended in the Neuropsychology Consultation at Hospital Universitario La Paz in Madrid from 2018 to 2023]

INTRODUCTION

Neuropsychology is an area of psychology that studies, through different methodologies, the relationship between the nervous system and behavior. This study aims to describe the profile of the patient who most frequently received a neuropsychological assessment at HULP between 2018 and 2023.

PATIENTS AND METHODS

This is a retrospective observational study of 394 clinical records of subjects aged 18 years or older who were attended in the neuropsychology consultation of HULP between 2018 and 2023. The data was recorded and anonymized in a database properly guarded. Frequency tables were extracted and χ2 tests were made. SPSS 26 was the programme used to make data analysis.

RESULTS

A total of 232 women (mean age of 46,47 years) and 162 men (mean age 43,31 years) were evaluated. The most frequent reason for consultation in both groups was suspicion of dementia (112 consultations for this reason in the female group; 76 in the male group). Regardless of the reason for consultation, there were more cases of patients who had studied up to the age of 18 years. Binomial analysis reveals a statistically significant relationship between being assessed in the neuropsychology consultation at HULP and having completed studies before the age of 18.

CONCLUSIONS

The profile of the patient who is most frequently attended at the neuropsychology consultation of HULP is a middle-aged woman with education up to 18 years of age, referred by the Mental Health Service for suspected dementia. The information obtained from this analysis will help to guide future planning of assessment strategies for patients with neuropsychological problems.

Understanding immune system dysfunction and its context in mood disorders: psychoneuroimmunoendocrinology and clinical interventions

Mood disorders include a set of psychiatric manifestations of increasing prevalence in our society, being mainly represented by major depressive disorder (MDD) and bipolar disorder (BD). The etiopathogenesis of mood disorders is extremely complex, with a wide spectrum of biological, psychological, and sociocultural factors being responsible for their appearance and development. In this sense, immune system dysfunction represents a key mechanism in the onset and pathophysiology of mood disorders, worsening mainly the central nervous system (neuroinflammation) and the periphery of the body (systemic inflammation). However, these alterations cannot be understood separately, but as part of a complex picture in which different factors and systems interact with each other. Psychoneuroimmunoendocrinology (PNIE) is the area responsible for studying the relationship between these elements and the impact of mind-body integration, placing the immune system as part of a whole. Thus, the dysfunction of the immune system is capable of influencing and activating different mechanisms that promote disruption of the psyche, damage to the nervous system, alterations to the endocrine and metabolic systems, and disruption of the microbiota and intestinal ecosystem, as well as of other organs and, in turn, all these mechanisms are responsible for inducing and enhancing the immune dysfunction. Similarly, the clinical approach to these patients is usually multidisciplinary, and the therapeutic arsenal includes different pharmacological (for example, antidepressants, antipsychotics, and lithium) and non-pharmacological (i.e., psychotherapy, lifestyle, and electroconvulsive therapy) treatments. These interventions also modulate the immune system and other elements of the PNIE in these patients, which may be interesting to understand the therapeutic success or failure of these approaches. In this sense, this review aims to delve into the relationship between immune dysfunction and mood disorders and their integration in the complex context of PNIE. Likewise, an attempt will be made to explore the effects on the immune system of different strategies available in the clinical approach to these patients, in order to identify the mechanisms described and their possible uses as biomarkers.

Neurobiology of attention-deficit hyperactivity disorder: historical challenges and emerging frontiers

Extensive investigations spanning multiple levels of inquiry, from genetic to behavioural studies, have sought to unravel the mechanistic foundations of attention-deficit hyperactivity disorder (ADHD), with the aspiration of developing efficacious treatments for this condition. Despite these efforts, the pathogenesis of ADHD remains elusive. In this Review, we reflect on what has been learned about ADHD while also providing a framework that may serve as a roadmap for future investigations. We emphasize that ADHD is a highly heterogeneous disorder with multiple aetiologies that necessitates a multifactorial dimensional phenotype, rather than a fixed dichotomous conceptualization. We highlight new findings that suggest a more brain-wide, 'global' view of the disorder, rather than the traditional localizationist framework, which asserts that a limited set of brain regions or networks underlie ADHD. Last, we underscore how underpowered studies that have aimed to associate neurobiology with ADHD phenotypes have long precluded the field from making progress. However, a new age of ADHD research with refined phenotypes, advanced methods, creative study designs and adequately powered investigations is beginning to put the field on a good footing. Indeed, the field is at a promising juncture to advance the neurobiological understanding of ADHD and fulfil the promise of clinical utility.

Early trajectories and moderators of autistic language profiles: A longitudinal study in preschoolers

LAY ABSTRACT

Language development can greatly vary among autistic children. Children who struggle with language acquisition often face many challenges and experience lower quality of life. However, little is known about the early language trajectories of autistic preschoolers and their moderators. Autistic language can be stratified into three profiles. Language unimpaired experience little to no language difficulties; language impaired show significant difficulties in language; minimally verbal never develop functional language. In this study, we used a longitudinal sample of preschoolers with autism and with typical development (aged 1.5-5.7 years). We replicated the three language profiles through a data-driven approach. We also found that different factors modulated the language outcome within each group. For instance, non-verbal cognition at age 2.4 moderated the participants' attribution to each language profile. Moreover, early intervention moderated verbal outcome in the language impaired profile. In conclusion, we provided a detailed description of how autistic preschoolers acquire language, and what factors might influence their trajectories. Our findings could inspire more personalized intervention for early autistic language difficulties.

Purposeful design in health professions' curriculum development

BACKGROUND

The content and delivery of a health professions' curriculum is often regarded as 'intuitive' and 'self-defining', based on commonly encountered professional roles, detailed knowledge of human structure, function and pathology, and opportunities to acquire necessary clinical and communication skills.  However, a curriculum tends to develop, sometimes in unexpected ways, due to scientific advances, changes in teaching faculty and variations in clinical placement experience. Trends come and go, often without careful scrutiny and evaluation. A common result is curriculum 'creep', where learning may drift away from the original plan.

METHODS

A review of available curriculum models produced several descriptions that appear to be based primarily on professional norms and traditions and not linked strongly to learning theories or expectations of employers and the community. Regulators accept variety in curriculua so long as agreed outcomes are achieved. Unless planned and maintained carefully, a curriculum may not necessarily prepare graduates well for a.

RESULTS AND DISCUSSION

Health professions curricula are required to produce graduates not only with higher education qualifications but also capable of providing healthcare services needed by regulators, employers and their communities. The design includes curriculum content, curriculum delivery and assessment of learning, topics often listed separately in standards but in fact closely intertwined, ideally demonstrating constructive alignment that sends consistent messages and facilitates achievement of graduate outcomes. Purposeful design is a systematic approach to defining, developing, and assessing learning that produces competent graduates who will maintain currency throughout their careers. Just as with the human body, everything is connected so a change anywhere is likely to have implications for other parts of the curriculum ecosystem. The role of clinical cases, level of integration, balance of assessment tasks, use of technology, on-site or remote delivery, and choice of assessment methods are important considerations in curriculum design. Any changes should be strategic and aligned with vision, mission, and graduate outcomes. This paper provides a framework analogous to the human body that may facilitate purposeful design of health professions curricula.

Demystifying environmental health-related diseases: Using ICD codes to facilitate environmental health clinical referrals

Background: The burden of disease of Aboriginal and Torres Strait Islander people is estimated as 2.3 times that of the broader Australian population, with between 30% and 50% of health inequalities attributable to poor environmental health. Objective: Although many Australian states and territories have clinical policy initiatives that seek to reduce the burden of preventable disease in this population, including field-based environmental health clinical referrals (EHCRs), there is little consistency across the jurisdictions, resulting in less potential to break the cycle of recurrent diseases within the home environment. Method and Results: This study addresses this inconsistency by recommending recognition and categorisation of environmental health risks to allow for accurate diagnosis and comparability across health services and locations by using the International Statistical Classification of Diseases and Related Health Problems (ICD) system, already in use in hospitals. Conclusion and Implications: Developing a list of mutually agreed environmental health attributable diseases for the EHCR process using assigned ICD-10-AM codes would influence the provision of primary care to include recognition of the impact of environmental health conditions and allow environmental health staff to provide a response and education at both community and household levels to break disease cycles.

A Method for Detecting Depression in Adolescence Based on an Affective Brain-Computer Interface and Resting-State Electroencephalogram Signals

Depression is increasingly prevalent among adolescents and can profoundly impact their lives. However, the early detection of depression is often hindered by the time-consuming diagnostic process and the absence of objective biomarkers. In this study, we propose a novel approach for depression detection based on an affective brain-computer interface (aBCI) and the resting-state electroencephalogram (EEG). By fusing EEG features associated with both emotional and resting states, our method captures comprehensive depression-related information. The final depression detection model, derived through decision fusion with multiple independent models, further enhances detection efficacy. Our experiments involved 40 adolescents with depression and 40 matched controls. The proposed model achieved an accuracy of 86.54% on cross-validation and 88.20% on the independent test set, demonstrating the efficiency of multimodal fusion. In addition, further analysis revealed distinct brain activity patterns between the two groups across different modalities. These findings hold promise for new directions in depression detection and intervention.

Efficacy and safety of acupuncture for pain relief: a systematic review and meta-analysis

OBJECTIVE

This study aims to evaluate the efficacy and safety of traditional acupuncture for pain relief based on rigorously designed RCTs with double-blind. The findings seek to provide valuable insights for clinical practice and inform future research.

METHODS

A literature search was conducted in PubMed, Web of Science, Cochrane Library, and Embase databases for randomized controlled trials on traditional acupuncture for pain management using a double-blind design, published from database inception to November 22, 2023. The Risk of Bias 2 (RoB2) tool was used to assess potential biases in the included studies, followed by a comprehensive analysis to evaluate efficacy and safety.

RESULTS

The findings show a significant positive effect on pain improvement, evidenced by changes in visual analog scale scores (mean difference 0.97 [95% confidence interval (CI) 0.66-1.27]). Safety analysis showed no significant differences in adverse reactions between the acupuncture and control groups (relative risk 1.40 [95% CI 0.52-3.74]), with no serious adverse effects reported.

CONCLUSION

Traditional acupuncture is effective and safe in pain management. This suggests that acupuncture can be a valuable approach in clinical practice. Future studies should explore optimal treatment durations and frequency, using larger sample sizes for more comprehensive insights.

Combating Antimicrobial Resistance Through a Data-Driven Approach to Optimize Antibiotic Use and Improve Patient Outcomes: Protocol for a Mixed Methods Study

BACKGROUND

It is projected that drug-resistant infections will lead to 10 million deaths annually by 2050 if left unabated. Despite this threat, surveillance data from resource-limited settings are scarce and often lack antimicrobial resistance (AMR)-related clinical outcomes and economic burden. We aim to build an AMR and antimicrobial use (AMU) data warehouse, describe the trends of resistance and antibiotic use, determine the economic burden of AMR in Uganda, and develop a machine learning algorithm to predict AMR-related clinical outcomes.

OBJECTIVE

The overall objective of the study is to use data-driven approaches to optimize antibiotic use and combat antimicrobial-resistant infections in Uganda. We aim to (1) build a dynamic AMR and antimicrobial use and consumption (AMUC) data warehouse to support research in AMR and AMUC to inform AMR-related interventions and public health policy, (2) evaluate the trends in AMR and antibiotic use based on annual antibiotic and point prevalence survey data collected at 9 regional referral hospitals over a 5-year period, (3) develop a machine learning model to predict the clinical outcomes of patients with bacterial infectious syndromes due to drug-resistant pathogens, and (4) estimate the annual economic burden of AMR in Uganda using the cost-of-illness approach.

METHODS

We will conduct a study involving data curation, machine learning-based modeling, and cost-of-illness analysis using AMR and AMU data abstracted from procurement, human resources, and clinical records of patients with bacterial infectious syndromes at 9 regional referral hospitals in Uganda collected between 2018 and 2026. We will use data curation procedures, FLAIR (Findable, Linkable, Accessible, Interactable and Repeatable) principles, and role-based access control to build a robust and dynamic AMR and AMU data warehouse. We will also apply machine learning algorithms to model AMR-related clinical outcomes, advanced statistical analysis to study AMR and AMU trends, and cost-of-illness analysis to determine the AMR-related economic burden.

RESULTS

The study received funding from the Wellcome Trust through the Centers for Antimicrobial Optimisation Network (CAMO-Net) in April 2023. As of October 28, 2024, we completed data warehouse development, which is now under testing; completed data curation of the historical Fleming Fund surveillance data (2020-2023); and collected retrospective AMR records for 599 patients that contained clinical outcomes and cost-of-illness economic burden data across 9 surveillance sites for objectives 3 and 4, respectively.

CONCLUSIONS

The data warehouse will promote access to rich and interlinked AMR and AMU data sets to answer AMR program and research questions using a wide evidence base. The AMR-related clinical outcomes model and cost data will facilitate improvement in the clinical management of AMR patients and guide resource allocation to support AMR surveillance and interventions.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

PRR1-10.2196/58116.

The Right ICD Code, Right Now: A Call to Action for Pragmatic Language Disorders After Right Hemisphere Stroke

PURPOSE

Diagnosis of language impairments after stroke is important to optimizing stroke outcomes. After right hemisphere brain damage (RHD), apragmatism can impact the comprehension and production of pragmatic language. However, despite decades of empirical evidence, there is no International Classification of Diseases (ICD) code for RHD pragmatic language impairments. The absence of an ICD code has far reaching ramifications that impact patient outcomes, including reduced clinical and public awareness, limited curricular content, and underdiagnosis. This viewpoint justifies the need to appropriately classify the pragmatic language symptomology after RHD with an ICD code.

CONCLUSION

An ICD code can positively influence health care practitioner knowledge, education, and practice while informing public health considerations vital to epidemiological analyses.

MedConceptsQA: Open source medical concepts QA benchmark

BACKGROUND

Clinical data often includes both standardized medical codes and natural language texts. This highlights the need for Clinical Large Language Models to understand these codes and their differences. We introduce a benchmark for evaluating the understanding of medical codes by various Large Language Models.

METHODS

We present MedConceptsQA, a dedicated open source benchmark for medical concepts question answering. The benchmark comprises of questions of various medical concepts across different vocabularies: diagnoses, procedures, and drugs. The questions are categorized into three levels of difficulty: easy, medium, and hard. We conduct evaluations of the benchmark using various Large Language Models.

RESULTS

Our findings show that most of the pre-trained clinical Large Language Models achieved accuracy levels close to random guessing on this benchmark, despite being pre-trained on medical data. However, GPT-4 achieves an absolute average improvement of 9-11% (9% for few-shot learning and 11% for zero-shot learning) compared to Llama3-OpenBioLLM-70B, the clinical Large Language Model that achieved the best results.

CONCLUSION

Our benchmark serves as a valuable resource for evaluating the abilities of Large Language Models to interpret medical codes and distinguish between medical concepts. We demonstrate that most of the current state-of-the-art clinical Large Language Models achieve random guess performance, whereas GPT-3.5, GPT-4, and Llama3-70B outperform these clinical models, despite their primary focus during pre-training not being on the medical domain. Our benchmark is available at https://huggingface.co/datasets/ofir408/MedConceptsQA.

[Tanslated article] Burnout Syndrome, Anxiety, and Depression in Dermatology Residents: A Cross-Sectional Study

BACKGROUND AND OBJECTIVE

Burnout syndrome is a mental health disorder due to chronic occupational stress. Both burnout and associated comorbidities are prevalent among health care professionals, being medical residents a vulnerable group. Despite this, the scientific medical literature currently available on this issue in dermatology residents is scarce. The aim of this study was to analyze the prevalence of the burnout syndrome, anxiety, and depression in dermatology residents, and the associated risk factors.

MATERIAL AND METHOD

This was a cross-sectional trial designed to include dermatology residents from Spain (from December 2022 through June 2023). A self-administered form was sent via online messaging applications, including validated scales to study professional quality of life, burnout syndrome, anxiety, and depression.

RESULTS

A total of 48 dermatology residents were included in the study, 50% of whom (24/48) were women, with a mean age of 27 years (1.25). A total of 58.33% (28/48) of the residents had some degree of anxiety, 22.9% (11/48) some degree of depression, and 23.4% a moderate risk of burnout (11/48). Workload was the main risk factor associated with the 3 disorders studied, while managerial support or intrinsic motivation seem to play a protective role.

CONCLUSIONS

Burnout syndrome and its comorbidities are both prevalent in dermatology residents in Spain and closely related to each other.

An open-source framework for end-to-end analysis of electronic health record data

With progressive digitalization of healthcare systems worldwide, large-scale collection of electronic health records (EHRs) has become commonplace. However, an extensible framework for comprehensive exploratory analysis that accounts for data heterogeneity is missing. Here we introduce ehrapy, a modular open-source Python framework designed for exploratory analysis of heterogeneous epidemiology and EHR data. ehrapy incorporates a series of analytical steps, from data extraction and quality control to the generation of low-dimensional representations. Complemented by rich statistical modules, ehrapy facilitates associating patients with disease states, differential comparison between patient clusters, survival analysis, trajectory inference, causal inference and more. Leveraging ontologies, ehrapy further enables data sharing and training EHR deep learning models, paving the way for foundational models in biomedical research. We demonstrate ehrapy's features in six distinct examples. We applied ehrapy to stratify patients affected by unspecified pneumonia into finer-grained phenotypes. Furthermore, we reveal biomarkers for significant differences in survival among these groups. Additionally, we quantify medication-class effects of pneumonia medications on length of stay. We further leveraged ehrapy to analyze cardiovascular risks across different data modalities. We reconstructed disease state trajectories in patients with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) based on imaging data. Finally, we conducted a case study to demonstrate how ehrapy can detect and mitigate biases in EHR data. ehrapy, thus, provides a framework that we envision will standardize analysis pipelines on EHR data and serve as a cornerstone for the community.

Burnout Syndrome, Anxiety, and Depression in Dermatology Residents: A Cross-Sectional Study

BACKGROUND AND OBJECTIVE

Burnout syndrome is a mental health disorder due to chronic occupational stress. Both burnout and associated comorbidities are prevalent among health care professionals, being medical residents a vulnerable group. Despite this, the scientific medical literature currently available on this issue in dermatology residents is scarce. The aim of this study was to analyze the prevalence of the burnout syndrome, anxiety, and depression in dermatology residents, and the associated risk factors.

MATERIAL AND METHOD

This was a cross-sectional trial designed to include dermatology residents from Spain (from December 2022 through June 2023). A self-administered form was sent via online messaging applications, including validated scales to study professional quality of life, burnout syndrome, anxiety, and depression.

RESULTS

A total of 48 dermatology residents were included in the study, 50% of whom (24/48) were women, with a mean age of 27 years (1.25). A total of 58.33% (28/48) of the residents had some degree of anxiety, 22.9% (11/48) some degree of depression, and 23.4% a moderate risk of burnout (11/48). Workload was the main risk factor associated with the 3 disorders studied, while managerial support or intrinsic motivation seem to play a protective role.

CONCLUSIONS

Burnout syndrome and its comorbidities are both prevalent in dermatology residents in Spain and closely related to each other.

A comparison of demographic profiles, clinical profile, course, and outcome of Bipolar I Disorder and Bipolar II Disorder: Findings from the Bipolar Disorder Course and Outcome study from India (BiD-CoIN study)

Background

There is lack of data on bipolar disorder (BD) type II from India.

Aim

To compare the demographic and clinical characteristics of patients with BD-I and BD-II using the data of the Bipolar Disorder Course and Outcome study from India (BiD-CoIN study).

Methodology

Using the data of the BiD-CoIN study, patients with BD-I and BD-II were compared for demographic and clinical variables.

Results

Out of the 773 patients, 59 (7.63%) participants had BD-II. Compared to BD-I, patients with BD-II had a higher income; were more often unemployed or housewives; had a higher mean number of episodes per year of illness, higher severity of depressive episodes, higher depressive affective morbidity, and a higher number of hypomanic episodes (in the lifetime, and number of episodes per year of illness); received lower doses of lithium and a lower number of medications; and had lower prevalence of alcohol dependence, higher prevalence of seasonality, a high proportion of them sought faith healing treatment, and a higher proportion of them have predominant depressive polarity.

Conclusions

Patients with BD-I and BD-II differ from each other on certain demographic and clinical variables. The difference in the clinical variables suggests that the patients with BD-II may require different treatment approaches for management.

An examination of relationships between transdiagnostic psychological processes and mental health disorders in athletes

INTRODUCTION

The aims of this study were (a) to identify links between transdiagnostic psychological processes and mental health disorder (MHD) symptoms, and (b) to examine differences in MHD and transdiagnostic psychological processes as a function of demographic variables, including, gender, status as an athlete (elite vs. non-elite), number of training sessions per week, previous severe injuries, and use of medical care following severe injuries.

METHODS

A total of 159 competitive athletes aged between 18 and 40 years old (44 % female; mean age = 24.20 ± 4.88 years) participated in this cross-sectional study. Participants completed a demographic questionnaire along with validated questionnaires evaluating MHD symptoms and transdiagnostic processes including: motivation to practice sport, emotional competencies, self-efficacy, repetitive negative thinking, meta-cognitive beliefs, and impulsive and perfectionist behaviors.

RESULTS

MHD symptoms were positively correlated with controlled forms of motivation, repetitive negative thinking, meta-cognitive beliefs, and impulsive and perfectionistic behaviors; and negatively correlated with emotional competencies and self-efficacy. Gender differences showed that women were more likely to experience MHD, higher levels of repetitive negative thinking, meta-cognitive beliefs, and lower levels of emotional competencies and self-efficacy than men. Finally, elite athletes showed higher personal standards than their non-elite counterparts and those training more than four times per week showed significantly higher perfectionistic behaviors.

DISCUSSION

These results are in line with previous findings in clinical psychology and shed light on the role of transdiagnostic processes and the risk of MHD in a sample of French athletes. Further research on the identification of key risk factors for MHD in competitive athletes is needed.

The relationship between teacher-student relationships and academic grades among Chinese rural high school students: the moderating role of mental health symptoms and the conditional moderating effect of academic resilience

Objective

This study examines the relationship between Teacher-Student Relationships and academic grades among Chinese rural high school students, focusing on the moderating role of mental health symptoms and the conditional moderating effect of academic resilience.

Method

A moderated moderation analysis was conducted via Mplus on data collected from a sample of rural Chinese high school students. SEM was used to test the direct and interactive effects of these variables on academic outcomes.

Results

Teacher-Student Relationships were found to have a significant positive association with students' academic grades. Academic resilience plays a conditional moderating role, with students who have higher levels of resilience better able to maintain their academic performance, even when facing psychological distress. This suggests that resilience can buffer the impact of challenges, enhancing the positive influence of TSRs on academic outcomes.

Advancing healthcare through data: the BETTER project's vision for distributed analytics

Introduction

Data-driven medicine is essential for enhancing the accessibility and quality of the healthcare system. The availability of data plays a crucial role in achieving this goal.

Methods

We propose implementing a robust data infrastructure of FAIRification and data fusion for clinical, genomic, and imaging data. This will be embedded within the framework of a distributed analytics platform for healthcare data analysis, utilizing the Personal Health Train paradigm.

Results

This infrastructure will ensure the findability, accessibility, interoperability, and reusability of data, metadata, and results among multiple medical centers participating in the BETTER Horizon Europe project. The project focuses on studying rare diseases, such as intellectual disability and inherited retinal dystrophies.

Conclusion

The anticipated impacts will benefit a wide range of healthcare practitioners and potentially influence health policymakers.

The mechanism and potential therapeutic target of piezo channels in pain

Pain is a common symptom of many clinical diseases; it adversely affects patients' physical and mental health, reduces their quality of life, and heavily burdens patients and society. Pain treatment is one of the most difficult problems today. There is an urgent need to explore the potential factors involved in the pathogenesis of pain to improve its diagnosis and treatment rate. Piezo1/2, a newly identified mechanosensitive ion channel opens in response to mechanical stimuli and plays a critical role in regulating pain-related diseases. Inhibition or downregulation of Piezo1/2 alleviates disease-induced pain. Therefore, in this study, we comprehensively discussed the biology of this gene, focusing on its potential relevance in pain-related diseases, and explored the pharmacological effects of drugs using this gene for the treatment of pain.

Diagnostic Utility of Whole Genome Sequencing After Negative Karyotyping/Chromosomal Microarray in Infants Born With Multiple Congenital Anomalies

BACKGROUND

Achieving a definitive genetic diagnosis of unexplained multiple congenital anomalies (MCAs) in neonatal intensive care units (NICUs) infants is challenging because of the limited diagnostic capabilities of conventional genetic tests. Although the implementation of whole genome sequencing (WGS) has commenced for diagnosing MCAs, due to constraints in resources and faculty, many NICUs continue to utilize chromosomal microarray (CMA) and/or karyotyping as the initial diagnostic approach. We aimed to evaluate the diagnostic efficacy of WGS in infants with MCAs who have received negative results from karyotyping and/or CMA.

METHODS

In this prospective study, we enrolled 80 infants with MCAs who were admitted to a NICU at a single center and had received negative results from CMA and/or karyotyping. The phenotypic characteristics were classified according to the International Classification of Diseases and the Human Phenotype Ontology. We assessed the diagnostic yield of trio-WGS in infants with normal chromosomal result and explored the process of diagnosing by analyzing both phenotype and genotype. Also, we compared the phenotype and clinical outcomes between the groups diagnosed with WGS and the undiagnosed group.

RESULTS

The diagnostic yield of WGS was 26% (21/80), of which 76% were novel variants. There was a higher diagnostic yield in cases of craniofacial abnormalities, including those of the eye and ear, and a lower diagnostic yield in cases of gastrointestinal and genitourinary abnormalities. In addition, higher rates of rehabilitation therapy and gastrostomy were observed in WGS-diagnosed infants than in undiagnosed infants.

CONCLUSION

This prospective cohort study assessed the usefulness of trio-WGS following chromosomal analysis for diagnosing MCAs in the NICU and revealed improvements in the diagnostic yield and clinical utility of WGS.

The Role of Secreted Frizzled-Related Protein 5 (Sfrp5) in Overweight and Obesity in Childhood and Adolescence

Background/Objective: Secreted frizzled-related protein 5 (Sfrp5) is an anti-inflammatory adipokine that has been implicated in the pathophysiology of obesity and its metabolic complications. Despite the fact that numerous studies have been carried out in adults, limited data on Sfrp5 exist for youth, especially in relation to overweight and obesity. Methods: In our study, we assessed the concentrations of Sfrp5, total oxidative (TOS) and antioxidative (TAS) status, high-sensitivity C-reactive protein (hs-CRP), and several cytokines (IL-1α, IL-1β, IL-2, IL-6, IL-8, IL-12, TNF-α) in 120 children and adolescents (mean age ± SE: 11.48 ± 0.25 years; 48 prepubertal, 72 pubertal; 74 males and 46 females) before and 1 year after the implementation of a personalized, structured, lifestyle intervention program of healthy diet, sleep, and physical exercise. Results: Based on the body mass index (BMI), participants were categorized as having morbid obesity (n = 63, 52.5%), obesity (n = 21, 17.5%), overweight (n = 22, 18.33%), or normal BMIs (n = 14, 11.67%), based on the International Obesity Task Force (IOTF) cut-off points. Following the 1-year lifestyle intervention program, a significant improvement in anthropometric measurements (BMI, BMI-z score, diastolic blood pressure, WHR, and WHtR), body-composition parameters, hepatic enzymes, lipid profile, inflammation markers, and the insulin-sensitivity profile (HbA1C, HOMA index) was observed in all subjects. Sfrp5 decreased in subjects with obesity (p < 0.01); however, it increased significantly (p < 0.05) in patients with morbid obesity. Linear regression analysis indicates that TNF-α and systolic blood pressure were the best positive predictors and hs-CRP was the best negative predictor for Sfpr5 concentration at initial assessment and glucose concentration for ΔSfrp5, while TNF-α and TAS were the best positive predictors for Sfpr5 concentration at annual assessment. Conclusions: These results indicate that Sfrp5 is associated with severe obesity and is increased following weight loss in children and adolescents with morbid obesity. It is also related to metabolic homeostasis, as well as inflammation and oxidative status.

Current state of data stewardship tools in life science

In today's data-centric landscape, effective data stewardship is critical for facilitating scientific research and innovation. This article provides an overview of essential tools and frameworks for modern data stewardship practices. Over 300 tools were analyzed in this study, assessing their utility, relevance to data stewardship, and applicability within the life sciences domain.

The Diagnostic Ability of GPT-3.5 and GPT-4.0 in Surgery: Comparative Analysis

BACKGROUND

ChatGPT (OpenAI) has shown great potential in clinical diagnosis and could become an excellent auxiliary tool in clinical practice. This study investigates and evaluates ChatGPT in diagnostic capabilities by comparing the performance of GPT-3.5 and GPT-4.0 across model iterations.

OBJECTIVE

This study aims to evaluate the precise diagnostic ability of GPT-3.5 and GPT-4.0 for colon cancer and its potential as an auxiliary diagnostic tool for surgeons and compare the diagnostic accuracy rates between GTP-3.5 and GPT-4.0. We precisely assess the accuracy of primary and secondary diagnoses and analyze the causes of misdiagnoses in GPT-3.5 and GPT-4.0 according to 7 categories: patient histories, symptoms, physical signs, laboratory examinations, imaging examinations, pathological examinations, and intraoperative findings.

METHODS

We retrieved 316 case reports for intestinal cancer from the Chinese Medical Association Publishing House database, of which 286 cases were deemed valid after data cleansing. The cases were translated from Mandarin to English and then input into GPT-3.5 and GPT-4.0 using a simple, direct prompt to elicit primary and secondary diagnoses. We conducted a comparative study to evaluate the diagnostic accuracy of GPT-4.0 and GPT-3.5. Three senior surgeons from the General Surgery Department, specializing in Colorectal Surgery, assessed the diagnostic information at the Chinese PLA (People's Liberation Army) General Hospital. The accuracy of primary and secondary diagnoses was scored based on predefined criteria. Additionally, we analyzed and compared the causes of misdiagnoses in both models according to 7 categories: patient histories, symptoms, physical signs, laboratory examinations, imaging examinations, pathological examinations, and intraoperative findings.

RESULTS

Out of 286 cases, GPT-4.0 and GPT-3.5 both demonstrated high diagnostic accuracy for primary diagnoses, but the accuracy rates of GPT-4.0 were significantly higher than GPT-3.5 (mean 0.972, SD 0.137 vs mean 0.855, SD 0.335; t285=5.753; P<.001). For secondary diagnoses, the accuracy rates of GPT-4.0 were also significantly higher than GPT-3.5 (mean 0.908, SD 0.159 vs mean 0.617, SD 0.349; t285=-7.727; P<.001). GPT-3.5 showed limitations in processing patient history, symptom presentation, laboratory tests, and imaging data. While GPT-4.0 improved upon GPT-3.5, it still has limitations in identifying symptoms and laboratory test data. For both primary and secondary diagnoses, there was no significant difference in accuracy related to age, gender, or system group between GPT-4.0 and GPT-3.5.

CONCLUSIONS

This study demonstrates that ChatGPT, particularly GPT-4.0, possesses significant diagnostic potential, with GPT-4.0 exhibiting higher accuracy than GPT-3.5. However, GPT-4.0 still has limitations, particularly in recognizing patient symptoms and laboratory data, indicating a need for more research in real-world clinical settings to enhance its diagnostic capabilities.

Advancing Post-Stroke Depression Research: Insights from Murine Models and Behavioral Analyses

Post-stroke depression (PSD) represents a significant neuropsychiatric complication that affects between 39% and 52% of stroke survivors, leading to impaired recovery, decreased quality of life, and increased mortality. This comprehensive review synthesizes our current knowledge of PSD, encompassing its epidemiology, risk factors, underlying neurochemical mechanisms, and the existing tools for preclinical investigation, including animal models and behavioral analyses. Despite the high prevalence and severe impact of PSD, challenges persist in accurately modeling its complex symptomatology in preclinical settings, underscoring the need for robust and valid animal models to better understand and treat PSD. This review also highlights the multidimensional nature of PSD, where both biological and psychosocial factors interplay to influence its onset and course. Further, we examine the efficacy and limitations of the current animal models in mimicking the human PSD condition, along with behavioral tests used to evaluate depressive-like behaviors in rodents. This review also sets a new precedent by integrating the latest findings across multidisciplinary studies, thereby offering a unique and comprehensive perspective of existing knowledge. Finally, the development of more sophisticated models that closely replicate the clinical features of PSD is crucial in order to advance translational research and facilitate the discovery of future effective therapies.

NCI's Proteomic Data Commons: A Cloud-Based Proteomics Repository Empowering Comprehensive Cancer Analysis through Cross-Referencing with Genomic and Imaging Data

Proteomics has emerged as a powerful tool for studying cancer biology, developing diagnostics, and therapies. With the continuous improvement and widespread availability of high-throughput proteomic technologies, the generation of large-scale proteomic data has become more common in cancer research, and there is a growing need for resources that support the sharing and integration of multi-omics datasets. Such datasets require extensive metadata including clinical, biospecimen, and experimental and workflow annotations that are crucial for data interpretation and reanalysis. The need to integrate, analyze, and share these data has led to the development of NCI's Proteomic Data Commons (PDC), accessible at https://pdc.cancer.gov. As a specialized repository within the NCI Cancer Research Data Commons (CRDC), PDC enables researchers to locate and analyze proteomic data from various cancer types and connect with genomic and imaging data available for the same samples in other CRDC nodes. Presently, PDC houses annotated data from more than 160 datasets across 19 cancer types, generated by several large-scale cancer research programs with cohort sizes exceeding 100 samples (tumor and associated normal when available). In this article, we review the current state of PDC in cancer research, discuss the opportunities and challenges associated with data sharing in proteomics, and propose future directions for the resource.

SIGNIFICANCE

The Proteomic Data Commons (PDC) plays a crucial role in advancing cancer research by providing a centralized repository of high-quality cancer proteomic data, enriched with extensive clinical annotations. By integrating and cross-referencing with complementary genomic and imaging data, the PDC facilitates multi-omics analyses, driving comprehensive insights, and accelerating discoveries across various cancer types.

Manual Therapy Improves Fibromyalgia Symptoms by Downregulating SIK1

Fibromyalgia (FM), classified by ICD-11 with code MG30.0, is a chronic debilitating disease characterized by widespread pain, fatigue, cognitive impairment, sleep, and intestinal alterations, among others. FM affects a large proportion of the worldwide population, with increased prevalence among women. The lack of understanding of its etiology and pathophysiology hampers the development of effective treatments. Our group had developed a manual therapy (MT) pressure-controlled custom manual protocol on FM showing hyperalgesia/allodynia, fatigue, and patient's quality of life benefits in a cohort of 38 FM cases (NCT04174300). With the aim of understanding the therapeutic molecular mechanisms triggered by MT, this study interrogated Peripheral Blood Mononuclear Cell (PBMC) transcriptomes from FM participants in this clinical trial using whole RNA sequencing (RNAseq) and reverse transcription followed by quantitative Polymerase Chain Reaction (RT-qPCR) technologies. The results show that the salt-induced kinase SIK1 gene was consistently downregulated by MT in FM, correlating with improvement of patient symptoms. In addition, this study compared the findings in a non-FM control cohort subjected to the same MT protocol, evidencing that those changes in SIK1 expression with MT only occurred in individuals with FM. This positions SIK1 as a potential biomarker to monitor response to MT and as a therapeutic target of FM, which will be further explored by continuation studies.

Prevalence of burnout among physiotherapists: a systematic review and meta-analysis

BACKGROUND

Work-related burnout is a significant concern amongst healthcare professionals, including physiotherapists. It can negatively impact on both staff well-being and the quality of care delivered to patients.

OBJECTIVES

To estimate the prevalence of burnout among physiotherapists.

DATA SOURCES

PubMed, CINAHL, Web of Science, Embase, Scopus and PsycINFO, from inception to February 1st, 2022.

STUDY SELECTION OR ELIGIBILITY CRITERIA

Studies reporting burnout prevalence among physiotherapists.

DATA EXTRACTION AND DATA SYNTHESIS

Prevalence of burnout. Sub-analyses were performed grouping studies based on countries where surveys were conducted, classified as developed or developing countries. The risk of bias was assessed using a modified version of the Newcastle-Ottawa Scale.

RESULTS

32 studies were included in the systematic review and 31 in the meta-analysis, enrolling a total of 5984 physiotherapists from 17 countries. Pooled prevalence (95% confidence interval) of burnout was 8% (4-15). Prevalence figures for Maslach Burnout Inventory dimensions were: (i) emotional exhaustion, 27% (21-34) (ii) depersonalization, 23% (15-32) (iii) low personal accomplishment, 25% (15-40). Both overall and single components prevalence was higher, although not significantly, in studies from developing than in developed countries.

LIMITATIONS

Tools used to assess burnout and cut-off scores chosen to identify the burnout prevalence differed across studies.

CONCLUSION AND IMPLICATIONS OF KEY FINDINGS

Prevalence of burnout reported by physiotherapists appears high worldwide, in particular in developing countries, and compares with that reported by nurses and physicians. Substantial heterogeneity in the prevalence of burnout, in its definition and assessment methods across studies, and limited quality of most studies precludes drawing definitive conclusions.

SYSTEMATIC REVIEW REGISTRATION NUMBER

PROSPERO CRD42022307876 CONTRIBUTION OF THE PAPER.

Association Between COVID-19 and Neurological Diseases: Evidence from Large-Scale Mendelian Randomization Analysis and Single-Cell RNA Sequencing Analysis

Observational studies have suggested that SARS-CoV-2 infection increases the risk of neurological diseases, but it remains unclear whether the association is causal. The present study aims to evaluate the causal relationships between SARS-CoV-2 infections and neurological diseases and analyzes the potential routes of SARS-CoV-2 entry at the cellular level. We performed Mendelian randomization (MR) analysis with CAUSE method to investigate causal relationship of SARS-CoV-2 infections with neurological diseases. Then, we conducted single-cell RNA sequencing (scRNA-seq) analysis to obtain evidence of potential neuroinvasion routes by measuring SARS-CoV-2 receptor expression in specific cell subtypes. Fast gene set enrichment analysis (fGSEA) was further performed to assess the pathogenesis of related diseases. The results showed that the COVID-19 is causally associated with manic (delta_elpd, - 0.1300, Z-score: - 2.4; P = 0.0082) and epilepsy (delta_elpd: - 2.20, Z-score: - 1.80; P = 0.038). However, no significant effects were observed for COVID-19 on other traits. Moreover, there are 23 cell subtypes identified through the scRNA-seq transcriptomics data of epilepsy, and SARS-CoV-2 receptor TTYH2 was found to be specifically expressed in oligodendrocyte and astrocyte cell subtypes. Furthermore, fGSEA analysis showed that the cell subtypes with receptor-specific expression was related to methylation of lysine 27 on histone H3 (H3K27ME3), neuronal system, aging brain, neurogenesis, and neuron projection. In summary, this study shows causal links between SARS-CoV-2 infections and neurological disorders such as epilepsy and manic, supported by MR and scRNA-seq analysis. These results should be considered in further studies and public health measures on COVID-19 and neurological diseases.