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Cryptococcosis, caused by fungi of the genus Cryptococcus, manifests in a broad range of clinical presentations, including severe pneumonia and disease of the central nervous system (CNS) and other tissues (bone and skin). Immune deficiency or development of overexuberant inflammatory responses can result in increased susceptibility or host damage, respectively, during fungal encounters. Leukotrienes help regulate inflammatory responses against fungal infections. Nevertheless, studies showed that Cryptococcus exploits host 5-lipoxygenase (5-LO), an enzyme central to the metabolism of arachidonic acid into leukotrienes, to facilitate transmigration across the brain-blood barrier. To investigate the impact of host 5-LO on the development of protective host immune responses and mortality during cryptococcosis, wild-type (C57BL/6) and 5-lipoxygenase-deficient (5-LO-/-) mice were given experimental pulmonary and systemic Cryptococcus sp., infections. Our results showed that 5-LO-/- mice exhibited reduced pathology and better disease outcomes (i.e., no mortality or signs associated with cryptococcal meningoencephalitis) following pulmonary infection with C. deneoformans, despite having detectable yeast in the brain tissues. In contrast, C57BL/6 mice exhibited classical signs associated with cryptococcal meningoencephalitis. Additionally, brain tissues of 5-LO-/- mice exhibited lower levels of cytokines (CCL2 and CCL3) clinically associated with Cryptococcus-related immune reconstitution inflammatory syndrome (C-IRIS). In a systemic mouse model of cryptococcosis, 5-LO-/- mice and those treated with a Federal Drug Administration (FDA)-approved 5-LO synthesis inhibitor, zileuton, displayed significantly reduced mortality compared to C57BL/6 infected mice. These results suggest that therapeutics designed to inhibit host 5-LO signaling could reduce disease pathology and mortality associated with cryptococcal meningoencephalitis. IMPORTANCE: Cryptococcosis is a mycosis with worldwide distribution and has a broad range of clinical manifestations, including diseases of the CNS. Globally, there is an estimated 179,000 cases of cryptococcal meningitis, resulting in approximately 112,000 fatalities per annum and 19% of AIDS-related deaths. Understanding how host immune responses are modulated during cryptococcosis is central to mitigating the morbidity and mortality associated with cryptococcosis. Leukotrienes (LTs) have been shown to modulate inflammatory responses during infection. In this study, we show that mice deficient in 5-lipoxygenase (5-LO), an enzyme central to the metabolism of arachidonic acid into leukotrienes, exhibit reduced pathology, disease, and neurological signs associated with cryptococcal meningitis. Additionally, mice given an experimental cryptococcal infection and subsequently treated with an FDA-approved 5-LO synthesis inhibitor exhibited significantly reduced mortality rates. These results suggest that therapeutics designed to inhibit host 5-LO activity could significantly reduce pathology and mortality rates associated with cryptococcal meningitis.

Asunto(s)

Araquidonato 5-Lipooxigenasa , Criptococosis , Meningoencefalitis , Ratones Endogámicos C57BL , Animales , Ratones , Araquidonato 5-Lipooxigenasa/metabolismo , Araquidonato 5-Lipooxigenasa/genética , Araquidonato 5-Lipooxigenasa/deficiencia , Meningoencefalitis/microbiología , Meningoencefalitis/inmunología , Meningoencefalitis/mortalidad , Criptococosis/inmunología , Criptococosis/microbiología , Criptococosis/mortalidad , Ratones Noqueados , Inflamación , Hidroxiurea/farmacología , Hidroxiurea/análogos & derivados , Modelos Animales de Enfermedad , Inhibidores de la Lipooxigenasa/farmacología , Femenino , Cryptococcus

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BACKGROUND AND PURPOSE: Post-stroke cognitive impairment (PSCI) is highly prevalent in modern society. However, there is limited study implying an accurate and explainable machine learning model to predict PSCI. The aim of this study is to develop and validate a web-based artificial intelligence (AI) tool for predicting PSCI. METHODS: The retrospective cohort study design was conducted to develop and validate a web-based prediction model. Adults who experienced a stroke between January 1, 2004, and September 30, 2017, were enrolled, and patients with PSCI were followed up from the stroke index date until their last follow-up. The model's performance metrics, including accuracy, area under the curve (AUC), recall, precision, and F1 score, were compared. RESULTS: A total of 3209 stroke patients were included in the study. The model demonstrated an accuracy of 0.8793, AUC of 0.9200, recall of 0.6332, precision of 0.9664, and F1 score of 0.7651. In the external validation phase, the accuracy improved to 0.9039, AUC to 0.9094, recall to 0.7457, precision to 0.9168, and F1 score to 0.8224. The final model can be accessed at https://psci-calculator.my.id/. CONCLUSION: Our results are able to produce a user-friendly interface that is useful for health practitioners to perform early prediction on PSCI. These findings also suggest that the provided AI model is reliable and can serve as a roadmap for future studies using AI models in a clinical setting.

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Disfunción Cognitiva , Internet , Valor Predictivo de las Pruebas , Accidente Cerebrovascular , Humanos , Femenino , Masculino , Anciano , Disfunción Cognitiva/diagnóstico , Disfunción Cognitiva/etiología , Disfunción Cognitiva/psicología , Accidente Cerebrovascular/complicaciones , Accidente Cerebrovascular/diagnóstico , Accidente Cerebrovascular/fisiopatología , Accidente Cerebrovascular/psicología , Estudios Retrospectivos , Persona de Mediana Edad , Reproducibilidad de los Resultados , Cognición , Pronóstico , Factores de Riesgo , Aprendizaje Automático , Medición de Riesgo , Factores de Tiempo , Anciano de 80 o más Años , Diagnóstico por Computador , China/epidemiología , Inteligencia Artificial

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INTRODUCTION: This review explores the transformative impact of machine learning (ML) on carcinogenicity prediction within drug development. It discusses the historical context and recent advancements, emphasizing the significance of ML methodologies in overcoming challenges related to data interpretation, ethical considerations, and regulatory acceptance. AREAS COVERED: The review comprehensively examines the integration of ML, deep learning, and diverse artificial intelligence (AI) approaches in various aspects of drug development safety assessments. It explores applications ranging from early-phase compound screening to clinical trial optimization, highlighting the versatility of ML in enhancing predictive accuracy and efficiency. EXPERT OPINION: Through the analysis of traditional approaches such as in vivo rodent bioassays and in vitro assays, the review underscores the limitations and resource intensity associated with these methods. It provides expert insights into how ML offers innovative solutions to address these challenges, revolutionizing safety assessments in drug development.

Asunto(s)

Inteligencia Artificial , Pruebas de Carcinogenicidad , Carcinógenos , Desarrollo de Medicamentos , Aprendizaje Automático , Humanos , Desarrollo de Medicamentos/métodos , Animales , Pruebas de Carcinogenicidad/métodos , Carcinógenos/toxicidad , Aprendizaje Profundo

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OBJECTIVE: The objective of this paper is to provide a comprehensive overview of the development and features of the Taipei Medical University Clinical Research Database (TMUCRD), a repository of real-world data (RWD) derived from electronic health records (EHRs) and other sources. METHODS: TMUCRD was developed by integrating EHRs from three affiliated hospitals, including Taipei Medical University Hospital, Wan-Fang Hospital and Shuang-Ho Hospital. The data cover over 15 years and include diverse patient care information. The database was converted to the Observational Medical Outcomes Partnership Common Data Model (OMOP CDM) for standardisation. RESULTS: TMUCRD comprises 89 tables (eg, 29 tables for each hospital and 2 linked tables), including demographics, diagnoses, medications, procedures and measurements, among others. It encompasses data from more than 4.15 million patients with various medical records, spanning from the year 2004 to 2021. The dataset offers insights into disease prevalence, medication usage, laboratory tests and patient characteristics. DISCUSSION: TMUCRD stands out due to its unique advantages, including diverse data types, comprehensive patient information, linked mortality and cancer registry data, regular updates and a swift application process. Its compatibility with the OMOP CDM enhances its usability and interoperability. CONCLUSION: TMUCRD serves as a valuable resource for researchers and scholars interested in leveraging RWD for clinical research. Its availability and integration of diverse healthcare data contribute to a collaborative and data-driven approach to advancing medical knowledge and practice.

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Bases de Datos Factuales , Registros Electrónicos de Salud , Humanos , Taiwán , Hospitales Universitarios

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BACKGROUND: Optimal timing for initiating maintenance dialysis in patients with chronic kidney disease (CKD) stages 3-5 is challenging. This study aimed to develop and validate a machine learning (ML) model for early personalised prediction of maintenance dialysis initiation within 1-year and 3-year timeframes among patients with CKD stages 3-5. METHODS: Retrospective electronic health record data from the Taipei Medical University clinical research database were used. Newly diagnosed patients with CKD stages 3-5 between 2008 and 2017 were identified. The observation period spanned from the diagnosis of CKD stages 3-5 until the maintenance dialysis initiation or a maximum follow-up of 3 years. Predictive models were developed using patient demographics, comorbidities, laboratory data and medications. The dataset was divided into training and testing sets to ensure robust model performance. Model evaluation metrics, including area under the curve (AUC), sensitivity, specificity, positive predictive value, negative predictive value and F1 score, were employed. RESULTS: A total of 6123 and 5279 patients were included for 1 year and 3 years of the model development. The artificial neural network demonstrated better performance in predicting maintenance dialysis initiation within 1 year and 3 years, with AUC values of 0.96 and 0.92, respectively. Important features such as baseline estimated glomerular filtration rate and albuminuria significantly contributed to the predictive model. CONCLUSION: This study demonstrates the efficacy of an ML approach in developing a highly predictive model for estimating the timing of maintenance dialysis initiation in patients with CKD stages 3-5. These findings have important implications for personalised treatment strategies, enabling improved clinical decision-making and potentially enhancing patient outcomes.

Asunto(s)

Aprendizaje Automático , Diálisis Renal , Insuficiencia Renal Crónica , Humanos , Femenino , Masculino , Estudios Retrospectivos , Insuficiencia Renal Crónica/terapia , Persona de Mediana Edad , Anciano , Registros Electrónicos de Salud , Taiwán , Medicina de Precisión

RESUMEN

This article contains up-to-date information on the features of ophthalmological and dermatological manifestations of human immunodeficiency virus (HIV) infection based on the analysis of studies published in 2018-2022. The article also presents a description of a clinical case of HIV infection in a 54-year-old female patient with synchronous manifestation of eye symptoms in the form of retinal vasculitis of the optic nerve head and Kaposi's sarcoma localized on the skin of the face.

Asunto(s)

Infecciones por VIH , Oftalmología , Disco Óptico , Femenino , Humanos , Persona de Mediana Edad , Infecciones por VIH/complicaciones , Infecciones por VIH/diagnóstico , Piel

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An advisory board meeting was held with experts in Vietnam (Hanoi, August 2022), to review the evidence on invasive meningococcal disease (IMD) epidemiology, clinical management, and meningococcal vaccines to reach a consensus on recommendations for meningococcal vaccination in Vietnam. IMD is a severe disease, with the highest burden in infants and children. IMD presents as meningitis and/or meningococcemia and can progress extremely rapidly. Almost 90% of deaths in children occur within the first 24 h, and disabling sequelae (e.g., limb amputations and neurological damage) occur in up to 20% of survivors. IMD patients are often hospitalized late, due to mild and nonspecific early symptoms and misdiagnosis. Difficulties related to diagnosis and antibiotic misuse mean that the number of reported IMD cases in Vietnam is likely to be underestimated. Serogroup B IMD is predominant in many regions of the world, including Vietnam, where 82% of IMD cases were due to serogroup B (surveillance data from 2012 to 2021). Four component meningococcal B vaccine (4CMenB) is used in many countries (and is part of the pediatric National Immunization Program in 13 countries), with infant vaccination starting from two months of age, and a 2 + 1 dosing schedule. Experts recommend 4CMenB vaccination as soon as possible in Vietnam, starting from two months of age, with a 2 + 1 dosing schedule, and at least completing one dose before 6 months of age.

RESUMEN

The study aims to develop machine-learning models to predict cardiac adverse events in female breast cancer patients who receive adjuvant therapy. We selected breast cancer patients from a retrospective dataset of the Taipei Medical University Clinical Research Database and Taiwan Cancer Registry between January 2004 and December 2020. Patients were monitored at the date of prescribed chemo- and/or -target therapies until cardiac adverse events occurred during a year. Variables were used, including demographics, comorbidities, medications, and lab values. Logistics regression (LR) and artificial neural network (ANN) were used. The performance of the algorithms was measured by the area under the receiver operating characteristic curve (AUC). In total, 1321 patients (an equal 15039 visits) were included. The best performance of the artificial neural network (ANN) model was achieved with the AUC, precision, recall, and F1-score of 0.89, 0.14, 0.82, and 0.2, respectively. The most important features were a pre-existing cardiac disease, tumor size, estrogen receptor (ER), human epidermal growth factor receptor 2 (HER2), cancer stage, and age at index date. Further research is necessary to determine the feasibility of applying the algorithm in the clinical setting and explore whether this tool could improve care and outcomes.

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Neoplasias de la Mama , Humanos , Femenino , Neoplasias de la Mama/tratamiento farmacológico , Estudios Retrospectivos , Terapia Combinada , Algoritmos , Aprendizaje Automático

Asunto(s)

Demencia , Diabetes Mellitus Tipo 2 , Humanos , Diabetes Mellitus Tipo 2/complicaciones , Diabetes Mellitus Tipo 2/diagnóstico , Diabetes Mellitus Tipo 2/epidemiología , Incidencia , Pacientes , Demencia/epidemiología

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We report on a 2023 outbreak of severe hand, foot, and mouth disease in southern Vietnam caused by an emerging lineage of enterovirus A71 subgenogroup B5. Affected children were significantly older than those reported during previous outbreaks. The virus should be closely monitored to assess its potential for global dispersal.

Asunto(s)

Infecciones por Enterovirus , Enterovirus , Enfermedad de Boca, Mano y Pie , Niño , Humanos , Enterovirus/genética , Vietnam/epidemiología , Enfermedad de Boca, Mano y Pie/epidemiología , Infecciones por Enterovirus/epidemiología , Extremidad Inferior , Antígenos Virales

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AIMS: The prevalence of Type 2 Diabetes Mellitus (T2DM) is projected to be 7 % in 2030. Despite its need for long-term diabetes care, the adherence rate of injectable medications such as insulin is around 60 %, lower than the acceptable threshold of 80 %. This study aims to create classification models to predict insulin adherence among adult T2DM naïve insulin users. METHODS: Clinical data were extracted from Taipei Medical University Clinical Research Database (TMUCRD) from January 1st, 2004 to December 30th, 2020. A patient was regarded as adherent if his/her medication possession ratio (MPR) was at least 80 %. Seven domains of predictors were created, including demographics, baseline medications, baseline comorbidities, baseline laboratory data, healthcare resource utilization, index insulins, and the concomitant non-insulin T2DM medications. We built two Xgboost models for internal and external testing respectively. RESULTS: Using a cohort of 4134 patients from Taiwan, our model achieved the Area Under the curve of the Receiver Operating Characteristic (AUROC) of the internal test was 0.782 and the AUROC of the external test was 0.771. the SHAP (SHapley Additive exPlanations) value showed that the number of prescribed medications, the number of outpatient visits, and laboratory data were predictive of future insulin adherence. CONCLUSIONS: This is the first study to predict adherence among adult naïve insulin users. The developed model is a potential clinical decision support tool to identify possible non-adherent patients for healthcare providers to design individualized education plans.

Asunto(s)

Diabetes Mellitus Tipo 2 , Humanos , Adulto , Masculino , Femenino , Diabetes Mellitus Tipo 2/tratamiento farmacológico , Diabetes Mellitus Tipo 2/complicaciones , Insulina/uso terapéutico , Estudios de Cohortes , Cumplimiento de la Medicación , Insulina Regular Humana/uso terapéutico , Aprendizaje Automático , Estudios Retrospectivos

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One new prenyl flavanone (1), (2S)-8-prenyl-5,6-dihydroxy-7-methoxyflavanone, and one new diarylbutanol (2), (7'S)-3'-hydroxy-linderagatin-A, were isolated from the stem bark of Uvaria siamensis (Annonaceae), along with five known compounds, eriodictyol (3), quercetin (4), paprazine (5), N-trans-caffeoyltyramine (6), and N-trans-feruloyltyramine (7). Their structures were determined through extensive spectroscopic analyses and comparison with the literature. The α-glucosidase inhibitory potential of 1-7 was evaluated. Compound 6 showed the highest inhibitory activity against α-glucosidase and exhibited superior potency compared to the positive control, with an IC50 value of 0.12 µM.

RESUMEN

Importance: Ranitidine, the most widely used histamine-2 receptor antagonist (H2RA), was withdrawn because of N-nitrosodimethylamine impurity in 2020. Given the worldwide exposure to this drug, the potential risk of cancer development associated with the intake of known carcinogens is an important epidemiological concern. Objective: To examine the comparative risk of cancer associated with the use of ranitidine vs other H2RAs. Design, Setting, and Participants: This new-user active comparator international network cohort study was conducted using 3 health claims and 9 electronic health record databases from the US, the United Kingdom, Germany, Spain, France, South Korea, and Taiwan. Large-scale propensity score (PS) matching was used to minimize confounding of the observed covariates with negative control outcomes. Empirical calibration was performed to account for unobserved confounding. All databases were mapped to a common data model. Database-specific estimates were combined using random-effects meta-analysis. Participants included individuals aged at least 20 years with no history of cancer who used H2RAs for more than 30 days from January 1986 to December 2020, with a 1-year washout period. Data were analyzed from April to September 2021. Exposure: The main exposure was use of ranitidine vs other H2RAs (famotidine, lafutidine, nizatidine, and roxatidine). Main Outcomes and Measures: The primary outcome was incidence of any cancer, except nonmelanoma skin cancer. Secondary outcomes included all cancer except thyroid cancer, 16 cancer subtypes, and all-cause mortality. Results: Among 1 183 999 individuals in 11 databases, 909 168 individuals (mean age, 56.1 years; 507 316 [55.8%] women) were identified as new users of ranitidine, and 274 831 individuals (mean age, 58.0 years; 145 935 [53.1%] women) were identified as new users of other H2RAs. Crude incidence rates of cancer were 14.30 events per 1000 person-years (PYs) in ranitidine users and 15.03 events per 1000 PYs among other H2RA users. After PS matching, cancer risk was similar in ranitidine compared with other H2RA users (incidence, 15.92 events per 1000 PYs vs 15.65 events per 1000 PYs; calibrated meta-analytic hazard ratio, 1.04; 95% CI, 0.97-1.12). No significant associations were found between ranitidine use and any secondary outcomes after calibration. Conclusions and Relevance: In this cohort study, ranitidine use was not associated with an increased risk of cancer compared with the use of other H2RAs. Further research is needed on the long-term association of ranitidine with cancer development.

Asunto(s)

Neoplasias Cutáneas , Neoplasias de la Tiroides , Femenino , Humanos , Persona de Mediana Edad , Masculino , Ranitidina/efectos adversos , Estudios de Cohortes , Antagonistas de los Receptores H2 de la Histamina/efectos adversos

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INTRODUCTION: Pancreatic cancer is associated with poor prognosis. Considering the increased global incidence of diabetes cases and that individuals with diabetes are considered a high-risk subpopulation for pancreatic cancer, it is critical to detect the risk of pancreatic cancer within populations of person living = with diabetes. This study aimed to develop a novel prediction model for pancreatic cancer risk among patients with diabetes, using = a real-world database containing clinical features and employing numerous artificial intelligent approach algorithms. METHODS: This retrospective observational study analyzed data on patients with Type 2 diabetes from a multisite Taiwanese EMR database between 2009 and 2019. Predictors were selected in accordance with the literature review and clinical perspectives. The prediction models were constructed using machine learning algorithms such as logistic regression, linear discriminant analysis, gradient boosting machine, and random forest. RESULTS: The cohort consisted of 66,384 patients. The Linear Discriminant Analysis (LDA) model generated the highest AUROC of 0.9073, followed by the Voting Ensemble and Gradient Boosting machine models. LDA, the best model, exhibited an accuracy of 84.03%, a sensitivity of 0.8611, and a specificity of 0.8403. The most significant predictors identified for pancreatic cancer risk were glucose, glycated hemoglobin, hyperlipidemia comorbidity, antidiabetic drug use, and lipid-modifying drug use. CONCLUSION: This study successfully developed a highly accurate 4-year risk model for pancreatic cancer in patients with diabetes using real-world clinical data and multiple machine-learning algorithms. Potentially, our predictors offer an opportunity to identify pancreatic cancer early and thus increase prevention and invention windows to impact survival in diabetic patients.

Asunto(s)

Diabetes Mellitus Tipo 2 , Neoplasias Pancreáticas , Humanos , Diabetes Mellitus Tipo 2/complicaciones , Diabetes Mellitus Tipo 2/epidemiología , Neoplasias Pancreáticas/epidemiología , Neoplasias Pancreáticas/etiología , Páncreas , Aprendizaje Automático , Neoplasias Pancreáticas

RESUMEN

The study used clinical data to develop a prediction model for breast cancer survival. Breast cancer prognostic factors were explored using machine learning techniques. We conducted a retrospective study using data from the Taipei Medical University Clinical Research Database, which contains electronic medical records from three affiliated hospitals in Taiwan. The study included female patients aged over 20 years who were diagnosed with primary breast cancer and had medical records in hospitals between January 1, 2009 and December 31, 2020. The data were divided into training and external testing datasets. Nine different machine learning algorithms were applied to develop the models. The performances of the algorithms were measured using the area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and F1-score. A total of 3914 patients were included in the study. The highest AUC of 0.95 was observed with the artificial neural network model (accuracy, 0.90; sensitivity, 0.71; specificity, 0.73; PPV, 0.28; NPV, 0.94; and F1-score, 0.37). Other models showed relatively high AUC, ranging from 0.75 to 0.83. According to the optimal model results, cancer stage, tumor size, diagnosis age, surgery, and body mass index were the most critical factors for predicting breast cancer survival. The study successfully established accurate 5-year survival predictive models for breast cancer. Furthermore, the study found key factors that could affect breast cancer survival in Taiwanese women. Its results might be used as a reference for the clinical practice of breast cancer treatment.

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Neoplasias de la Mama , Humanos , Femenino , Adulto , Estudios Retrospectivos , Aprendizaje Automático , Valor Predictivo de las Pruebas , Curva ROC

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Objectives: A vast amount of literature has been conducted for investigating the association of different lunar phases with human health; and it has mixed reviews for association and non-association of diseases with lunar phases. This study investigates the existence of any impact of moon phases on humans by exploring the difference in the rate of outpatient visits and type of diseases that prevail in either non-moon or moon phases. Methods: We retrieved dates of non-moon and moon phases for eight years (1st January 2001-31st December 2008) from the timeanddate.com website for Taiwan. The study cohort consisted of 1 million people from Taiwan's National Health Insurance Research Database (NHIRD) followed over eight years (1st January 2001-31st December 2008). We used the two-tailed, paired-t-test to compare the significance of difference among outpatient visits for 1229 moon phase days and 1074 non-moon phase days by using International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) codes from NHIRD records. Results: We found 58 diseases that showed statistical differences in number of outpatient visits in the non-moon and moon phases. Conclusions: The results of our study identified diseases that have significant variations during different lunar phases (non-moon and moon phases) for outpatient visits in the hospital. In order to fully understand the reality of the pervasive myth of lunar effects on human health, behaviors and diseases, more in-depth research investigations are required for providing comprehensive evidence covering all the factors, such as biological, psychological and environmental aspects.

RESUMEN

Debate regarding vaccinating high-risk infants with penta- and hexavalent vaccines persists, despite their good immunogenicity and acceptable safety profile in healthy full-term infants. We report the findings of a systematic literature search that aimed to present data on the immunogenicity, efficacy, effectiveness, safety, impact, compliance and completion of penta- and hexavalent vaccination in high-risk infants, including premature newborns. Data from the 14 studies included in the review showed that the immunogenicity and the safety profile of penta- and hexavalent vaccines in preterm infants was generally similar to those seen in full-term infants, with the exception of an increase in cardiorespiratory adverse events such as apnea, bradycardia and desaturation following vaccination in preterm infants. Despite recommendations of vaccinating preterm infants according to their actual age, and the relatively high completion rate of the primary immunization schedule, vaccination was often delayed, increasing the vulnerability of this high-risk population to vaccine-preventable diseases.

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Combined vaccines such as penta- and hexavalent vaccines against multiple childhood diseases are widely used in healthy babies born at term. However, it is still debated whether these vaccines act the same way in babies considered to be high-risk: babies born prematurely at <34 weeks of pregnancy, those with a birthweight of <1500 g or babies with chronic diseases. We did a systematic literature search to find studies on such high-risk babies vaccinated with penta- or hexavalent vaccines; we focused on their antibody levels following vaccination, side effects, and protection from the diseases against which they were vaccinated. We also analyzed whether they were vaccinated on time and with all the doses recommended for healthy full-term babies. We found 14 studies that included premature babies. The results of these studies suggest that premature babies' immune systems respond to penta- and hexavalent vaccines in largely the same way as those of full-term babies; side effects of penta- and hexavalent vaccines are also mostly similar to those seen in full-term babies. However, side effects like pauses in breathing, slow heart rate or low blood oxygen levels seem to be more common in preterm babies; for safety, these babies should be monitored closely after vaccination. Preterm babies are often vaccinated with a delay compared to the recommended schedule. No studies reported data on protection from the diseases covered by penta- and hexavalent vaccinations in preterm babies. More research is needed on penta- and hexavalent vaccination of other high-risk babies besides those born prematurely.

Asunto(s)

Enfermedades del Recién Nacido , Rubiaceae , Lactante , Recién Nacido , Humanos , Recien Nacido Prematuro , Vacunas Combinadas/efectos adversos , Vacunación/efectos adversos , Esquemas de Inmunización

RESUMEN

BACKGROUND: Psoriasis (PsO) is a chronic, systemic, immune-mediated disease with multiorgan involvement. Psoriatic arthritis (PsA) is an inflammatory arthritis that is present in 6%-42% of patients with PsO. Approximately 15% of patients with PsO have undiagnosed PsA. Predicting patients with a risk of PsA is crucial for providing them with early examination and treatment that can prevent irreversible disease progression and function loss. OBJECTIVE: The aim of this study was to develop and validate a prediction model for PsA based on chronological large-scale and multidimensional electronic medical records using a machine learning algorithm. METHODS: This case-control study used Taiwan's National Health Insurance Research Database from January 1, 1999, to December 31, 2013. The original data set was split into training and holdout data sets in an 80:20 ratio. A convolutional neural network was used to develop a prediction model. This model used 2.5-year diagnostic and medical records (inpatient and outpatient) with temporal-sequential information to predict the risk of PsA for a given patient within the next 6 months. The model was developed and cross-validated using the training data and was tested using the holdout data. An occlusion sensitivity analysis was performed to identify the important features of the model. RESULTS: The prediction model included a total of 443 patients with PsA with earlier diagnosis of PsO and 1772 patients with PsO without PsA for the control group. The 6-month PsA risk prediction model that uses sequential diagnostic and drug prescription information as a temporal phenomic map yielded an area under the receiver operating characteristic curve of 0.70 (95% CI 0.559-0.833), a mean sensitivity of 0.80 (SD 0.11), a mean specificity of 0.60 (SD 0.04), and a mean negative predictive value of 0.93 (SD 0.04). CONCLUSIONS: The findings of this study suggest that the risk prediction model can identify patients with PsO at a high risk of PsA. This model may help health care professionals to prioritize treatment for target high-risk populations and prevent irreversible disease progression and functional loss.

Asunto(s)

Artritis Psoriásica , Psoriasis , Humanos , Artritis Psoriásica/diagnóstico , Artritis Psoriásica/terapia , Registros Electrónicos de Salud , Estudios de Casos y Controles , Aprendizaje Automático , Progresión de la Enfermedad

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Invasive meningococcal disease (IMD), caused by Neisseria meningitidis, is life-threatening with a high case fatality rate (CFR) and severe sequelae. We compiled and critically discussed the evidence on IMD epidemiology, antibiotic resistance and disease management in Vietnam, focusing on children. PubMed, Embase and gray literature searches for English, Vietnamese and French publications, with no date restrictions, retrieved 11 eligible studies. IMD incidence rate (/100,000 population) was 7.4 [95% confidence interval 3.6-15.3] in children under 5 years of age; driven by high rates in infants (e.g. 29.1 [8.0-106.0] in 7-11 month-olds). Serogroup B IMD was predominant. Neisseria meningitidis strains may have developed resistance to streptomycin, sulfonamides, ciprofloxacin, and possibly ceftriaxone. There was a lack of current data on diagnosis and treatment of IMD, which remain challenging. Healthcare professionals should be trained to rapidly recognize and treat IMD. Preventive measures, such as routine vaccination, could help address the medical need.

Asunto(s)

Infecciones Meningocócicas , Vacunas Meningococicas , Neisseria meningitidis Serogrupo B , Neisseria meningitidis , Niño , Preescolar , Humanos , Lactante , Incidencia , Infecciones Meningocócicas/diagnóstico , Infecciones Meningocócicas/epidemiología , Infecciones Meningocócicas/prevención & control , Serogrupo , Vietnam/epidemiología

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BACKGROUND AND OBJECTIVE: The promising use of artificial intelligence (AI) to emulate human empathy may help a physician engage with a more empathic doctor-patient relationship. This study demonstrates the application of artificial empathy based on facial emotion recognition to evaluate doctor-patient relationships in clinical practice. METHODS: A prospective study used recorded video data of doctor-patient clinical encounters in dermatology outpatient clinics, Taipei Municipal Wanfang Hospital, and Taipei Medical University Hospital collected from March to December 2019. Two cameras recorded the facial expressions of four doctors and 348 adult patients during regular clinical practice. Facial emotion recognition was used to analyze the basic emotions of doctors and patients with a temporal resolution of 1 second. In addition, a physician-patient satisfaction questionnaire was administered after each clinical session, and two standard patients gave impartial feedback to avoid bias. RESULTS: Data from 326 clinical session videos showed that (1) Doctors expressed more emotions than patients (t [326] > = 2.998, p < = 0.003), including anger, happiness, disgust, and sadness; the only emotion that patients showed more than doctors was surprise (t [326] = -4.428, p < .001) (p < .001). (2) Patients felt happier during the latter half of the session (t [326] = -2.860, p = .005), indicating a good doctor-patient relationship. CONCLUSIONS: Artificial empathy can offer objective observations on how doctors' and patients' emotions change. With the ability to detect emotions in 3/4 view and profile images, artificial empathy could be an accessible evaluation tool to study doctor-patient relationships in practical clinical settings.

Asunto(s)

Empatía , Relaciones Médico-Paciente , Adulto , Humanos , Estudios Prospectivos , Inteligencia Artificial , Emociones