Could scalp pain be a type of unclassified headache? / A dor no couro cabeludo poderia ser um tipo de dor de cabeça não classificada?

The scalp is the structure that covers the skull. It is commonly affected by painful processes resulting from infestations, infectious or inflammatory diseases. This pain located in the scalp does not have well-defined clinical characteristics and is not yet included in the ICHD-3 diagnostic criteria. The authors suggest including this pain in the next classification of headaches as a headache attributed to a scalp disorder.

O couro cabeludo é a estrutura que cobre o crânio. É comumente acometida por processos dolorosos decorrentes de infestações, doenças infecciosas ou inflamatórias. Essa dor localizada no couro cabeludo não possui características clínicas bem definidas e ainda não está incluída nos critérios diagnósticos da ICHD-3. Os autores sugerem incluir esta dor na próxima classificação de dores de cabeça como dor de cabeça atribuída a um distúrbio do couro cabeludo.

CFNCM: Collaborative filtering neighborhood-based model for predicting miRNA-disease associations.

MicroRNAs have a significant role in the emergence of various human disorders. Consequently, it is essential to understand the existing interactions between miRNAs and diseases, as this will help scientists better study and comprehend the diseases' biological mechanisms. Findings can be employed as biomarkers or drug targets to advance the detection, diagnosis, and treatment of complex human disorders by foretelling possible disease-related miRNAs. This study proposed a computational model for predicting potential miRNA-disease associations called the Collaborative Filtering Neighborhood-based Classification Model (CFNCM), in light of the shortcomings of conventional and biological experiments, which are expensive and time-consuming. The model generated integrated miRNA and disease similarity matrices using the validated associations and miRNA and disease similarity information and used them as the input features for CFNCM. To produce class labels, we first determined the association scores for brand-new pairs using user-based collaborative filtering. With zero as the threshold, the associations with scores >0 were labelled 1, indicating a potential positive association, otherwise, it is marked as 0. Then, we developed classification models using various machine-learning algorithms. By comparison, we discovered that the support vector machine (SVM) produced the best AUC of 0.96 with 10-fold cross-validation through the GridSearchCV technique for identifying optimal parameter values. In addition, the models were evaluated and verified by analyzing the top 50 breast and lung neoplasms-related miRNAs, of which 46 and 47 associations were verified in two authoritative databases, dbDEMC and miR2Disease.

Consumption of benzylpenicillin as a syphilis control indicator

Abstract Syphilis is a disease with compulsory and mandatory notification to the Notifiable Diseases Information System (SINAN), with benzathine benzylpenicillin being the treatment of choice. The aim of the study was to compare the consumption of benzylpenicillin benzathine, from the dispensation, between the health regions of a capital in the southern region of the country, according to the georeferencing of notified cases of syphilis. This is a descriptive, cross-sectional, retrospective study of the use of benzylpenicillin benzathine and of reported cases of syphilis. Data on syphilis cases were obtained from notifications made in SINAN, and drug consumption data were obtained from the Municipal Health Department computerized system for Drug Dispensing from January 1st, 2019 to December 31st, 2019. Notifications and drug consumption were georeferenced according to 8 health regions. From the compilation of data, the rates of cases and consumption in relation to the population of each region were calculated. A total of 3188 notifications and a total of 35191 vials of benzathine benzylpenicillin were analyzed. The ratio of vials by SINAN notifications showed that each patient took 11 vials of the drug, which is a higher value if we consider that the complete treatment is 2 to 6 vials per case.

Development, physicochemical evaluation, and in vivo permeation studies of topical formulations containing 0.1% tacrolimus

Abstract The objective of this paper was to develop and evaluate two semi-solid pharmaceutical forms containing 0.1% tacrolimus: cream (CRT01) and gel (GLT01). For the evaluation of physicochemical stability, at times 0, 30, 60 and 90 days, at 23°C and at 40°C, High Performance Liquid Chromatography coupled with a Diode Array Detector (HPLC-DAD) was employed. This method was developed and validated for tacrolimus quantification. The occlusivity test and skin permeation assay were also performed, using an animal model (Wistar rats), and the CRT01 and GLT01 were compared to the 0.1% tacrolimus ointment (PFU01) obtained from the University Pharmacy, Federal University of Rio de Janeiro, Brazil. CRT01 and GLT01 presented a homogeneous aspect and consistency adequate for topical products, along with sensory characteristics above PFU01. They also presented adequate physicochemical stability for 90 days and a lower occlusive effect than PFU01 (p<0.05). CRT01 showed greater affinity for the skin when compared to PFU01 and GLT01, with low systemic absorption. The CRT01 semi-solid formulation was considered the most adequate one to treat patients with atopic dermatitis or other dermatologic inflammatory diseases, promoting rational use of tacrolimus

MoSBi: Automated signature mining for molecular stratification and subtyping.

The improving access to increasing amounts of biomedical data provides completely new chances for advanced patient stratification and disease subtyping strategies. This requires computational tools that produce uniformly robust results across highly heterogeneous molecular data. Unsupervised machine learning methodologies are able to discover de novo patterns in such data. Biclustering is especially suited by simultaneously identifying sample groups and corresponding feature sets across heterogeneous omics data. The performance of available biclustering algorithms heavily depends on individual parameterization and varies with their application. Here, we developed MoSBi (molecular signature identification using biclustering), an automated multialgorithm ensemble approach that integrates results utilizing an error model-supported similarity network. We systematically evaluated the performance of 11 available and established biclustering algorithms together with MoSBi. For this, we used transcriptomics, proteomics, and metabolomics data, as well as synthetic datasets covering various data properties. Profiting from multialgorithm integration, MoSBi identified robust group and disease-specific signatures across all scenarios, overcoming single algorithm specificities. Furthermore, we developed a scalable network-based visualization of bicluster communities that supports biological hypothesis generation. MoSBi is available as an R package and web service to make automated biclustering analysis accessible for application in molecular sample stratification.

Questioning Established Theories and Treatment Methods Related to Iron and Other Metal Metabolic Changes, Affecting All Major Diseases and Billions of Patients.

The medical and scientific literature is dominated by highly cited historical theories and findings [...].

Determine independent gut microbiota-diseases association by eliminating the effects of human lifestyle factors.

Lifestyle and physiological variables on human disease risk have been revealed to be mediated by gut microbiota. Low concordance between case-control studies for detecting disease-associated microbe existed due to limited sample size and population-wide bias in lifestyle and physiological variables. To infer gut microbiota-disease associations accurately, we propose to build machine learning models by including both human variables and gut microbiota. When the model's performance with both gut microbiota and human variables is better than the model with just human variables, the independent gut microbiota -disease associations will be confirmed. By building models on the American Gut Project dataset, we found that gut microbiota showed distinct association strengths with different diseases. Adding gut microbiota into human variables enhanced the classification performance of IBD significantly; independent associations between occurrence information of gut microbiota and irritable bowel syndrome, C. difficile infection, and unhealthy status were found; adding gut microbiota showed no improvement on models' performance for diabetes, small intestinal bacterial overgrowth, lactose intolerance, cardiovascular disease. Our results suggested that although gut microbiota was reported to be associated with many diseases, a considerable proportion of these associations may be very weak. We proposed a list of microbes as biomarkers to classify IBD and unhealthy status. Further functional investigations of these microbes will improve understanding of the molecular mechanism of human diseases.

A genome-wide association study of serum proteins reveals shared loci with common diseases.

With the growing number of genetic association studies, the genotype-phenotype atlas has become increasingly more complex, yet the functional consequences of most disease associated alleles is not understood. The measurement of protein level variation in solid tissues and biofluids integrated with genetic variants offers a path to deeper functional insights. Here we present a large-scale proteogenomic study in 5,368 individuals, revealing 4,035 independent associations between genetic variants and 2,091 serum proteins, of which 36% are previously unreported. The majority of both cis- and trans-acting genetic signals are unique for a single protein, although our results also highlight numerous highly pleiotropic genetic effects on protein levels and demonstrate that a protein's genetic association profile reflects certain characteristics of the protein, including its location in protein networks, tissue specificity and intolerance to loss of function mutations. Integrating protein measurements with deep phenotyping of the cohort, we observe substantial enrichment of phenotype associations for serum proteins regulated by established GWAS loci, and offer new insights into the interplay between genetics, serum protein levels and complex disease.

Coding and regulatory variants are associated with serum protein levels and disease.

Circulating proteins can be used to diagnose and predict disease-related outcomes. A deep serum proteome survey recently revealed close associations between serum protein networks and common disease. In the current study, 54,469 low-frequency and common exome-array variants were compared to 4782 protein measurements in the serum of 5343 individuals from the AGES Reykjavik cohort. This analysis identifies a large number of serum proteins with genetic signatures overlapping those of many diseases. More specifically, using a study-wide significance threshold, we find that 2021 independent exome array variants are associated with serum levels of 1942 proteins. These variants reside in genetic loci shared by hundreds of complex disease traits, highlighting serum proteins' emerging role as biomarkers and potential causative agents of a wide range of diseases.

Characteristics of Disease-Specific and Generic Diagnostic Pitfalls: A Qualitative Study.

Importance: Progress in understanding and preventing diagnostic errors has been modest. New approaches are needed to help clinicians anticipate and prevent such errors. Delineating recurring diagnostic pitfalls holds potential for conceptual and practical ways for improvement. Objectives: To develop the construct and collect examples of "diagnostic pitfalls," defined as clinical situations and scenarios vulnerable to errors that may lead to missed, delayed, or wrong diagnoses. Design, Setting, and Participants: This qualitative study used data from January 1, 2004, to December 31, 2016, from retrospective analysis of diagnosis-related patient safety incident reports, closed malpractice claims, and ambulatory morbidity and mortality conferences, as well as specialty focus groups. Data analyses were conducted between January 1, 2017, and December 31, 2019. Main Outcomes and Measures: From each data source, potential diagnostic error cases were identified, and the following information was extracted: erroneous and correct diagnoses, presenting signs and symptoms, and areas of breakdowns in the diagnostic process (using Diagnosis Error Evaluation and Research and Reliable Diagnosis Challenges taxonomies). From this compilation, examples were collected of disease-specific pitfalls; this list was used to conduct a qualitative analysis of emerging themes to derive a generic taxonomy of diagnostic pitfalls. Results: A total of 836 relevant cases were identified among 4325 patient safety incident reports, 403 closed malpractice claims, 24 ambulatory morbidity and mortality conferences, and 355 focus groups responses. From these, 661 disease-specific diagnostic pitfalls were identified. A qualitative review of these disease-specific pitfalls identified 21 generic diagnostic pitfalls categories, which included mistaking one disease for another disease (eg, aortic dissection is misdiagnosed as acute myocardial infarction), failure to appreciate test result limitations, and atypical disease presentations. Conclusions and Relevance: Recurring types of pitfalls were identified and collected from diagnostic error cases. Clinicians could benefit from knowledge of both disease-specific and generic cross-cutting pitfalls. Study findings can potentially inform educational and quality improvement efforts to anticipate and prevent future errors.

Design and evaluation of dental pastes Containing anti-inflammatory drugs

Abstract Periodontitis is an oral disease associated with inflammation and pain with swollen and bleeding gums. In the present study, dental pastes containing NSAIDs, namely, diclofenac sodium and nimesulide (1 % w/w) were prepared to treat periodontitis. Dental pastes of diclofenac sodium and nimesulide (1 % w/w) were prepared with/without mucoadhesive hydrocolloid polymers such as sodium carboxy methyl cellulose (NaCMC), hydroxyl ethyl cellulose (HEC) and methyl cellulose (MC) by conventional trituration method. The pH, drug content, viscosity, tube spreadability and tube extrudability of these prepared dental pastes were measured. These dental pastes of diclofenac sodium and nimesulide (1 % w/w) were characterized by FTIR analyses for drug-excipient compatibility. The in vitro drug releases from these dental pastes in 6.4 pH phosphate buffer solution displayed sustained release over longer period and the drug release rate was found to be decreased when the concentration of mucoadhesive polymer was increased. These dental pastes displayed good adhesion to the oral mucosa revealing more retention time in mouth when tested for ex vivo mucoadhesion using bovine cheek pouch. The stability study results reveal that the DC3 and NC3 dental paste formulations were found stable enough over a longer period in different storage conditions. The present study revealed that the prepared mucoadhesive dental pastes of diclofenac sodium and nimesulide (1 % w/w) had good adhesion with the oral mucosa to maintain consistent release of drugs over prolonged time.

Anti-hyperlipidemic and anti-diabetic evaluation of ethanolic leaf extract of Catharanthus roseus alone and in combination therapy

The use of plants in disease treatment is cost effective and relatively safe. This study was designed to investigate anti-hyperlipidemic and anti-diabetic activity of ethanolic leaf extract of Catharanthus roseus alone and in combination therapy in hyperlipidemic & diabetic mice. Eight groups comprising five mice each were used. Group A was hyperlipidemic control, group B, C, D received atorvastatin (20 mg/kg), leaf extract (200 mg/kg) and leaf extract in combination with atorvastatin (200 mg/kg and 20 mg/kg) orally for 15 days. Group E was diabetic control. Group F, G, H received sitagliptin (40 mg/kg), leaf extract (200 mg/kg) and extract in combination with sitagliptin (200 mg/kg and 40 mg/kg) orally for 7 days. Blood cholesterol levels were measured at 1st, 5th, 10th and 15th day and fasting blood sugar levels were measured at 2, 12, 24, 72 and 168 hours during treatment. One-way ANOVA with tukey- kramer multiple comparison test was used. The chemical characterization of ethanolic extract of Catharanthus roseus leaves showed presence of alkaloids, saponins, tannins and flavonoids. Ethanolic extract of Catharanthus roseus has significant anti-hyperlipidemic & anti-diabetic effects (p<0.05, p<0.01) when compared with control but had not cause significantly increase in anti-hyperlipidemic effects of atorvastatin. While significantly increased the antidiabetic effect of sitagliptin (p<0.05)

Network medicine for disease module identification and drug repurposing with the NeDRex platform.

Traditional drug discovery faces a severe efficacy crisis. Repurposing of registered drugs provides an alternative with lower costs and faster drug development timelines. However, the data necessary for the identification of disease modules, i.e. pathways and sub-networks describing the mechanisms of complex diseases which contain potential drug targets, are scattered across independent databases. Moreover, existing studies are limited to predictions for specific diseases or non-translational algorithmic approaches. There is an unmet need for adaptable tools allowing biomedical researchers to employ network-based drug repurposing approaches for their individual use cases. We close this gap with NeDRex, an integrative and interactive platform for network-based drug repurposing and disease module discovery. NeDRex integrates ten different data sources covering genes, drugs, drug targets, disease annotations, and their relationships. NeDRex allows for constructing heterogeneous biological networks, mining them for disease modules, prioritizing drugs targeting disease mechanisms, and statistical validation. We demonstrate the utility of NeDRex in five specific use-cases.

História da obesidade na classificação internacional de doenças (CID): de 1900 a 2018 / History of obesity in the international classification of diseases (ICD): 1900 to 2018

O presente artigo teve como objetivo descrever o histórico da inserção da condição clínica denominada atualmente de "obesidade" nas onze revisões da Classificação Internacional de Doenças (CID), publicadas pela Organização Mundial da Saúde (OMS), entre os anos 1900 e 2018. Para tanto, buscou-se pela palavra-chave "obesity" nos documentos, realizando, posteriormente, uma descrição e uma análise da presença, modo de inserção e as mudanças ocorridas ao longo do tempo. Os resultados demonstraram que a condição já foi e continua sendo inserida como sintoma, morbidade, coREVSmorbidade, causa de mortalidade e/ou doença. Concluiu-se que há uma grande inconsistência lógica nos princípios que regem a classificação.

This article aimed at describing the history of insertion of the clinical condition currently referred to as "obesity" in the eleven revisions of the International Classification of Diseases (ICD) published by the World Health Organization (WHO) between 1900 and 2018. For this purpose, a search for the keyword obesity was performed in the documents, with subsequent description and analysis of the presence, mode of insertion, and changes occurring over time. The results demonstrated that the condition has been and continues to be inserted as symptom, morbidity, comorbidity, cause of mortality and/or disease. It can be concluded that there is a massive logical inconsistency in the principles that govern the classification.

Cross-sectional study of the ambulance transport between healthcare facilities with medical support via telemedicine: Easy, effective, and safe tool.

BACKGROUND: Feasibility and safety of ambulance transport between healthcare facilities with medical support exclusively via telemedicine are unknown. METHODS: This was a retrospective study with a single telemedicine center reference for satellite emergency departments of the same hospital. The study population was all critically ill patients admitted to one of the peripheral units from November 2016 to May 2020 and who needed to be transferred to the main building. Telemedicine-assisted transportation was performed by an emergency specialist. The inclusion criteria included patients above the age of 15 and initial stabilization performed at the emergency department. Unstable, intubated, ST-elevation myocardial infarction and acute stroke patients were excluded. There was a double-check of safety conditions by the nurse and the remote doctor before the ambulance departure. The primary endpoint was the number of telemedicine-guided interventions during transport. RESULTS: 2840 patients were enrolled. The population was predominantly male (53.2%) with a median age of 60 years. Sepsis was the most prevalent diagnosis in 28% of patients, followed by acute coronary syndromes (8.5%), arrhythmia (6.7%), venous thromboembolism (6.1%), stroke (6.1%), acute abdomen (3.6%), respiratory distress (3.3%), and heart failure (2.5%). Only 22 (0.8%) patients required telemedicine-assisted support during transport. Administration of oxygen therapy and analgesics were the most common recommendations made by telemedicine emergency physicians. There were no communication problems in the telemedicine-assisted group. CONCLUSIONS: Telemedicine-assisted ambulance transportation between healthcare facilities of stabilized critically ill patients may be an option instead of an onboard physician. The frequency of clinical support requests by telemedicine is minimal, and most evaluations are of low complexity and easily and safely performed by trained nurses.

SCMFMDA: Predicting microRNA-disease associations based on similarity constrained matrix factorization.

miRNAs belong to small non-coding RNAs that are related to a number of complicated biological processes. Considerable studies have suggested that miRNAs are closely associated with many human diseases. In this study, we proposed a computational model based on Similarity Constrained Matrix Factorization for miRNA-Disease Association Prediction (SCMFMDA). In order to effectively combine different disease and miRNA similarity data, we applied similarity network fusion algorithm to obtain integrated disease similarity (composed of disease functional similarity, disease semantic similarity and disease Gaussian interaction profile kernel similarity) and integrated miRNA similarity (composed of miRNA functional similarity, miRNA sequence similarity and miRNA Gaussian interaction profile kernel similarity). In addition, the L2 regularization terms and similarity constraint terms were added to traditional Nonnegative Matrix Factorization algorithm to predict disease-related miRNAs. SCMFMDA achieved AUCs of 0.9675 and 0.9447 based on global Leave-one-out cross validation and five-fold cross validation, respectively. Furthermore, the case studies on two common human diseases were also implemented to demonstrate the prediction accuracy of SCMFMDA. The out of top 50 predicted miRNAs confirmed by experimental reports that indicated SCMFMDA was effective for prediction of relationship between miRNAs and diseases.

Deep Dive on the Proteome of Human Body Fluids: A Valuable Data Resource for Biomarker Discovery.

BACKGROUND/AIM: Body fluids are considered to be a rich source of disease biomarkers. Proteins in many body fluids have potential clinical applications for disease diagnostic and prognostic purposes. The aim of this study was to establish an in-depth multi-body fluid proteome. MATERIALS AND METHODS: Ten body fluids associated with 8 types of cancers collected from 23 patients involved in 19 common diseases underwent liquid chromatography tandem mass spectrometry (MS) analysis after gel-based protein separation (SDS-PAGE) or peptide-based fractionations. Bioinformatic analysis, including principal component analysis (PCA), consensus clustering, and hierarchical clustering analysis were also performed. The biological function was determined using the Database for Annotation, Visualization and Integrated Discovery (DAVID). RESULTS: We profiled the proteome of ten body fluids, including ascites, bile, cerebrospinal fluid (CSF), hydrothorax, knee joint fluid (KJF), plasma, saliva, serum, tears, and urine. A total of 3,396 nonredundant proteins were identified, of which 304 were shared among ten body fluids, with common functions in focal adhesion and complement/coagulation cascades. A total of 41.5% (1,409) of the proteins were detected in only one body fluid and were closely related to their adjacent tissues by function. The functional analysis of the remaining 1,683 proteins showed that similar functions might be shared among different body fluids, which further highlighted the close connection of body fluids in the human body. CONCLUSION: A deep proteome of multi-body fluids originated from patients diagnosed with 19 common diseases provides a valuable data resource, and might indicate the potential application of body fluids for biomarker discovery.

DisBalance: a platform to automatically build balance-based disease prediction models and discover microbial biomarkers from microbiome data.

How best to utilize the microbial taxonomic abundances in regard to the prediction and explanation of human diseases remains appealing and challenging, and the relative nature of microbiome data necessitates a proper feature selection method to resolve the compositional problem. In this study, we developed an all-in-one platform to address a series of issues in microbiome-based human disease prediction and taxonomic biomarkers discovery. We prioritize the interpretation, runtime and classification accuracy of the distal discriminative balances analysis (DBA-distal) method in selecting a set of distal discriminative balances, and develop DisBalance, a comprehensive platform, to integrate and streamline the workflows of disease model building, disease risk prediction and disease-related biomarker discovery for microbiome-based binary classifications. DisBalance allows the de novo model-building and disease risk prediction in a very fast and convenient way. To facilitate the model-driven and knowledge-driven discoveries, DisBalance dedicates multiple strategies for the mining of microbial biomarkers. The independent validation of the models constructed by the DisBalance pipeline is performed on seven microbiome datasets from the original article of DBA-distal. The implementation of the DisBalance platform is demonstrated by a complete analysis of a shotgun metagenomic dataset of Ulcerative Colitis (UC). As a free and open-source, DisBlance can be accessed at http://lab.malab.cn/soft/DisBalance. The source code and demo data for Disbalance are available at https://github.com/yangfenglong/DisBalance.