In this review, recent advances regarding the integration of machine learning into electrochemical analysis are overviewed, focusing on the strategies to increase the analytical context of electrochemical data for enhanced machine learning applications. While information-rich electrochemical data offer great potential for machine learning applications, limitations arise when sensors struggle to identify or quantitatively detect target substances in a complex matrix of non-target substances. Advanced machine learning techniques are crucial, but equally important is the development of methods to ensure that electrochemical systems can generate data with reasonable variations across different targets or the different concentrations of a single target. We discuss five strategies developed for building such electrochemical systems, employed in the steps of preparing sensing electrodes, recording signals, and analyzing data. In addition, we explore approaches for acquiring and augmenting the datasets used to train and validate machine learning models. Through these insights, we aim to inspire researchers to fully leverage the potential of machine learning in electroanalytical science.

The current review examines the state of knowledge and research on machine learning (ML) applications in horticultural production and the potential for predicting fresh produce losses and waste. Recently, ML has been increasingly applied in horticulture for efficient and accurate operations. Given the health benefits of fresh produce and the need for food and nutrition security, efficient horticultural production and postharvest management are important. This review aims to assess the application of ML in preharvest and postharvest horticulture and the potential of ML in reducing postharvest losses and waste by predicting their magnitude, which is crucial for management practices and policymaking in loss and waste reduction. The review starts by assessing the application of ML in preharvest horticulture. It then presents the application of ML in postharvest handling and processing, and lastly, the prospects for its application in postharvest loss and waste quantification. The findings revealed that several ML algorithms perform satisfactorily in classification and prediction tasks. Based on that, there is a need to further investigate the suitability of more models or a combination of models with a higher potential for classification and prediction. Overall, the review suggested possible future directions for research related to the application of ML in postharvest losses and waste quantification.

Background: Numerous studies have aimed to predict prenatal and neonatal outcomes for pregnancies complicated by congenital cytomegalovirus (CMV). Presently, assessing CMV severity prenatally relies largely on fetal imaging. A controversy exists regarding CMV viral load (VL) and its association with fetal and neonatal sequelae. Objective: To perform a systematic review and meta-analysis investigating the association between CMV DNA VL in amniotic fluid and fetal and neonatal outcomes in pregnancies with congenital CMV. Results: All cohort, case-control and observational studies that compared outcomes of fetuses with congenital CMV and provided information on individual patient CMV VL quantified in copies per milliliter (c/mL) from inception to January 2023 were included, with no geographical or language restrictions. A total of 1251 citations were reviewed with eight studies meeting inclusion criteria and included in meta-analysis. Affected pregnancies had a higher VL in the amniotic fluid compared to those unaffected with a mean difference of 2.2e+7 (range 1.5e+7 to 2.8e+7). In subgroup analysis, the VL was significantly higher in the fetuses, with imaging findings related to CMV compared to asymptomatic fetuses with a mean difference of 4.1e+7 (95% CI 2.8e+7-5.4e+7). However, among babies with congenital CMV, the VL was not significantly different between symptomatic and asymptomatic babies. Conclusions: Amniotic fluid CMV VL is associated with fetal sequalae in congenital CMV, with a higher VL conferring a greater risk for prenatal injury.

The food sector faces difficulty meeting the expectations for high-quality food items with safe and clean perceptions in light of customers\' increased concern and economic sanctions of synthetic and hazardous chemicals. Besides their widespread use as decoration, flowers are known to be consumed as a traditional food or a component of complementary therapy in many different civilizations worldwide. Because of their nutritional importance as a source of nutrients, proteins, essential amino acids, bioactive compounds, etc., many edible flowers can be viewed as a food source rather than just a delicacy or decoration. Polyphenols, flavonoids, and carotenoids are the phytochemicals that make up the bioactive components of edible flowers. These substances have anti-inflammatory, antibacterial, and antioxidant properties that can improve the nutritional profile of dairy products. Nanoparticles have become a cutting-edge strategy to make use of these advantages. In addition to encapsulating and protecting medicinal substances, nanoparticles made from edible flowers also enable regulated release, increasing bioavailability and durability. Numerous opportunities exist for the addition of edible flower- nanoparticles to dairy products. Their inclusion can add distinctive flavours, colours, and sensations, boosting the consumer\'s sensory perception. This review quotes the recent studies and discusses different aspects such as nanoparticle synthesis, quantification and characterization, health benefits, novel ingredient for the development of functional food, and the bioactive compounds for different varieties of edible flowers.Kindly check and confirm the edit made in the title.  The final title  is : \"Bioactive compounds,nanoparticles synthesis, health benefits andpotential utilization of edible flowers for thedevelopment of functional dairy products: areview\".
UNASSIGNED:

In the pharmaceutical industry, the unexpected appearance of crystalline forms could impact the therapeutic efficacy of an Active Pharmaceutical Ingredient (API). For quality control, a thorough qualitative and quantitative monitoring of pharmaceutical solid forms is essential to ensure the detection and the quantification of crystalline forms, wither different or with the same chemical composition (polymorphs) at a low detection level. The purpose of this paper was to review and highlight the importance of choosing adequate solid-state techniques for detection and quantification APIs that present polymorphism - based on limits of detection (LOD) and quantification (LOQ), pharmacopeias specifications, international guidelines and studies reported in the literature. To this study, the powder X-ray diffraction (PXRD), differential scanning calorimetry (DSC), Infrared and Raman spectroscopies and solid-state nuclear magnetic resonance (NMR) were the solid-state techniques analyzed. Additionally, the Argentine, Brazilian, British, European, International, Japanese, Mexican and the United States of America pharmacopeias were reviewed. Based on the analysis performed, the advantages and disadvantages of these techniques, as well as the LOD and LOQ values of APIs were reported. In comparison to these solid-state techniques, reference material used for identification analyses should be previously identified with the corresponding polymorph. Without this previous procedure, the patterns, the spectra, and DSC curves of the reference material can only be used to confirm the mixture of solid forms, not being able to specify which polymorphs are contained in the sample. A major advantage of PXRD is the use of the calculated diffraction patterns obtained from the Crystallographic Information Frameworks (CIFs) files which could be used as a reference pattern without any other information, assistance technique, or physical standards. Regarding the quantification aspect, different pharmacopeias suggest various methods such as the PXRD combining with Rietveld method, which can be used to obtain lower LOD values for minority phases in the mixture of different substances without the need for a calibration curve. Raman spectroscopy can detect polymorphs in small particles and solid-state NMR spectroscopy is a powerful technique for quantification not only crystalline but also crystalline-amorphous mixtures. Finally, this review intends to be a useful tool to control, with efficiency and accuracy, the polymorphism of APIs in pharmaceutical compounds.

BACKGROUND: The anatomy laboratory can provide the ideal setting for the preclinical phase of neurosurgical research. Our purpose is to comprehensively and critically review the preclinical anatomical quantification methods used in cranial neurosurgery.
METHODS: A systematic review was conducted following the PRISMA guidelines. The PubMed, Ovid MEDLINE, and Ovid EMBASE databases were searched, yielding 1667 papers. A statistical analysis was performed using R.
RESULTS: The included studies were published from 1996 to 2023. The risk of bias assessment indicated high-quality studies. Target exposure was the most studied feature (81.7%), mainly with area quantification (64.9%). The surgical corridor was quantified in 60.9% of studies, more commonly with the quantification of the angle of view (60%). Neuronavigation-based methods benefit from quantifying the surgical pyramid features that define a cranial neurosurgical approach and allowing post-dissection data analyses. Direct measurements might diminish the error that is inherent to navigation methods and are useful to collect a small amount of data.
CONCLUSIONS: Quantifying neurosurgical approaches in the anatomy laboratory provides an objective assessment of the surgical corridor and target exposure. There is currently limited comparability among quantitative neurosurgical anatomy studies; sharing common research methods will provide comparable data that might also be investigated with artificial intelligence methods.

Lean Thinking and clinical pathways are commonly used concepts to improve healthcare. However, little is known on how to use Lean Thinking for the optimization of pathways or the quantification of both concepts. This study aims to create a framework to analyze pathways with Lean Thinking on a system level, by quantifying the seven wastes, flow and pull. A systematic literature review was performed. Inclusion criteria were the focus of the article on a well-defined group of patients and studied a pathway optimization with Lean Thinking. Data were extracted on measured outcomes, type of intervention and type of researched pathway. Thirty-six articles were included. No articles described the implementation of the Lean Thinking philosophy or studied the development of their people and partners (\"4 P\" model). Most articles used process optimization tools or problem-solving tools. The majority of the studies focused on process measures. The measures found in the review were used as input for our suggested framework to identify and quantify wastes, flow, and pull in a clinical pathway. The proposed framework can be used to create an overview of the improvement potential of a pathway or to analyze the level of improvement after an enhancement is introduced to a pathway. Further research is needed to study the use of the suggested quantifications.

UNASSIGNED: Vascularized lymph node transfer (VLNT) is an effective microsurgical method for the treatment of lymphedema. Knowledge of lymph node quantity and regional anatomy of donor sites are essential for surgery optimization. The aim of the study was to quantify the level I lymph nodes of the neck (submental and submandibular lymph nodes), describe the regional anatomy and review the current literature.
UNASSIGNED: Level I lymph nodes were identified from pathology specimens of 73 patients in Melbourne, Australia who underwent radical, modified, and selective neck dissection were quantified. Level I was further subcategorized into levels IA (submental) and IB (submandibular), with data assessed qualitatively and quantitatively. PubMed, Google Scholar, Science Direct, Cochrane CENTRAL, and trial registries (http://clinicialtrials.gov/) were searched for relevant studies published from infinity to December 2021.
UNASSIGNED: A mean of 5.2±2.9 lymph nodes were identified in the level I neck region. Subcategorization into level IA and level IB showed means of 3.7±1.6 and 4.7±3.0 lymph nodes, respectively. No statistical differences were detected regarding patients\' gender (P=0.8) and age (P=0.5). Current literature shows level I neck VLNT is a suitable treatment for lymphedema.
UNASSIGNED: Level I of the neck offers a consistent quantity of lymph nodes and is suitable for free lymph node transfer. Current literature states VLNT is an effective technique for lymphedema treatment. This study details the regional anatomy of the level I neck lymph nodes, which will assist surgeons to optimize their surgeries and prevent donor-site morbidity.

Many emerging factors and circumstances urge the need to develop and optimize the detection and quantification techniques of mycotoxins in solid food and feed. The diversity of mycotoxins, which have different properties and affinities, makes the standardization of the analytical procedures and the adoption of a single protocol that covers the attributes of all mycotoxins a tedious or even an impossible mission. Several modifications and improvements have been undergone in order to optimize the performance of these methods including the extraction solvents, the extraction methods, the clean-up procedures, and the analytical techniques. The techniques range from the rapid screening methods, which lack sensitivity and specificity such as TLC, to a spectrum of more advanced protocols, namely, ELISA, HPLC, and GC-MS and LC-MS/MS. This review aims at assessing the current studies related to these analytical techniques of mycotoxins in solid food and feed. It discusses and evaluates, through a critical approach, various sample treatment techniques, and provides an in-depth examination of different mycotoxin detection methods. Furthermore, it includes a comparison of their actual accuracy and a thorough analysis of the observed benefits and drawbacks.

Deposition of amyloid and tau pathology can be quantified in vivo using positron emission tomography (PET). Accurate longitudinal measurements of accumulation from these images are critical for characterizing the start and spread of the disease. However, these measurements are challenging; precision and accuracy can be affected substantially by various sources of errors and variability. This review, supported by a systematic search of the literature, summarizes the current design and methodologies of longitudinal PET studies. Intrinsic, biological causes of variability of the Alzheimer\'s disease (AD) protein load over time are then detailed. Technical factors contributing to longitudinal PET measurement uncertainty are highlighted, followed by suggestions for mitigating these factors, including possible techniques that leverage shared information between serial scans. Controlling for intrinsic variability and reducing measurement uncertainty in longitudinal PET pipelines will provide more accurate and precise markers of disease evolution, improve clinical trial design, and aid therapy response monitoring.