Preparation and characterization of a novel Co(II) optode based on polymer inclusion membrane

Exploring the design and performance of a tellurium optical sensor utilizing a plasticizer-free polymer inclusion membrane

A highly responsive, discerning, and uncomplicated technique has been devised for immobilizing reagents onto a plasticizer-free optical sensor membrane, employing polymer inclusion membranes (PIMs). This procedural strategy relies on a physical immobilization approach, specifically encapsulation, resulting in the creation of an optical sensing membrane. The responsive PIM is composed of poly(vinyl chloride) (PVC) as the fundamental polymer, Aliquat 336 as an extractant, and 4-(4 -chlorobenzylideneimino)-3-methyl-5-mercapto-1,2,4-triazole (CBIMMT) as the reagent. The optimized sensor demonstrates a linear range of 6.00-156 ng/mL for Te(IV), along with detection and quantification limits of 1.75 and 5.60 ng/mL, respectively. The sensor response time is 3.0 min, confirming its reproducibility. Effective regeneration of the sensor is achieved using a 0.2 mol/L HCl solution. The sensor membrane's selectivity is evaluated against various interfering ions, underscoring minimal interference. The sensor membrane efficacy is demonstrated through successful applications in quantifying Te(IV) levels, including natural water, chalcogenides, milk, vegetables, and soil samples.

A Novel Polymer Inclusion Membrane-Based Green Optical Sensor for Selective Determination of Iron: Design, Characterization, and Analytical Applications

The design, characterization, and analytical application of a green optical sensor for the selective determination of Fe(II) ions is proposed. The sensor is based on the immobilization of the chromogenic reagent picolinaldehyde salicyloylhydrazone (SHPA) within a polymer inclusion membrane. To reduce solvent usage, the reagent was synthesized using a green mechanochemical procedure. The components for sensor preparation were optimized with a sequential simplex method and the optimal composition was found to be 0.59 g cellulose triacetate (base polymer), 0.04 g SHPA (chemosensor reagent), 4.9 mL dibutyl phthalate (plasticizer), and 38 mL dichloromethane (solvent). The conditions of iron analysis were also optimized resulting in pH 6 for aqueous solution, 90 min exposure time and 10 min short-term stability. The optical sensor showed a linear range from the limit of detection (0.48 µmol L-1) to 54 µmol L-1 Fe(II). The precision of the method was found to be 1.44% and 1.19% for 17.9 and 45 µmol L-1 Fe(II), respectively. The characteristics of the sensor allowed the design of a Fe(II)/Fe(III) speciation scheme. The methodology was successfully applied to the determination of iron in food preservatives, food additives, and dietary supplement. Additionally, the Fe speciation scheme was successfully applied to an agricultural fertilizer.

Improved interpretation of 18F-florzolotau PET in progressive supranuclear palsy using a normalization-free deep-learning classifier

While 18F-florzolotau tau PET is an emerging biomarker for progressive supranuclear palsy (PSP), its interpretation has been hindered by a lack of consensus on visual reading and potential biases in conventional semi-quantitative analysis. As clinical manifestations and regions of elevated 18F-florzolotau binding are highly overlapping in PSP and the Parkinsonian type of multiple system atrophy (MSA-P), developing a reliable discriminative classifier for 18F-florzolotau PET is urgently needed. Herein, we developed a normalization-free deep-learning (NFDL) model for 18F-florzolotau PET, which achieved significantly higher accuracy for both PSP and MSA-P compared to semi-quantitative classifiers. Regions driving the NFDL classifier's decision were consistent with disease-specific topographies. NFDL-guided radiomic features correlated with clinical severity of PSP. This suggests that the NFDL model has the potential for early and accurate differentiation of atypical parkinsonism and that it can be applied in various scenarios due to not requiring subjective interpretation, MR-dependent, and reference-based preprocessing.

ConvNets for automatic detection of polyglutamine SCAs from brain MRIs: state of the art applications

Polyglutamine spinocerebellar ataxias (polyQ SCAs) are a group of neurodegenerative diseases, clinically and genetically heterogeneous, characterized by loss of balance and motor coordination due to dysfunction of the cerebellum and its connections. The diagnosis of each type of polyQ SCA, alongside with genetic tests, includes medical images analysis, and its automation may help specialists to distinguish between each type. Convolutional neural networks (ConvNets or CNNs) have been recently used for medical image processing, with outstanding results. In this work, we present the main clinical and imaging features of polyglutamine SCAs, and the basics of CNNs. Finally, we review studies that have used this approach to automatically process brain medical images and may be applied to SCAs detection. We conclude by discussing the possible limitations and opportunities of using ConvNets for SCAs diagnose in the future.

Convolutional Neural Networks for Segmenting Cerebellar Fissures from Magnetic Resonance Imaging

The human cerebellum plays an important role in coordination tasks. Diseases such as spinocerebellar ataxias tend to cause severe damage to the cerebellum, leading patients to a progressive loss of motor coordination. The detection of such damages can help specialists to approximate the state of the disease, as well as to perform statistical analysis, in order to propose treatment therapies for the patients. Manual segmentation of such patterns from magnetic resonance imaging is a very difficult and time-consuming task, and is not a viable solution if the number of images to process is relatively large. In recent years, deep learning techniques such as convolutional neural networks (CNNs or convnets) have experienced an increased development, and many researchers have used them to automatically segment medical images. In this research, we propose the use of convolutional neural networks for automatically segmenting the cerebellar fissures from brain magnetic resonance imaging. Three models are presented, based on the same CNN architecture, for obtaining three different binary masks: fissures, cerebellum with fissures, and cerebellum without fissures. The models perform well in terms of precision and efficiency. Evaluation results show that convnets can be trained for such purposes, and could be considered as additional tools in the diagnosis and characterization of neurodegenerative diseases.

Utilization of a plasticized PVC optical sensor for the selective and efficient detection of cobalt(ii) in environmental samples

A novel sensitive, selective, and reversible cobalt(ii) ion optical sensor was prepared by the incorporation of 5-[o-carboxyphenylazo]2,4-dihydroxybenzoic acid [CPDB] and sodium tetraphenylborate (NaTPB) in a plasticized polyvinyl chloride (PVC) membrane containing dioctyl adipate (DOA) as a plasticizer. The influence of several parameters such as pH, base matrix, solvent mediator and reagent concentration was optimized. A comparison of the obtained results with those of previously reported sensors revealed that the proposed method, in addition to being fast and simple, provided a good linear range (0.05–45.20 μM) and low detection limit (0.015 μM). Low detection and quantification limits and excellent selectivity in the presence of interfering ions such as Fe³⁺, Cu²⁺, Ni²⁺, Ag⁺, Au³⁺, Cr³⁺, Cd²⁺, Zn²⁺, Hg²⁺, and SO₄²⁻ make it feasible to monitor Co²⁺ ion content accurately and repeatedly in environmental samples with complicated matrices. The optode was regenerated successfully using 0.3 M nitric acid (HNO₃) solution while its response was reversible with a relative standard deviation (RSD) lower than 1.9% for seven replicate determinations of 20 μM Co²⁺ in various membranes. The optode was stable and was stored for at least 15 days without observing any change in its sensitivity.

A novel sensitive, selective, and reversible cobalt(ii) ion optical sensor was prepared by the incorporation of [CPDB] and (NaTPB) in a (PVC) membrane containing (DOA) as a plasticizer.

Sensing Cd(II) Using a Disposable Optical Sensor Based on a Schiff Base Immobilisation on a Polymer-Inclusion Membrane. Applications in Water and Art Paint Samples

A disposable colour-changeable optical sensor based on an interesting polymer inclusion-membrane (PIM) was designed to determine Cd(II) ions in aqueous medium. The Schiff base 2-acetylpyridine benzoylhydrazone (2-APBH) immobilised on the polymer membrane was used as a sensing molecule. The amounts of the PIM components were optimised by a 3² fractional factorial design with two central points and two blocks. The best optical sensor composition consisted of 2.5 g of poly(vinylchloride) (PVC) as a base polymer, 3 mL of tributyl phosphate (TBP) as a plasticiser, and 0.02 g of 2-APBH as a reagent. The sensor showed a good linear response in the range from 0.02 mg L⁻¹ (limit of detection) to 1 mg L⁻¹ of Cd(II) under the following experimental conditions: pH 9.5 (adjusted using ammonium chloride buffer solution at 0.337 mol L⁻¹), 60 min of exposure time plus 2 min of sonication (pulses at 2 s intervals), and 10 min of short-term stability. The relative standard deviation of the method was determined to be 4.04% for 0.4 mg L⁻¹ of Cd(II). The optical sensor was successfully applied to the determination of Cd(II) in natural-water and art-paint samples.

Ensemble of transfer learnt classifiers for recognition of cardiovascular tissues from histological images

Recognition of tissues and organs is a recurrent step performed by experts during analyses of histological images. With advancement in the field of machine learning, such steps can be automated using computer vision methods. This paper presents an ensemble-based approach for improved classification of non-pathological tissues and organs in histological images using convolutional neural networks (CNNs). With limited dataset size, we relied upon transfer learning where pre-trained CNNs are re-used for new classification problems. The transfer learning was done using eleven CNN architectures upon 6000 image patches constituting training and validation subsets of a public dataset containing six cardiovascular categories. The CNN models were fine-tuned upon a much larger dataset obtained by augmenting training subset to obtain agreeable performance on validation subset. Lastly, we created various ensembles of trained classifiers and evaluate them on testing subset of 7500 patches. The best ensemble classifier gives, precision, recall, and accuracy of 0.876, 0.869 and 0.869, respectively upon test images. With an overall F₁-score of 0.870, our ensemble-based approach outperforms previous approaches with single fine-tuned CNN, CNN trained from scratch, and traditional machine learning by 0.019, 0.064 and 0.183, respectively. Ensemble approach can perform better than individual classifier-based ones, provided the constituent classifiers are chosen wisely. The empirical choice of classifiers reinforces the intuition that models which are newer and outperformed in their native domain are more likely to outperform in transferred-domain, since the best ensemble dominantly consists of more lately proposed and better architectures.

Diagnosis of Cervical Cancer based on Ensemble Deep Learning Network using Colposcopy Images

Traditional screening of cervical cancer type classification majorly depends on the pathologist's experience, which also has less accuracy. Colposcopy is a critical component of cervical cancer prevention. In conjunction with precancer screening and treatment, colposcopy has played an essential role in lowering the incidence and mortality from cervical cancer over the last 50 years. However, due to the increase in workload, vision screening causes misdiagnosis and low diagnostic efficiency. Medical image processing using the convolutional neural network (CNN) model shows its superiority for the classification of cervical cancer type in the field of deep learning. This paper proposes two deep learning CNN architectures to detect cervical cancer using the colposcopy images; one is the VGG19 (TL) model, and the other is CYENET. In the CNN architecture, VGG19 is adopted as a transfer learning for the studies. A new model is developed and termed as the Colposcopy Ensemble Network (CYENET) to classify cervical cancers from colposcopy images automatically. The accuracy, specificity, and sensitivity are estimated for the developed model. The classification accuracy for VGG19 was 73.3%. Relatively satisfied results are obtained for VGG19 (TL). From the kappa score of the VGG19 model, we can interpret that it comes under the category of moderate classification. The experimental results show that the proposed CYENET exhibited high sensitivity, specificity, and kappa scores of 92.4%, 96.2%, and 88%, respectively. The classification accuracy of the CYENET model is improved as 92.3%, which is 19% higher than the VGG19 (TL) model.

Integration of Response Surface Methodology (RSM) and Principal Component Analysis (PCA) as an Optimization Tool for Polymer Inclusion Membrane Based-Optodes Designed for Hg(II), Cd(II), and Pb(II)

An optimization of the composition of polymer inclusion membrane (PIM)-based optodes, and their exposure times to metal ion solutions (Hg(II), Cd(II), and Pb(II)) was performed using two different chromophores, diphenylthiocarbazone (dithizone) and 1-(2-pyridylazo)-2-naphthol (PAN). Four factors were evaluated (chromophore (0.06-1 mg), cellulose triacetate (25-100 mg) and plasticizer amounts (25-100 mg), and exposure time (20-80 min)). Derringer's desirability functions values were employed as response variables to perform the optimization obtained from the results of three different processes of spectral data treatment: two full-spectrum methods (M1 and M3) and one band-based method (M2). The three different methods were compared using a heatmap of the coefficients and dendrograms of the Principal Component Analysis (PCA)reductions of their desirability functions. The final recommended M3 processing method, i.e., using the scores values of the first two principal components in PCA after subtraction of the normalized spectra of the membranes before and after complexation, gave more discernable differences between the PIMs in the Design of Experiments (DoE), as the nodes among samples appeared at longer distances and varyingly distributed in the dendrogram analysis. The optimal values were time of 35-65 min, 0.53 mg-1.0 mg of chromophores, plasticizers 34.4-71.9 of chromophores, and 62.5-100 mg of CTA, depending on the metal ion. In addition, the method yielded the best outcomes in terms of interpretability and an easily discernable color change so that it is recommended as a novel optimization method for this kind of PIM optode.

A 3D origami paper-based analytical device combined with PVC membrane for colorimetric assay of heavy metal ions: Application to determination of Cu(II) in water samples

Microfluidic paper-based analytical devices (μPADs) as a potentially powerful analytical platform have recently gained significant attention for on-site monitoring of heavy metal ions, which are one of the most significant environmental concern because of non-degradability and high toxicity. The commonly applied μPADs suffers from some defects, such as heterogeneous deposition of reagent, resulting in poor detection limits and low sensitivity. So, in this work, a three-dimensional origami μPAD combined with PVC Membrane was developed, which can manage problems of movement of colored products or leaching out the dye and leading to color heterogeneity in the detection zones. Furthermore, a waste layer was added to μPAD for loading of more amounts of the analyte, which results in improvement of detection limit. As a proof of concept, the μPAD was used for the analysis of Cu²⁺ ion. For this purpose, pyrocatechol violet and chrome azurol S as colorimetric reagents were doped into PVC membrane and injected in the detection zone. The proposed μPAD was presented good linearity in the ranges of 5.0-1400.0 and 5.0-200.0 mg L^-1, and the limits of detections of 1.7 and 1.9 mg L^-1 in presence of chrome azurol S and pyrocatechol violet, respectively.

Chromium(VI) Removal by Polyvinyl Chloride (PVC)/Aliquat-336 Polymeric Inclusion Membranes in a Multiframe Flat Sheet Membrane Module

A new multiframe flat sheet membrane contactor module containing several flat membranes was designed and implemented. Each frame contains a chamber (central hole) in which the feed and the receiving phases are put in contact with polyvinyl chloride (PVC)/Aliquat-336 polymeric flat sheet membranes for Cr(VI) removal from aqueous solutions (feed phase). To evaluate the efficiency of the system, the experimental design methodology was used to analyze the effect of temperature (T, °C), PVC/Aliquat-336 ratio, and Cr (VI) concentration in the feed phase and the concentration of sodium chloride (NaOH-NaCl) in the receiving phase. Two representative mathematical models of the two responses (extraction and back-extraction) were respectively obtained. A good correlation between the experimental results and those predicted (RS2 = 97.77 and RR2 = 97.87) was achieved, allowing the optimization of the different factors selected for each response, separately. The proposed system showed a good separation performance, leading to Cr(VI) extractions up to 93% when working at the optimized operating conditions.