Low-cost Tool Research Articles

Utilizing Modified Coopland's Scoring System to Identify and Predict the Outcome of High-Risk Pregnancies in Resource-Limited Settings: A Retrospective Review.

Introduction A high-risk pregnancy is associated with adverse maternal and foetal outcomes. Women with high-risk pregnancies are at a greater risk of developing antepartum haemorrhage, miscarriages, and the need for surgical interventions. Neonatal complications include preterm births, low birth weight (LBW), intra-uterine deaths and an increased need for NICU admission. The utilisation of low-cost scoring tools for identifying high-risk women can aid in early diagnosis and timely implementation of therapeutic interventions. Objective The retrospective record-based study sought to calculate the proportion of high-risk pregnancies using modified Coopland's scoring system and compare the maternal and foetal outcomes among high-risk pregnancies. Methods The study retrospectively analysed the records of antenatal women in their third trimester from the years December 2018 to December 2021. Each record was then numerically assessed according to the modified Coopland's scoring system and categorised according to the risk status. Maternal and neonatal outcomes were then compared across the risk groups. Results The data included 300 cases over a three-year period.According to modifiedCoopland's scoring system, we found that the overall proportion of high-risk pregnancies was 18.3%. Adverse maternal and fetal outcomes were increased in high-risk pregnancy groups when compared to low-risk pregnancies, miscarriages (31.6% vs 15.8%) and antepartum haemorrhage (55.6% vs 11.1%). Babies born to high-risk mothers had a higher chance of developing LBW status (52.0%) and respiratory distress (45.5%) when compared to those born to low-risk mothers: 8.0% and 13.6%, respectively. Conclusion A notable portion of pregnant women were classified as high-risk using modified Coopland's scoring tool and would benefit from targeted obstetric care.

Design and Investigation of a low-cost elbow rehabilitation tool

Electronic medical development focuses on creating an efficient rehabilitation device that will strengthen all surrounding muscles and enhance elbow performance. The elbow rehabilitation tool (ERT) provides sophisticated methods like exercise and motion analysis. The initiative is notable for its advanced assessment methods and adaptable training curricula, which offer users a thorough and successful therapeutic experience. The ERT includes elements like a stepper motor, variable resistor, steel wire, microcontroller, motor driver, and components created using a 3D printer. The experiment results show that the average systematic error percentage is about 82.857%, where seven healthy people have tested the ERT aged between 22 and 55 (five males and two females). The ERT also has achievement evaluation, which improves motivation and dedication to the recovery methods through an effective rehabilitation experience for users.

3D-printed neck phantoms with detailed anatomy for ultrasound-guided procedure and device testing.

With rapid advances in ultrasound-guided procedures, there is an unmet need for echogenic phantoms with sufficient anatomical details for artificial intelligence and ultrasound-guided device testing. We developed a method for creating neck phantoms for novel otolaryngology-related device testing. To achieve accurate representation of the anatomy, we utilized CT scans and 3D printing technology to create customized agar molds, thus providing high-fidelity yet cost-effective tools. Based on previous studies, the key components in our neck phantom include the cervical vertebrae, trachea, common carotid arteries, internal jugular veins, thyroid gland, and surrounding soft tissue. Open-source image analysis software were employed to process CT data to generate high fidelity 3D models of the target structures. Resin molds were 3D printed and filled with various agar mixtures to mimic anatomical echogenicity. Following the method proposed, we successfully assembled the neck phantom which provided a detailed representation of the target structures. To evaluate the results, ultrasound data was collected on the phantom and living tissue and analyzed with ImageJ. We were able to demonstrate echogenicity comparable to that of living tissue. The proposed method for building neck phantoms with detailed anatomical features offers a valuable, detailed, low-cost tool for medical training and device testing in otolaryngology, particularly for novel devices that involve artificial intelligence (AI) guidance and robotic-based needle insertion. Additional anatomical refinements and validation studies could further enhance the consistency and accuracy, thus paving the way for future advancements in ultrasound training and research, and ultimately benefiting patient care and safety.

Muscle Network Connectivity Study in Diabetic Peripheral Neuropathy Patients.

Diabetic peripheral neuropathy (DPN) is a prevalent complication of chronic diabetes mellitus and has a significant impact on quality of life. DPN typically manifests itself as a symmetrical, length-dependent sensorimotor polyneuropathy with severe effects on gait. Surface electromyography (sEMG) is a valuable low-cost tool for assessing muscle activation patterns and precise identification of abnormalities. For the present study, we used information theory methods, such as cross-correlation (CC), normalized mutual information (NMI), conditional granger causality (CG-Causality), and transfer entropy (TE), to evaluate muscle network connectivity in three population groups: 33 controls (healthy volunteers, CT), 10 diabetic patients with a low risk of DPN (LW), and 17 moderate/high risk patients (MH). The results obtained indicated significant alterations in the intermuscular coupling mechanisms due to diabetes and DPN, with the TE group showing the best performance in detecting differences. The data revealed a significant increase in information transfer and muscle connectivity in the LW group over the CT group, while the MH group obtained significantly lower values for these metrics than the other two groups. These findings highlight the sEMG coupling metrics' potential to reveal neuromuscular mechanisms that could aid the development of targeted rehabilitation strategies and help monitor DPN patients.

Effect of Physics Inquiry Learning with Low-cost Experiment Tool on Students’ Problem-solving Skills and Self-efficacy

The low problem-solving skills and self-efficacy of students in physics learning significantly impact their overall learning processes and outcomes. In order to address this issue, an inquiry learning model was implemented, along with the low-cost experiment tool. This learning model is designed to be valid and effective in enhancing students' problem-solving skills and self-efficacy. The research followed a non-equivalent control group design and involved 66 students to ensure the development of a high-quality learning model that meets the required standards and criteria. By utilising learning instruments, such as students' response questionnaires, problem-solving pre- and post-tests, and self-efficacy questionnaires, the focus of the physics topic in this research was the simple mathematic pendulum. The validity test results show that the learning instrument has high validity and reliability criteria, making it feasible to use in physics learning. Furthermore, the statistical effectiveness test results show that this learning model is effective in improving students' problem-solving skills and self-efficacy. As students engage in hands-on investigations and collaborate on meaningful projects, they are prompted to apply critical thinking and decision-making skills, directly contributing to the development of effective problem-solving strategies. The intrinsic motivation derived from successfully navigating the challenges of inquiry learning, coupled with the personal relevance of the content, nurtures students' confidence in their abilities, ultimately strengthening their self-efficacy as independent and capable learners. Research implies the development of innovative learning models, effectively enhancing students' PSS and SE.

Artificial Intelligence Models for the Detection of Microsatellite Instability from Whole-Slide Imaging of Colorectal Cancer.

With the advent of whole-slide imaging (WSI), a technology that can digitally scan whole slides in high resolution, pathology is undergoing a digital revolution. Detecting microsatellite instability (MSI) in colorectal cancer is crucial for proper treatment, as it identifies patients responsible for immunotherapy. Even though universal testing for MSI is recommended, particularly in patients affected by colorectal cancer (CRC), many patients remain untested, and they reside mainly in low-income countries. A critical need exists for accessible, low-cost tools to perform MSI pre-screening. Here, the potential predictive role of the most relevant artificial intelligence-driven models in predicting microsatellite instability directly from histology alone is discussed, focusing on CRC. The role of deep learning (DL) models in identifying the MSI status is here analyzed in the most relevant studies reporting the development of algorithms trained to this end. The most important performance and the most relevant deficiencies are discussed for every AI method. The models proposed for algorithm sharing among multiple research and clinical centers, including federal learning (FL) and swarm learning (SL), are reported. According to all the studies reported here, AI models are valuable tools for predicting MSI status on WSI alone in CRC. The use of digitized H&E-stained sections and a trained algorithm allow the extraction of relevant molecular information, such as MSI status, in a short time and at a low cost. The possible advantages related to introducing DL methods in routine surgical pathology are underlined here, and the acceleration of the digital transformation of pathology departments and services is recommended.

Development and implementation of nested single-nucleotide polymorphism (SNP) assays for breeding and genetic research applications.

SoySNP50K and SoySNP6K are commonly used for soybean (Glycine max) genotyping. The SoySNP50K assay has been used to genetically analyze the entire USDA Soybean Germplasm Collection, while the SoySNP6K assay, containing a subset of 6000 single-nucleotide polymorphisms (SNPs) from SoySNP50K, has been used for quantitative trait loci mapping of different traits. To meet the needs for genomic selection, selection of parents for crosses, and characterization of breeding populations, especially early selection of ideal offspring from thousands of lines, we developed two assays, SoySNP3K and SoySNP1K, containing 3072 and 1252 SNPs, respectively, based on SoySNP50K and SoySNP6K mark sets. These two assays also contained the trait markers reported or contributed by soybean breeders. The SNPs in the SoySNP3K are a subset from SoySNP6K, while the SNPs in the SoySNP1K are a subset from SoySNP3K. These SNPs were chosen to reduce the SNP number in the large linkage blocks while capturing as much of the haplotype diversity as possible. They are highly polymorphic and of high quality. The mean minor allele frequencies of the SNPs in the southern and northern US elites were 0.25 and 0.27 for SoySNP3K, respectively, and 0.29 and 0.33 for SoySNP1K. The selected SNPs are a valuable source for developing targeted amplicon sequencing assay or beadchip assay in soybean. SoySNP3K and SoySNP1K assays are commercialized by Illumina Inc. and AgriPlex Genomics, respectively. Together with SoySNP50K and SoySNP6K, a series of nested assays with different marker densities will serve as additional low-cost genomic tools for genetic, genomic, and breeding research.

HEROIC: a platform for remote collection of electroencephalographic data using consumer-grade brain wearables

The growing number of portable consumer-grade electroencephalography (EEG) wearables offers potential to track brain activity and neurological disease in real-world environments. However, accompanying open software tools to standardize custom recordings and help guide independent operation by users is lacking. To address this gap, we developed HEROIC, an open-source software that allows participants to remotely collect advanced EEG data without the aid of an expert technician. The aim of HEROIC is to provide an open software platform that can be coupled with consumer grade wearables to record EEG data during customized neurocognitive tasks outside of traditional research environments. This article contains a description of HEROIC’s implementation, how it can be used by researchers and a proof-of-concept demonstration highlighting the potential for HEROIC to be used as a scalable and low-cost EEG data collection tool. Specifically, we used HEROIC to guide healthy participants through standardized neurocognitive tasks and captured complex brain data including event-related potentials (ERPs) and powerband changes in participants’ homes. Our results demonstrate HEROIC’s capability to generate data precisely synchronized to presented stimuli, using a low-cost, remote protocol without reliance on an expert operator to administer sessions. Together, our software and its capabilities provide the first democratized and scalable platform for large-scale remote and longitudinal analysis of brain health and disease.

Gait Assessment Using Smartphone Applications in Older Adults: A Scoping Review.

Spatiotemporal parameters such as gait velocity and stride length are simple indicators of functional status and can be used to predict major adverse outcomes in older adults. A smartphone can be used for gait analysis by providing spatiotemporal parameters useful for improving the diagnosis and rehabilitation processes in frail people. The aim of this study was to review articles published in the last 20 years (from 2004 to 2024) concerning the application of smartphones to assess the spatiotemporal parameters of gait in older adults. This systematic review was performed in line with Preferred Reporting Items for Systematic Review and Meta-Analyses (PRISMA), and original articles were identified by searching seven electronic databases: SciVerse (ScienceDirect), Excerpta Medica Database (EMBASE), Medline, Scopus, PubMed, Web of Science and the Cochrane Library. Studies were rigorously screened using the inclusion criteria of smartphones and mobile apps, older adults and spatiotemporal gait parameters, and results were narratively synthesized. Seventy-three articles were initially identified while searching the scientific literature regarding this topic. Eleven articles were selected and included in this review. Analysis of these studies covered information about gait assessment using mobile apps recorded in 723 older adults and 164 control cases. Analysis of data related to the application of smartphones to assess spatiotemporal parameters of gait in older adults showed moderate-to-excellent test-retest reliability and validity (ICCs around 0.9) of gait speed, the most common parameter reported. Additionally, gait speeds recorded with mobile apps showed excellent agreement when compared to gold standard systems. Smartphones and mobile apps are useful, non-invasive, low-cost and objective tools that are being extensively used to perform gait analysis in older adults. Smartphones and mobile apps can reliably identify spatiotemporal parameters related to adverse outcomes, such as a slow gait speed, as predictors and outcomes in clinical practice and research involving older adults.

Artificial intelligence system for outcome evaluations of human in vitro fertilization-derived embryos.

In vitro fertilization (IVF) has emerged as a transformative solution for infertility. However, achieving favorable live-birth outcomes remains challenging. Current clinical IVF practices in IVF involve the collection of heterogeneous embryo data through diverse methods, including static images and temporal videos. However, traditional embryo selection methods, primarily reliant on visual inspection of morphology, exhibit variability and are contingent on the experience of practitioners. Therefore, an automated system that can evaluate heterogeneous embryo data to predict the final outcomes of live births is highly desirable. We employed artificial intelligence (AI) for embryo morphological grading, blastocyst embryo selection, aneuploidy prediction, and final live-birth outcome prediction. We developed and validated the AI models using multitask learning for embryo morphological assessment, including pronucleus type on day 1 and the number of blastomeres, asymmetry, and fragmentation of blastomeres on day 3, using 19,201 embryo photographs from 8271 patients. A neural network was trained on embryo and clinical metadata to identify good-quality embryos for implantation on days or day 5, and predict live-birth outcomes. Additionally, a 3D convolutional neural network was trained on 418 time-lapse videos of preimplantation genetic testing (PGT)-based ploidy outcomes for aneuploidy prediction and consequent live-birth outcomes. These two approaches enabled us to automatically assess the implantation potential. By combining embryo and maternal metrics in an ensemble AI model, we evaluated live-birth outcomes in a prospective cohort that achieved higher accuracy than experienced embryologists (46.1% vs. 30.7% on day 3, 55.0% vs. 40.7% on day 5). Our results demonstrate the potential for AI-based selection of embryos based on characteristics beyond the observational abilities of human clinicians (area under the curve: 0.769, 95% confidence interval: 0.709-0.820). These findings could potentially provide a noninvasive, high-throughput, and low-cost screening tool to facilitate embryo selection and achieve better outcomes. Our study underscores the AI model's ability to provide interpretable evidence for clinicians in assisted reproduction, highlighting its potential as a noninvasive, efficient, and cost-effective tool for improved embryo selection and enhanced IVF outcomes. The convergence of cutting-edge technology and reproductive medicine has opened new avenues for addressing infertility challenges and optimizing IVF success rates.

Automatic Identification of Sea Rice Grains in Complex Field Environment Based on Deep Learning

The number of grains per sea rice panicle is an important parameter directly related to rice yield, and it is also a very important agronomic trait in research related to sea rice breeding. However, the grain number per sea rice panicle still mainly relies on manual calculation, which has the disadvantages of being time-consuming, error-prone, and labor-intensive. In this study, a novel method was developed for the automatic calculation of the grain number per rice panicle based on a deep convolutional neural network. Firstly, some sea rice panicle images were collected in complex field environment and annotated to establish the sea rice panicle image data set. Then, a sea grain detection model was developed using the Faster R-CNN embedded with a feature pyramid network (FPN) for grain identification and location. Also, ROI Align was used to replace ROI pooling to solve the problem of relatively large deviations in the prediction frame when the model detected small grains. Finally, the mAP (mean Average Precision) and accuracy of the sea grain detection model were 90.1% and 94.9%, demonstrating that the proposed method had high accuracy in identifying and locating sea grains. The sea rice grain detection model can quickly and accurately predict the number of grains per panicle, providing an effective, convenient, and low-cost tool for yield evaluation, crop breeding, and genetic research. It also has great potential in assisting phenotypic research.

WhatsApp as a Tool to Facilitate Continued Adjustment of First Time Entering Students Into University During COVID–19 Lockdown Restrictions

The Covid-19 pandemic forced universities into emergency remote learning. For universities situated in a country where internet connectivity prices are high, network coverage is low, and most students live in rural areas, this presented a unique set of problems. One of the issues is that traditional orientations were face-to-face for first-time entering students (FTENs) at universities, and they had to be moved online due to lockdown restrictions and the desire to save the academic year. For the university concerned, this presented challenges in accessing students due to their low digital literacy skills and infrastructure limitations. The study employed a quantitative exploratory research approach to assess whether WhatsApp, a low-cost communication tool offering features like affordable data bundles, group creation, and accessibility, could facilitate student engagement and support during the COVID-19 lockdown at a rural university campus. The aim was to investigate if utilizing WhatsApp could foster a sense of belonging among students, potentially mitigating dropout rates and preventing students from falling behind. The hypothesis posited a positive correlation between using WhatsApp for academic support and student retention, ultimately leading to enhanced success rates. The study used the 'Academic Dropout Wheel, a theoretical framework developed by Naaman (2021), which identifies key factors contributing to academic dropout and provides strategies for intervention. The results of a quantitative questionnaire on 181 of the 2022 cohort of FTENS at the campus revealed that students felt more at ease, had a feeling of belonging and ownership of the university, and knew where to ask for help through the effective use of WhatsApp. This paper concludes that if professionally managed, WhatsApp can be an effective tool to facilitate continued adjustment and integration of FTENs into the university.

Toward on-demand measurements of greenhouse gas emissions using an uncrewed aircraft AirCore system

Abstract. This paper evaluates the performance of a multirotor uncrewed aircraft and AirCore system (UAAS) for measuring vertical profiles of wind velocity (speed and direction) and the mole fractions of methane (CH4) and carbon dioxide (CO2), and it presents a use case that combines UAAS measurements and dispersion modeling to quantify CH4 emissions from a dairy farm. To evaluate the atmospheric sensing performance of the UAAS, four field deployments were performed at three locations in the San Joaquin Valley of California where CH4 hotspots were observed downwind of dairy farms. A comparison of the observations collected on board the UAAS and an 11 m meteorological tower show that the UAAS can measure wind velocity trends with a root mean squared error varying between 0.4 and 1.1 m s−1 when the wind magnitude is less than 3.5 m s−1. Findings from UAAS flight deployments and a calibration experiment also show that the UAAS can reliably resolve temporal variations in the mole fractions of CH4 and CO2 occurring over periods of 10 s or longer. Results from the UAAS and dispersion modeling use case further demonstrate that UAASs have great potential as low-cost tools for detecting and quantifying CH4 emissions in near real time.

PPG-based Vital Signs Measurement Using SmartPhone Camera: Review

Due to their growing popularity and rapid performance improvements, smartphones have the potential to be an accurate, low-cost physiological monitoring tool that is useful outside of the clinical setting. Since the cardiac pulse is responsible for the slight color changes in skin, a pulsatile signal that is also known as a photoplethysmographic (PPG) signal can be evaluated by utilizing a digital camera to record a video of your Region Of Interest (ROI). This review Study focuses on several research works that have used PPG-based video smartphone camera methods to measure Heart Rate (HR), Blood Pressure (BP), Oxygen Saturation (SpO2), and Respiratory Rate (RR). Comparing the approaches and results of each study (HR measurement studies produced acceptable findings with error rates less than 5%, BP error rates of results less than 10%, and SpO2 =<3.5%, by comparing the predicted models reading with the standard references reading) shows how smartphone-based physiological monitoring might be used in medical settings.

MeNPs-PEDOT Composite-Based Detection Platforms for Epinephrine and Quercetin.

The development of low-cost, sensitive, and simple analytical tools for biomolecule detection in health status monitoring is nowadays a growing research topic. Sensing platforms integrating nanocomposite materials as recognition elements in the monitoring of various biomolecules and biomarkers are addressing this challenging objective. Herein, we have developed electrochemical sensing platforms by means of a novel fabrication procedure for biomolecule detection. The platforms are based on commercially available low-cost conductive substrates like glassy carbon and/or screen-printed carbon electrodes selectively functionalized with nanocomposite materials composed of Ag and Au metallic nanoparticles and an organic polymer, poly(3,4-ethylenedioxythiophene). The novel fabrication method made use of alternating currents with controlled amplitude and frequency. The frequency of the applied alternating current was 100 mHz for the polymer deposition, while a frequency value of 50 mHz was used for the in situ electrodeposition of Ag and Au nanoparticles. The selected frequency values ensured the successful preparation of the composite materials. The use of readily available composite materials is intended to produce cost-effective analytical tools. The judicious modification of the organic conductive matrix by various metallic nanoparticles, such as Ag and Au, extends the potential applications of the sensing platform toward a range of biomolecules like quercetin and epinephrine, chosen as benchmark analytes for proof-of-concept antioxidant and neurotransmitter detection. The sensing platforms were tested successfully for quercetin and epinephrine determination on synthetic and real samples. Wide linear response ranges and low limit-of-detection values were obtained for epinephrine and quercetin detection.

A Study on Autonomic Dysfunction in Type 2 Diabetes Mellitus with Peripheral Neuropathy

Abstract Introduction: Subclinical Autonomic Neuropathy is found in association with distal symmetric polyneuropathy in diabetic patients. The Aim of this study was to compare the Cardiac Autonomic Function Test parameters in Type 2 Diabetes Mellitus patients with and without Distal Peripheral Neuropathy. The Primary Objective was to compare the mean of Valsalva ratio in Type 2 Diabetes Mellitus patients with and without Distal Peripheral Neuropathy. The secondary Objective of this study was to find the correlation between Michigan Neuropathic Screening Instrument Score and Autonomic Function test parameters in type 2 Diabetes Mellitus patients. Methods: This was a single centre, cross sectional study conducted from July 2022 to Feb 2023. The study was commenced after obtaining Institute Ethics Committee clearance. Subjects who satisfied the inclusion and exclusion criteria, and gave informed consent for participation in this study were eligible for enrolment. The inclusion Criteria were patients aged between 18 and 65 years with diagnosis of Diabetes mellitus. The number required to study Diabetic patients with peripheral neuropathy was 34 and for Diabetic patients without peripheral neuropathy was 34. Total sample size of 68 patients was taken. Once the patient was enrolled a detailed history, clinical examination, ANS testing and MSNI scoring was done as per attached pre verified proforma duly vetted by institutional scientific committee.Patients were asked to refrain from vigorous exercise for the 24 hours before to the cardiovascular testing as well as from eating, drinking, or smoking for at least 2 hours before the autonomic testing. At the conclusion of the checkup, all antidiabetic and other prescriptions were given. After data collection, test for normality of data was done. Appropriate statistical tests were applied according to data distribution and analysed by using SPSS criteria 2011. Results: A total of 68 patients, 34 with symptoms of Diabetic Peripheral Neuropathy and 34 without Diabetic Peripheral Neuropathy were studied from July 2022 to December 2023. The age, BMI, duration of disease, HbA1c, resting systolic blood pressure and heart rate didn’t show any significant difference between the two groups and thus the groups were comparable. The median (interquartile range) of MNSI score of diabetic patients without neuropathy was 1(1,1) and diabetic patients with neuropathy was 8 (7,9) with p value < 0.005. There was a significant difference in E: I ratio (P<0.0001) between diabetic patients with and without neuropathy. The mean ± SD of Valsalva ratio, in diabetic patients without neuropathy was 1.632 ± 0.115 and with neuropathy was 1.366 ± 0.045. There was a significant difference in Valsalva ratio (P<0.0001). There was a good negative correlation between MNSI score and Valsalva ratio with r= -0.769 and p value < 0.0001 in Type 2 Diabetes mellitus patients. Conclusion: This study found that the Cardiac Autonomic Function Test parameters in Type 2 Diabetes Mellitus patients with and without Distal Peripheral Neuropathy, were significantly deranged in patients with symptomatic DPN. The mean of Valsalva ratio was significantly different in two study groups. The correlation between Michigan Neuropathy Screening Instrument Score and Autonomic Function test parameters in Type 2 Diabetes Mellitus patients showed strong negative correlation. The patients with symptomatic DSN were found to have higher MSNI scores, who in turn were having severe cardiac autonomic dysfunction. Recommendation of Study: Mean Valsalva ratio and MNSI testing are recommended to be used as a screening tool to assess cardiac autonomic functions as a low cost tool in patients of T2DM having symptoms suggestive of DSN.

Study of the Lipophilicity and ADMET Parameters of New Anticancer Diquinothiazines with Pharmacophore Substituents.

Lipophilicity is one of the principal parameters that describe the pharmacokinetic behavior of a drug, including its absorption, distribution, metabolism, elimination, and toxicity. In this study, the lipophilicity and other physicochemical, pharmacokinetic, and toxicity properties that affect the bioavailability of newly synthesized dialkylaminoalkyldiquinothiazine hybrids as potential drug candidates are presented. The lipophilicity, as RM0, was determined experimentally by the RP-TLC method using RP18 plates and acetone-TRIS buffer (pH 7.4) as the mobile phase. The chromatographic parameters of lipophilicity were compared to computationally calculated partition coefficients obtained by various types of programs such as iLOGP, XLOGP3, WLOGP, MLOGP, SILCOS-IT, LogP, logP, and milogP. In addition, the selected ADMET parameters were determined in silico using the SwissADME and pkCSM platforms and correlated with the experimental lipophilicity descriptors. The results of the lipophilicity study confirm that the applied algorithms can be useful for the rapid prediction of logP values during the first stage of study of the examined drug candidates. Of all the algorithms used, the biggest similarity to the chromatographic value (RM0) for certain compounds was seen with iLogP. It was found that both the SwissADME and pkCSM web tools are good sources of a wide range of ADMET parameters that describe the pharmacokinetic profiles of the studied compounds and can be fast and low-cost tools in the evaluation of examined drug candidates during the early stages of the development process.

Simple and solvent-free synthesis of Zeolitic Tetrazole Framework based MOF for selective and sensitive electroanalysis of silver ions in drinking water

Human silver poisoning through water consumption constitutes a real public health concern nowadays. Thus, the development of sensitive, accurate and low-cost tools for silver ions (Ag+) determination in drinking water would significantly help in facing this great challenge. Herein, a cost effective and eco-friendly material namely Zeolitic Tetrazole Framework (ZTF-8) was synthesized and used as electrode material for Ag+ ions electroanalysis. XRD, FT-IR, TGA, XPS and FE-SEM were utilized to evidence the successful synthesis of the electrode material. Electrochemical characterization of the associated sensor revealed its good affinity towards Ag+ ions as well as the satisfactory repeatability and reproducibility of the obtained results. The sensor optimization showed that to achieve its best performance, 3 μL of ZTF-8 suspension, 1 mg/mL should be utilized for its fabrication. Given that the sensing mechanism was based on the deposition of Ag0 on the sensor surface followed by their redissolution, the sensor optimization also revealed that the optimal deposition potential and time are −0.1 V vs. SCE and 200 s, respectively. Calibrated in concentration range of 0.2 to 1.6 µM, the prepared sensor exhibited a linear relationship (0.997) with a sensitivity of (0.56 ± 0.01) µA/µM; the statistic limit of detection was found to be 0.9 nM. Moreover, the fabricated sensor showed great potential for real samples analysis and satisfactory recoveries in mineral water, tap water, water obtained from a reverse osmosis plant and in an AAS standard solution. Furthermore, obtained results were validated using FAAS and ICP-OES.

Rapid, Simple and Low-Cost Reverse-Phase High-Performance Liquid Chromatography Tool for Estimation of Methotrexate: Testing Application in Standard Laboratory Bulk, Marketed Dosage Form, and Release kinetics in Nanoformulation

Rapid, Simple and Low-Cost Reverse-Phase High-Performance Liquid Chromatography Tool for Estimation of Methotrexate: Testing Application in Standard Laboratory Bulk, Marketed Dosage Form, and Release kinetics in Nanoformulation

Graphite/Aluminum oxide/polylactic acid composite material: A valuable strategy for additively manufacturing cheap and improved electrochemical platforms for sensing sulfamethoxazole in honey samples

Composite materials with properties aimed at the additive manufacturing of affordable electrochemical sensors have received special attention in the scientific scenario. For this reason, we proposed a new composite material based on alumina oxide (Al2O3) and graphite (Gpt) dispersed in polylactic acid (PLA) biopolymer. Various proportions between the materials were studied, and a better compromise between printability and electrochemical performance was obtained using Gpt/Al2O3/PLA (30:10:60% w/w). The electrodes were manufactured using a 3D pen, a portable, user-friendly, and low-cost tool. Characterizations by Raman and infrared spectroscopy, X-ray photoelectron spectroscopy, and scanning electron microscopy indicated that Gpt and Al2O3 were successfully incorporated into the PLA matrix. Preliminary studies using species with well-known electrochemical behavior revealed that Al2O3 positively impacts the electrochemical response of the sensor. As a proof of concept, sulfamethoxazole (SMZ), a low-cost antibiotic widely used in beekeeping practices, was selected as the target analyte, and a simple, fast, and selective method using square wave voltammetry was proposed. Using the 3D-printed Gpt/Al2O3/PLA electrode, a wider linear range (2.0 to 40.0 μmol L−1), a 6.5-fold increase in sensitivity, limits of detection (LOD = 0.4 μmol L−1) and quantification (LOQ = 1.2 μmol L−1) were achieved compared to the Al2O3-free electrode (Gpt/PLA). In addition, the proposed sensor was selective against other antibiotics commonly used in beekeeping practices. Three honey samples were analyzed after simple dilution in 0.12 mol L−1 Britton-Robinson buffer, pH 7.0 (background electrolyte), and recoveries close to 100% as well as statistically comparable results from chromatographic analysis certified the accuracy and reliability of the analysis. The proposed approach is innovative and valuable in contributing to the advancement of additive manufacturing in electrochemical sensing, especially in the on-site analysis of antibiotic species in food, a public health challenge.