Disease Categories Research Articles

How was the impact of heatwaves on daily hospital admissions in Portugal (2000-2018)?

Abstract Background Climate change has increased the frequency, intensity, and duration of heatwaves, posing a serious threat to public health. While the link between high temperatures and premature mortality has been extensively studied, the comprehensive quantification of heatwave impacts on morbidity remains understudied. Methods We assessed the relationship between heatwaves and daily hospital admissions at a county level in Portugal, considering all major diagnostic categories and age groups, over a 19-year period from 2000 to 2018. Our nationwide study included a comprehensive geospatial analysis, integrating over 12 million hospital admission records with heatwave events indexed by the Excess Heat Factor (EHF), covering all 278 mainland counties. To estimate the impact of heatwaves on hospital admissions, we applied negative binomial regression models at both national and county levels. Results A statistically significant overall 18·9% increase in daily hospital admissions was found during heatwave days (IRR=1·189, 95% CI: 1·179-1·198). This impact affected all age groups, with pediatric ages being the most affected (21·7%), followed by the working-age (19·7%) and elderly individuals (17·2%). All 25 Major Disease Diagnostic Categories experienced significant increases, particularly Burns (34·3%), Multiple Significant Trauma (26·8%), and Infectious and Parasitic Diseases (25·4%). Notable rises were also observed in Endocrine, Nutritional, and Metabolic (25·1%), Mental (23%), Respiratory (22·4%), and Circulatory (15·8%) diseases. Conclusions Our results provide statistically significant evidence of the association between heatwaves and increased hospitalizations across all age groups and for all major causes of disease. This is the first study to estimate the full extent of heatwaves’ impact on hospitalizations using the EHF index over a comprehensive 19-year period, encompassing an entire country, and spanning 25 disease categories during multiple heatwave events. Key messages • There is a clear association between heatwaves and increased hospital admissions across all age groups and Major Diagnostic Categories. • Our data offer crucial information to guide policymakers in effectively and efficiently allocating resources to address the profound healthcare consequences resulting from climate change.

Development of the Unbiased Disease Network Using Health Insurance Claims Big Data in South Korea

Abstract Background Research on the overall relationship between disease is needed. The disease network model can comprehensively show the complex relationships between diseases and progression patterns. Existing disease network studies were estimated by univariate relative risks. We aim to develop the unbiased disease network. Methods We used NHIS national data of 50 million patients, collected from 2008 to 2022. We used only the primary diagnosis codes (three-digit codes) and defined 603 disease categories based on the special tabulation list for morbidity in the detailed subcategories of ICD-10, prior research, and the frequency of diseases in the dataset. To develop a more unbiased disease network, we first adjusted for age, sex, period, season, and encounter type, then added the confounding disease exposure according to the Directed Acyclic Graph (DAG) and performed Poisson regression to estimate relative risk (RR) applying Inverse Probability Weighting (IPW). We identified statistically significant disease pairs (RR > 1.10, p < 0.001) and repeated the IPW analysis up to the third round because the network would be updated every round. The significant disease pairs identified in the last round were connected to develop the overall network and subnetworks by age and sex. Results We repeated the process up to the third round for all 346,948 disease pairs and fitted 9,024 pairs as significant. We identified general population disease network and subnetwork per age and sex subgroups. Conclusions These disease networks represented the overall progression relationship between diagnosis codes. Key messages • We developed the unbiased disease network that shows progression patterns by adjusting for confounders and using IPW. • The disease network can be used to extract features from various diagnosis codes in EHR/Claims data, analyze causal effects, and predict disease or healthcare usage using RR or graph embedding.

AgriScan: Next.js powered cross-platform solution for automated plant disease diagnosis and crop health management

Plant diseases present a formidable challenge to the agricultural sector worldwide, leading to significant losses, with the US experiencing annual losses amounting to one-third of crop production. Diagnosis of crop diseases through optical observation of leaf symptoms is particularly daunting for farmers with limited resources. Therefore, there is an urgent need for enhanced detection, monitoring, and prediction methods to mitigate agricultural losses effectively. Harnessing the power of computer vision and deep learning, this paper introduces a cross-platform system designed to automate plant leaf disease diagnosis. The system employs convolutional neural networks to classify 46 disease categories, trained on a dataset comprising 96,206 images of healthy and infected plant leaves. The user interface, accessible across multiple platforms including Android, iOS, Windows, and Linux, allows farmers to capture photos of infected leaves and receive real-time disease classification along with confidence percentages. By empowering farmers to maintain crop health and prevent the application of incorrect fertilizers, the system aims to optimize crop productivity. Performance evaluation includes metrics such as classification accuracy and processing time, with the model achieving an impressive overall accuracy of 93.45% across 46 common disease classes spanning 16 crop species.

Decoding tumor-fibrosis interplay: mechanisms, impact on progression, and innovative therapeutic strategies

Malignant tumors are a category of diseases that possess invasive and metastatic capabilities, with global incidence and mortality rates remaining high. In recent years, the pivotal role of fibrosis in tumor progression, drug resistance, and immune evasion has increasingly been acknowledged. Fibrosis enhances the proliferation, migration, and invasion of tumor cells by modifying the composition and structure of the extracellular matrix, thereby offering protection for immune evasion by tumor cells. The activation of cancer-associated fibroblasts (CAFs) plays a significant role in this process, as they further exacerbate the malignant traits of tumors by secreting a variety of cytokines and growth factors. Anti-fibrotic tumor treatment strategies, including the use of anti-fibrotic drugs and inhibition of fibrosis-related signaling pathways such as Transforming Growth Factor-β (TGF-β), have demonstrated potential in delaying tumor progression and improving the effectiveness of chemotherapy, targeted therapy, and immunotherapy. In the future, by developing novel drugs that target the fibrotic microenvironment, new therapeutic options may be available for patients with various refractory tumors.

An ethnobotanical study of medicinal and wild food plants in Kırşehir (Türkiye)

This study compiles information regarding the use of certain plants found in Kırşehir and its surrounding regions in the Central Anatolia Region in folk medicine and other ethnobotanical characteristics. A total of 180 people participated in this study. Face-to-face interviews were conducted with the participants. As a result of the study, demographic characteristics of the participants, types of medicinal plant used by the people of the region, preparation techniques, usage patterns and frequency were determined. Local people benefit from 80 plants belonging to 28 families. The most commonly encountered medicinal plant family was Asteraceae (22 taxa). Twenty-two disease categories were identified in which traditional medicinal plants were used. Local people frequently use medicinal plants for the treatment of various diseases. These include dermatologic ailments (ICF 0.87), hematology issues (ICF 0.98), high cholesterol (ICF 0.98), gastrointestinal problems (ICF 0.88). Medicinal plants are also employed for treating cancer (ICF 0.95), hand tremors (ICF 1.00), cataracts (ICF 1.00), and gonorrhea (ICF 1.00), and kidney stones (ICF 1.00). The highest UV was found for Mentha arvensis and M. pulegium. This research is important in terms of transferring traditional knowledge to future generations. Moreover, the findings obtained should be investigated in fields such as pharmacology and phytotherapy.

Evidence gaps in conservative non-pharmacological interventions and guideline implementation for high-burden non-communicable diseases: protocol for an overview of reviews

Non-communicable diseases (NCDs) represent a high burden for the society and affected individuals. Conservative non-pharmacological interventions play a first-line role in the treatment and management of most NCDs. Systematic reviews...

Self-Supervised Learning for Feature Extraction from Glomerular Images and Disease Classification with Minimal Annotations.

Deep learning has great potential in digital kidney pathology. However, its effectiveness depends heavily on the availability of extensively labeled datasets, which are often limited due to the specialized knowledge and time required for their creation. This limitation hinders the widespread application of deep learning for the analysis of kidney biopsy images. We applied self-distillation with no labels (DINO), a self-supervised learning method, to a dataset of 10,423 glomerular images obtained from 384 PAS-stained kidney biopsy slides. Glomerular features extracted from the DINO-pretrained backbone were visualized using principal component analysis (PCA). We then performed classification tasks by adding either k-nearest neighbor (kNN) classifiers or linear head layers to the DINO-pretrained or ImageNet-pretrained backbones. These models were trained on our labeled classification dataset. Performance was evaluated using metrics such as the area under the receiver operating characteristic curve (ROC-AUC). The classification tasks encompassed four disease categories (minimal change disease, mesangial proliferative glomerulonephritis, membranous nephropathy, and diabetic nephropathy) as well as clinical parameters such as hypertension, proteinuria, and hematuria. PCA visualization revealed distinct principal components corresponding to different glomerular structures, demonstrating the capability of the DINO-pretrained backbone to capture morphological features. In disease classification, the DINO-pretrained transferred model (ROC-AUC = 0.93) outperformed the ImageNet-pretrained fine-tuned model (ROC-AUC = 0.89). When the labeled data were limited, the ImageNet-pretrained fine-tuned model's ROC-AUC dropped to 0.76 (95% confidence interval [CI], 0.72-0.80), whereas the DINO-pretrained transferred model maintained superior performance (ROC-AUC 0.88, 95% CI 0.86-0.90). The DINO-pretrained transferred model also exhibited higher AUCs for the classification of several clinical parameters. External validation using two independent datasets confirmed DINO pre-training's superiority, particularly when labeled data were limited. The application of DINO to unlabeled PAS-stained glomerular images facilitated the extraction of histological features that can be effectively utilized for disease classification.

Maternal Mental Health Spillovers From Child Illness and Disability: A Dynamic Panel Analysis

ObjectivesThere is growing recognition of the consequences of a person’s health and illness experience for the health and wider welfare of those close to them. However, estimation of these health spillovers is challenging. This study adopts a longitudinal approach to examine maternal mental health spillovers associated with various forms of child illness and disability. MethodsDynamic panel models are used in estimating maternal mental health spillovers related to 7 subcategories of chronic child illness and disability. In particular, we use longitudinal data from the Growing Up in Ireland study and a system generalized method of moments approach. We also consider heterogeneity in these spillovers by the severity of the child’s illness/disability and by household deprivation. ResultsWe find that a child’s experience of chronic nervous system conditions and chronic mental and behavioral disorders are associated with 10.8 and 5.1 percentage point increases in the probability of maternal depression, respectively. Similar associations were not observed for other health conditions. Spillover magnitude is also found to be strongly related to illness/disability severity. Finally, subsample analyses reveal a larger association between severe child illness and maternal depression among deprived households. ConclusionsThis analysis, in observing health spillovers related to certain disease categories but not others, draws further attention to their context specificity. Our findings also further corroborate calls for inclusion of caregiver and family member outcomes in the economic evaluation of child health services and support consensus guidelines for collection of these outcomes alongside patient outcomes in clinical trials.

Practical Approach to Orbital Lesions by Anatomic Compartments.

A wide range of pathologic conditions can originate in the orbit. While it is common to approach the differential diagnosis based on disease categories, such as neoplastic and inflammatory, segmenting the orbit into anatomic compartments can direct the radiologist toward the most common pathologic conditions for each manifestation and space. The orbit can be divided into intraconal, conal, and extraconal compartments. Additionally, the optic nerve sheath complex and lacrimal apparatus can be partitioned into separate compartments due to their unique functions and pathologic features. By using this anatomic approach, the authors review the most common pathologic conditions affecting the orbit and discuss clinical and imaging findings that can guide the differential diagnosis for lesions with similar appearances. Published under a CC BY 4.0 license. Supplemental material is available for this article.

High Burden of Ileus and Pneumonia in Clozapine-Treated Individuals With Schizophrenia: A Finnish 25-Year Follow-Up Register Study.

The authors used longitudinal biobank data with up to 25 years of follow-up on over 2,600 clozapine users to derive reliable estimates of the real-world burden of clozapine adverse drug events (ADEs). A total of 2,659 participants in the FinnGen biobank project had a schizophrenia diagnosis and clozapine purchases with longitudinal electronic health record follow-up for up to 25 years after clozapine initiation. Diseases and health-related events enriched during clozapine use were identified, adjusting for disease severity. The incidence and recurrence of ADEs over years of clozapine use, their effect on clozapine discontinuation and deaths, and their pharmacogenetics were studied. Median follow-up time after clozapine initiation was 12.7 years. Across 2,157 diseases and health-related events, 27 were enriched during clozapine use, falling into five disease categories: gastrointestinal hypomotility, seizures, pneumonia, other acute respiratory tract infections, and tachycardia, along with a heterogeneous group including neutropenia and type 2 diabetes, among others. Cumulative incidence estimates for ileus (severe gastrointestinal hypomotility) and pneumonia were 5.3% and 29.5%, respectively, 20 years after clozapine initiation. Both events were significantly associated with increased mortality among clozapine users (ileus: odds ratio=4.5; pneumonia: odds ratio=2.8). Decreased genotype-predicted CYP2C19 and CYP1A2 activities were associated with higher pneumonia risk. Clozapine-induced ileus and pneumonia were notably more frequent than has previously been reported and were associated with increased mortality. Two CYP genes influenced pneumonia risk. Pneumonia and ileus call for improved utilization of available preventive measures.

Cassava crop disease prediction and localization using object detection

In agriculture, early detection and localization of plant diseases in time using deep learning techniques can help farmers contain the spread of plant diseases. In this work, we apply object detection models to identify and localize various categories of cassava plant leaf diseases. These include You Only Look Once (YOLO) as well as Generalized Efficient Layer Aggregation Network(GELAN) models. We applied YOLO v9-e, YOLO v9-c, as well as GELAN-e and GELAN-c models. The models were successfully trained using a custom cassava dataset. Several evaluation indicators that include precision, recall and mean average precision(mAP) were analysed result. The results have been compared with an earlier version of YOLO model and show an improvement in evaluation indicators reaching above 80% in the majority of diseases.

Rebalancing immune homeostasis in combating disease: the impact of medicine food homology plants and gut microbiome

BackgroundGut microbiota plays an important role in multiple human physiological processes and an imbalance in it, including the species, abundance, and metabolites can lead to diseases. These enteric microorganisms modulate immune homeostasis by presenting a myriad of antigenic determinants and microbial metabolites. Medicinal and food homologous (MFH) plants, edible herbal materials for both medicine and food, are important parts of Traditional Chinese Medicine (TCM). MFH plants have drawn much attention due to their strong biological activity and low toxicity. However, the interplay of MFH and gut microbiota in rebalancing the immune homeostasis in combating diseases needs systematic illumination. PurposeThe review discusses the interaction between MFH and gut microbiota, including the effect of MFH on the major group of gut microbiota and the metabolic effect of gut microbiota on MFH. Moreover, how gut microbiota influences the immune system in terms of innate and adaptive immunity is addressed. Finally, the immunoregulatory mechanisms of MFH in regulation of host pathophysiology via gut microbiota are summarized. MethodsLiterature was searched, analyzed, and collected using databases, including PubMed, Web of Science, and Google Scholar using relevant keywords. The obtained articles were screened and summarized by the research content of MFH and gut microbiota in immune regulation. ResultsThe review demonstrates the interaction between MFH and gut microbiota in disease prevention and treatment. Not only do the intestinal microorganisms and intestinal mucosa constitute an important immune barrier of the human body, but also lymphoid tissue and diffused immune cells within the mucosa participate in the response of innate immunity and adaptive immunity. MFH modulates immune regulation by affecting intestinal flora, helps maintain the balance of the immune system and interfere with the occurrence and development of a broad category of diseases. ConclusionBeing absorbed from the gastrointestinal tract, MFH can have profound effects on gut microbiota. In turn, the gut microbiota also actively participate in the bioconversion of complex constituents from MFH, which could further influence their physiological and pharmacological properties. The review deepens the understanding of the relationship among MFH, gut microbiota, immune system, and human diseases and further promotes the progression of additional relevant research.

S827 Global Insights: Glucagon-Like Peptide-1 Receptor Agonists are Linked to Gastrointestinal Functional Disorders ACG Outstanding Research Award in the Functional Bowel Disease Category (Trainee)

S827 Global Insights: Glucagon-Like Peptide-1 Receptor Agonists are Linked to Gastrointestinal Functional Disorders ACG Outstanding Research Award in the Functional Bowel Disease Category (Trainee)

Educational and income inequalities across diseases in Denmark: a register-based cohort study.

Educational attainment and income are often, but not always, associated with disease incidence. Existing research typically examines single diseases, resulting in disparate analyses with little comparability. In this study, we aimed to assess educational and income inequalities across diseases in Denmark. This register-based study included all Danish residents aged at least 30 years between Jan 1, 2013, and Dec 31, 2022, who were born after 1920, and with known educational attainment and income. We used a disease-wide approach to assess associations between education and income and the incidence of 751 diagnostic codes determined upon hospital admission. We estimated age-standardised incidence rates and incidence rate ratios (IRRs) using Poisson regression, adjusted for birth cohort and stratified by sex. Participants were followed up until time of diagnosis, death, emigration, or until Dec 31, 2022. 4 541 309 individuals aged 30 years and older were registered as living in Denmark between Jan 1, 2013, and Dec 31, 2022. 121 083 were excluded due to limited or missing information about educational attainment. 4 420 226 individuals were included in the analysis of educational inequalities (2 232 200 [50%] were female and 2 188 026 [50%] were male). 23 708 were excluded due to absence of income information, and 4 396 518 were included in the analysis of income inequalities (2 223 217 (51%) were female and 2 173 301 (49%) were male). Socioeconomic differences in incidence rates were observed across all disease groups; incidence rates of most diseases decreased with higher educational attainment and income. The magnitude of the socioeconomic inequalities varied substantially. Among non-communicable diseases, the strongest positive association with regard to education was observed in chronic obstructive pulmonary disease for female individuals (low education vs high education, IRR 2·7 [95% CI 2·4-3·0]) and schizophrenia for male inividuals (low education vs high education, IRR 4·4 [2·2-8·8]), and the strongest negative association was in melanoma and other skin cancers for females (low education vs high education, IRR 0·7 [0·7-0·8]) and melanoma and skin cancers for males (low education vs high education, 0·7 [0·6-0·8]). With regard to income, for females, the strongest positive association was observed in schizophrenia (quartile 1 [Q1] vs quartile 4 [Q4], IRR 10·1 [6·1-17·2]), whereas the strongest negative association was in melanoma and other skin cancers (Q1 vs Q4, IRR 0·5 [0·5-0·6]). For males, the strongest positive assocation was schizophrenia (Q1 vs Q4, IRR 18·4 [95% CI 8·5-39·9]) and the strongest negative association was also melanoma and other skin cancers (Q1 vs Q4, IRR 0·5 [0·5-0·6]). The most prevalent disease category, other digestive diseases, was also strongly positively associated with education (low education vs high education, IRR 1·6 [95% CI 1·6-1·6] for females; IRR 1·5 [1·4-1·5] for males) and income (Q1 vs Q4, IRR 1·5 [1·5-1·5] for females; IRR 1·3 [1·3-1·4] for males). Our study provides a detailed representation of the association between two socioeconomic indicators and disease incidence. A broad spectrum of diseases, and not only the most prevalent, show socioeconomic disparities. This finding highlights the need for not only policies that address specific diseases, but also universal policies addressing the root causes of socioeconomic disparities and their health consequences. The Novo Nordisk Foundation.

Diurnal temperature range and cardiopulmonary health in Taiwan: Evaluating impacts, thresholds, and vulnerable groups

The health impacts of the diurnal temperature range (DTR), which may be affected by climate change, have received little attention. The objectives of this study were (1) to evaluate the association of DTR and cardiopulmonary outcomes, (2) to select the proper thresholds for a DTR warning system, and (3) to identify vulnerable groups. The weather and health records in Taiwan from 2000 to 2019, with a maximum DTR of 12.8 °C, were analyzed using generalized additive models. The health outcomes included cardiovascular (CVD) and respiratory disease (RD) categories and several sub-categories, such as ischemic heart disease, stroke, pneumonia, asthma, and chronic obstructive pulmonary disease. The results showed that the associations of DTR and cardiopulmonary outcomes were as significant as, and sometimes even stronger than, those of the daily maximum temperature and daily minimum apparent temperature in the warm and cold seasons, respectively. The significant association began at DTR of 6 °C, lower than previously reported. The identified DTR warning thresholds were 8.5 and 11 °C for the warm and cold seasons, respectively. DTR is statistically significantly associated with a 5–36% and a 9–20% increase in cardiopulmonary emergency and hospitalized cases in the warm season with a 1 °C increase above 8.5 °C, respectively. In the cold season, DTR is significantly associated with 7–41%, 4–30%, and 36–100% increases in cardiopulmonary emergency, hospitalized, and mortality with a 1 °C increase above 11 °C, respectively. People with hypertension, hyperglycemia, and hyperlipidemia had even higher risks. Vulnerable age and sex groups were identified if they had a lower DTR–health threshold than the general population, which can be integrated into a warning system. In conclusion, DTR may be increased on a local or city scale under climate change; a DTR warning system and vulnerable group identification may be warranted in most countries for health risk reduction.

18F-FDG PET/CT biomarkers as predictors of long term outcomes and survival rates in patients with high risk malignant pulmonary masses/nodules treated with stereotactic ablative radiotherapy.

Stereotactic ablative body radiotherapy (SABR) is a standard treatment option for patients with malignant pulmonary masses (including primary and metastatic lesions) who are unfit for surgery or who are medically operable but refuse surgery. Flourine-18 flurodeoxyglucose positron emission tomography (18F-FDG PET) volumetric metabolic parameters, i.e., metabolic tumour volume (MTV) and total lesion glycolysis (TLG) play an important role in assessing the biological characteristics of some tumours and its role as potential prognostic factors has also been introduced. The aim of this retrospective study is to assess the value of baseline metabolic volumetric parameters as prognostic imaging biomarkers in patients with pulmonary masses/nodules treated with SABR. 70 patients were included in this retrospective study (39 male and 31 female, age range 47-91years, mean 76years). Standardized uptake value (SUVmax), SUVmean, MTV and TLG for all the patients were calculated on baseline 18F-FDG PET/CT. Patient outcome was divided into 3 categories free of disease, stable disease and disease progression. There was no significant statistical difference in the SUVmax and SUVmean in all the three categories. Mean SUVmax ranges from 7.13 to 8.08 with its highest value in the stable disease and lowest value in the progressive disease categories. Similarly, the average SUVmean was 4.9 in the free of disease category and 4.68 in the progressive disease category. MTV and TLG were low in the free of disease and the highest in progressive disease. MTV increased from 2.25 cm3 in free of disease category to 3.23 cm3 and 7.29 cm3 in stable disease and progressive disease, respectively. TLG has increased from 11.7 in the disease-free survival category to 18.77 and 40.39 in the stable and progressive disease, respectively. Patients with low MTV had longer overall survival (OS) than patients with high MTV (37months versus 27months, p value = 0. 0018). In addition, OS was longer in patients with low TLG (36months versus 24months, p value = 0.016). TLG and MTV are more useful than SUVmax and SUVmean for predicting outcome, OS and progression-free survival (PFS) in patients receiving SABR. The TLG and MTV measurement on 18F-FDG PET imaging may be routinely recommended in baseline 18F-FDG PET/CT prior to SABR.

Comprehensive analysis of the cerebrospinal fluid and serum metabolome in neurological diseases

BackgroundComprehensive characterization of the metabolome in cerebrospinal fluid (CSF) and serum by Nuclear Magnetic Resonance (NMR) spectroscopy may identify biomarkers and contribute to the understanding of the pathophysiology of neurological diseases.MethodsMetabolites were determined by NMR spectroscopy in stored CSF/serum samples of 20 patients with Parkinson’s disease, 25 patients with other neuro-degenerative diseases, 22 patients with cerebral ischemia, 48 patients with multiple sclerosis, and 58 control patients with normal CSF findings. The data set was analysed using descriptive and multivariate statistics, as well as machine learning models.ResultsCSF glucose and lactic acid measured by NMR spectroscopy and routine clinical chemistry showed a strong correlation between both methods (glucose, R2 = 0.87, n = 173; lactic acid, R2 = 0.74, n = 173). NMR spectroscopy detected a total of 99 metabolites; 51 in both, CSF and serum, 16 in CSF only, and 32 in serum only. CSF concentrations of some metabolites increased with age and/or decreasing blood–brain-barrier function. Metabolite detection rates were overall similar among the different disease groups. However, in two-group comparisons, absolute metabolite levels in CSF and serum discriminated between multiple sclerosis and neurodegenerative diseases (area under the curve (AUC) = 0.96), multiple sclerosis and Parkinson’s disease (AUC = 0.89), and Parkinson’s disease and control patients (AUC = 0.91), as demonstrated by random forest statistical models. Orthogonal partial least square discriminant analysis using absolute metabolite levels in CSF and serum furthermore permitted separation of Parkinson’s disease and neurodegenerative diseases. CSF propionic acid levels were about fourfold lower in Parkinson’s disease as compared to neurodegenerative diseases.ConclusionsThese findings outline the landscape of the CSF and serum metabolome in different categories of neurological diseases and identify age and blood–brain-barrier function as relevant co-factors for CSF levels of certain metabolites. Metabolome profiles as determined by NMR spectroscopy may potentially aid in differentiating groups of patients with different neurological diseases, including clinically meaningful differentiations, such as Parkinson’s disease from other neurodegenerative diseases.

Peptide Biomarkers - An Emerging Diagnostic Tool and Current Applicable Assay.

In the past few decades, impressive progress achieved in technology development and improvementhasaccelerated the application of peptides as diagnostic biomarkers for various diseases. We outline the advantages of peptides as good diagnostic targets, since they serve as molecular surrogates of enzyme activities, much more specific biomarkers than proteins, and also play vital roles in many biological processes. On the basis of an extensive literature survey, peptide markers with high specificity and sensitivity that are currently applied in clinical tests, as well as recently identified, are summarized for the following four major categories of diseases: neurodegenerative disease, heart failure, infectious disease, and cancer. In addition, we summarize a few prevalent techniques used in peptide biomarker discovery and analysis, such as immunoassays, nanopore-based and nanoparticle-based peptide detection, and also MS-based peptide analysis techniques, and their pros and cons. Currently, there are plenty of analytical technologies available to achieve fast, sensitive and reliable peptide analyses, benefiting from the developments of hardware and instrumentation, as well as data analysis software and databases. Thus, with peptides emerging as sensitive, specific and reliable biomarkers for early detection of diseases, therapeutic monitoring, clinical treatment decisions and disease prognosis, the medical need for peptide biomarkers will increase strongly in the future.

Nine Neuroimaging-AI Endophenotypes Unravel Disease Heterogeneity and Partial Overlap across Four Brain Disorders: A Dimensional Neuroanatomical Representation.

Disease heterogeneity poses a significant challenge for precision diagnostics. Recent work leveraging artificial intelligence has offered promise to dissect this heterogeneity by identifying complex intermediate brain phenotypes, herein called dimensional neuroimaging endophenotypes (DNEs). We advance the argument that these DNEs capture the degree of expression of respective neuroanatomical patterns measured, offering a dimensional neuroanatomical representation for studying disease heterogeneity and similarities of neurologic and neuropsychiatric diseases. We investigate the presence of nine such DNEs derived from independent yet harmonized studies on Alzheimer's disease (AD1-2)1, autism spectrum disorder (ASD1-3)2, late-life depression (LLD1-2)3, and schizophrenia (SCZ1-2)4, in the general population of 39,178 participants in the UK Biobank study. Phenome-wide associations revealed prominent associations between the nine DNEs and phenotypes related to the brain and other human organ systems. This phenotypic landscape aligns with the SNP-phenotype genome-wide associations, revealing 31 genomic loci associated with the nine DNEs (Bonferroni corrected P-value < 5×10-8/9). The DNEs exhibited significant genetic correlations, colocalization, and causal relationships with multiple human organ systems and chronic diseases. A causal effect (odds ratio=1.25 [1.11, 1.40], P-value=8.72×10-4) was established from AD2, characterized by focal medial temporal lobe atrophy, to AD. The nine DNEs, along with their polygenic risk scores, significantly enhanced the predictive accuracy for 14 systemic disease categories, particularly for conditions related to mental health and the central nervous system, as well as mortality outcomes. These findings underscore the potential of the nine DNEs to capture the expression of disease-related brain phenotypes in individuals of the general population and to relate such measures with genetics, lifestyle factors, and chronic diseases. All results are publicly available at https://labs-laboratory.com/medicine/.

Factors associated with cost of adverse events in the intensive care unit: an economic evaluation performed in Bogotá, Colombia

BackgroundThe costs of adverse health events represent a significant burden of disease. Management of these adverse events also entails high costs to healthcare systems, reaching up to US$110 516.25 in...