Detection and Validation of Organic Metabolites in Urine for Clear Cell Renal Cell Carcinoma Diagnosis.

BACKGROUND: Clear cell renal cell carcinoma (ccRCC) comprises the majority, approximately 70-80%, of renal cancer cases and often remains asymptomatic until incidentally detected during unrelated abdominal imaging or at advanced stages. Currently, standardized screening tests for renal cancer are lacking, which presents challenges in disease management and improving patient outcomes. This study aimed to identify ccRCC-specific volatile organic compounds (VOCs) in the urine of ccRCC-positive patients and develop a urinary VOC-based diagnostic model. METHODS: This study involved 233 pretreatment ccRCC patients and 43 healthy individuals. VOC analysis utilized stir-bar sorptive extraction coupled with thermal desorption gas chromatography/mass spectrometry (SBSE-TD-GC/MS). A ccRCC diagnostic model was established via logistic regression, trained on 163 ccRCC cases versus 31 controls, and validated with 70 ccRCC cases versus 12 controls, resulting in a ccRCC diagnostic model involving 24 VOC markers. RESULTS: The findings demonstrated promising diagnostic efficacy, with an Area Under the Curve (AUC) of 0.94, 86% sensitivity, and 92% specificity. CONCLUSIONS: This study highlights the feasibility of using urine as a reliable biospecimen for identifying VOC biomarkers in ccRCC. While further validation in larger cohorts is necessary, this study's capability to differentiate between ccRCC and control groups, despite sample size limitations, holds significant promise.

Green synthesized CeO2 nanoparticles-based chitosan/PVA composite films: Enhanced antimicrobial activities and mechanical properties for edible berry tomato preservation.

The current study is intended to enhance unique bioactive and eco-friendly composite films following a simple solvent-casting approach by incorporating cerium oxide nanoparticles (CeO2 NPs) with a chitosan (CS)/polyvinyl alcohol (PVA) matrix. Antimicrobial activity, preservation impact, mechanisms for the edible berry tomatoes and physicochemical properties of the produced films were tested. FTIR, SEM-EDX, XRD, UV-vis spectroscopy and contact angle were used to characterize the films. Incorporated (3.0 wt%) CeO2 NPs practically developed composite film's thermal stability, structural, mechanical, bioactive, antioxidant, barrier and wettability properties. The tomatoes' look, weight loss and stiffness were better preserved after 25 days of storage at room temperature (25 ± 5 °C) when 3.0 wt% CeO2 NPs films were used instead of the original CS/PVA film. CS and CeO2 NPs have unique physiochemical and antibacterial properties. Food packaging extensively investigates the modified films as antimicrobials and preservatives to increase the shelf life of packaged foods, owing to their ability to inhibit gram-positive bacteria (Bacillus cereus and Staphylococcus aureus), gram-negative bacteria (Klebsiella pneumoniae and Pseudomonas aeruginosa), and filamentous fungi (Bipolaris sorokiniana, Fusarium op., and Alternaria sp.). Our findings indicated that the CeO2/CS/PVA composite films could be used as effective wrapping materials for food preservation.

Levodopa Equivalent Daily Dosage: Geographical Variations and Real-Life Modules in Parkinson's Disease.

BACKGROUND: The Levodopa Equivalent Daily Dosage (LEDD) calculation algorithms help in capturing and harmonization of Parkinson's Disease (PD) therapies. Analyzing these updates is essential for validating their effectiveness. OBJECTIVE: To assess updated LEDD conversion factors in capturing the newer therapies in PD and therapy modules in different geographical cohorts. METHODS: Data were sourced from 10 Centers from 6 countries representing 2 different continents. The study compared the LEDD conversion factors proposed by Tomlinson et al and Jost et al, alongside investigating demographic disparities. RESULTS: The analysis involved 2943 subjects; 87% (n = 2577) met the UK Brain Bank criteria for PD. The LEDD differed significantly across methodologies (Tomlinson vs. Jost, 598 mg vs 610 mg, P < 0.0001). Geographical disparities highlighted variations in PD onset age (P < 0.0001). Jost and Tomlinson's calculations demonstrated consistency within but significant differences across countries (P < 0.0001).Age at onset revealed statistically significant differences in LEDD requirements (P < 0.0001), which were particularly higher in 21-50 years (718 mg vs 566 mg). This subgroup also demonstrated increased usage of non-Levodopa therapies (P < 0.0001). Men exhibited higher total LEDD (P = 0.001). 34% reported dyskinesia, associated with higher LEDD (756 mg, P < 0.0001). Surgically treated patients also had higher LEDD (P < 0.0001) and a significant difference between Jost and Tomlinson dosages (761 mg vs716mg) reflecting the incorporation of newer therapeutic molecules. CONCLUSION: This analysis delineates the importance of updated LEDD algorithms and intricacies in the landscape of PD treatment, underscored by geographical, age-related, and gender-specific variations, in real-life management scenarios.

Deep learning-driven forward and inverse design of nanophotonic nanohole arrays: streamlining design for tailored optical functionalities and enhancing accessibility.

In nanophotonics, nanohole arrays (NHAs) are periodic arrangements of nanoscale apertures in thin films that provide diverse optical functionalities essential for various applications. Fully studying NHAs' optical properties and optimizing performance demands understanding both materials and geometric parameters, which presents a computational challenge due to numerous potential combinations. Efficient computational modeling is critical for overcoming this challenge and optimizing NHA-based device performance. Traditional approaches rely on time-consuming numerical simulation processes for device design and optimization. However, using a deep learning approach offers an efficient solution for NHAs design. In this work, a deep neural network within the forward modeling framework accurately predicts the optical properties of NHAs by using device structure data such as periodicity and hole radius as model inputs. We also compare three deep learning-based inverse modeling approaches-fully connected neural network, convolutional neural network, and tandem neural network-to provide approximate solutions for NHA structures based on their optical responses. Once trained, the DNN accurately predicts the desired result in milliseconds, enabling repeated use without wasting computational resources. The models are trained using over 6000 samples from a dataset obtained by finite-difference time-domain (FDTD) simulations. The forward model accurately predicts transmission spectra, while the inverse model reliably infers material attributes, lattice geometries, and structural parameters from the spectra. The forward model accurately predicts transmission spectra, with an average Mean Squared Error (MSE) of 2.44 × 10-4. In most cases, the inverse design demonstrates high accuracy with deviations of less than 1.5 nm for critical geometrical parameters. For experimental verification, gold nanohole arrays are fabricated using deep UV lithography. Validation against experimental data demonstrates the models' robustness and precision. These findings show that the trained DNN models offer accurate predictions about the optical behavior of NHAs.

Dual Ambient Sampling Hollow Cathode Discharge Ionization-Mass Spectrometry System: An Approach for Detecting Explosives and Drugs of Abuse at Trace to Ultratrace Levels.

Detection of illicit compounds like explosives and drugs of abuse at trace levels is crucial to provide public security and health safety. A dual ambient sampling system hollow cathode discharge (HCD) ion source was developed to investigate its performance. Here, trinitrotoluene (TNT), trinitrobenzene (TNB), hexamethylene triperoxide diamine (HMTD), and triacetone triperoxide (TATP) as explosives and methamphetamine (MA) as drugs of abuse were taken as model compounds. Two sample inlets, inlet-1 and inlet-2, are available for ambient sampling. In negative ion mode, N2 and air HCD plasmas are confined close to inlet-1, but in positive ion mode, they are confined close to inlet-2. Special design of the ion source makes it feasible to generate multiple ions from a single analyte, which assists in understanding the gas phase ionization mechanism. In negative ion mode, both TNT and TNB gave radical ions, [M]-•, as major ions for N2 HCD plasma as they were introduced via inlet-1 or inlet-2. TNB gave radical ions for air and N2 HCD plasmas, while TNT exhibited adduct ions, [TNT-H]-, by using air HCD plasma. In positive ion mode, HMTD gave [HMTD + H]+ m/z 209 ions, while TATP only produced adduct ions with ammonia, [TATP + NH4]+ m/z 240. Regardless of ion source inlet, MA showed protonated molecule ions, [MA + H]+ m/z 150. As analytes were introduced via inlet-1, the stability of the HCD background ion signal reduced, leading to a decrease in sensitivity. Unlike that in negative ion mode, introduction of ambient air in positive ion mode enhanced the sensitivity of the air HCD ion source through the formation of hydronium ions, which gave protonated molecule ions. Ionization mechanisms are also discussed.

MDDBranchNet: A Deep Learning Model for Detecting Major Depressive Disorder Using ECG Signal.

Major depressive disorder (MDD) is a chronic mental illness which affects people's well-being and is often detected at a later stage of depression with a likelihood of suicidal ideation. Early detection of MDD is thus necessary to reduce the impact, however, it requires monitoring vitals in daily living conditions. EEG is generally multi-channel and due to difficulty in signal acquisition, it is unsuitable for home-based monitoring, whereas, wearable sensors can collect single-channel ECG. Classical machine-learning based MDD detection studies commonly use various heart rate variability features. Feature generation, which requires domain knowledge, is often challenging, and requires computation power, often unsuitable for real time processing, MDDBranchNet is a proposed parallel-branch deep learning model for MDD binary classification from a single channel ECG which uses additional ECG-derived signals such as R-R signal and degree distribution time series of horizontal visibility graph. The use of derived branches was able to increase the model's accuracy by around 7%. An optimal 20-second overlapped segmentation of ECG recording was found to be beneficial with a 70% prediction threshold for maximum MDD detection with a minimum false positive rate. The proposed model evaluated MDD prediction from signal excerpts, irrespective of location (first, middle or last one-third of the recording), instead of considering the entire ECG signal with minimal performance variation stressing the idea that MDD phenomena are likely to manifest uniformly throughout the recording.

Atmospheric emissions of hexachlorobutadiene in fine particulate matter from industrial sources.

Hexachlorobutadiene (HCBD) is a concerning chemical that is included in the United States Toxic Substances Control Act, and the Stockholm Convention. Knowledge of the sources of HCBD is insufficient and is pivotal for accurate inventory and implementing global action. In this study, unintentional HCBD release and source emission factors of 121 full-scale industrial plants from 12 industries are investigated. Secondary copper smelting, electric arc furnace steelmaking, and hazardous waste incineration show potential for large emission reductions, which are found of high HCBD emission concentrations of > 20 ng/g in fine particulate matter in this study. The highest HCBD emission concentration is observed for the secondary copper smelting industry (average: 1380 ng/g). Source emission factors of HCBD for the 12 industries range from 0.008 kg/t for coal fire power plants to 0.680 kg/t for secondary lead smelting, from which an estimation of approximately 8452.8 g HCBD emissions annually worldwide achieved. The carcinogenic risks caused by HCBD emissions from countries and regions with intensive 12 industrial sources are 1.0-80 times higher than that without these industries. These results will be useful for formulating effective strategies of HCBD control.

Mass Spectrometry Imaging Techniques: Non-Ambient and Ambient Ionization Approaches.

Molecular information can be acquired from sample surfaces in real time using a revolutionary molecular imaging technique called mass spectrometry imaging (MSI). The technique can concurrently provide high spatial resolution information on the spatial distribution and relative proportion of many different compounds. Thus, many scientists have been drawn to the innovative capabilities of the MSI approach, leading to significant focus in various fields during the past few decades. This review describes the sampling protocol, working principle and applications of a few non-ambient and ambient ionization mass spectrometry imaging techniques. The non-ambient techniques include secondary ionization mass spectrometry and matrix-assisted laser desorption ionization, while the ambient techniques include desorption electrospray ionization, laser ablation electrospray ionization, probe electro-spray ionization, desorption atmospheric pressure photo-ionization and femtosecond laser desorption ionization. The review additionally addresses the advantages and disadvantages of ambient and non-ambient MSI techniques in relation to their suitability, particularly for biological samples used in tissue diagnostics. Last but not least, suggestions and conclusions are made regarding the challenges and future prospects of MSI.

Interactions of Co(II)- and Zn(II)porphyrin of 5,10,15,20-tetrakis(1-methyl-4-pyridinio)porphyrin with DNA in Aqueous Solution and Their Antimicrobial Activities.

Antibiotics are frequently used to treat, prevent, or control bacterial infections, but in recent years, infections resistant to all known classes of conventional antibiotics have significantly grown. The development of novel, nontoxic, and nonincursive antimicrobial methods that work more quickly and efficiently than the present antibiotics is required to combat this growing public health issue. Here, Co(II) and Zn(II) derivatives of tetrakis(1-methylpyridinium-4yl)porphyrin [H2TMPyP]4+ as a tetra(ρ-toluenesulfonate) were synthesized and purified to investigate their interactions with DNA (pH 7.40, 25 °C) using UV-vis, fluorescence techniques, and antimicrobial activity. UV-vis results showed that [H2TMPyP]4+ had a high hypochromicity (∼64%) and a substantial bathochromic shift (Δλ, 14 nm), while [Co(II)TMPyP]4+ and [Zn(II)TMPyP]4+ showed little hypochromicity (∼37%) and a small bathochromic shift (Δλ, 3-6 nm). Results reveal that [H2TMPyP]4+ interacts with DNA via intercalation, while Co(II)- and [Zn(II)TMPyP]4+ interact with DNA via outside self-stacking. Fluorescence results also confirmed the interaction of [H2TMPyP]4+ and the metalloporphyrins with DNA. Results of the antimicrobial activity assay revealed that the metalloporphyrins showed inhibitory effects on Gram-positive and Gram-negative bacteria and fungi, but that neither the counterions nor [H2TMPyP]4+ exhibited any inhibitory effects. Mechanism of antimicrobial activities of metalloporphyrins are discussed.

Green Synthesized Chitosan Nanoparticles for Controlling Multidrug-Resistant mecA- and blaZ-Positive Staphylococcus aureus and aadA1-Positive Escherichia coli.

Antimicrobial resistance has recently been considered an emerging catastrophe globally. The public health and environmental threats were aggravated by the injudicious use of antibiotics in animal farming, aquaculture, and croup fields, etc. Consequently, failure of antibiotic therapies is common because of the emergence of multidrug-resistant (MDR) bacteria in the environment. Thus, the reduction in antibiotic spillage in the environment could be an important step for overcoming this situation. Bear in mind, this research was focused on the green synthesis of chitosan nanoparticles (ChiNPs) using Citrus lemon (Assam lemon) extract as a cross-linker and application in controlling MDR bacteria to reduce the antibiotic spillage in that sector. For evaluating antibacterial activity, Staphylococcus aureus and Escherichia coli were isolated from environmental specimens, and their multidrug-resistant pattern were identified both phenotypically by disk diffusion and genotypically by detecting methicillin- (mecA), penicillin- (blaZ), and streptomycin (aadA1)-resistance encoding genes. The inhibitory zone's diameter was employed as a parameter for determining the antibacterial effect against MDR bacteria revealing 30 ± 0.4 mm, 34 ± 0.2 mm, and 36 ± 0.8 mm zones of inhibition against methicillin- (mecA) and penicillin (blaZ)-resistant S. aureus, and streptomycin (aadA1)-resistant E. coli, respectively. The minimum inhibitory concentration at 0.31 mg/mL and minimum bactericidal concentration at 0.62 mg/mL of yielded ChiNPs were used as the broad-spectrum application against MDR bacteria. Finally, the biocompatibility of ChiNPs was confirmed by showing a negligible decrease in BHK-21 cell viability at doses less than 2 MIC, suggesting their potential for future application in antibiotic-free farming practices.

Development of sustainable dual core-spun yarns using several filaments and recycled cotton sourced from pre-consumer fabric waste.

Textile industries are now focusing on sustainable issues in manufacturing operations to save the environment. The study focuses on the use of cotton fibers (recycled) sourced from fabric (knitted) waste (pre-consumer) to manufacture elastic yarn (dual-core) for denim fabric. The study involves the production of yarns (dual-core) using a redesigned ring spinning method with different elastomeric components, including T400® (Polyethylene terephthalate)/Polytrimethylene terephthalate), Polybutylene terephthalate (PBT), Polyester (PES), Lycra® (elastane), virgin cotton and cotton (recycled) fiber. The study investigates various yarn (Ne 18/1) characteristics such as strength, IPI (imperfection index), elongation %, unevenness %, and hairiness. It is noticed that the elongation and strength of recycled yarn (double core) are lower and IPI (Imperfection index), unevenness %, and hairiness values are higher than 100 % cotton (virgin) yarn (double core). One-way ANOVA (statistical analysis) is employed to assess the significance of differences among yarns manufactured from various core materials and found significant variation for all characteristics. Additionally, the article introduces the MOORA (multi-objective optimization based on ratio analysis) technique as a decision-making tool to determine the best yarn among three alternatives (PES yarn, PBT yarn, and T400 yarn) based on their properties, considering attributes and finding T400 filament containing yarn as the best option. The study introduces a sustainable approach using recycled cotton in yarn (double core) production and employs decision-making tools to assess and rank the performance of different yarn alternatives.

Continuous monitoring of body temperature for objective detection of health and safety risks in construction sites: An analysis of the accuracy and comfort of off-the-shelf wearable sensors.

Recent studies have shown the potential of wearable sensors for objective detection of health and safety risks in construction workers through their collected physiological data. Body temperature, as the focus of the current study, is one of the most important physiological parameters that can help to detect various health and safety risks such as heat stress, physical fatigue, and infectious diseases. This study aims to assess the applicability and performance of off-the-shelf wearable sensor devices to monitor workers' body temperature in construction sites by evaluating the accuracy of temperature measurements as well as the comfort of the devices. A total of nine off-the-shelf wearable sensor devices available on the market were initially trialed in the laboratory, and three devices were shortlisted considering a set of selection criteria for further assessment. Over three weeks, the shortlisted wearable sensors were tested on 26 workers in two large construction sites in Australia. The reliability/validity of the selected wearable sensors in measuring body temperature was investigated using Bland-Altman analysis. Human factors were also investigated in terms of the comfort of the devices, their impact on workers' performance, and the acceptability of being worn for an extended period (i.e., 8 h or more). It was found that all selected devices measured body temperature with a bias of less than one indicating a slight difference in measurements compared to the reference hospital-grade thermometers. Two devices out of the three were also comfortable. The achieved results indicate that it is feasible to develop a continuous temperature monitoring platform using off-the-shelf wearable sensors to detect a range of significant health and safety risks in construction sites objectively. Considering the rapid advancements in manufacturing wearable sensors, future research can adopt a similar approach to include the newly introduced off-the-shelf temperature sensors and select the most appropriate device.

Urinary fatty acid biomarkers for prostate cancer detection.

The lack of accuracy in the current prostate specific antigen (PSA) test for prostate cancer (PCa) screening causes around 60-75% of unnecessary prostate biopsies. Therefore, alternative diagnostic methods that have better accuracy and can prevent over-diagnosis of PCa are needed. Researchers have examined various potential biomarkers for PCa, and of those fatty acids (FAs) markers have received special attention due to their role in cancer metabolomics. It has been noted that PCa metabolism prefers FAs over glucose substrates for continued rapid proliferation. Hence, we proposed using a urinary FAs based model as a non-invasive alternative for PCa detection. Urine samples collected from 334 biopsy-designated PCa positive and 232 biopsy-designated PCa negative subjects were analyzed for FAs and lipid related compounds by stir bar sorptive extraction coupled with gas chromatography/mass spectrometry (SBSE-GC/MS). The dataset was split into the training (70%) and testing (30%) sets to develop and validate logit models and repeated for 100 runs of random data partitioning. Over the 100 runs, we confirmed the stability of the models and obtained optimal tuning parameters for developing the final FA based model. A PSA model using the values of the patients' PSA test results was constructed with the same cohort for the purpose of comparing the performances of the FA model against PSA test. The FA final model selected 20 FAs and rendered an AUC of 0.71 (95% CI = 0.67-0.75, sensitivity = 0.48, and specificity = 0.83). In comparison, the PSA model performed with an AUC of 0.51 (95% CI = 0.46-0.66, sensitivity = 0.44, and specificity = 0.71). The study supports the potential use of urinary FAs as a stable and non-invasive alternative test for PCa diagnosis.

Aflatoxin M1 Analysis in Urine of Mill Workers in Bangladesh: A Pilot Study.

Presence of aflatoxin B1 (AFB1) in food and feed is a serious problem, especially in developing countries. Human exposure to this carcinogenic mycotoxin can occur through dietary intake, but also through inhalation or dermal contact when handling and processing AFB1-contaminated crops. A suitable biomarker of AFB1 exposure by all routes is the occurrence of its hydroxylated metabolite aflatoxin M1 (AFM1) in urine. To assess mycotoxin exposure in mill workers in Bangladesh, we analyzed AFM1 levels in urine samples of this population group who may encounter both dietary and occupational AFB1 exposure. In this pilot study, a total of 76 participants (51 mill workers and 25 controls) were enrolled from the Sylhet region of Bangladesh. Urine samples were collected from people who worked in rice, wheat, maize and spice mills and from controls with no occupational contact to these materials. A questionnaire was used to collect information on basic characteristics and normal food habits of all participants. Levels of AFM1 in the urine samples were determined by a competitive enzyme linked immunosorbent assay. AFM1 was detected in 96.1% of mill workers' urine samples with a range of LOD (40) of 217.7 pg/mL and also in 92% of control subject's urine samples with a range of LOD of 307.0 pg/mL). The mean level of AFM1 in mill workers' urine (106.5 ± 35.0 pg/mL) was slightly lower than that of the control group (123.3 ± 52.4 pg/mL), whilst the mean AFM1 urinary level adjusted for creatinine was higher in mill workers (142.1 ± 126.1 pg/mg crea) than in the control group (98.5 ± 71.2 pg/mg crea). Yet, these differences in biomarker levels were not statistically significant. Slightly different mean urinary AFM1 levels were observed between maize mill, spice mill, rice mill, and wheat mill workers, yet biomarker values are based on a small number of individuals in these subgroups. No significant correlations were found between the study subjects' urine AFM1 levels and their consumption of some staple food items, except for a significant correlation observed between urinary biomarker levels and consumption of groundnuts. In conclusion, this pilot study revealed the frequent presence of AFM1 in the urine of mill workers in Bangladesh and those of concurrent controls with dietary AFB1 exposure only. The absence of a statistical difference in mean biomarker levels for workers and controls suggests that in the specific setting, no extra occupational exposure occurred. Yet, the high prevalence of non-negligible AFM1 levels in the collected urines encourage further studies in Bangladesh regarding aflatoxin exposure.

Occurrence of ochratoxin A in breast milk and urine samples of nursing mothers in Bangladesh.

The mycotoxin ochratoxin A (OTA) is a potent nephrotoxin with carcinogenic properties and, thus, of concern as a food contaminant. Since food contaminant data are scarce in Bangladesh, we applied human biomonitoring to gain more insights into OTA exposure in the country's population. OTA concentrations in human milk and urine samples of nursing mothers were determined with the aim to assess also exposure to this mycotoxin in breastfed infants. Breastfeeding mothers (n = 74) from three districts of Bangladesh (Sylhet, Cumilla, and Mymensingh region) participated in this study. They provided demographic data, along with breast milk and urine samples. OTA levels were measured by a competitive enzyme-linked immunosorbent assay (ELISA) with a detection limit of 60 ng/L for milk and 30 ng/L for urine.OTA was detected in 62.2% of all breast milk samples (mean 74.8 ± 49.0 ng/L, range < LOD-243.3 ng/L) and in 51.4% of all urine samples (mean 44.3 ± 63.5 ng/L, range < LOD-519.3 ng/L). The differences observed between regions for mean breast milk or for urinary OTA levels were relatively small. No significant correlation was observed between OTA levels in breast milk and food consumption patterns among nursing mothers. Regarding infant exposure, the estimated average daily intake of OTA for all was 15.0 ng/kg bw/day (range 4.5-45 ng/kg bw/day). In 34.5% of these infants, their estimated daily OTA intake exceeded a preliminary TDI value set by EFSA (17 ng/kg bw/day). The mean OTA intake was slightly higher (16.2 ± 7.8 ng/kg bw/day) in 1-2 months babies than in older infants (< 2 to 12 months), although the difference was not significant. Presence of OTA in most milk and urine samples of nursing mothers documents their widespread dietary mycotoxin exposure. Although based on a relatively small number of participants, the present analysis indicates non-negligible exposure of some nursed infants in Bangladesh. Therefore, further biomonitoring studies and investigations on major sources of OTA in food commodities are encouraged.

Impact of synthetic noise signature and physiologic ECG signal on designing ML-based ECG noise detection framework.

Automatic signal analysis using artificial intelligence is getting popular in digital healthcare, such as ECG rhythm analysis, where ECG signals are collected from traditional ECG machines or wearable ECG sensors. However, the risk of using an automated system for ECG analysis when noise is present can lead to incorrect diagnosis or treatment decisions. A noise detector is crucial to minimise the risk of incorrect diagnosis. Machine learning (ML) models are used in ECG noise detection before clinical decision-making systems to mitigate false alarms. However, it is essential to prove the generalisation capability of the ML model in different situations. ML models performance is 50% lesser when the model is trained with synthetic and tested with physiologic ECG datasets compared to trained and tested with physiologic ECG datasets. This suggests that the ML model must be trained with physiologic ECG datasets rather than synthetic ones or add more various types of noise in synthetic ECG datasets that can mimic physiologic ECG.Clinical relevance- ML model trained with synthetic noisy ECG can increase the 50% misclassification rate in ECG noise detection compared to training with physiologic ECG datasets. The wrong classification of noise-free and noisy ECG will lead to misdiagnosis regarding the patient's condition, which could be a cause of death.

Investigating the effects of storage conditions on urinary volatilomes for their reliability in disease diagnosis.

BACKGROUND: Cancer detection presents challenges regarding invasiveness, cost, and reliability. As a result, exploring alternative diagnostic methods holds significant clinical importance. Urinary metabolomic profiling has emerged as a promising avenue; however, its application for cancer diagnosis may be influenced by sample preparation or storage conditions. OBJECTIVE: This study aimed to assess the impact of sample storage and processing conditions on urinary volatile organic compounds (VOCs) profiles and establish a robust standard operating procedure (SOP) for such diagnostic applications. METHODS: Five key variables were investigated: storage temperatures, durations, freeze-thaw cycles, sample collection conditions, and sample amounts. The analysis of VOCs involved stir bar sorptive extraction coupled with thermal desorption-gas chromatography/mass spectrometry (SBSE-TD-GC-MS), with compound identification facilitated by the National Institute of Standards and Technology Library (NIST). Extensive statistical analysis, including combined scatterplot and response surface (CSRS) plots, partial least squares-discriminant analysis (PLS-DA), and probability density function plots (PDFs), were employed to study the effects of the factors. RESULTS: Our findings revealed that urine storage duration, sample amount, temperature, and fasting/non-fasting sample collection did not significantly impact urinary metabolite profiles. This suggests flexibility in urine sample collection conditions, enabling individuals to contribute samples under varying circumstances. However, the influence of freeze-thaw cycles was evident, as VOC profiles exhibited distinct clustering patterns based on the number of cycles. This emphasizes the effect of freeze-thaw cycles on the integrity of urinary profiles. CONCLUSIONS: The developed SOP integrating SBSE-TD-GC-MS and statistical analyses can serve as a valuable tool for analyzing urinary organic compounds with minimal preparation and sensitive detection. The findings also support that urinary VOCs for cancer screening and diagnosis could be a feasible alternative offering a robust, non-invasive, and sensitive approach for cancer screening.

Removal of hazardous textile dye from simulated wastewater by municipal organic solid waste charcoal using machine learning approaches: Kinetics, isotherm, and thermodynamics.

This study focuses on the probable use of municipal organic solid waste charcoal (MOSWC) as an adsorbent for Methyl orange (MO) adsorption. The prepared MOSWC is characterized by FE-SEM and FT-IR. Batch adsorption experiments were conducted with the influencing of different operational conditions namely time of contact (1-180 min), adsorbate concentration (60-140 mg/L), adsorbent dose (1-5 g/L), pH (3-11), and temperature (25-60 °C). The high coefficient value (R2 = 0.96) of the process optimization model suggests that this model was significant, where pH and adsorbent dose expressively stimulus adsorption efficiency including 40.11 mg/g at pH (3), MO concentration (100 mg/L), and MOSWC dose (1 g/L). Furthermore, the machine learning approaches (ANN and BB-RSM) revealed a good association between the tested and projected values. The highest monolayer adsorption capacity of MO was 90.909 mg/g. Pseudo-second-order was the well-suited kinetics, where Langmuir isotherm could explain better for equilibrium adsorption data. Thermodynamic study shows MO adsorption is favourable, exothermic, and spontaneous. Finally, this study indicates that MOSWC could be a potential candidate for the adsorption of MO from wastewater.

Omics data analysis reveals common molecular basis of small cell lung cancer and COVID-19.

The impact of COVID-19 infection on individuals with small cell lung cancer (SCLC) poses a serious threat. Unfortunately, the molecular basis of this severe comorbidity has yet to be elucidated. The present study addresses this gap utilizing publicly available omics data of COVID-19 and SCLC to explore the key molecules and associated pathways involved in the convergence of these diseases. Findings revealed 402 genes, that exhibited differential expression patterns in SCLC patients and also play a pivotal role in COVID-19 pathogenesis. Subsequent functional enrichment analyses identified relevant ontologies and pathways that are significantly associated with these genes, revealing important insights into their potential biological, molecular and cellular functions. The protein-protein interaction network, constructed under four combinatorial topological assessments, highlighted SMAD3, CAV1, PIK3R1, and FN1 as the primary components to this comorbidity. Our results suggest that these components significantly regulate this cross-talk triggering the PI3K-AKT and TGF-ß signaling pathways. Lastly, this study made a multi-step computational attempt and identified corylifol A and ginkgetin from natural sources that can potentially inhibit these components. Therefore, the outcomes of this study offer novel perspectives on the common molecular mechanisms underlying SCLC and COVID-19 and present future opportunities for drug development.Communicated by Ramaswamy H. Sarma.

Carbon Coated Iron-Cobalt Nanoparticles for Magnetic Particle Imaging.

Magnetic particle imaging (MPI) is an emerging imaging modality that provides direct and quantitative mapping of iron oxide tracers. To achieve high sensitivity and good spatial resolution images, a magnetic nanoparticle with a higher contrast intensity needs to be developed. Currently, a majority of MPIs being developed for potential clinical application are composed of iron oxide nanoparticles with a spherical shape. In this project, we intend to report development of high-performance carbon (C) coated iron-cobalt (FeCo) nanoparticles (FeCo/C) and investigate their feasibility as a MPI agent. We have synthesized FeCo/C through a facile and simple method at mild temperature that is safe, easy, and up-scalable. We studied the structural and functional relationships and biocompatibility of this MPI agent in vitro. However, to enhance the aqueous solubility and biocompatibility, the surface of FeCo/C was modified with polyethylene glycol (PEG). We found that variation in the ratio of Fe and Co plays a vital role in their physical properties and functionality. In vitro imaging confirms that the Fe3Co1/C nanoparticle has highly competitive MPI intensity compared to VivoTrax, a commercially available MPI agent. Confocal laser scanning microscopy imaging with Rhodamine B labeled FeCo/C displays cellular internalization by the A375 cancer cells. The in vitro toxicity analysis concludes that there is no significant toxicity of FeCo/C nanoparticles. Therefore, the newly developed MPI agent holds strong promise for biomedical imaging and could be further validated in vivo in small animals.