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Prediction of pulmonary embolism by an explainable machine learning approach in the real world
Spillover effect of children’s education on parental physical functioning over the life course
Mendelian randomization analysis reveals causal relationship between depression, antidepressants and benign paroxysmal vertigo
Complex Diophantine interval-valued Pythagorean normal set for decision-making processes
Nanobody-based indirect competitive ELISA for the detection of aflatoxin M1 in dairy products
AbstractAflatoxin M1 (AFM1) is known to be carcinogenic, mutagenic, and teratogenic and poses a serious threat to food safety and human health, which makes its surveillance critical. In this study, an indirect competitive ELISA (icELISA) based on a nanobody (Nb M4) was developed for the sensitive and rapid detection of AFM1 in dairy products. In our previous work, Nb M4 was screened from a Bactrian-camel-immunized phage-displayed library. It exhibits VH-like features, possesses higher thermal stability than monoclonal antibody (mAb 1E6) and tightly binds to AFM1–BSA with a KD value of 2.5 nM. Under the optimal conditions, its half-maximal inhibitory concentration was 0.338 ng/mL, the limit of detection was 0.051 ng/mL, and linearity was noted in the range of 0.168–0.679 ng/mL. Nb M4 displayed almost no cross-reactivity with other mycotoxins. No matrix effect was observed in milk and milk powder samples, and the matrix effect in yogurt samples could be weakened by 2-fold dilution. Furthermore, validation studies in spiked samples (milk, yogurt, and milk powder) resulted in good recoveries of 95.40–111.33%, with a low coefficient of variation (2.89–6.78%). High-performance liquid chromatography was used to evaluate the accuracy and reliability of the developed icELISA, which indicated a satisfactory consistent correlation (R2 = 0.9722). This study highlights the potential of Nb M4 as a promising component for detecting AFM1 in dairy products.
Sleep deprivation and corneal chronobiology: reevaluating overnight corneal changes
AbstractThis prospective cohort study is aimed to investigate circadian variations in corneal parameters, focusing on sleep-deprived subjects. Sixty-four healthy individuals (age range: 21–76 years) actively participated in this study, undergoing examinations at least five times within a 24-hour timeframe. The analysis encompassed keratometric parameters of the cornea’s front (F) and back (B) surfaces, refractive power in flattest and steepest axes (K1, K2), astigmatism (Astig) and its axis (Axis), aspheric coefficient (Asph), corneal pachymetry values of thinnest corneal thickness (Pachy Min) and corneal thickness in the center of the pupil (Pachy Pupil), volume relative to the 3 and 10 mm corneal diagonal (Vol D3, Vol D10) and surface variance index (ISV). Circadian changes were assessed using a hierarchical, mixed-effects linear regression adjusted for age and night shift. A total of 1,636 measurements revealed significant circadian changes in various corneal parameters, including K1 F, K2 F/B, Astig F/B, Asph F/B, Pachy Min/Pupil, Vol D3/10, and ISV (p < 0.0001). Moreover, K1 B exhibited a significant circadian change (p = 0.0002), while Axis F/B remained unchanged. Notably, Corneal thickness peaked before 6 o’clock in the morning and reached its minimum after 12 o’clock. Contrary to previous notions linking corneal diurnal changes with eyelid closure during sleep, our study reveals that these changes persist in the absence of sleep. This research contributes valuable insights into the impact of sleep deprivation on corneal properties, warranting further investigations to deepen our understanding of daily variations in visual quality and guide the planning of refractive eye surgery interventions.
Rectal glands and tergal glands as sources of volatile pheromones in cucumber fruit fly, Zeugodacus Cucumis
A preliminary exploration of establishing a mice model of hypoxic training
Puerarin pretreatment provides protection against myocardial ischemia/reperfusion injury via inhibiting excessive autophagy and apoptosis by modulation of HES1
Electrical impedance-based tissue classification for bladder tumor differentiation
AbstractIncluding sensor information in medical interventions aims to support surgeons to decide on subsequent action steps by characterizing tissue intraoperatively. With bladder cancer, an important issue is tumor recurrence because of failure to remove the entire tumor. Impedance measurements can help to classify bladder tissue and give the surgeons an indication on how much tissue to remove. Over the years of research, it became obvious that electrical impedance spectroscopy is a very promising tool for tissue differentiation, but also a very sensitive one. While differentiation in preliminary studies shows great potential, challenges arise when transferring this concept to real, intraoperative conditions, mainly due to the influence of preoperative radiotherapy, possibly different tumor types, and mechanical tissue deformations due to peristalsis or unsteady contact force of the sensor. This work proposes a patient-based classification approach that evaluates the distance of an unknown measurement to a healthy reference of the same patient, essentially a relative classification of the difference in impedance that is robust against inter-individual differences and systematic errors. A diversified dataset covering multiple disturbance scenarios is recorded. Two alternatives to define features from the impedance data are investigated, namely using measurement points and model-based parameters. Based on the distance of the feature vector of a unknown measurement to a healthy reference, a Gaussian process classifier is trained. The approach achieves a high classification accuracy of up to 100% on noise-free impedance data recorded under controlled conditions. Even when the differentiation is more ambiguous due to external disturbances, the presented approach still achieves a classification accuracy of 80%. These results are a starting point to tackle intraoperative bladder tissue characterization and decrease the recurrence rate.
Using transformer-based models and social media posts for heat stroke detection
Development and validation of a new nomogram for self-reported OA based on machine learning: a cross-sectional study
An integrated DEA-fuzzy AHP method for prioritization of renewable energy sources in India
AbstractAs India’s population grows and urbanization accelerates, energy demand is increasing sharply while conventional sources fall behind. To tackle energy shortages and climate change, India must prioritize renewable energy sources (RES), which offer sustainable solutions. The country is rich in RES, which can enhance fuel mix for electricity generation. This study analyzes various RES in India-solar, geothermal, hydro, biomass, wave, onshore, and offshore wind energy -using an integrated data envelopment analysis (DEA) and fuzzy analytic hierarchy process (Fuzzy AHP) methodology. Four main parameters-technical, economic, environmental, and socio-political -are identified and supported by 19 criteria, with environmental parameters including both desirable and undesirable criteria. In first phase, undesirable criteria are transformed into desirable criteria using Modified Ratio model. DEA is then applied to calculate initial efficiency score of RES under each parameter category. Fuzzy AHP determines weights for each parameter. The weights and initial efficiency scores are then combined to calculate overall efficiency score and ranking of RES. Sensitivity analysis shows that results obtained from proposed methodology are significant, and robust. Offshore wind ranks highest in efficiency, followed by hydro and onshore wind, while geothermal scores lowest. This methodology could benefit developing nations and guide policymakers in adopting RES.
The tight balance state and mechanism of disaster-resilient resources in karst small towns: a Chinese karst landform case study
Surgical frequency analysis of patients clustered according to postoperative pain trajectory: a retrospective study
Cognitive load detection through EEG lead wise feature optimization and ensemble classification
Differential regulatory effects of the N-terminal region in SYK-fusion kinases reveal unique activation-inducible nuclear translocation of ITK-SYK
Novel numerical and artificial neural computing with experimental validation towards unsteady micropolar nanofluid flow across a Riga plate
Enhancing electric vehicle battery lifespan: integrating active balancing and machine learning for precise RUL estimation
AbstractElectric vehicles (EVs) rely heavily on lithium-ion battery packs as essential energy storage components. However, inconsistencies in cell characteristics and operating conditions can lead to imbalanced state of charge (SOC) levels, resulting in reduced capacity and accelerated degradation. This study presents an active cell balancing method optimized for both charging and discharging scenarios, aiming to equalize SOC across cells and improve overall pack performance. The proposed system includes two balancing strategies: a charging balance that redistributes excess charge from high-SOC cells to maximize capacity, and a discharging balance that addresses low-SOC cells to extend discharge duration. Experimental results confirm that this method effectively reduces SOC disparities, enhancing both charging and discharging capacities. Additionally, to accurately predict battery lifespan and remaining useful life (RUL), seven machine learning models are evaluated using R-squared (R2) and Mean Absolute Error (MAE) metrics. Among these, k-nearest Neighbors and Random Forest models deliver the highest accuracy, achieving R2 values of 0.996 and above with low MAE, demonstrating strong predictive capability. The integration of active balancing and RUL prediction enables a feedback loop where balanced SOC levels promote battery health, and RUL predictions inform optimal balancing strategies. This comprehensive approach advances EV battery management, enhancing lifespan and reliability through proactive balancing and predictive insights.