Behind the Headset: Predictive Accuracy of Patient-Reported Outcome Measures for Voice Symptoms in Call Centers.

Objective: This study examines factors predicting self-reported voice symptoms in call center workers. Methods: Multivariate analysis and predictive modeling assess personal, work-related, acoustic, and behavioral factors. Generalized Linear Models (GLMs) and Receiver Operating Characteristic (ROC) curves are employed. Results: Age and sleep patterns impacted voice quality and effort, while workplace factors influenced symptom perception. Unhealthy vocal behaviors related to tense voice and increased effort, while hydration was protective. Voice acoustics showed diagnostic potential, supported by ROC data. These findings emphasize voice symptom complexity in call center professionals, necessitating comprehensive assessment. Limitations: This study recognizes its limitations, including a moderate-sized convenience sample and reliance on PROM metrics. Future research should incorporate more objective measures in addition to self-reports and acoustic analysis. Value: This research provides novel insights into the interplay of personal, occupational, and voice-related factors in developing voice symptoms among call center workers. Predictive modeling enhances risk assessment and understanding of individual susceptibility to voice disorders. Conclusion: Results show associations between various factors and self-reported voice symptoms. Protective factors include sleeping more than six hours and consistent hydration, whereas risk factors include working conditions, such as location and behaviors like smoking. Diagnostic models indicate good accuracy for some voice symptom PROMs, emphasizing the need for comprehensive models considering work factors, vocal behaviors, and acoustic parameters to understand voice issues complexity.

Objetivo: Este estudio examina los factores que predicen los síntomas de voz en los trabajadores de call centers. Métodos: Se utilizan análisis multivariados y modelos predictivos para evaluar factores personales, laborales, acústicos y de comportamiento. Se emplean Modelos Lineales Generalizados (GLM) y curvas ROC. Resultados: La edad y los patrones de sueño afectaron la calidad vocal y el esfuerzo, mientras que los factores laborales influyeron en la percepción de síntomas. Los comportamientos vocales no saludables se relacionaron con voz tensa y mayor esfuerzo, mientras que la hidratación fue protectora. Los parámetros acústicos de voz mostraron potencial diagnóstico respaldado por datos de ROC. Los hallazgos subrayan complejidad de síntomas vocales en profesionales de centros de llamadas, requiriendo una evaluación integral. Limitaciones: Este estudio reconoce sus limitaciones, que incluyen una muestra de conveniencia de tamaño moderado y la dependencia de medidas PROMs. Futuras investigaciones deberían incorporar medidas objetivas, además de los autorreportes y análisis acústico. Importancia: Esta investigación aporta nuevos conocimientos sobre factores personales, laborales y síntomas de voz en trabajadores de call centers. El modelado predictivo mejora la evaluación de riesgos y la comprensión de la susceptibilidad individual a trastornos de la voz. Conclusión: Los resultados muestran asociaciones entre diversos factores y los síntomas vocales reportados. Los factores de protección incluyen dormir más de seis horas y una hidratación constante; los factores de riesgo incluyen las condiciones de trabajo, como la ubicación y comportamientos como fumar. Los modelos de diagnóstico indican una buena precisión para algunas PROMs de síntomas de la voz, lo que subraya la necesidad de modelos integrales que tengan en cuenta los factores laborales, los comportamientos vocales y los parámetros acústicos para comprender la complejidad de los problemas de la voz.

Salivary biomarkers: a promising approach for predicting immunotherapy response in head and neck cancers.

Head and neck cancers, including cancers of the mouth, throat, voice box, salivary glands, and nose, are a significant global health issue. Radiotherapy and surgery are commonly used treatments. However, due to treatment resistance and disease recurrence, new approaches such as immunotherapy are being explored. Immune checkpoint inhibitors (ICIs) have shown promise, but patient responses vary, necessitating predictive markers to guide appropriate treatment selection. This study investigates the potential of non-invasive biomarkers found in saliva, oral rinses, and tumor-derived exosomes to predict ICI response in head and neck cancer patients. The tumor microenvironment significantly impacts immunotherapy efficacy. Oral biomarkers can provide valuable information on composition, such as immune cell presence and checkpoint expression. Elevated tumor mutation load is also associated with heightened immunogenicity and ICI responsiveness. Furthermore, the oral microbiota may influence treatment outcomes. Current research aims to identify predictive salivary biomarkers. Initial studies indicate that tumor-derived exosomes and miRNAs present in saliva could identify immunosuppressive pathways and predict ICI response. While tissue-based markers like PD-L1 have limitations, combining multiple oral fluid biomarkers could create a robust panel to guide treatment decisions and advance personalized immunotherapy for head and neck cancer patients.

Pupillary responses to directional uncertainty while intercepting a moving target.

Pupillary responses serve as sensitive indicators of cognitive processes, attentional shifts and decision-making dynamics. Our study investigates how directional uncertainty and target speed (V T) influence pupillary responses in a foveal tracking task involving the interception of a moving dot. Directional uncertainty, reflecting the unpredictability of the target's direction changes, was manipulated by altering the angular range (AR) from which random directions for the moving dot were extracted. Higher AR values were associated with reduced pupillary diameters, indicating that heightened uncertainty led to smaller pupil sizes. Additionally, an inverse U-shaped relationship between V T and pupillary responses suggested maximal diameters at intermediate speeds. Analysis of saccade-triggered responses showed a negative correlation between pupil diameter and directional uncertainty. Dynamic linear modelling revealed the influence of past successful collisions and other behavioural parameters on pupillary responses, emphasizing the intricate interaction between task variables and cognitive processing. Our results highlight the dynamic interplay between the directional uncertainty of a single moving target, V T and pupillary responses, with implications for understanding attentional mechanisms, decision-making processes and potential applications in emerging technologies.

Do Temperature Abuses Along the Frozen Açaí Pulp Value Chain Increase Microbial Hazards?

Does temperature abuse during storage, distribution, marketing, and consumption of unpasteurized frozen açaí pulp increase microbial hazards? This study investigated the behavior of potentially pathogenic (Escherichia coli, Listeria monocytogenes and Salmonella spp.) and spoilage (mesophilic bacteria, yeasts and molds) microorganisms in two simulated thawing conditions: under refrigeration and at room temperature. The effect of repeated cold chain abuse was observed by thawing and refreezing (-20°C) açaí pulp four times over a period of 90 days. Freezing resulted in inhibition of all microorganisms except for mesophilic aerobic bacteria in one single sample. After thawing at 5°C, the kinetic parameters obtained by the Weibull model indicated that mesophilic aerobic bacteria, yeasts and molds and L. monocytogenes showed a longer inactivation time with δ values reaching 35, 126, and 46 days, respectively. The shortest inactivation time for a reduction of 4 log CFU.g-1 was for E. coli. The concentration of Salmonella spp. and L. monocytogenes in control samples was higher (p < 0.01) than in samples exposed to abusive conditions after 90 days of storage. The results indicate that the abusive thawing conditions studied do not increase the potential hazards of pathogens.

Modeling nonlinear inactivation of hygiene indicator bacteria in pig carcasses during scalding at different pHs.

This study aimed to investigate the influence of adding an alkalizing agent to the scalding water of a slaughterhouse in Brazil to inactivate hygiene indicator bacteria in pig carcasses. Scalding is critical during carcass processing because slaughterhouses' scalding water is constantly renewed; therefore, it is usually contaminated with organic matter, such as faeces and dirt from the previous carcasses. The treatments evaluated consisted of counting Enterobacteriaceae and mesophilic bacteria in pork jowls at 62 °C, 65 °C, 68 C, and 72 °C after 0.0, 1.5, 3.0, 4.5, 6.0, and 7.5 min of simulated scalding at the pHs of 7.0 (control) and 11.0 (after addition of alkalizing agent). Decimal reduction times of hygiene indicator bacteria for all treatments were estimated with different nonlinear bacterial inactivation models. As a result, adding the alkalizing agent did not significantly inactivate most of the bacteria in the studied samples. However, it contributed to the inactivation of some bacteria, mostly belonging to the mesophilic group, at some specific temperatures. The results obtained in the current study can provide useful insights into dealing with pig carcass contamination in a real-world scenario and applying the obtained information in the industrial environment.

Unlocking Biological Activity and Metabolomics Insights: Primary Screening of Cyanobacterial Biomass from a Tropical Reservoir.

Cyanobacterial harmful algal blooms can pose risks to ecosystems and human health worldwide due to their capacity to produce natural toxins. The potential dangers associated with numerous metabolites produced by cyanobacteria remain unknown. Only select classes of cyanopeptides have been extensively studied with the aim of yielding substantial evidence regarding their toxicity, resulting in their inclusion in risk management and water quality regulations. Information about exposure concentrations, co-occurrence, and toxic impacts of several cyanopeptides remains largely unexplored. We used liquid chromatography-mass spectrometry (LC-MS)-based metabolomic methods associated with chemometric tools (NP Analyst and Data Fusion-based Discovery), as well as an acute toxicity essay, in an innovative approach to evaluate the association of spectral signatures and biological activity from natural cyanobacterial biomass collected in a eutrophic reservoir in southeastern Brazil. Four classes of cyanopeptides were revealed through metabolomics: microcystins, microginins, aeruginosins, and cyanopeptolins. The bioinformatics tools showed high bioactivity correlation scores for compounds of the cyanopeptolin class (0.54), in addition to microcystins (0.54-0.58). These results emphasize the pressing need for a comprehensive evaluation of the (eco)toxicological risks associated with different cyanopeptides, considering their potential for exposure. Our study also demonstrated that the combined use of LC-MS/MS-based metabolomics and chemometric techniques for ecotoxicological research can offer a time-efficient strategy for mapping compounds with potential toxicological risk. Environ Toxicol Chem 2024;43:2222-2231. © 2024 SETAC.

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.

The Expression Profiles of lncRNAs Are Associated with Neoadjuvant Chemotherapy Resistance in Locally Advanced, Luminal B-Type Breast Cancer.

lncRNAs are noncoding transcripts with tissue and cancer specificity. Particularly, in breast cancer, lncRNAs exhibit subtype-specific expression; they are particularly upregulated in luminal tumors. However, no gene signature-based laboratory tests have been developed for luminal breast cancer identification or the differential diagnosis of luminal tumors, since no luminal A- or B-specific genes have been identified. Particularly, luminal B patients are of clinical interest, since they have the most variable response to neoadjuvant treatment; thus, it is necessary to develop diagnostic and predictive biomarkers for these patients to optimize treatment decision-making and improve treatment quality. In this study, we analyzed the lncRNA expression profiles of breast cancer cell lines and patient tumor samples from RNA-Seq data to identify an lncRNA signature specific for luminal phenotypes. We identified an lncRNA signature consisting of LINC01016, GATA3-AS1, MAPT-IT1, and DSCAM-AS1 that exhibits luminal subtype-specific expression; among these lncRNAs, GATA3-AS1 is associated with the presence of residual disease (Wilcoxon test, p < 0.05), which is related to neoadjuvant chemotherapy resistance in luminal B breast cancer patients. Furthermore, analysis of GATA3-AS1 expression using RNA in situ hybridization (RNA ISH) demonstrated that this lncRNA is detectable in histological slides. Similar to estrogen receptors and Ki67, both commonly detected biomarkers, GATA3-AS1 proves to be a suitable predictive biomarker for clinical application in breast cancer laboratory tests.

Construct, Face, and Predictive Validity of Parkinson's Disease Rodent Models.

Parkinson's disease (PD) is the second most common neurodegenerative disease globally. Current drugs only alleviate symptoms without halting disease progression, making rodent models essential for researching new therapies and understanding the disease better. However, selecting the right model is challenging due to the numerous models and protocols available. Key factors in model selection include construct, face, and predictive validity. Construct validity ensures the model replicates pathological changes seen in human PD, focusing on dopaminergic neurodegeneration and a-synuclein aggregation. Face validity ensures the model's symptoms mirror those in humans, primarily reproducing motor and non-motor symptoms. Predictive validity assesses if treatment responses in animals will reflect those in humans, typically involving classical pharmacotherapies and surgical procedures. This review highlights the primary characteristics of PD and how these characteristics are validated experimentally according to the three criteria. Additionally, it serves as a valuable tool for researchers in selecting the most appropriate animal model based on established validation criteria.

Rentabilidad de la ratio angiogénica en el seguimiento de gestantes de alto riesgo de preeclampsia precoz tratadas con ácido acetilsalicílico / Profitability of the angiogenic ratio in the follow-up of pregnant women at high risk of early preeclampsia treated with acetylsalicylic acid

Objetivo: Evaluar el valor predictivo negativo de la ratio antigénica y conocer su rentabilidad para descartar preeclampsia precoz en pacientes de alto riesgo de desarrollarla, con profilaxis de ácido acetilsalicílico. Métodos: Se realizó un estudio descriptivo transversal que recogió a las gestantes con cribado de preeclampsia precoz de alto riesgo (384 gestantes) en el Hospital Santa Lucía durante el año 2021, para lo que se usó test Elecsys® tabulado a un riesgo mayor a 1/150 en primer trimestre, y que tomaran ácido acetilsalicílico antes de la semana 16, quedando en 368 gestantes vistas en las semanas 20, 26, 31 y 36. Se realizó biometría, ratio angiogénica y doppler. Resultados: La incidencia de preeclampsia precoz en la población fue 4 casos (incidencia 1,08 %). Son significativos por su alto valor predictivo negativo del 100 % de preeclampsia precoz: la ratio angiogénica mayor a 38 en la semana 26 y el doppler de las uterinas en semana 20 y 26. Conclusión: En gestaciones con cribado de alto riesgo de preeclampsia que tomen ácido acetilsalicílico, una ratio angiogénica menor a 38 en la semana 26, además de un doppler uterino normal en semana 20 y 26 permite reducir el seguimiento gestacional(AU)

Objective: Our main objective was to evaluate the negative predictive value of the angiogenic ratio and to know its profitability to rule out early preeclampsia in patients at high risk of early preeclampsia with acetylsalicylic acid prophylaxis. Methods: A cross-sectional descriptive study was carried out that included pregnant women with high-risk early preeclampsia screening (384 pregnant women) at the Santa Lucía Hospital during the year 2021, for which the Elecsys® test tabulated at a risk >1/ was used. 150 in the first trimester, and who take acetylsalicylic acid before week 16, leaving 368 pregnant women seen in weeks 20, 26, 31 and 36, with biometry, angiogenic ratio and Doppler performed. Results: The incidence of early preeclampsia in the population was 4 cases (incidence 1.08%). They are significant due to their high negative predictive value of 100% of early preeclampsia: Angiogenic ratio > 38 in week 26, uterine Doppler in weeks 20 and 26. Conclusion: Pregnancies with high risk screening for preeclampsia who take acid acetylsalicylic acid, an angiogenic ratio < 38 at week 26 in addition to a normal uterine Doppler at weeks 20 and 26 allows for reduced gestational follow-up(AU)

Degree-day models for predicting adult Delia platura (Diptera: Anthomyiidae) spring flight and first emergence in New York State.

The seedcorn maggot, Delia platura (Meigen), is a pest affecting many crops, including corn. The early spring emergence of adults and belowground seed damage by maggots leave no room for rescue treatments during the short growing season in New York State. Degree-day (DD) models play a crucial role in predicting insect emergence and adult peak activity and are essential for effective pest management. The current D. platura DD model was launched on the Network for Environment and Weather Applications (NEWA) in 2022, using existing scientific literature from other North American regions. The NEWA model predicted adult D. platura first emergence at an average of 471 (39°F) DD in 2022. To gain an accurate and precise understanding of D. platura adult spring emergence and activity, we used interpolated temperature data to calculate the DD for each specific location where adults were captured in the field. DD calculations were performed using the average method, setting a biofix on January 1st and a base temperature of 39°F. In 2023, overwintering adults emerged at an average of 68 DD, and in 2022, adult activity was registered at an average of 282 DD. Accurately predicting the emergence of D. platura could contribute to informing integrated pest management strategies that incorporate timing and cultural practices over chemical solutions to protect crops and the environment.

Predictive value of dynamic arterial elastance for vasopressor withdrawal: a systematic review and meta-analysis.

BACKGROUND: Dynamic arterial elastance (Eadyn) has been investigated for its ability to predict hypotension during the weaning of vasopressors. Our study focused on assessing Eadyn's performance in the context of critically ill adult patients admitted to the intensive care unit, regardless of diagnosis. MAIN BODY: Our study was conducted in accordance with the Preferred Reported Items for Systematic Reviews and Meta-Analysis checklist. The protocol was registered in PROSPERO (CRD42023421462) on May 26, 2023. We included prospective observational studies from the MEDLINE and Embase databases through May 2023. Five studies involving 183 patients were included in the quantitative analysis. We extracted data related to patient clinical characteristics, and information about Eadyn measurement methods, results, and norepinephrine dose. Most patients (76%) were diagnosed with septic shock, while the remaining patients required norepinephrine for other reasons. The average pressure responsiveness rate was 36.20%. The synthesized results yielded an area under the curve of 0.85, with a sensitivity of 0.87 (95% CI 0.74-0.93), specificity of 0.76 (95% CI 0.68-0.83), and diagnostic odds ratio of 19.07 (95% CI 8.47-42.92). Subgroup analyses indicated no variations in the Eadyn based on norepinephrine dosage, the Eadyn measurement device, or the Eadyn diagnostic cutoff to predict cessation of vasopressor support. CONCLUSIONS: Eadyn, evaluated through subgroup analyses, demonstrated good predictive ability for the discontinuation of vasopressor support in critically ill patients.

Effect of prior beliefs and cognitive deficits on learning in first-episode schizophrenia patients.

Introduction: It is known that cognitive deficits are a core feature of schizophrenia and that in the general population, prior beliefs significantly influence learning and reasoning processes. However, the interaction of prior beliefs with cognitive deficits and their impact on performance in schizophrenia patients is still poorly understood. This study investigates the role of beliefs and cognitive variables (CVs) like working memory, associative learning, and processing speed on learning processes in individuals with schizophrenia. We hypothesize that beliefs will influence the ability to learn correct predictions and that first-episode schizophrenia patients (FEP) will show impaired learning due to cognitive deficits. Methods: We used a predictive-learning task to examine how FEP (n = 23) and matched controls (n = 23) adjusted their decisional criteria concerning physical properties during the learning process when predicting the sinking behavior of two transparent containers filled with aluminum discs when placed in water. Results: On accuracy, initial differences by group, trial type, and interaction effects of these variables disappeared when CVs were controlled. The differences by conditions, associated with differential beliefs about why the objects sink slower or faster, were seen in patients and controls, despite controlling the CVs' effect. Conclusions: Differences between groups were mainly explained by CVs, proving that they play an important role than what is assumed in this type of task. However, beliefs about physical events were not affected by CVs, and beliefs affect in the same way the decisional criteria of the control or FEP patients' groups.

LMI-Based MPC Design Applied to the Single-Phase PWM Inverter with LC Filter under Uncertain Parameters.

This work proposes a design methodology for predictive control applied to the single-phase PWM inverter with an LC filter. In the design, we considered that the PWM inverter has parametric uncertainties in the filter inductance and output load resistance. The control system purpose is to track a sinusoidal signal at the inverter output. The designed control system with an embedded integrator uses the principle of receding horizon control, which underpinned predictive control. The methodology was described by linear matrix inequalities, which can be solved efficiently using convex programming techniques, and the optimal solution is obtained. MATLAB-Simulink and real-time FPGA-in-the-loop simulations illustrate the viability of the proposed control system. The LMI-based MPC reveals an effective performance for tracking of a sinusoidal reference signal and disturbance rejection of input voltage and load perturbations for the inverter subject to uncertainties.

Enhancing Cervical Cancer Screening with 7-Type HPV mRNA E6/E7 Testing on Self-Collected Samples: Multicentric Insights from Mexico.

Cervical cancer remains a significant public health issue, particularly in regions with low screening uptake. This study evaluates the effectiveness of self-sampling and the 7-type HPV mRNA E6/E7 test in improving cervical cancer screening outcomes among a referral population in Mexico. A cohort of 418 Mexican women aged 25 to 65, referred for colposcopy and biopsy due to abnormal cytology results (ASC-US+), participated in this study. Self-samples were analyzed using both the 14-type HPV DNA test and the 7-type HPV mRNA E6/E7 test. The study assessed the sensitivity, specificity, positive predictive value (PPV), and the necessity of colposcopies to detect CIN3+ lesions. Participant acceptability of self-sampling was also evaluated through a questionnaire. The 7-type HPV mRNA E6/E7 test demonstrated equivalent sensitivity but significantly higher specificity (77.0%) and PPV for CIN3+ detection compared to the 14-type HPV DNA test (specificity: 45.8%, p < 0.001). The use of the HPV mRNA test as a triage tool reduced the number of colposcopies needed per CIN3+ case detected from 16.6 to 7.6 (p < 0.001). Self-sampling was highly accepted among participants, with the majority reporting confidence in performing the procedure, minimal discomfort, and willingness to undertake self-sampling at home. Self-sampling combined with the 7-type HPV mRNA E6/E7 testing offers a promising strategy to enhance cervical cancer screening by improving accessibility and ensuring precise diagnostics. Implementing these app roaches could lead to a significant reduction in cervical cancer morbidity and mortality, especially in underserved populations. Future research should focus on the long-term impact of integrating these methods into national screening programs and explore the cost-effectiveness of widespread implementation.

Predictive Models for Non-Alcoholic Fatty Liver Disease Diagnosis in Mexican Patients with Gallstone Disease: Sex-Specific Insights.

(1) Background: Evidence regarding Non-Alcoholic Fatty Liver Disease (NAFLD) diagnosis is limited in the context of patients with gallstone disease (GD). This study aimed to assess the predictive potential of conventional clinical and biochemical variables as combined models for diagnosing NAFLD in patients with GD. (2) Methods: A cross-sectional study including 239 patients with GD and NAFLD diagnosed by ultrasonography who underwent laparoscopic cholecystectomy and liver biopsy was conducted. Previous clinical indices were also determined. Predictive models for the presence of NAFLD stratified by biological sex were obtained through binary logistic regression and sensitivity analyses were performed. (3) Results: For women, the model included total cholesterol (TC), age and alanine aminotransferase (ALT) and showed an area under receiver operating characteristic curve (AUC) of 0.727 (p < 0.001), sensitivity of 0.831 and a specificity of 0.517. For men, the model included TC, body mass index (BMI) and aspartate aminotransferase (AST), had an AUC of 0.898 (p < 0.001), sensitivity of 0.917 and specificity of 0.818. In both sexes, the diagnostic performance of the designed equations was superior to the previous indices. (4) Conclusions: These models have the potential to offer valuable guidance to healthcare providers in clinical decision-making, enabling them to achieve optimal outcomes for each patient.

Prediction of Bioactive Compounds and Antioxidant Activity in Bananas during Ripening Using Non-Destructive Parameters as Input Data.

Vegetable quality parameters are established according to standards primarily based on visual characteristics. Although knowledge of biochemical changes in the secondary metabolism of plants throughout development is essential to guide decision-making about consumption, harvesting and processing, these determinations involve the use of reagents, specific equipment and sophisticated techniques, making them slow and costly. However, when non-destructive methods are employed to predict such determinations, a greater number of samples can be tested with adequate precision. Therefore, the aim of this work was to establish an association capable of modeling between non-destructive-physical and colorimetric aspects (predictive variables)-and destructive determinations-bioactive compounds and antioxidant activity (variables to be predicted), quantified spectrophotometrically and by HPLC in 'Nanicão' bananas during ripening. It was verified that to predict some parameters such as flavonoids, a regression equation using predictive parameters indicated the importance of R2, which varied from 83.43 to 98.25%, showing that some non-destructive parameters can be highly efficient as predictors.

Neutrophil-to-lymphocyte ratio may predict complications and patency in bile duct injury repair.

BACKGROUND: Bile duct injury (BDI) repair surgery is usually associated with morbidity/mortality. The neutrophil-to-lymphocyte ratio (NLR) easily assesses a patient's inflammatory status. The study aims to determine the possible relationship between preoperative NLR (pNLR) with postoperative outcomes in BDI repair surgery. METHODS: Approved Ethics/Research Committee retrospective study, in patients who had a Bismuth-Strasberg type E BDI repair (2008-2023). Data registered was: morbidity, mortality, and long-term outcomes (primary patency and loss of primary patency) (Kaplan-Meier). Group comparison (U Mann-Whitney), receiver operator characteristic (ROC): area under curve [AUC]; cut-off value, and Youden index [J], and logistic regression analysis were used for pNLR evaluation. RESULTS: Seventy-three patients were studied. Mean age was 44.4 years. E2 was the commonest BDI (38.4%). Perioperative morbidity/mortality was 31.5% and 1.4%. Primary patency was 95.9%. 8.2% have lost primary patency (3-year actuarial patency: 85.3%). Median pNLR was higher in patients who had any complication (4.84 vs. 2.89 p = 0.015), biliary complications (5.29 vs. 2.86 p = 0.01), and patients with loss of primary patency (5.22 vs. 3.1 p = 0.08). AUC's, cut-off values and (J) were: any complication (0.678, pNLR = 4.3, J = 0.38, p = 0.007), serious complication (0.667, pNLR = 4.3, J = 0.34, p = 0.04), biliary complications (0.712, pNLR = 3.64, J = 0.46, p = 0.001), and loss of primary patency (0.716, pNLR = 3.24, J = 0.52, p = 0.008). Logistic regression was significant in any complication (Exp [B]: 0.1, p = 0.002), serious complications (Exp [B]: 0.2, p = 0.03), and biliary complications (Exp [B]: 8.1, p = 0.003). CONCLUSIONS: pNLR is associated with complications in BDI repair with moderate to acceptable predictive capacity. pNLR could potentially predict patency of a BDI repair.

Advancing groundwater quality predictions: Machine learning challenges and solutions.

Machine learning (ML) is revolutionizing groundwater quality research by enhancing predictive accuracy and management strategies for contamination. This comprehensive review explores the evolution of ML technologies and their integration into environmental science, assessing 230 papers to understand the advancements and challenges in groundwater quality research. It reveals that a substantial portion of the research neglects critical preprocessing steps, crucial for model accuracy, with 83 % of the studies overlooking this phase. Furthermore, while model optimization is more commonly addressed, being implemented in 65 % of the papers, there is a noticeable gap in model interpretability, with only 15 % of the research providing explanations for model outcomes. Comparative evaluation of ML algorithms and careful selection of evaluation metrics are deemed essential for determining model fitness and reliability. The review underscores the need for interdisciplinary collaboration, methodological rigor, and continuous innovation to advance ML in groundwater management. By addressing these challenges and implementing solutions, the full potential of ML can be harnessed to tackle complex environmental issues and ensure sustainable groundwater management. This comprehensive and critical review paper can serve as a guiding framework to establish minimum standards for developing ML in groundwater quality studies.

Models for predicting the risk of illness in leprosy contacts in Brazil: Leprosy prediction models in Brazilian contacts.

OBJECTIVE: This study aims to develop and validate predictive models that assess the risk of leprosy development among contacts, contributing to an enhanced understanding of disease occurrence in this population. METHODS: A cohort of 600 contacts of people with leprosy treated at the National Reference Center for Leprosy and Health Dermatology at the Federal University of Uberlândia (CREDESH/HC-UFU) was followed up between 2002 and 2022. The database was divided into two parts: two-third to construct the disease risk score and one-third to validate this score. Multivariate logistic regression models were used to construct the disease score. RESULTS: Of the four models constructed, model 3, which included the variables anti-phenolic glycolipid I immunoglobulin M positive, absence of Bacillus Calmette-Guérin vaccine scar and age ≥60 years, was considered the best for identifying a higher risk of illness, with a specificity of 89.2%, a positive predictive value of 60% and an accuracy of 78%. CONCLUSIONS: Risk prediction models can contribute to the management of leprosy contacts and the systematisation of contact surveillance protocols.