Reliable Method Research Articles

Factors influencing speech pathology practice in dysphagia after stroke: A qualitative focus group study.

Dysphagia affects over half of adults after stroke. Dysphagia rehabilitation aims to improve swallowing and reduce negative outcomes for these adults. However, significant variability exists in dysphagia rehabilitation. Research is needed to explore the underlying clinician-specific and workplace factors that contribute to variability in dysphagia rehabilitation after stroke. To explore factors influencing speech pathology practice in dysphagia rehabilitation after stroke. We used a phenomenological approach with an interpretivist perspective. Twenty speech pathologists working in dysphagia rehabilitation participated from different workplace settings around Australia. Five semi-structured focus groups were conducted online. Data were inductively analysed using thematic analysis with a coding reliability method. Four themes were discussed within focus groups: (1) relationship between experienced and less experienced clinicians: 'Following what other people have done', (2) need for collaborative learning: 'A safe space to share and train', (3) variation between settings impacts on continuity of care: 'There's a difference between community and acute', and (4) working effectively with multidisciplinary teams (MDT): 'An MDT which can listen to the voice of speech pathology'. Relationships between senior and junior speech pathologists, within speech pathology and MDT, and across inpatient and community settings influenced speech pathology practice. Flattened hierarchies in speech pathology, collaborative learning in workplaces, mutual respect within teams and connection across inpatient and community settings could improve the quality and consistency of dysphagia rehabilitation after stroke. What is already known on this subject Dysphagia rehabilitation can improve swallowing after a stroke. However, dysphagia rehabilitation is characterised by variability in clinical practice. Clinician-specific and workplace factors influence clinical practice and may contribute to variability in dysphagia rehabilitation. What this study adds Professional relationships influence speech pathologists' clinical practice, including relationships between senior and junior clinicians, between inpatient and community settings and with peers and multidisciplinary teams. Workplace norms and hierarchies, poor continuity of care between settings and competing priorities from other disciplines can hinder dysphagia rehabilitation. However, collaborative learning, positive workplace cultures and respectful transdisciplinary care can improve the quality and consistency of clinical practice. What are the clinical implications of this work? Flattening hierarchies in the workplace can foster a safe learning space. Further, questioning workplace norms and seeking out peer learning within and across settings can build clinical skills and confidence. Developing positive workplace cultures that support continuous development may be key for empowering speech pathologists to provide high-quality and consistent dysphagia rehabilitation.

High-Throughput Bacteriophage Testing with Potency Determination: Validation of an Automated Pipetting and Phage Drop-Off Method.

The development of bacteriophages (phages) as active pharmaceutical ingredients for the treatment of patients is on its way and regulatory agencies are calling for reliable methods to assess phage potency. As the number of phage banks is increasing, so is the number of phages that need to be tested to identify therapeutic candidates. Currently, assessment of phage potency on a semi-solid medium to observe plaque-forming units is unavoidable and proves to be labor intensive when considering dozens of phage candidates. Here, we present a method based on automated pipetting and phage drop-off performed by a liquid-handling robot, allowing high-throughput testing and phage potency determination (based on phage titer and efficiency of plaquing). Ten phages were tested, individually and assembled into one cocktail, against 126 Escherichia coli strains. This automated method was compared to the reference one (manual assay) and validated in terms of reproducibility and concordance (ratio of results according to the Bland and Altman method: 0.99; Lin's concordance correlation coefficient: 0.86). We found that coefficients of variation were lower with automated pipetting (mean CV: 13.3% vs. 24.5%). Beyond speeding up the process of phage screening, this method could be used to standardize phage potency evaluation.

Long-acting porcine ACTH stimulated salivary cortisol reduces the overdiagnosis of adrenal insufficiency compared to serum cortisol in cirrhosis liver.

There are no reliable methods in clinical practice to diagnose adrenal insufficiency (AI) in patients with cirrhosis owing to variable cortisol-binding protein levels. This leads to unreliable results in ACTH stimulated serum cortisol test. We aimed to estimate the long-acting porcine (LA)ACTH-stimulated serum and salivary cortisol levels of patients at different stages of cirrhosis using second generation electrochemiluminescence and to determine the prevalence of true adrenal insufficiency in these patients. We included 135 noncritical patients with cirrhosis (45 each from CHILD A, Band C) and 45 healthy controls. Serum and salivary samples were collected at baseline in the morning and at 1and 2 h after LA-ACTH injection. In healthy subjects, the 2.5th centile of 2 h ACTH stimulated serum and salivary cortisol were 19.8and 0.97 μg/dL, which were used as cut-offs for defining AI based on serum and saliva respectively. The median (interquartile-range) 2-h stimulated salivary cortisol in Child A, B, C categories and controls were 1.36(1.23-2.38), 1.46(1.18-2.22), 1.72(1.2-2.2) and 2.12(1.42-2.72) μg/dLrespectively. Six subjects (4.4%) were diagnosed to have AI based on stimulated salivary cortisol cut-off, whereas 39 (28.9%) cirrhosis subjects had inadequately stimulated serum cortisol. Three patients (symptomatic) required steroid replacement therapy. Hypoalbuminemia was identified as a major risk factor for the misdiagnosis of adrenal insufficiency by serum cortisol-based testing. Long-acting porcine ACTH stimulated salivary cortisol reduces the overdiagnosis of adrenal insufficiency compared to serum cortisol in cirrhosis liver. Stimulated salivary cortisol is a promising investigation for evaluation of adrenal function in cirrhosis and more studies are required for its further validation before clinical use.

Correlation between electrical conductivity in saturated paste extracts and different diluted extracts (1/2.5, 1/5) of coarse-textured soils

Soil salinity poses a significant challenge to agricultural productivity, necessitating precise measurement methods. Rather than relying on complex determinations like saturated paste extracts (ECe), a practical alternative involves assessing soil salinity through the Electrical Conductivity (EC) of soil-water extracts. This simplified yet reliable method is crucial for understanding the impact of soil salinity on agricultural yields. To improve accuracy in representing soluble salts relevant to plant growth, the conversion of EC from soil water extracts to ECe becomes pivotal.In an experimental study, a clear relationship was sought between the Electrical Conductivity (EC) of saturated paste extracts (ECe) and the ratios found in soil-water extracts (1/2.5 and 1/5). Fifty samples of coarse-textured soil were collected from the Djamâa palm grove in Sidi Yahia village, located in the Oued Righ region within the Djamaa Zone. The analysis of the collected data revealed compelling findings, indicating a robust and statistically significant correlation between values obtained from ECe and those derived from EC1/2.5 and EC1/5 (correlation coefficient of R = 0.93 and 0.91, at a significance level of P = 0.05). These results highlight the promising potential of accurately estimating ECe in coarse-textured soils by employing EC1/2.5 and EC1/5, paving the way for streamlining assessment methodologies for soil salinity. This study not only elucidates the correlation between conductivity measures but also proposes a practical means to estimate soil salinity more efficiently. The findings contribute to informed decision-making in agricultural practices within the Northern Sahara's Djamâa palm grove and similar regions, offering valuable insights for sustainable soil management.

Clinical usefulness of the baby vision test in young children and its correlation with the Snellen chart.

To investigate the efficacy of a new visual acuity (VA) screening method, the baby vision test for young children. A total 105 eyes of 65 children aged 2-8y were included in the study. Acuity testing was conducted using a standardized recognition acuity chart (Snellen visual chart: at 3 m) and the baby vision model assessment. The baby vision device includes a screen, a near infrared camera and a computer. Children were seated at a measured distance of 33-40 cm from a display for testing. VA was estimated according to the highest resolution the children could follow. Decimal VA data were converted to logarithm of the minimum angle of resolution (logMAR) for statistical analysis. The VA results for each child were recorded and analyzed for consistency. The mean VA measured using the Snellen visual chart was 0.62±0.32, and that assessed using the baby vision test was 0.66±0.27. The 95% limit of agreement was -0.609 to 0.695, with 95.2% (100/105) plots within the 95% limits of agreement. VA values of the baby vision test were significantly correlated with those of the Snellen chart (R=0.274, P=0.005). The baby vision test can be used as a relatively reliable method for estimating VA in young children. This new acuity assessment might be a valid predictor of optotype-measured acuity later in preverbal children.

A simple and reliable method for claustrum localization across age in mice

The anatomical organization of the rodent claustrum remains obscure due to lack of clear borders that distinguish it from neighboring forebrain structures. Defining what constitutes the claustrum is imperative for elucidating its functions. Methods based on gene/protein expression or transgenic mice have been used to spatially outline the claustrum but often report incomplete labeling and/or lack of specificity during certain neurodevelopmental timepoints. To reliably identify claustrum projection cells in mice, we propose a simple immunolabelling method that juxtaposes the expression pattern of claustrum-enriched and cortical-enriched markers. We determined that claustrum cells immunoreactive for the claustrum-enriched markers Nurr1 and Nr2f2 are devoid of the cortical marker Tle4, which allowed us to differentiate the claustrum from adjoining cortical cells. Using retrograde tracing, we verified that nearly all claustrum projection neurons lack Tle4 but expressed Nurr1/Nr2f2 markers to different degrees. At neonatal stages between 7 and 21 days, claustrum projection neurons were identified by their Nurr1-postive/Tle4-negative expression profile, a time-period when other immunolabelling techniques used to localize the claustrum in adult mice are ineffective. Finally, exposure to environmental novelty enhanced the expression of the neuronal activation marker c-Fos in the claustrum region. Notably, c-Fos labeling was mainly restricted to Nurr1-positive cells and nearly absent from Tle4-positive cells, thus corroborating previous work reporting novelty-induced claustrum activation. Taken together, this method will aid in studying the claustrum during postnatal development and may improve histological and functional studies where other approaches are not amenable.

Critical role of growth medium for detecting drug interactions in Gram-negative bacteria that model in vivo responses.

Drug-resistant bacterial infections are a growing concern and have only continued to increase during the SARS-CoV-2 pandemic. Though not routinely used for Gram-negative bacteria, drug combinations are sometimes used for serious infections and may become more widely used as the prevalence of extremely drug-resistant organisms increases. To date, reliable methods are not available for identifying beneficial drug combinations for a particular infection. Our study shows variability across strains in how drug interactions are impacted by growth conditions. It also demonstrates that testing drug combinations in tissue-relevant growth conditions for some strains better models what happens during infection and may better inform combination therapy selection.

Predicting construction equipment resale price: machine learning model

Purpose The equipment resale price plays an important role in calculating the optimum time for equipment replacement. Some of the existing models that predict the equipment resale price do not take many of the influencing factors on the resale price into account. Other models consider more factors that influence equipment resale price, but they still with low accuracy because of the modeling techniques that were used. An easy tool is required to help in forecasting the resale price and support efficient decisions for equipment replacement. This research presents a machine learning (ML) computer model helping in forecasting accurately the equipment resale price.Design/methodology/approach A measuring method for the influencing factors that have impacts on the equipment resale price was determined. The values of those factors were measured for 1,700 pieces of equipment and their corresponding resale price. The data were used to develop a ML model that covers three types of equipment (loaders, excavators and bulldozers). The methodology used to develop the model applied three ML algorithms: the random forest regressor, extra trees regressor and decision tree regressor, to find an accurate model for the equipment resale price. The three algorithms were verified and tested with data of 340 pieces of equipment.Findings Using a large number of data to train the ML model resulted in a high-accuracy predicting model. The accuracy of the extra trees regressor algorithm was the highest among the three used algorithms to develop the ML model. The accuracy of the model is 98%. A computer interface is designed to make the use of the model easier.Originality/value The proposed model is accurate and makes it easy to predict the equipment resale price. The predicted resale price can be used to calculate equipment elements that are essential for developing a dependable equipment replacement plan. The proposed model was developed based on the most influencing factors on the equipment resale price and evaluation of those factors was done using reliable methods. The technique used to develop the model is the ML that proved its accuracy in modeling. The accuracy of the model, which is 98%, enhances the value of the model.

Dynamic S-Box Construction Using Mordell Elliptic Curves over Galois Field and Its Applications in Image Encryption

Elliptic curve cryptography has gained attention due to its strong resilience against current cryptanalysis methods. Inspired by the increasing demand for reliable and secure cryptographic methods, our research investigates the relationship between complex mathematical structures and image encryption. A substitution box (S-box) is the single non-linear component of several well-known security systems. Mordell elliptic curves are used because of their special characteristics and the immense computational capacity of Galois fields. These S-boxes are dynamic, which adds a layer of complexity that raises the encryption process’s security considerably. We suggest an effective technique for creating S-boxes based on a class of elliptic curves over GF(2n),n≥8. We demonstrate our approach’s robustness against a range of cryptographic threats through thorough examination, highlighting its practical applicability. The assessment of resistance of the newly generated S-box to common attack methods including linear, differential, and algebraic attacks involves a thorough analysis. This analysis is conducted by quantifying various metrics such as non-linearity, linear approximation, strict avalanche, bit independence, and differential approximation to gauge the S-box’s robustness against these attacks. A recommended method for image encryption involves the use of built-in S-boxes to quickly perform pixel replacement and shuffling. To evaluate the efficiency of the proposed strategy, we employed various tests. The research holds relevance as it can provide alternative guidelines for image encryption, which could have wider consequences for the area of cryptography as a whole. We believe that our findings will contribute to the development of secure communication and data protection, as digital security is becoming increasingly important.

FACIAL EXPRESSION EMOTION RECOGNITION USING LOCAL BINARY PATTERNS HISTOGRAMS

Local Binary Patterns (LBP) histograms are used as texture descriptors by the proposed facial expression emotion detection system to identify various face expressions in photos. Localized facial patterns are represented by histograms formed by the extraction of LBP features based on pixel intensity comparisons after pre-processing operations such as picture normalization and scaling. Using a labeled emotion dataset, these histograms are used as feature vectors to train a machine learning model. The model's performance is evaluated using evaluation measures, such as accuracy, precision, recall, and F1 score. The system, which was developed with OpenCV and scikit-learn, shows a reliable and useful method for recognizing facial expressions that can be used in practical settings. Key Words: Facial Recognition, Deep Learning, Image Recognition

Assessment and development of saturated and subcooled heat transfer coefficient correlations for cryogenic flow boiling in tubes

The escalating interest in cryogenic technologies for space-related applications has led to an unprecedented demand for reliable prediction methods for cryogenic two-phase flow and heat transfer. Regrettably, existing heat transfer coefficient (HTC) correlations developed for conventional fluids prove inadequate and provide subpar predictions when applied to cryogenic flow boiling conditions. Therefore, it is imperative to develop a set of new HTC correlations specifically tailored for cryogenic flow boiling. In this study, comprehensive databases are constructed consolidating experimental data from previous studies of the present authors and historical data extracted from open literatures, spanning a wide range of operating conditions, flow orientations, and various cryogenic fluids. Subsequently, based on the constructed databases, an assessment of the predictive accuracy of seminal HTC correlations is conducted, followed by the development of new HTC correlations for cryogenic saturated and subcooled flow boiling. The newly developed correlations demonstrate very good predictive accuracy, with an overall mean absolute error (MAE) of 23.84 % and 21.24 % for LN2 saturated and subcooled HTC, respectively, under terrestrial gravity conditions. When assessed against a microgravity dataset, these correlations exhibit equally good predictive accuracy, yielding MAE values of 16.73 % and 25.99 % for LN2 saturated and subcooled HTC, respectively. Furthermore, the universal applicability of the new HTC correlations is ascertained by assessing the correlations across a multitude of cryogenic fluids, including LN2, LHe, LAr, LCH4, and LH2. Impressively, these correlations display outstanding predictive accuracy, with MAE values of 24.01 % and 21.29 % for saturated and subcooled HTC, respectively, underscoring their superior performance across a wide range of cryogenic fluids, validated against 2,445 saturated cryogenic HTC datapoints and 1,553 subcooled cryogenic HTC datapoints. Overall, the new HTC correlations consistently outperform all prior seminal correlations, yielding good predictive accuracy across a diverse spectrum of operating conditions, irrespective of flow orientation and gravity level, for various cryogenic fluids.

A comparative study of LightGBM, XGBoost, and GEP models in shear strength management of SFRC-SBWS

Steel fiber reinforced concrete (SFRC) was revealed to have superior mechanical properties compared to plain concrete, especially under shear loads. However, Due to a lack of prediction models for SFRC-Slender Beams without Stirrups (SFRC-SBWS) in the available literature, the application of SFRC-SBWS in reinforced concrete (RC) constructions is limited. This research aims to fill a literature gap by analyzing different Machine Learning (ML) algorithms, including Extreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LightGBM), and Gene Expression Programming (GEP), to forecast the shear strength of SFRC-SBWS specimens. A database of 333 tested SFRC-SBWS specimens was collected and analyzed. The results revealed the LightGBM and XGBoost algorithms surpassed other studied algorithms with Coefficient of determination (R2) = 95.74% and 93.27%, Root Mean Square Error (RMSE) = 20.04 kN and 25.19 kN, respectively. A GEP-based closed-form model was suggested to forecast the shear strength, considering the most important factors, including beam depth effect, beam width, and longitudinal steel reinforcement ratio. The proposed GEP model has an R2 = 91.12% and RMSE = 101.44 kN, showing a slightly lower prediction accuracy. The effectiveness of the studied models was evaluated from various angles, and the results demonstrate the importance of managing new technological advancements to create reliable support methods. Engineers that use these approaches get a dependable tool for estimating shear resistance, which is crucial in guaranteeing the safety and lifespan of RC structures. The successful combination of classic engineering approaches and revolutionary ML strategies indicates the potential for improving accuracy and reducing bias in prediction-oriented investigations. As the engineering field supports technological improvements, combining robust and efficient prediction models, as described in this paper, can contribute considerably to resilient structure design and safety assurance. This leads to sustainable and robust management of structures with greater confidence in predictions.

Explainable based approach for the air quality classification on the granular computing rule extraction technique

Air pollution corresponds to one of the considerable challenges and disastrous sides of the environment that causes severe damage to all its biodiversity, including humans. As a result, establishing efficient, reliable, and interpretable methods and techniques to predict and control air quality is a must to preserve the environment and consider the necessary precautions. Most traditional machine learning models often lack transparency, making it challenging to interpret their decisions, especially in vital domains like air pollution. This paper proposes a novel approach that leverages granular computing to extract interpretable rules for air quality classification. We demonstrate the effectiveness of our approach through experiments on a real-world air quality dataset, showcasing the interpretability of the extracted rules and their accuracy in classifying air quality levels. The output of the proposed GrC model is a tree-like structure minimizing the entropy, allowing an easier interpretation of the classification results. A comparison is conducted with some widely used machine learning algorithms, including decision tree classifier, random forest, and CatBoost. The results indicate that the proposed granular computing rule extraction approach shows a competitive performance according to traditional black-box models in terms of accuracy (79%), transparency and reliability. The developed GrC model and the findings of this study not only contribute to advancing the field of air quality classification but also bear broader implications for environmental research and management for relevant and informed decision-making.

Micrometeorological monitoring reveals that canopy temperature is a reliable trait for the screening of heat tolerance in rice.

Micrometeorological monitoring is not just an effective method of determining the impact of heat stress on rice, but also a reliable way of understanding how to screen for heat tolerance in rice. The aim of this study was to use micrometeorological monitoring to determine varietal differences in rice plants grown under two weather scenarios-Long-term Heat Scenario (LHS) and Normal Weather Scenario (NWS)- so as to establish reliable methods for heat tolerance screening. Experiments were conducted with two heat susceptible varieties-Mianhui 101 and IR64-and two heat tolerant varieties, Quanliangyou 681 and SDWG005. We used staggered sowing method to ensure that all varieties flower at the same time. Our results showed that heat tolerant varieties maintained lower canopy temperature compared to heat susceptible varieties, not just during the crucial flowering period of 10 am to 12 pm, but throughout the entire day and night. The higher stomatal conductance rate observed in heat tolerant varieties possibly decreased their canopy temperatures through the process of evaporative cooling during transpiration. Conversely, we found that panicle temperature cannot be used to screen for heat tolerance at night, as we observed no significant difference in the panicle temperature of heat tolerant and heat susceptible varieties at night. However, we also reported that higher panicle temperature in heat susceptible varieties decreased spikelet fertility rate, while low panicle temperature in heat tolerant varieties increased spikelet fertility rate. In conclusion, the results of this study showed that canopy temperature is probably the most reliable trait to screen for heat tolerance in rice.

A Scoping Review to Find Reliable Smile Analysis Method

Background The smile is one of the most fundamental facial expressions, used to convey friendship, agreement, and admiration. There are various smile analysis method available. Objective The aim of this scoping review to find most reliable smile analysis method. Which smile analysis method is most reliable and accurate while assessing the smile of patients undergoing orthodontic treatment? Design An electronic search was executed on PUBMED, Cochrane, SCOPUS, hand search was made on google scholar and references of the articles selected. Keyword used while searching the databases as “((smile analysis) AND methods) AND comparison.” Results A preliminary search of databases and grey literature yielded 525 articles. Following the application of the eligibility criteria, 16 articles were included. Conclusion With the use of latest technologies and knowledge in literature, a clinician can correctly identify the problem, assess the treatment need and give a satisfactory outcome to the patient.

Unusual foreign bodies in upper aero digestive tract – Our experience at tertiary care centre

Foreign bodies ingestion is especially common in the pediatric age group, whereas in adults occur accidently or among those with psychiatric disorders, mental retardation, prisoners and alcoholics, Preexisting GI tract abnormalities, such as previous surgery, strictures, or functional abnormalities, increases the risk of a swallowed foreign body becoming lodged at the site of the abnormality.This retrospective study was conducted in 32 patients with upper aero digestive tract unusual foreign bodies presenting to our department on emergency basis in a duration of one year. This Study was conducted in ENT department tertiary care Centre in duration of 2021-2022, 32 patients presented with unusual FB in upper aero digestive tract were treated in our department on an emergency basis. There were 14 male (43.75%) and 18 female (56.25%) ranging in age from 2 to 62 years. The main symptoms patients complained were difficulty in swallowing and pain. Detailed history with examination of nasal cavity, oral cavity and pharynx by indirect laryngoscopy and finally X-ray or CT scan were the diagnostic tools as required. The location of the FB was in the esophagus, Pyriform sinus, hypopharynx in decreasing order.The treatment consisted of rigid endoscopy and foreign body removal under general anesthesia or sedation. No major complications occurred in procedure except one in with iatrogenic esophageal perforation that was managed conservatively. Endoscopy is a reliable method in the treatment of upper aero digestive tract foreign body (FB) impaction.

Farmers Perspectives on Negative Impacts of Chemical Pesticide Usage in Vegetable Cultivations: Case Study from the Periyaneelavanai Area, Ampara District

Indiscriminate use of pesticides causes health hazards to humans and long-lasting negative impacts to the environment. The major objectives of this study are to evaluate the current status of pesticide usage in the Periyaneelavani area, to evaluate farmers‘ perception on health, environmental effects due to usage, of pesticides, to identify the awareness level of farmers about proper usage of chemical pesticides and alternatives. This study was carried out in two GN divisions of Periyaneelavanai area, Ampara district. To represent 20% respondents, 50 farmers from each GN devisions which upto total 100 were randomly surveyed using a structured questionnaire. In order to gather the necessary data on vegetable cultivation, a preliminary study involving interviews with the Agricultural Instructor-Officer in Charge and affiliated farmers in Periyaneelavanai area were conducted before the actual survey. Discriptive analysis, P test, T test, anova were performed using minitab 14.0 version to conduct data analysis. The present study revealed that Chili, Brinjal, Okra, Tomato, Long beans, Cucurbites, Onion and Leafy vegetables were primarily grown in this area. Abamactin, Coragan, Applaud and Virtako were used as insecticides and Abamactin was the most popular (42%) among farmers. Mancozeb, Propineb and Canazole were used as fungicides and the highest usage was Mancozeb (45%). Mortin rat kill was used as a rodenticide by all the respondent farmers, and they have not used herbicides for their vegetable cultivation; instead, they manually remove the weeds. Skin irritation was the most prominent (60%), acute disease, and Asthma was the most common chronic disease (31%), that can be seen among farmers. The most common adverse environmental impact in this area was the empty pesticide containers piling up along roadsides (95%). All the farmers use at least one PPE while applying pesticide though the frequency of wearing PPE is different among each other. The majority of respondents use gloves, eye protection, and foot ware but lack of awareness towards face, head and body protection. The highest number of farmers selected indoor places (51%) for pesticide storage which cause to have a high risk of exposure among farmers. Most of the farmers selected the option of burying or burning pesticide bottles after using it. Majority of respondents preferred to use chemical pesticides (76%), rather than alternative methods due to the reliability of those methods. It is proposed to educate farmers on application, storage, cleaning and carrying the pesticides, the methods of effectiveness of different individual protection facilities, and utilizing the behavior to reduce the exposure to poisons should be developed and be available to farmers. This will help to reduce negative health and environmental consequences. Moreover, it is recommended to introduce reliable and effective alternative methods for chemical pesticides.
 Keywords: Periyaneelavanai area, Vegetable cultivation, Pesticides, Environmental impacts, Disease

Sub-ppb detection limit of Cr, Pb, and Cu in water by surface-enhanced LIBS with femtosecond laser

Water pollution caused by heavy metal contamination significantly threatens human health. Detecting trace levels of heavy metals in water is of utmost importance. Surface-enhanced laser-induced breakdown spectroscopy (SE-LIBS) has become a reliable detection method. In this study, we improved the analytical sensitivity of trace heavy metals in water by introducing femtosecond lasers into the SE-LIBS technique, replacing the traditional nanosecond lasers. An aluminum plate was utilized as a substrate to transform the liquid into a solid, overcoming the inadequacy in sensitivity and precision of LIBS when applied to water samples. We developed individual calibration curves for Cr, Pb, and Cu to determine their respective limit of detection (LOD). The LODs obtained were 0.3, 2.5, and 0.08 ng/mL for Cr, Pb, and Cu, respectively. These LODs were lower than traditional nanosecond SE-LIBS, and even lower than the LODs obtained by combining other LIBS enhancement techniques and SE-LIBS. We also determined good accuracy through the coefficient of determination R2, which was 0.97, 0.99, and 0.97, respectively, for Cr, Pb, and Cu. These results provide valuable and insightful information for the sensitive analysis of trace heavy metals in water using SE-LIBS with a femtosecond laser.

Challenges and potential for detecting and quantifying titanium dioxide in food.

Titanium dioxide (TiO2) is banned in some countries but its use is still permitted in others. The global food supply chain is therefore challenged with the need to use rapid and reliable testing methods to either detect the presence of TiO2 or to quantify its concentration. The goal of this study was to determine the feasibility of using color, texture profile analysis, Raman microscopy, and X-ray fluorescence (XRF) spectroscopy to detect and quantify TiO2 in fillings used in the pastry and confectionery industry. In this study, two types of fillings were investigated: vanilla based and chocolate based. All fillings were prepared in four different variations - without TiO2 and with three concentrations as follows: 0.25 g*kg-1, 0.5 g*kg-1, or 0.75 g*kg-1 TiO2 per sample. The methods were selected for their ability to analyze the samples in a short period of time. All of the methods showed moderate to high potential for detecting TiO2 in the samples. The results reveal how TiO2 affects the food matrix color and texture. Use of Raman microscopy confirms its detectability, although concentrations of TiO2 do not follow a pattern. X-ray fluorescence spectroscopy showed the greatest potential as it can not only detect TiO2 but can also quantify its concentration in the samples. The highest potential for quantifying the concentration of this food additive was achieved with XRF. © 2024 Society of Chemical Industry.

Cochlear Implantation in Charcot-Marie-Tooth Patients: Speech Perception and Quality of Life.

There is a limited understanding of the impact of cochlear implantation (CI) in patients with Charcot-Marie-Tooth disease (CMT), given the scarcity of reported cases. We aim to evaluate the audiological outcomes and quality of life (QoL) after CI in CMT. Multi-institutional, university-affiliated, tertiary-referral centers, retrospective chart review.Our cohort includes 5 patients with CMT. Patients' charts were reviewed for demographic characteristics, operation notes, and pre- and post-implantation audiology evaluation. Patients completed the Cochlear Implant Quality of Life-10 (CIQOL-10) Global questionnaire. Pre-implantation, the mean pure tone average was 84.1 ± 7.2 dB, and the mean word recognition score was 2.4% in the implanted ear. AzBio sentence test was performed in quiet, revealing a mean of 4 ± 1.4% in the implanted ear. Post-implantation, PTA results were all within the mild hearing loss range (mean 33.0 ± 5.9 dB). Post-CI, AZ-Bio test results were 5%, 65%, and 74% (for 3 patients), and HINT scores were 55% and 58% (for 2 patients). The mean score of the CIQOL-10 questionnaire was 42.7 ± 10.47 (range 1-100). Patients were most satisfied with their ability to listen to the television or radio, have conversations in a quiet environment, and feel comfortable being themselves. To the best of our knowledge, this is the most extensive series of CI in CMT-associated sensorineural hearing loss and auditory neuropathy. Our cohort suggests that CI is a safe and reliable method for hearing rehabilitation that can achieve good speech performance and improve QoL in CMT patients.