Improvement In Sensitivity Research Articles

Fluorogenic RNA aptamer based artificial membraneless organelles for small molecule and cell sensing

Artificial membraneless organelle with molecularly crowded and confined microenvironment shows great potential for improving functional RNA properties and developing biosensing. However, the performance of current artificial membraneless organelles are impaired with polycationic components. In this work, we demonstrate the construction of a purely fluorescent RNA aptamer based artificial membraneless organelle (FRAME) for improving RNA performance and biosensing. The fluorescent RNA aptamer in FRAME exhibited higher enzymatic resistance, thermostability, photostability and binding affinity than that dispersed in solution. The FRAME can be easily designed as a biosensor for tetracycline detection in lake water and tap water, which eliminated the adverse effects of polycationic components and demonstrated about 42.8-fold sensitivity improvement than the biosensor prepared with Poly-L-Lysine and RNA. By introducing epithelial cell adhesion molecule (EpCAM) aptamer to specific recognition of tumor cells, the FRAME is easily engineered as an ultra-sensitive probe to precisely identify cancer cell phenotypes. We believe that the proposed FRAME system would provide a universal platform for biosensing.

The role of metformin in modulating cardiometabolic risk in obese pediatric populations with metabolic syndrome: A systematic review

Introduction: Obesity significantly increases the risk of developing cardiometabolic complications, which are precursors to cardiovascular diseases (CVD). In children and adolescents, metabolic dysfunctions such as insulin resistance, dyslipidemia, and hypertension are key contributors to early-onset cardiovascular risk. Metformin, a well-established antidiabetic agent, has shown potential benefits in addressing these metabolic imbalances. However, the long-term impact of metformin on cardiovascular outcomes in obese pediatric patients remains unclear. Aim: This systematic review aims to evaluate the effect of metformin on cardiometabolic parameters in obese children and adolescents with metabolic syndrome, focusing on insulin sensitivity, body mass index (BMI), blood pressure, and lipid profiles. Methods: A systematic review was conducted by searching databases including PubMed, Cochrane Library, Embase, and Web of Science. The inclusion criteria targeted randomized controlled trials (RCTs), cohort studies, systematic reviews, and meta-analyses published between 2004 and 2024. Pediatric patients aged 6-18 years with obesity and metabolic syndrome were included. The primary outcomes assessed were BMI, insulin sensitivity, lipid profiles, blood pressure, and cardiovascular outcomes. Results: A total of 23 studies, including 10 RCTs and 1 meta-analysis, were reviewed, involving 777 patients. Metformin demonstrated consistent improvements in insulin sensitivity and BMI in obese children and adolescents. Several studies reported modest reductions in BMI, improved insulin resistance, and better lipid profiles. However, variability in results was observed regarding long-term cardiovascular outcomes. While metformin reduced systolic blood pressure and carotid intimal-medial thickness (CIMT), the long-term impact on preventing major cardiovascular events remained inconclusive. Discussion: Metformin’s benefits on cardiometabolic risk factors in obese children are primarily attributed to its effects on insulin sensitivity, lipid metabolism, and inflammatory pathways. Its ability to activate AMP-activated protein kinase (AMPK) leads to improved metabolic profiles and reduced inflammation and is crucial for mitigating obesity-related cardiovascular risks. Despite these positive effects, the evidence for long-term prevention of cardiovascular events is limited and requires further exploration. Conclusions: Metformin offers significant short-term benefits in improving BMI, insulin sensitivity, and cardiometabolic parameters in obese children and adolescents. However, its long-term efficacy in preventing cardiovascular events remains uncertain. Further studies are necessary to establish its role in long-term cardiovascular protection in pediatric populations

Evaluating XGBoost’s predictive accuracy on surgical site Infections in cardiac surgery

Abstract Background Surgical site infections (SSIs) pose a significant threat in cardiac surgery (CS), profoundly impacting patient prognosis. SSI incidence varies widely, reported between 3.5% and 26.8%. The study used a decade-long dataset from SSI surveillance in a specialized hospital in Italy, applying machine learning (ML) techniques to predict the SSI incidence among CS patients. Methods Data collected from 2013-2023 through the surveillance system of SSI in patients undergoing CS were used to train a predictive ML algorithm (XGBoost). Data included information on demographics, risk factors, surgery variables (incision site, prophylaxis, etc.), and the infection outcome derived from follow-up interviews at 30 days post-surgery, or 90 days for patients with prosthetic materials, according to the ECDC case definition of SSI. We used the R libraries “caret”, “smotefamily” and “xgboost” to train the algorithm. A train-test split of 70-30 was applied. Both downsampling of the majority class (no SSI) and oversampling of SSI cases with SMOTE were used in the training set to address class imbalance. Results A total of 10,534 subjects (65.9% males, mean age 68.3 years) who underwent CS (64.3% with prosthetic materials) were included, among which 533 SSIs were identified (mean incidence of 5.06%), with 430 cases (80.7%) occurring after discharge (38.7% deep SSI) and 103 (19.3%) before discharge (55.5% deep SSI). The trained XGBoost algorithm achieved an AUC of 0.62, sensitivity of 69%, and specificity of 50% in the prediction of SSI on the test dataset. Conclusions These findings suggest that while the XGBoost model provides a fair predictive capability, there is significant room for improvement in sensitivity and specificity. The use of artificial intelligence offers a promising opportunity to identify patients at risk of SSI. However, further research and comprehensive data are needed to refine the predictive model and effectively improve prevention measures. Key messages • Predominantly occurring post-discharge, SSIs in cardiac surgery necessitate enhanced prevention strategies, highlighting the critical phase after hospital discharge where surveillance is paramount. • Artificial intelligence may enhance post-discharge SSI surveillance, promising to identify patients at increased risk. Further research is needed to refine these AI tools for optimal sensitivity.

Deep learning improves detection of aortic stenosis in b-mode ultrasound: results of a pivotal multicenter validation study using artificial intelligence

Abstract Background Aortic stenosis (AS) is a highly prevalent and morbid condition, with moderate or severe AS present in 9% of patients over 75 years. Despite improved diagnostic and therapeutic methods, AS remains underdiagnosed, particularly in Black patients and underserved communities. Echocardiography (echo) is effective for detecting AS but requires expertise to quantify and interpret valve gradients and valve area. We hypothesized that a deep learning (DL) algorithm could be trained to detect AS severity using B-mode (non-Doppler) echo alone, the modality most likely to be available in point-of-care ultrasound (POCUS) studies. Here, we present the results of a multicenter pivotal trial to test whether artificial intelligence (AI) can help increase the detection of AS from B-mode echoes. Methods A convolutional neural network (CNN) was trained to classify the severity of aortic stenosis from 3 B-mode echo views of the aortic valve (parasternal long- and short-axis and apical 5-chamber). The training dataset had over 500,000 cine loops from 29,527 echoes in 26,981 patients. A fully crossed multireader multicase study was conducted using 220 echoes never seen during algorithm development. 5 level II-trained cardiologists (referred to as POCUS readers as they only reviewed B-mode clips) were presented with these echoes twice at least a month apart, once with and again without AI interpretation of AS severity. Readers identified 2 dichotomous severity levels (either "suggestive of moderate to severe AS" or not), with ground truth established by 3 level III-trained cardiology echo experts. While experts had access to full studies (including Doppler) to adjudicate severity, POCUS readers had no Doppler or measurements of any kind available. Sensitivity and specificity were compared for the 5 POCUS readers when aided by the AI algorithm vs unaided, with similar tests of inter-reader agreement. Results There was a statistically significant improvement in sensitivity (+5.5%, 95% CI [1.5%, 9.5%], p=0.007) with stable specificity (-0.3%, 95% CI [-3.6%, 3.0%], p=NS) when the 5 POCUS readers received the assistance of the AI software. Fig 1 shows 2,500 bootstrapping assessments to better show consistency of these findings. In addition, the POCUS inter-reader agreement (overall percent agreement) improved by approximately 7% (p<0.001), becoming comparable to that of expert readers (Fig 2). Conclusions In a comprehensive multireader pivotal trial, an AI algorithm improved the performance of POCUS cardiologist readers to identify significant AS from limited B-mode echoes as might be obtained in screening or outreach settings. Sensitivity for detection of moderate-severe AS increased significantly by 5.5% with stable specificity. Inter-reader agreement improved to resemble that of expert cardiologists given access to full study information, including Doppler. AI may have a role in assisting detection of significant AS from limited B-mode echoes.Fig 1Fig 2

Association between dyspnea, chest pain, nt-probnp and cardiac impairment in people with cystic fibrosis

Abstract Background Cystic fibrosis (CF) is an autosomal recessive disease affecting multiple organs. Emerging evidence indicates an elevated risk of cardiovascular complications in people with CF (pwCF), with some studies implicating CF-related cardiomyopathy as an underlying mechanism. Whether screening for cardiac symptoms and N-terminal pro B-type natriuretic peptide (NT-proBNP) levels may aid in identifying cardiac impairment in pwCF remains unknown. Purpose To assess prevalence of dyspnea and chest pain in pwCF and investigate whether symptoms and NT-pro-BNP are predictive of cardiac impairment. Methods We interviewed 104 adult pwCF and 104 age- and sex-matched controls from the general population about symptoms of dyspnea and chest pain. All participants were examined with transthoracic echocardiography and NT-proBNP. Cardiac impairment was defined as left ventricular (LV) ejection fraction (LVEF) <50%, LV global longitudinal strain (GLS) < 16% or presence of diastolic dysfunction. Results Chest pain was more frequent in pwCF than in controls: (29% vs. 1%, p<0.001), although primarily characterized as atypical and non-exertional chest pain (27%). PwCF also suffered more from dyspnea than controls (89% vs. 18%, p<0.001) (Figure 1) and more pwCF had abnormal cardiac function compared to controls (44% versus 22%, p<0.001). Median NT-proBNP in pwCF was 50.0 pg/ml (40.7; 100.8) and increasing NT-proBNP level was associated with decreasing FEV1 (L) (estimate: -0.008, p<0.001). NT-proBNP level was not associated with neither dyspnea nor chest pain (p=0.60 and p=0.15, respectively). PwCF with dyspnea did not differ from those without on clinical or echocardiographic parameters. However, pwCF with chest pain had lower absolute GLS compared to pwCF without (17.4% vs.18.5%, p=0.026). Neither dyspnea nor chest pain was more frequent in pwCF with cardiac impairment versus normal cardiac function (54% vs. 39%, p=0.17 and 29% vs. 28%, p=0.91, respectively). Decreasing absolute GLS was associated with increased odds of chest pain (OR 1.25, 95% CI 1.02; 1.53, p=0.030) (Figure 2). Neither dyspnea or chest pain alone, nor in combination with NT-proBNP were able to predict abnormal cardiac function. While dyspnea and chest pain combined showed a slight improvement in sensitivity and positive predictive value for diagnosing cardiac impairment, the combination of symptoms and increased NT-proBNP did not enhance diagnostic accuracy. Conclusions In this cross-sectional study, dyspnea and atypical, non-exertional chest pain were common symptoms in adult pwCF. While abnormal cardiac function was prevalent in pwCF, neither symptoms, NT-proBNP nor a combination of these were associated with increased odds of cardiac impairment. Dyspnea and chest pain, though prevalent, are multifactorial in pwCF and do not seem to serve as sensitive markers of cardiac impairment, even when combined with NT-proBNP level.

Comparison of DNA extraction procedures for detection of Mycoplasma bovis directly from extended bovine semen straw samples using a commercial M. bovis PCR

BackgroundMycoplasma bovis is a global pathogen of cattle but was detected for the first time in New Zealand in 2017, triggering a response under their Biosecurity Act as an “unwanted organism”. Following a lengthy eradication campaign, the Ministry of Primary Industries (MPI) now requires all bovine semen destined for export to New Zealand to be screened with an M. bovis-specific real-time PCR (rtPCR) compliant with amended import health standard (IHS) test requirements aimed at preventing the accidental importation of M. bovis. The standard stipulates that semen samples cannot be centrifuged prior to DNA extraction. To comply with these strict requirements, one of the listed tests was validated together with different DNA preparation steps and compared with existing in-house procedures. DNA was extracted from semen straws using the current in-house semi-automated platform procedures for processing culture, tissue and body fluid sample submissions and was compared with the stipulated test requirements. DNA from centrifuged and unspun semen samples spiked with M. bovis was also compared.ResultsThe rtPCR had a sensitivity and specificity of 100% (95% confidence interval = 79–100% and 74–100%, respectively) when testing DNA from other Mycoplasma species or bovine semen spiked with the latter, with a high level of repeatability for within- and between- run replicates. The consistent limit of detection was 0.001 pg/µl M. bovis DNA and between 5.3 × 102 and 7.5 × 102 CFU/ml M. bovis when artificially spiked in semen. DNA extracted using the KingFisher Flex was detected with lower Cq values than the Maxwell 16, but the comparable improvements in sensitivity were mainly associated with non-centrifuged samples (p < 0.001). None of the procedures tested impeded the detection sensitivity of M. bovis in the presence of competitor organisms Acholeplasma laidlawii, Mycoplasma bovigenitalium and Ureaplasma diversum, confirming M. bovis specificity of the polC target.ConclusionsUnder the experimental conditions applied, this rtPCR test efficiently detected M. bovis in extended bovine semen straw samples from DNA extracted using both semi-automated extraction platforms, irrespective of prior centrifugation of extended semen.

Improvement of angle measurement sensitivity using second harmonic wave interference

This paper presents the angular displacement measurement based on second harmonic generation (SHG). In our previous study, the angular resolution of sub-arcsecond was achieved. In this paper, three kinds of sensors based on SHG are reported. In these three sensors, the angular displacement is determined from the intensity of the SHG signal since the SHG is sensitive to the angle of the incident light. Two of these sensors in this study utilize the interference of SHG to improve the sensitivity performance. Comparing with the sensors which have been studied before, interference type sensors in this study allow the femtosecond laser to pass through the nonlinear optical crystal twice, which leads to a significant sensitivity improvement. The principles were verified by both simulation and experiment. In the simulation part, the sensitivities of these three kinds of sensors are compared. In the experiment, the sensitivities were confirmed to be consistent with the simulation. Additionally, the resolution of the presented systems is evaluated and sub-0.1-arcsecond is achieved.

Sensitivity improvement of a deuterium-deuterium neutron generator based in vivo neutron activation analysis (IVNAA) system.

Our lab has been developing a deuterium-deuterium (DD) neutron generator-based neutron activation analysis (NAA) system to quantify metals and elements in the human body in vivo. The system has been used to quantify metals such as manganese, aluminum, sodium in bones of a living human. The technology provides a useful way to assess metal exposure and to estimate elemental deposition, storage and biokinetics. It has great potential to be applied in the occupational and environmental health fields to study the association of metal exposure and various health outcomes, as well as in the nutrition field to study the intake of essential elements and human health. However, the relatively low sensitivity of the system has greatly limited its applications. Neutron moderation plays an important role in designing an IVNAA facility, as it affects thermal neutron flux in irradiation cave and radiation exposure to the human subject. This study aims to develop a novel thermal neutron enhancement method to improve the sensitivity of the in vivo neutron activation analysis (IVNAA) system for elemental measurement but still maintain radiation dose. Utilizing a compact DD neutron source, we propose a new and practical moderator design that combines high density polyethylene with heavy water to enhance thermal neutrons by reducing thermal neutron absorption. All material dimensions are calculated by PHITS, a general-purpose Monte Carlo simulation program. The improvement of the new design predicted by the Monte Carlo simulation for the quantification of one of the elements, manganese was verified by experimental irradiation of manganese-doped bone equivalent phantoms. For the same radiation dose, a 67.9% thermal neutron flux enhancement is reached. With only 4.2% increase of radiation dose, the simulated thermal neutron flux and activation can be further increased by 84.2%. A 100% thermal neutron enhancement ratio is also achievable with a 20% dose increase. The experimental results clearly show higher manganese activation gamma ray counts for each specific phantom, with a significantly reduced minimum detection limit. Additionally, the photon dose was suppressed. The thermal neutron enhancement method can increase the number of useful neutrons significantly but maintain the radiation dose. This greatly decreased the detection limit of the system for elemental quantification at an acceptable dose, which will broadly expand the application of the technology in research and clinical use. The method can also be applied to other neutron medical applications, including neutron imaging and radiotherapy.

Fiber-Reinforced Flexible Self-Healing Strain Sensor with Failure-Improving Sensitivity Recovery.

In this work, we address the inherent limitations of porous, flexible, fibrous, and self-healing strain sensors. Specifically, we tackle issues such as the fatigue failure of carbon-fibrous materials and the long-term flow and low mechanical stability of self-healing materials. We achieve this by combining self-healing carbon/PBS blends with fibrous materials, creating a fiber-reinforced self-healing composite. The self-healing carbon/PBS blends provide strain sensitivity and the ability to recover after fatigue and impact failure, while the fibers prevent the long-term flow of material and the scattering of pieces during impact and fatigue failure within the elastic deformation regime, enabling shape recovery. We fabricated composite wearable strain sensors with a viscoelastic functional layer composed of two continuous phases: (i) a self-healing polymer-carbon blend and (ii) long electrospun fibers of commercial polyurethane. This setup also eliminates the other drawbacks of bulk materials, such as nonlinearity of volt-ampere characteristics, irreversibility of deformation, and a low working factor, and allows improvement of the working factor after failure and healing. Most importantly, we discovered that hindered self-healing, like in the case of the MWCNT/PBS system, enables improvement of sensor sensitivity after large strains and failure, which is due to partial failure of the network formed by conductive particles.

An improved AlexNet deep learning method for limb tumor cancer prediction and detection.

Synovial sarcoma (SS) is a rare cancer that forms in the soft tissues around joints, where early detection is crucial for improving patient survival rates. This study introduces a convolutional neural network (CNN) using an improved AlexNet deep learning classifier to enhance the diagnosis of SS from digital pathological images. Key preprocessing steps such as dataset augmentation and noise reduction techniques like adaptive median filtering (AMF) and histogram equalization were employed to improve image quality. Feature extraction was conducted using the Gray-Level Co-occurrence Matrix (GLCM) and Improved Linear Discriminant Analysis (ILDA), while image segmentation targeted spindle-shaped cells using repetitive phase-level set segmentation (RPLSS). The improved AlexNet architecture features additional convolutional layers and resized input images, leading to superior performance. The model demonstrated significant improvements in accuracy, sensitivity, specificity, and AUC, outperforming existing methods by 3%, 1.70%, 6.08%, and 8.86%, respectively, in predicting SS.

Improvements in visual function early after cataract surgery in eyes implanted with multifocal intraocular lenses.

To compare the improvements in all-distance visual acuity (VA) and contrast sensitivity early after cataract surgery among eyes implanted with multifocal and monofocal intraocular lenses (IOLs). Nonrandomized comparative study. Forty-two eyes with a trifocal IOL (Alcon, PanOptix®), 42 eyes with a combined bifocal and extended-depth-of-focus (EDF) IOL (Johnson & Johnson, Synergy®), and 84 eyes with a monofocal IOL were recruited. Uncorrected or corrected VA at various distances, and photopia and mesopic contrast VA were examined at 1day and 3 months postoperatively. Improvements in these VAs were compared between time points and among groups. Mean uncorrected as well as corrected VA improved significantly between 1day and 3 months postoperatively at almost all distances in the trifocal and combined bifocal + EDF groups (P ≤ 0.018), and at the intermediate distance of 1.0m and far distances in the monofocal group (P ≤ 0.031). Mean improvements in near uncorrected VA at 0.3m, intermediate VA at 0.5m, and far VA at 2.0, 3.0, and 5.0m were significantly greater in the multifocal groups than in the monofocal group (P ≤ 0.032). Improvements in photopic and mesopic contrast VA at most contrasts were significantly better in the multifocal groups (P ≤ 0.021). Mean uncorrected or corrected VA at almost all distances and contrast sensitivity at most contrasts improved significantly early after surgery in eyes implanted with multifocal IOLs, and the improvements in VA at most distances and in contrast sensitivity were better with multifocal IOLs than with monofocal IOLs.

Empirical assessment of the enrichment-based metagenomic methods in identifying diverse respiratory pathogens.

Probe-based nucleic acid enrichment represents an effective route to enhance the detection capacity of next-generation sequencing (NGS) in a set of clinically diverse and relevant microbial species. In this study, we assessed the effect of the enrichment-based sequencing on identifying respiratory infections using tiling RNA probes targeting 76 respiratory pathogens and sequenced using both Illumina and Oxford Nanopore platforms. Forty respiratory swab samples pre-tested for a panel of respiratory pathogens by qPCR were used to benchmark the sequencing data. We observed a general improvement in sensitivity after enrichment. The overall detection rate increased from 73 to 85% after probe capture detected by Illumina. Moreover, enrichment with probe sets boosted the frequency of unique pathogen reads by 34.6 and 37.8-fold for Illumina DNA and cDNA sequencing, respectively. This also resulted in significant improvements on genome coverage especially in viruses. Despite these advantages, we found that library pooling may cause reads mis-assignment, probably due to crosstalk issues arise from post-capture PCR and from pooled sequencing, thus increasing the risk of bleed-through signal. Taken together, an overall improvement in the breadth and depth of pathogen coverage is achieved using enrichment-based sequencing method. For future applications, automated library processing and pooling-free sequencing could enhance the precision and timeliness of probe enrichment-based clinical metagenomics.

THE FORMATION OF PROFESSIONALLY IMPORTANT MOBILE QUALITIES IN THE PROFESSIONAL AND APPLIED TRAINING OF STUDENTS NATIONAL TECHNICAL UNIVERSITY OF UKRAINE “IGOR SIKORSKY KYIV POLYTECHNIC INSTITUTE”

Vocational applied physical training is a specialized pedagogical process of targeted training that promotes the development and improvement of motor functions and skills in accordance with professional requirements and the nature of the educational process. The importance of professional-applied physical training in promoting health is to increase the nonspecific resistance of the body, expand the adaptive potential, increase the functional capabilities of the body and prevent diseases. The block (development of functions) determines the improvement of muscle-joint sensitivity and coordination of movements, the increase in muscle strength and endurance, the development of tactile sensitivity and dexterity, improving the reaction to a moving object and sensorimotor reactions. The essence of the block (increasing the efficiency of professional activity) lies in the fact that physical labor sports are based on a general motor stereotype that helps improve the functions and systems necessary to acquire professional qualifications. Special exercises determine the level of professional suitability, the strength of mastery of professional skills, the development of professional abilities, the improvement of professionally significant functions, and the increase in the level of professional performance. The leading principles of professional-applied physical training are: professional-applied orientation of physical education classes; the use of professionally applied physical training against the background of high physical activity; stages in the formation of professionally significant functions; coincidence of the functional orientation of sports and the requirements of professional activity; adequacy of forms and methods of physical education to the nature of work in professional activities; the complexity of the use of physical education means; improvement of individual psychophysiological properties that ensure the success of training for a specific professional activity; transfer of trainability of motor qualities from sports to professional activities.

Use of a Single-Fiber Optical Probe for the Detection of Tumor Fluorescence in High-Grade Glioma.

High-grade glioma (HGG) is the most common primary brain tumor in adults. The overall median survival is between 14 and 16 months. Fluorescence-guided surgery by detection of protoporphyrin IX (PpIX) fluorescence has been shown to improve the extent of resection, translating to improved progression-free survival. Microscope-based fluorescence detection techniques are associated with several pain points, some of which may be addressed by using contact-based spectroscopy. We aimed to investigate the accuracy of an optical fiber at detecting PpIX fluorescence by performing real-time spectroscopy in patients with HGG. Adult patients undergoing fluorescence-guided surgery for suspected HGG were recruited prospectively. Intraoperatively, samples from cortex, white matter, and tumor were taken. These samples were interrogated using standard white and blue light microscope techniques and the optical fiber. These specimens were then assessed by neuropathology to determine whether the tumor tissue was present within them. We collected 89 samples from 28 patients. There was an equal ratio of men to women, with a median age of 66.5 years. The accuracy of the probe for detecting PpIX fluorescence was 75.9%, compared with 56.6% for the operating microscope, with a significant improvement in sensitivity (χ2 = 11.84, P < .001). The optical fiber probe was more accurate than the operating microscope for detecting PpIX fluorescence. This technology has potential value in improving the accuracy of fluorescence-guided surgery and has potential practical benefits relating to surgical workflow.

Self-Powered Tactile Sensors Embedded with Self-Healing Electrodes for Multifunctional Applications.

Tactile-based sensing technology represents one of the most promising methods for interacting with their surrounding environment. Consequently, flexible tactile sensing has garnered significant attention from researchers worldwide. In this study, triboelectricity and piezoelectricity were combined to propose a multifunctional self-powered tactile sensor (MSPTS). Notably, new and innovative self-healing electrodes were embedded and synthesized from HPDMS (poly(dimethylsiloxane)) and boric acid in the MSPTS, MSPTS demonstrated a linear detection range of 0.25-5 kPa with a sensitivity of 246.28 mV/kPa, indicating substantial improvements in sensor sensitivity, output performance, and anti-interference capability. Our method of forming hydrogen bonds between a self-healing material (SHM) and a liquid metal (LM) effectively addressed the issue of LM leakage within the flexible matrix under pressure, thus preventing sensor failure. The borate dynamic bonding conferred self-healing properties to the electrode when it was damaged. The MSPTS was successfully applied to pressure detection, material identification, and human body movement detection, significantly broadening the application range of flexible electronic devices.

Toward Upconversion (Yb-Er) and near-Infrared (Yb-Ho-Er, Nd-Yb) Thermometry with Sea Urchin Type GdPO4 Nanoarchitectures.

In recent years, optical temperature probes operating in the second near-infrared (BW-II) and third near-infrared (BW-III) biological windows have garnered significant attention in the scientific community. For biological applications these probes offer distinct advantages, including enhanced tissue penetration depth, minimal autofluorescence, and a remarkable improvement in imaging sensitivity and spatial resolution. Moving toward theranostic applications, there is a growing demand for the development of materials that integrate both BW-II and BW-III thermometry systems with drug delivery functionalities. In this study, we concentrate on the development of GdPO4 materials, utilizing both hard and sacrificial template routes to synthesize (hollow) GdPO4 porous sea urchin-like particles. We first investigated the development of a Boltzmann-type thermometer utilizing an Yb-Er upconversion system, designed to operate within the physiological temperature range. Our exploration extends to the potential of GdPO4 particles in near-infrared (NIR) thermometry, spanning the first, second, and third biological windows with systems like Yb-Ho-Er, Nd-Yb, and Ho-Yb, respectively. We further examined the temperature impact of the Yb-Ho-Er system on the NIR emission within a biologically relevant setting, using a phantom that replicates biological tissue. Furthermore, we illustrate the successful loading of these materials with doxorubicin (DOX·HCl), a model anticancer drug, showing these particles exhibit pH-dependent DOX release. This demonstrates the versatility of these materials as upconversion and NIR thermometers while simultaneously serving as an on-demand drug carrier. The investigation involves assessing their cytotoxicity on specific human cells (Normal Human Dermal Fibroblasts (NHDFs)), to determine their viability for potential use in biological applications. The study also investigates how effectively loading the particles with DOX enables targeted delivery to a cellular model of lymphoma (Jurkat E6-1), resulting in cell death. This comprehensive analysis highlights the promising potential of GdPO4 particles for medical applications.

Muscle Mass and Glucagon-Like Peptide-1 Receptor Agonists: Adaptive or Maladaptive Response to Weight Loss?

Recent studies have shown that pharmacologic weight loss with glucagon-like peptide-1 receptor agonists (GLP-1 RAs) and combination therapies is approaching magnitudes achieved with surgery. However, as more weight loss is achieved, there is concern for potential adverse effects on muscle quantity, composition, and function. This primer aims to address whether muscle-related changes associated with weight loss treatments such as GLP-1 RAs may be maladaptive (ie, adversely affecting muscle health or function), adaptive (ie, a physiologic response to weight loss maintaining or minimally affecting muscle health or function), or perhaps an enhanced response to weight loss (ie, improved muscle health or function after treatment). Based on contemporary evidence with the addition of studies using magnetic resonance imaging, skeletal muscle changes with GLP-1 RA treatments appear to be adaptive: changes in muscle volume z-score indicate a change in muscle volume that is commensurate with what is expected given aging, disease status, and weight loss achieved, and the improvement in insulin sensitivity and muscle fat infiltration likely contributes to an adaptive process with improved muscle quality, lowering the probability for loss in strength and function. Nevertheless, factors such as older age and prefrailty may influence the selection of appropriate candidates for these therapies because of risk for sarcopenia. Several pharmacologic treatments to maintain or improve muscle mass designed in combination with GLP-1-based therapies are under development. For future development of GLP-1-based therapies (and other therapies) designed for weight loss, as well as for patient-centered treatment optimization, the introduction of more objective and comprehensive ways of assessing muscle health (including accurate and meaningful assessments of muscle quantity, composition, function, mobility, and strength) is important for the substantial numbers of patients who will likely be taking these medications well into the future.

NMDA Receptors in POMC Neurons Connect Exercise With Insulin Sensitivity.

Increased arcuate proopiomelanocortin (POMC) neuron activity improves glucose metabolism and reduces appetite, facilitating weight loss. We recently showed that arcuate POMC neurons are activated by exercise. However, the role of excitatory glutamatergic input in these neurons and the metabolic outcomes of exercise remains undefined. To investigate this, we developed a mouse model with NMDA receptors (NMDARs) selectively deleted from POMC neurons of adult mice. We performed metabolic assessments, including the monitoring of body weight, body composition analysis, and glucometabolic tolerance tests. We also examined the metabolic outcomes of these mice in response to exercise, including changes in arcuate POMC neuronal activity and insulin sensitivity. Loss of NMDARs in POMC neurons failed to alter body weight or body composition. Notably, however, we did observe a marked impairment in glucose tolerance and insulin sensitivity. Additionally, exercise resulted in activation of arcuate POMC neurons and a sustained improvement in insulin sensitivity, an effect that was abrogated in mice deficient for NMDARs in POMC neurons when compared with their respective sedentary controls. This underscores an important link among exercise, hypothalamic neuron function, and metabolic health. Moreover, this highlights an underappreciated role of hypothalamic POMC neurons in mediating beneficial effects of exercise on glucose metabolism.

Nanofloral BiVO4/rGO composites for sensitive electrochemical detection of furazolidone in water

Antibiotics are widely used in agriculture, animal husbandry, and aquaculture, and the development of efficient, sensitive, and convenient antibiotic trace detection techniques is beneficial for tracking their transfer behavior in the environment. Electrochemical sensors have recently shown significant promise in the detection of antibiotics, however, the complex composition of the aqueous environment may interfere with the sensitivity and accuracy of the electrodes during electrochemical detection, and the selection of appropriate nanocomposites for modifying the electrodes has been considered as an effective solution. Nanofloral BiVO4/rGO electrocatalysts were developed for the electrochemical detection of furazolidone (FZD) based on a robust green sonochemical approach. the large specific surface area and excellent electrical conductivity of the BiVO4/rGO composites efficiently catalyzed the electrochemical reaction of FZD, which led to the improvement in the detection sensitivity and stability of the sensors. It exhibits current amplification and electrocatalytic effects in the reduction reaction of furazolidone. The assay exhibited a wide linear range, low detection limit, good reproducibility, stability and anti-interference properties, and a simple and convenient preparation process. The effects of conditions such as scanning rate (v), electrolyte pH, and electrode loading on the detection effect were investigated, developed BiVO4/rGO composites exhibited detection limits of 1.46 and 22.4 nM and sensitivities of 24.4 and 1.94 μA μM−1 cm−2, respectively, for FZD. The objective of the study is to provide a novel, low-cost, convenient and highly sensitive assay for the detection of antibiotics in the aqueous environment. BiVO4/rGO electrocatalysts are ideal candidates for low-cost, convenient and highly sensitive detection of antibiotics in aqueous environments, promoting the technological development and application in this field.

Ultrasensitive Ultrasoft Buckled Crack‐Based Sensor for Respiration Measurement and Enhanced Human–Machine Interface

Wearable strain sensors have transformed the real‐time monitoring of health conditions and human–machine interactions. However, recently developed wearable strain sensors exhibit several limitations. For example, when a sensor is designed with high sensitivity to detect strain, it struggles to accurately measuring the deformation of low‐stiffness materials like skin. Additionally, finding the optimal balance between sensitivity, durability, hysteresis, and strain range in sensor design is challenging. To address these challenges, a Buckled, Ultrasoft, Crack‐based, Large strain, EpiDermal (BUCKLED) sensor is developed. This sensor integrates the benefits of soft structure engineering with high sensitivity of crack‐based sensing mechanisms to ensure optimal skin deformation measurements. The BUCKLED sensor exhibits significant improvements in compliance (18 500 mm N−1), stretchability (100%), hysteresis (2%), durability (10 000 cycles with 100% strain), and force sensitivity () owing to its buckled shape, confirming its ability to detect subtle movements with enhanced accuracy. The sensor's high compliance allows it to accurately measure low‐stiffness objects, ensuring reliable performance. Furthermore, the sensor's tunability is demonstrating its effectiveness in applications such as respiratory monitoring, facial expression recognition, and silent speech interfaces. Consequently, the proposed sensor is versatile and holds great potential for a wide range of sensing applications.