Predictive modeling of gestational weight gain: a machine learning multiclass classification study.

Background: Gestational weight gain (GWG) is a critical factor influencing maternal and fetal health. Excessive or insufficient GWG can lead to various complications, including gestational diabetes, hypertension, cesarean delivery, low birth weight, and preterm birth. This study aims to develop and evaluate machine learning models to predict GWG categories: below, within, or above recommended guidelines.

Accuracy of COVID-19 diagnostic tests via infrared spectroscopy: A systematic review and meta-analysis.

This study aims to synthesize the evidence on the accuracy parameters of COVID-19 diagnosis methods using infrared spectroscopy (FTIR). A systematic review with searches in PubMed and Embase was performed (September 2023). Studies reporting data on test specificity, sensitivity, true positive, true negative, false positive, and false negative using different human samples were included.

approaches supporting drug repurposing for Leishmaniasis: a scoping review.

The shortage of treatment options for leishmaniasis, especially those easy to administer and viable for deployment in the world's poorest regions, highlights the importance of employing these strategies to cost-effectively investigate repurposing candidates. This scoping review aims to map the studies using methodologies for drug repurposing against leishmaniasis. This study followed JBI recommendations for scoping reviews.

Methodological quality and clinical recommendations of guidelines on the management of dyslipidaemias for cardiovascular disease risk reduction: a systematic review and an appraisal through AGREE II and AGREE REX tools.

Background: Clinical practice guidelines (CPGs) are statements to assist practitioners and stakeholders in decisions about healthcare. Low methodological quality guidelines may prejudice decision-making and negatively affect clinical outcomes in non-communicable diseases, such as cardiovascular diseases worsted by poor lipid management. We appraised the quality of CPGs on dyslipidemia management and synthesized the most updated pharmacological recommendations.

Novel COVID-19 biomarkers identified through multi-omics data analysis: N-acetyl-4-O-acetylneuraminic acid, N-acetyl-L-alanine, N-acetyltriptophan, palmitoylcarnitine, and glycerol 1-myristate.

This study aims to apply machine learning models to identify new biomarkers associated with the early diagnosis and prognosis of SARS-CoV-2 infection.Plasma and serum samples from COVID-19 patients (mild, moderate, and severe), patients with other pneumonia (but with negative COVID-19 RT-PCR), and healthy volunteers (control) from hospitals in four different countries (China, Spain, France, and Italy) were analyzed by GC-MS, LC-MS, and NMR. Machine learning models (PCA and PLS-DA) were developed to predict the diagnosis and prognosis of COVID-19 and identify biomarkers associated with these outcomes.

Safety evaluation of Mikania glomerata and Mikania laevigata in healthy volunteers: A randomized, open label and multiple dose phase I clinical trial.

Ethnopharmacological Relevance: Mikania glomerata (MG) and Mikania laevigata (ML) leaves are used interchangeably in Brazilian ethno-medicine, mainly for the treatment of respiratory diseases, but there is no clinical trial for the safety evaluation of these species at different doses.

Aim Of The Study: To evaluate the safety of two oral solutions from each species, across two weeks of use and two doses.

Materials And Methods: A randomized, open-label, multiple dose, two-arm trial.

Naringenin-4'-glucuronide as a new drug candidate against the COVID-19 Omicron variant: a study based on molecular docking, molecular dynamics, MM/PBSA and MM/GBSA.

This study aimed to identify natural bioactive compounds (NBCs) as potential inhibitors of the spike (S1) receptor binding domain (RBD) of the COVID-19 Omicron variant using computer simulations (. NBCs with previously proven biological activity were obtained from the ZINC database and analyzed through virtual screening, molecular docking, molecular dynamics (MD), molecular mechanics/Poisson-Boltzmann surface area (MM/PBSA), and molecular mechanics/generalized Born surface area (MM/GBSA). Remdesivir was used as a reference drug in docking and MD calculations.

Status and Future Scope of Soft Nanoparticles-Based Hydrogel in Wound Healing.

Wounds are alterations in skin integrity resulting from any type of trauma. The healing process is complex, involving inflammation and reactive oxygen species formation. Therapeutic approaches for the wound healing process are diverse, associating dressings and topical pharmacological agents with antiseptics, anti-inflammatory, and antibacterial actions.

Diagnosis and prognosis of COVID-19 employing analysis of patients' plasma and serum via LC-MS and machine learning.

Objective: To implement and evaluate machine learning (ML) algorithms for the prediction of COVID-19 diagnosis, severity, and fatality and to assess biomarkers potentially associated with these outcomes.

Material And Methods: Serum (n = 96) and plasma (n = 96) samples from patients with COVID-19 (acute, severe and fatal illness) from two independent hospitals in China were analyzed by LC-MS. Samples from healthy volunteers and from patients with pneumonia caused by other viruses (i.

A validated LC-MS/MS assay for the quantification of phosphodiesterase-5 inhibitors in human plasma.

Phosphodiesterase inhibitors (PDE5i) are considered the first line therapy for erectile dysfunction. All PDE5i available on the market are structurally related; their main differences relate to their pharmacokinetic parameters. For these treatments to be effective and safe, it is necessary that these drugs are in the appropriate doses and that they reach adequate concentrations in the plasma.

Diagnosis and prediction of COVID-19 severity: can biochemical tests and machine learning be used as prognostic indicators?

Objective: This study aimed to implement and evaluate machine learning based-models to predict COVID-19' diagnosis and disease severity.

Methods: COVID-19 test samples (positive or negative results) from patients who attended a single hospital were evaluated. Patients diagnosed with COVID-19 were categorised according to the severity of the disease.

Chemical composition, cytotoxicity, and antibacterial activity of propolis from Africanized honeybees and three different Meliponini species.

Ethnopharmacological Relevance: Propolis extracts are widely used in traditional folk medicine and exhibit several properties such as antitumor, anti-inflammatory, and antimicrobial. However, these products have not been investigated in combination with medicines used in clinical practice.

Aim Of The Study: This study aimed to evaluate the chemical composition of propolis extracts from Apis mellifera scutellata and different Meliponini species and characterize their cytotoxicity against tumor cells, antibacterial effects, and interference with the actions of doxorubicin and gentamicin.

A multivariate analysis of risk factors associated with death by Covid-19 in the USA, Italy, Spain, and Germany.

Aim: Our aim was to investigate the risk factors associated with death from COVID-19 in four countries: The USA, Italy, Spain, and Germany.

Subject And Methods: We used data from the Institute for Health Metrics and Evaluation with projection information from January-August 2020. A multivariate analysis of logistic regression was performed.

Risk factors associated with delay in diagnosis and mortality in patients with COVID-19 in the city of Rio de Janeiro, Brazil.

We investigated the predictors of delay in the diagnosis and mortality of patients with COVID-19 in Rio de Janeiro, Brazil. A cohort of 3,656 patients were evaluated (Feb-Apr 2020) and patients' sociodemographic characteristics, and social development index (SDI) were used as determinant factors of diagnosis delays and mortality. Kaplan-Meier survival analyses, time-dependent Cox regression models, and multivariate logistic regression analyses were conducted.

LC-QToF-MS method for quantification of ethambutol, isoniazid, pyrazinamide and rifampicin in human plasma and its application.

In this research, we developed and validated a liquid chromatography coupled to mass spectrometry (LC-QToF-MS) method for simultaneous quantification of the anti-tuberculosis drugs ethambutol, isoniazid, pyrazinamide and rifampicin in human plasma. Plasma samples spiked with cimetidine (internal standard) were extracted using protein precipitation with acetonitrile containing 1% formic acid. Separation was performed using a C column under flow gradient conditions with water and acetonitrile, both containing 5 mm ammonium formate and 0.