Integrating whole genome sequencing and machine learning for predicting antimicrobial resistance in critical pathogens: a systematic review of antimicrobial susceptibility tests.

Background: Infections caused by antibiotic-resistant bacteria pose a major challenge to modern healthcare. This systematic review evaluates the efficacy of machine learning (ML) approaches in predicting antimicrobial resistance (AMR) in critical pathogens (CP), considering Whole Genome Sequencing (WGS) and antimicrobial susceptibility testing (AST). Methods: The search covered databases including PubMed/MEDLINE, EMBASE, Web of Science, SCOPUS, and SCIELO, from their inception until June 2024. The review protocol was officially registered on PROSPERO (CRD42024543099). Results: The review included 26 papers, analyzing data from 104,141 microbial samples. Random Forest (RF), XGBoost, and logistic regression (LR) emerged as the top-performing models, with mean Area Under the Receiver Operating Characteristic (AUC) values of 0.89, 0.87, and 0.87, respectively. RF showed superior performance with AUC values ranging from 0.66 to 0.97, while XGBoost and LR showed similar performance with AUC values ranging from 0.83 to 0.91 and 0.76 to 0.96, respectively. Most studies indicate that integrating WGS and AST data into ML models enhances predictive performance, improves antibiotic stewardship, and provides valuable clinical decision support. ML shows significant promise for predicting AMR by integrating WGS and AST data in CP. Standardized guidelines are needed to ensure consistency in future research.

Biochemical markers for prediction of the first half pregnancy losses: a review.

Objective: 26% of all pregnancies end in miscarriage, and up to 10% of clinically diagnosed pregnancies, and recurrent pregnancy loss is 5% among couples of childbearing ages. Although there are several known causes of pregnancy loss in the first half, including recurrent pregnancy loss, including parental chromosomal abnormalities, uterine malformations, endocrinological disorders, and immunological abnormalities, about half of the cases of pregnancy loss in its first half remain unexplained. Methods: The review includes observational controlled studies (case-control or cohort, longitudinal studies, reviews, meta-analyses), which include the study of biochemical factors for predicting pregnancy losses in the first half, in singlet pregnancy. The Newcastle-Ottawa Scale (NOS) was used to assess the research quality. Results: Finally, 27 studies were included in the review, which has 134904 examined patients. The results of the review include estimates of ß-human chorionic gonadotropin, progesterone, pregnancy-associated protein - A, angiogenic vascular factors, estradiol, α-fetoprotein, homocysteine and CA-125 as a predictors or markers of the first half pregnancy losses. Conclusion: It may be concluded that to date, research data indicate the unavailability of any reliable biochemical marker for predicting pregnancy losses in its first half and require either a combination of them or comparison with clinical evidence. A fairly new model shall be considered for the assessment of α-fetoprotein in vaginal blood, which may have great prospects in predicting spontaneous miscarriages.

Development and validation of interpretable machine learning models to predict glomerular filtration rate in chronic kidney disease Colombian patients.

BACKGROUND: ML predictive models have shown their capability to improve risk prediction and assist medical decision-making, nevertheless, there is a lack of accuracy systems to early identify future rapid CKD progressors in Colombia and even in South America. OBJECTIVE: The purpose of this study was to develop a series of interpretable machine learning models that predict GFR at 6-months, 9-months, and 12-months. STUDY DESIGN AND SETTING: Over 29,000 CKD patients stage 1 to 3b (estimated GFR, <60 mL/min/1.73 m2) with an average of 3-year follow-up data were included. We used the machine learning extreme gradient boosting (XGBoost) to build three models to predict the next eGFR. Models were internally and externally validated. In addition, we included SHapley Additive exPlanation (SHAP) values to offer interpretable global and local prediction models. RESULTS: All models showed a good performance in development and external validation. However, the 6-months XGBoost prediction model showed the best performance in internal (MAE average = 6.07; RSME = 78.87), and in external validation (MAE average = 6.45, RSME = 18.94). The top 3 most influential features that pushed the predicted eGFR value to lower values were the interpolated values for eGFR and creatinine, and eGFR at baseline. CONCLUSION: In the current study we have developed and validated machine learning models to predict the next eGFR value at different intervals. Furthermore, we attempted to approach the need for prediction explanation by offering transparent predictions.

MODY calculator applied in patients with clinical diagnosis of type 1 diabetes mellitus: Is a higher cutoff needed?

Aim: This study aimed to evaluate the mean post-test probability (PTP) of the Maturity-onset diabetes of the young (MODY) calculator in a multiethnic cohort of patients previously diagnosed with type 1 diabetes (T1DM). Materials and methods: The MODY probability calculator proposed by Shields and colleagues (2012) was applied to 117 patients from a T1DM outpatient clinic at a tertiary hospital in Brazil. Additionally, two exons of the HNF1A gene were sequenced in eight patients who hadn't received insulin treatment within six months after the diagnosis. Results: 17.1 % of patients achieved PTP >10 %; 11.1 % achieved PTP >25 % (and all patients >30 %), and 7.7 % achieved PTP >40 %. Among the patients who were selected for genetic sequencing, 100 % presented PTP >30 %, with 66.6 % achieving PTP >40 % and 41.6 % achieving PTP >75 %. These cutoffs are as suggested for the Brazilian population, according to previous investigations. No mutation was observed in the sequenced exons. Conclusion: Considering that only around 10 % of the evaluated cases achieved PTP >30 %, it is highly probable that the most suitable cutoff to select patients for genetic sequencing in a Brazilian cohort of T1DM is higher than the cutoff used in Caucasian populations.

Left atrial function in patients with rheumatic mitral stenosis: addressing prognostic insights beyond atrial fibrillation prediction.

Aims: Rheumatic mitral stenosis (MS) frequently leads to impaired left atrial (LA) function because of pressure overload, highlighting the underlying atrial pathology. Two-dimensional speckle tracking echocardiography (2D-STE) offers early detection of LA dysfunction, potentially improving risk assessment in patients with MS. This study aims to evaluate the predictive value of LA function assessed by 2D-STE for clinical outcomes in patients with MS. Methods and results: Between 2011 and 2021, patients with MS underwent LA function assessment using 2D-STE, with focus on the reservoir phase (LASr). Atrial fibrillation (AF) development constituted the primary outcome, with death or valve replacement as the secondary outcome. Conditional inference trees were employed for analysis, validated through sample splitting. The study included 493 patients with MS (mean valve area 1.1 ± 0.4 cm2, 84% female). At baseline, 166 patients (34%) had AF, with 62 patients (19%) developing AF during follow-up. LASr emerged as the primary predictor for new-onset AF, with a threshold of 17.9%. Over a mean 3.8-year follow-up, 125 patients (25%) underwent mitral valve replacement, and 32 patients (6.5%) died. A decision tree analysis identified key predictors such as age, LASr, severity of tricuspid regurgitation (TR), net atrioventricular compliance (C n), and early percutaneous mitral valvuloplasty, especially in patients aged ≤49 years, where LASr, with a threshold of 12.8%, significantly predicted adverse outcomes. Conclusion: LASr emerged as a significant predictor of cardiovascular events in this MS cohort, validated through a decision tree analysis. Patients were stratified into low- or high-risk categories for adverse outcomes, taking into account LASr, age, TR severity, and C n.

STAS: New explorations and challenges for thoracic surgeons.

Spread through air spaces (STAS) represents a relatively novel concept in the pathology of lung cancer, and it specifically refers to the dissemination of tumour cells into the parenchymal air spaces adjacent to the primary tumour. In 2015, the World Health Organization (WHO) classified STAS as a new invasive form of lung adenocarcinoma (LUAD). Many studies investigated the role of STAS and revealed its association with the prognosis of LUAD and its influence on the outcomes of other malignant pulmonary neoplasms. Additionally, the underlying mechanisms and predictive models of STAS have received considerable attention in recent years. This paper provides a comprehensive overview of the research advancements and prospects of STAS by examining it from multiple perspectives.

Training set optimization is a feasible alternative for perennial orphan crop domestication and germplasm management: an Acrocomia aculeata example.

Orphan perennial native species are gaining importance as sustainability in agriculture becomes crucial to mitigate climate change. Nevertheless, issues related to the undomesticated status and lack of improved germplasm impede the evolution of formal agricultural initiatives. Acrocomia aculeata - a neotropical palm with potential for oil production - is an example. Breeding efforts can aid the species to reach its full potential and increase market competitiveness. Here, we present genomic information and training set optimization as alternatives to boost orphan perennial native species breeding using Acrocomia aculeata as an example. Furthermore, we compared three SNP calling methods and, for the first time, presented the prediction accuracies of three yield-related traits. We collected data for two years from 201 wild individuals. These trees were genotyped, and three references were used for SNP calling: the oil palm genome, de novo sequencing, and the A. aculeata transcriptome. The traits analyzed were fruit dry mass (FDM), pulp dry mass (PDM), and pulp oil content (OC). We compared the predictive ability of GBLUP and BayesB models in cross- and real validation procedures. Afterwards, we tested several optimization criteria regarding consistency and the ability to provide the optimized training set that yielded less risk in both targeted and untargeted scenarios. Using the oil palm genome as a reference and GBLUP models had better results for the genomic prediction of FDM, OC, and PDM (prediction accuracies of 0.46, 0.45, and 0.39, respectively). Using the criteria PEV, r-score and core collection methodology provides risk-averse decisions. Training set optimization is an alternative to improve decision-making while leveraging genomic information as a cost-saving tool to accelerate plant domestication and breeding. The optimized training set can be used as a reference for the characterization of native species populations, aiding in decisions involving germplasm collection and construction of breeding populations.

Molecular and Evolutionary Characteristics of Chicken Parvovirus (ChPV) Genomes Detected in Chickens with Runting-Stunting Syndrome.

Chicken Parvovirus (ChPV) belongs to the genus Aveparvovirus and is implicated in enteric diseases like runting-stunting syndrome (RSS) in poultry. In RSS, chicken health is affected by diarrhea, depression, and increased mortality, causing significant economic losses in the poultry industry. This study aimed to characterize the ChPV genomes detected in chickens with RSS through a metagenomic approach and compare the molecular and evolutionary characteristics within the Aveparvovirus galliform1 species. The intestinal content of broiler flocks affected with RSS was submitted to viral metagenomics. The assembled prevalent genomes were identified as ChPV after sequence and phylogenetic analysis, which consistently clustered separately from Turkey Parvovirus (TuPV). The strain USP-574-A presented signs of genomic recombination. The selective pressure analysis indicated that most of the coding genes in A. galliform1 are evolving under diversifying (negative) selection. Protein modeling of ChPV and TuPV viral capsids identified high conservancy over the VP2 region. The prediction of epitopes identified several co-localized antigenic peptides from ChPV and TuPV, especially for T-cell epitopes, highlighting the immunological significance of these sites. However, most of these peptides presented host-specific variability, obeying an adaptive scenario. The results of this study show the evolutionary path of ChPV and TuPV, which are influenced by diversifying events such as genomic recombination and selective pressure, as well as by adaptation processes, and their subsequent immunological impact.

Association of neutrophil-to-lymphocyte ratio with age and 180-day mortality after emergency surgery.

BACKGROUND: To examine the relationship between neutrophil-to-lymphocyte ratio (NLR), age, and mortality rates after emergency surgery. METHODS: In this observational study, a total of 851 patients undergoing emergency surgery between January 2022 and January 2023 were retrospective examined. Using 30 and 180 days mortality data, NLR differences and receiver operating characteristic (ROC) curves were analyzed using a 65-year threshold. A multiple logistic regression model was constructed incorporating age and NLR. Finally, Kaplan-Meier curves were constructed for mortality. RESULTS: Among 851 patients, the 30 and 180 days mortality rates were 5.2% and 10.8%, respectively. Median NLR in 30 days was 5.6 (3.1 to 9.6) in survivors and 8.7 (4.6 to 13.4) in deceased patients (p < 0.0001); in 180 days, it was 5.5 (3.1 to 9.8) and 8.8 (4.8 to 14.5), respectively (p < 0.0001). In the 30- and 180-days mortality analyses, median NLRs were 5.1 (2.9 to 8.9) and 4.9 (2.9 to 8.8) in survivors and 10.6 (6.9 to 16.6) and 9.3 (5.4 to 14.9) in deceased patients aged < 65 years, respectively. The ROC AUC in patients younger than 65 years was higher for 30 days (AUC 0.75; 95% CI 0.72 to 0.87) and 180 days (AUC 0.73; 95% CI 0.64 to 0.81). Multivariate logistic regression revealed that the NLR (odds ratio, 1.03 [95% CI 1.005 to 1.053; p = 0.0133) and age (odds ratio, 1.05 [95% CI 1.034 to 1.064; p < 0.0001) significantly contributed to the model. Survival analysis revealed differences in the 180 days mortality (p = 0.0006). CONCLUSION: We observed differences in preoperative NLR between patients who survived and those who died after emergency surgery. Age impacts the use of NLR as a mortality risk factor. TRIAL REGISTRATION: NCT06549101, retrospectively registered.

Hybrid Class Balancing Approach for Chemical Compound Toxicity Prediction.

INTRODUCTION: Computational methods are crucial for efficient and cost-effective drug toxicity prediction. Unfortunately, the data used for prediction is often imbalanced, resulting in biased models that favor the majority class. This paper proposes an approach to apply a hybrid class balancing technique and evaluate its performance on computational models for toxicity prediction in Tox21 datasets. METHODS: The process begins by converting chemical compound data structures (SMILES strings) from various bioassay datasets into molecular descriptors that can be processed by algorithms. Subsequently, Undersampling and Oversampling techniques are applied in two different schemes on the training data. In the first scheme (Individual), only one balancing technique (Oversampling or Undersampling) is used. In the second scheme (Hybrid), the training data is divided according to a ratio (e.g., 90-10), applying a different balancing technique to each proportion. We considered eight resampling techniques (four Oversampling and four Undersampling), six molecular descriptors (based on MACCS, ECFP, and Mordred), and five classification models (KNN, MLP, RF, XGB and SVM) over 10 bioassay datasets to determine the configurations that yield the best performance. RESULTS: We defined three testing scenarios: without balancing techniques (baseline), Individual, and Hybrid. We found that using the ENN technique in the MACCS-MLP combination resulted in a 10.01% improvement in performance. The increase for ECFP6-2048 was 16.47% after incorporating a combination of the SMOTE (10%) and RUS (90%) techniques. Meanwhile, using the same combination of techniques, MORDRED-XGB showed the most significant increase in performance, achieving a 22.62% improvement. CONCLUSION: Integrating any of the class balancing schemes resulted in a minimum of 10.01% improvement in prediction performance compared to the best baseline configuration. In this study, Undersampling techniques were more appropriate due to the significant overlap among samples. By eliminating specific samples from the predominant class that are close to the minority class, this overlap is greatly reduced.

Identification of common MicroRNAs expression signatures in antiphospholipid syndrome and thromboembolic disease: A scoping review.

BACKGROUND: Antiphospholipid syndrome (APS) is an acquired autoimmune disorder characterized by distinct pathophysiological mechanisms leading to heterogeneous manifestations, including venous and arterial thrombosis. Despite the lack of specific markers of thrombosis risk in APS, some of the mechanisms responsible for thrombosis in APS may overlap with those of other thromboembolic diseases. Understanding these similarities is important for improving the assessment of thrombosis risk in APS. MicroRNAs (MiRNAs) are RNA molecules that regulate gene expression and may influence the autoimmune response and coagulation. PURPOSE: In this scoping review we aimed to investigate shared miRNAs profiles associated with APS and other thromboembolic diseases as a means of identifying markers indicative of a pro-thrombotic profile among patients with APS. DATA COLLECTION AND RESULTS: Through a comprehensive search of scientific databases, 45 relevant studies were identified out of 1020 references. miRs-124-3p, 125b-5p, 125a-5p, and 17-5p, were associated with APS and arterial thrombosis, while miRs-106a-5p, 146b-5p, 15a-5p, 222-3p, and 451a were associated with APS and venous thrombosis. Additionally, miR-126a-3p was associated with APS and both arterial and venous thrombosis. CONCLUSION: We observed that APS shares a common miRNAs signature with non-APS related thrombosis, suggesting that miRNA expression profiles may serve as markers of thrombotic risk in APS. Further validation of a pro-thrombotic miRNA signature in APS is warranted to improve risk assessment, diagnosis, and management of APS.

Racial and ethnic disparities in the natural history of alcohol-associated liver disease in the United States.

BACKGROUND: Outcomes in alcohol-associated liver disease (ALD) are influenced by several race and ethnic factors, yet its natural history across the continuum of patients in different stages of the disease is unknown. METHODS: We conducted a retrospective cohort study of U.S. adults from 2011 to 2018, using three nationally representative databases to examine potential disparities in relevant outcomes among racial and ethnic groups. Our analysis included logistic and linear regressions, along with competing risk analysis. RESULTS: Black individuals had the highest daily alcohol consumption (12.6 g/day) while Hispanic participants had the largest prevalence of heavy episodic drinking (33.5%). In a multivariable-adjusted model, Hispanic and Asian participants were independently associated with a higher ALD prevalence compared to Non-Hispanic White interviewees (OR: 1.4, 95% CI: 1.1-1.8 and OR: 1.5 95% CI:1.1-2.0, respectively), while Blacks participants had a lower ALD prevalence (OR: .7 95% CI: .6-.9), and a lower risk of mortality during hospitalization due to ALD (OR: .83 95% CI: .73-.94). Finally, a multivariate competing-risk analysis showed that Hispanic ethnicity had a decreased probability of liver transplantation if waitlisted for ALD (SHR: .7, 95% CI: .6-.8) along with female Asian population (HR: .40, 95% CI: .26-.62). CONCLUSIONS: After accounting for key social and biological health determinants, the Hispanic population showed an increased risk of ALD prevalence, even with lower alcohol consumption. Additionally, Hispanic and Asian female patients had reduced access to liver transplantation compared to other enlisted patients.

Development of machine learning-based personalized predictive models for risk evaluation of hepatocellular carcinoma in hepatitis B virus-related cirrhosis patients with low levels of serum alpha-fetoprotein.

INTRODUCTION AND OBJECTIVES: The increasing incidence of hepatocellular carcinoma (HCC) in China is an urgent issue, necessitating early diagnosis and treatment. This study aimed to develop personalized predictive models by combining machine learning (ML) technology with a demographic, medical history, and noninvasive biomarker data. These models can enhance the decision-making capabilities of physicians for HCC in hepatitis B virus (HBV)-related cirrhosis patients with low serum alpha-fetoprotein (AFP) levels. PATIENTS AND METHODS: A total of 6,980 patients treated between January 2012 and December 2018 were included. Pre-treatment laboratory tests and clinical data were obtained. The significant risk factors for HCC were identified, and the relative risk of each variable affecting its diagnosis was calculated using ML and univariate regression analysis. The data set was then randomly partitioned into validation (20 %) and training sets (80 %) to develop the ML models. RESULTS: Twelve independent risk factors for HCC were identified using Gaussian naïve Bayes, extreme gradient boosting (XGBoost), random forest, and least absolute shrinkage and selection operation regression models. Multivariate analysis revealed that male sex, age >60 years, alkaline phosphate >150 U/L, AFP >25 ng/mL, carcinoembryonic antigen >5 ng/mL, and fibrinogen >4 g/L were the risk factors, whereas hypertension, calcium <2.25 mmol/L, potassium ≤3.5 mmol/L, direct bilirubin >6.8 µmol/L, hemoglobin <110 g/L, and glutamic-pyruvic transaminase >40 U/L were the protective factors in HCC patients. Based on these factors, a nomogram was constructed, showing an area under the curve (AUC) of 0.746 (sensitivity = 0.710, specificity=0.646), which was significantly higher than AFP AUC of 0.658 (sensitivity = 0.462, specificity=0.766). Compared with several ML algorithms, the XGBoost model had an AUC of 0.832 (sensitivity = 0.745, specificity=0.766) and an independent validation AUC of 0.829 (sensitivity = 0.766, specificity = 0.737), making it the top-performing model in both sets. The external validation results have proven the accuracy of the XGBoost model. CONCLUSIONS: The proposed XGBoost demonstrated a promising ability for individualized prediction of HCC in HBV-related cirrhosis patients with low-level AFP.

A stacked autoencoder-based aid system for severity degree classification of knee ligament rupture.

BACKGROUND: Knee ligament rupture is one of the most common injuries, but the diagnosis of its severity tends to require the use of complex methods and analyses that are not always available to patients. AIM: The objective of this research is the investigation and development of a diagnostic aid system to analyze and determine patterns that characterize the presence of the injury and its degree of severity. METHODS: Implement a novel proposal of a framework based on stacked auto-encoder (SAE) for ground reaction force (GRF) signals analysis, coming from the GaitRec database. Analysis of the raw data is used to determine the main features that allow us to diagnose the presence of a knee ligament rupture and classify its severity as high, mid or mild. RESULTS: The process is divided into two stages to determine the presence of the lesion and, if necessary, evaluate variations in features to classify the degree of severity as high, mid, and mild. The framework presents an accuracy of 87 % and a F1-Score of 90 % for detecting ligament rupture and an accuracy of 86.5 % and a F1-Score of 87 % for classifying severity. CONCLUSION: This new methodology aims to demonstrate the potential of SAE in physiotherapy applications as an evaluation and diagnostic tool, identifying irregularities associated with ligament rupture and its degree of severity, thus providing updated information to the specialist during the rehabilitation process.

Predicting autoimmune diseases: A comprehensive review of classic biomarkers and advances in artificial intelligence.

Autoimmune diseases comprise a spectrum of disorders characterized by the dysregulation of immune tolerance, resulting in tissue or organ damage and inflammation. Their prevalence has been on the rise, significantly impacting patients' quality of life and escalating healthcare costs. Consequently, the prediction of autoimmune diseases has recently garnered substantial interest among researchers. Despite their wide heterogeneity, many autoimmune diseases exhibit a consistent pattern of paraclinical findings that hold predictive value. From serum biomarkers to various machine learning approaches, the array of available methods has been continuously expanding. The emergence of artificial intelligence (AI) presents an exciting new range of possibilities, with notable advancements already underway. The ultimate objective should revolve around disease prevention across all levels. This review provides a comprehensive summary of the most recent data pertaining to the prediction of diverse autoimmune diseases and encompasses both traditional biomarkers and the latest innovations in AI.

Enhancing genomic prediction with Stacking Ensemble Learning in Arabica Coffee.

Coffee Breeding programs have traditionally relied on observing plant characteristics over years, a slow and costly process. Genomic selection (GS) offers a DNA-based alternative for faster selection of superior cultivars. Stacking Ensemble Learning (SEL) combines multiple models for potentially even more accurate selection. This study explores SEL potential in coffee breeding, aiming to improve prediction accuracy for important traits [yield (YL), total number of the fruits (NF), leaf miner infestation (LM), and cercosporiosis incidence (Cer)] in Coffea Arabica. We analyzed data from 195 individuals genotyped for 21,211 single-nucleotide polymorphism (SNP) markers. To comprehensively assess model performance, we employed a cross-validation (CV) scheme. Genomic Best Linear Unbiased Prediction (GBLUP), multivariate adaptive regression splines (MARS), Quantile Random Forest (QRF), and Random Forest (RF) served as base learners. For the meta-learner within the SEL framework, various options were explored, including Ridge Regression, RF, GBLUP, and Single Average. The SEL method was able to predict the predictive ability (PA) of important traits in Coffea Arabica. SEL presented higher PA compared with those obtained for all base learner methods. The gains in PA in relation to GBLUP were 87.44% (the ratio between the PA obtained from best Stacking model and the GBLUP), 37.83%, 199.82%, and 14.59% for YL, NF, LM and Cer, respectively. Overall, SEL presents a promising approach for GS. By combining predictions from multiple models, SEL can potentially enhance the PA of GS for complex traits.

Towards better mortality prediction in cancer patients in the ICU: a comparative analysis of prognostic scales: systematic literature review.

OBJECTIVE: To evaluate the predictive ability of mortality prediction scales in cancer patients admitted to intensive care units (ICUs). DESIGN: A systematic review of the literature was conducted using a search algorithm in October 2022. The following databases were searched: PubMed, Scopus, Virtual Health Library (BVS), and Medrxiv. The risk of bias was assessed using the QUADAS-2 scale. SETTING: ICUs admitting cancer patients. PARTICIPANTS: Studies that included adult patients with an active cancer diagnosis who were admitted to the ICU. INTERVENTIONS: Integrative study without interventions. MAIN VARIABLES OF INTEREST: Mortality prediction, standardized mortality, discrimination, and calibration. RESULTS: Seven mortality risk prediction models were analyzed in cancer patients in the ICU. Most models (APACHE II, APACHE IV, SOFA, SAPS-II, SAPS-III, and MPM II) underestimated mortality, while the ICMM overestimated it. The APACHE II had the SMR (Standardized Mortality Ratio) value closest to 1, suggesting a better prognostic ability compared to the other models. CONCLUSIONS: Predicting mortality in ICU cancer patients remains an intricate challenge due to the lack of a definitive superior model and the inherent limitations of available prediction tools. For evidence-based informed clinical decision-making, it is crucial to consider the healthcare team's familiarity with each tool and its inherent limitations. Developing novel instruments or conducting large-scale validation studies is essential to enhance prediction accuracy and optimize patient care in this population.

Development of a new definition of maternal near miss based on organ dysfunction in Latin America and the Caribbean: A prospective multicenter cohort study.

BACKGROUND: There has been debate over whether the existing World Health Organization (WHO) criteria accurately represent the severity of maternal near misses. OBJECTIVE: This study assessed the diagnostic accuracy of two WHO clinical and laboratory organ dysfunction markers for determining the best cutoff values in a Latin American setting. METHODS: A prospective multicenter cohort study was conducted in five Latin American countries. Patients with severe maternal complications were followed up from admission to discharge. Organ dysfunction was determined using clinical and laboratory data, and participants were classified according to severe maternal outcomes. This study compares the diagnostic criteria of Latin American Centre for Perinatology, Network for Adverse Maternal Outcomes (CLAP/NAMO) to WHO standards. RESULTS: Of the 698 women studied, 15.2% had severe maternal outcomes. Most measured variables showed significant differences between individuals with and without severe outcomes (all P-values <0.05). Alternative cutoff values suggested by CLAP/NAMOs include pH ≤7.40, lactate ≥2.3 mmol/L, respiratory rate ≥ 24 bpm, oxygen saturation ≤ 96%, PaO2/FiO2 ≤ 342 mmHg, platelet count ≤189 × 109 × mm3, serum creatinine ≥0.8 mg/dL, and total bilirubin ≥0.67 mg/dL. No significant differences were found when comparing the diagnostic performance of the CLAP/NAMO criteria to that of the WHO standards. CONCLUSION: The CLAP/NAMO values were comparable to the WHO maternal near-miss criteria, indicating that the WHO standards might not be superior in this population. These findings suggest that maternal near-miss thresholds can be adapted regionally, improving the identification and management of severe maternal complications in Latin America.

Functional prediction based on 16S rRNA metagenome data from bacterial microbiota associated with macroalgae from the Peruvian coast.

Macroalgae are vital reservoirs for essential epibiotic microorganisms. Among these are growth-promoting bacteria that support the growth and healthy development of their host macroalgae, and these macroalgae can be utilized in agriculture as biostimulants, offering an alternative to traditional agrochemicals. However, to date, no comparative studies have been conducted on the functional profile and bacterial diversity associated with coastal macroalgae of Peru. In this study, we employed amplicon sequencing of the V3-V4 region of 16S rRNA gene in twelve host macroalgae collected from two rocky shores in central Peru to compare their bacterial communities. The results revealed high bacterial diversity across both sites, but differences in microbial composition were noted. The phyla Bacteroidota and Pseudomonadota were predominant. The functional prediction highlighted 44 significant metabolic pathways associated with the bacterial microbiota when comparing host macroalgae. These active pathways are related to metabolism and genetic and cellular information processing. No direct association was detected between the macroalgal genera and the associated microbiota, suggesting that the bacterial community is largely influenced by their genetic functions than the taxonomic composition of their hosts. Furthermore, some species of Chlorophyta and Rhodophyta were observed to host growth-promoting bacteria, such as Maribacter sp. and Sulfitobacter sp.

Integrating plasma exosomal miRNAs, ultrasound radiomics and tPSA for the diagnosis and prediction of early prostate cancer: a multi-center study.

INTRODUCTION: This multi-center study aims to explore the roles of plasma exosomal microRNAs (miRNAs), ultrasound (US) radiomics, and total prostate-specific antigen (tPSA) levels in early prostate cancer detection. METHODS: We analyzed the publicly available dataset GSE112264 to identify the differentially expressed miRNAs associated with prostate cancer. Then, PyRadiomics was used to extract image features, and least absolute shrinkage and selection operator (LASSO) was used to screen the data. Subsequently, according to strict inclusion and exclusion criteria, the internal dataset (n = 199) was used to construct a diagnostic model, and the receiver operating characteristic (ROC) curve, calibration curve, decision curve analysis (DCA), and DeLong test were used to evaluate its diagnostic performance. Finally, we used an external dataset (n = 158) for further validation. RESULTS: The number of features extracted by PyRadiomics was 851, and the number of features screened by LASSO was 23. We combined the hsa-miR-320c, hsa-miR-944, radiomics, and tPSA features to construct a joint model. The area under the ROC curve of the combined model was 0.935. In the internal validation, the area under the curve (AUC) of the training set was 0.943, and the AUC of the test set was 0.946. The AUC of the external data set was 0.910. The calibration curve and decision curve were consistent with the performance of the combined model. There was a significant difference in the prediction ability between the combined prediction model and the single index prediction model, indicating the high credibility and accuracy of the combined model in predicting PCa. CONCLUSIONS: The combined prediction model, consisting of plasma exosomal miRNAs (hsa-miR-320c and hsa-miR-944), US radiomics, and clinical tPSA, can be utilized for the early diagnosis of prostate cancer.