Significance of maximum intensity projection technique of multimodal ultrasound imaging in differentiating follicular thyroid carcinoma from benign lesions.

Objective: Preoperative diagnosis for follicular thyroid cancer (FTC) remains challenging. The purpose of this study was to explore the maximum intensity projection (MIP) features, which can be utilized for reconstructing and characterizing the structure of microvascular in tissue, associated with FTC, and to explore the independent risk factors for FTC in combination with multimodal ultrasonography and blood indicators. Methods: This single-center, prospective, single-blind, observational study included patients with suspected follicular thyroid carcinoma based on preoperative ultrasonography findings. All patients underwent routine ultrasonography, contrast-enhanced ultrasonography (CEUS), and correlated blood indexes tests. Offline MIP reconstruction of the CEUS images was performed. The tumor was histologically diagnosed postoperatively. Multivariable logistics regression was utilized for analyzing MIP characteristics combined with multimodal ultrasonography and preoperative blood indicators to identify independent risk factors for FTC. Results: In this study, 61 thyroid nodules were finally included according to the atretic criteria. (1) Compared with traditional color profile ultrasonography and CEUS, MIP technology can provide more information regarding microvascular characteristics inside thyroid tumors. The short, rod-like, crossed, curved and firework-like features of MIP images revealed statistically significant differences between the benign and malignant groups. (2) Multivariable logistic regression analysis indicated that the firework-like MIP characteristics of microvascular, thyroglobulin (Tg) level and vessel intensity (VI) value were independent risk factors for malignancy. Conclusion: (1) MIP technology has potential applications in the differential diagnosis of follicular thyroid carcinoma from benign lesions. (2) Firework MIP microvascular characteristics, Tg values and VI values can serve as parameters for the differential diagnosis of follicular thyroid carcinoma from benign lesions. This study provides a novel approach idea for preoperative multimodal differentiation of follicular thyroid carcinoma from benign lesions.

Measurement of the Tilt Angle of the Optic Disc Using Spectral-Domain Optical Coherence Tomography and Related Factors in Myopia.

Purpose: This study presents a novel, three-dimensional method for measuring the tilt angle of the tilted optic disc (TOD) using spectral-domain optical coherence tomography (SD-OCT) and investigates the correlation between ocular-related parameters and TOD. Methods: We included the right eyes of 243 healthy young individuals, categorized by axial length. We measured the ovality index (OI) and dihedral angle (DA) using SD-OCT infrared ray fundus photographs and high-resolution cross-sectional images of the optic disc, respectively. The relationships between OI, DA, and ocular-related parameters were analyzed. Results: Eyes in the longer axial length group exhibited a lower OI and a higher DA, along with thinner nasal and inferonasal circumpapillary retinal nerve fiber layer (cpRNFL) and thicker temporal and superotemporal cpRNFL. There was a significant relationship between DA and cpRNFL thickness. The new method utilizing DA to measure the tilt angle of TOD demonstrated high repeatability. Conclusions: We propose a novel, three-dimensional, and quantitative method for evaluating the tilt degree of TOD. A higher degree of myopia indicated a greater tilt angle of the TOD, and a greater TOD suggested additional changes in cpRNFL thickness. These findings should be considered when interpreting increased susceptibility and early assessment of glaucoma in myopia. Translational Relevance: DA could serve as a superior indicator for describing TOD morphology during eyeball elongation and evaluating its impact on related parameters of the optic disc and peripapillary structures in the myopic population.

Prediction of sepsis within 24 hours at the triage stage in emergency departments using machine learning.

BACKGROUND: Sepsis is one of the main causes of mortality in intensive care units (ICUs). Early prediction is critical for reducing injury. As approximately 36% of sepsis occur within 24 h after emergency department (ED) admission in Medical Information Mart for Intensive Care (MIMIC-IV), a prediction system for the ED triage stage would be helpful. Previous methods such as the quick Sequential Organ Failure Assessment (qSOFA) are more suitable for screening than for prediction in the ED, and we aimed to find a light-weight, convenient prediction method through machine learning. METHODS: We accessed the MIMIC-IV for sepsis patient data in the EDs. Our dataset comprised demographic information, vital signs, and synthetic features. Extreme Gradient Boosting (XGBoost) was used to predict the risk of developing sepsis within 24 h after ED admission. Additionally, SHapley Additive exPlanations (SHAP) was employed to provide a comprehensive interpretation of the model's results. Ten percent of the patients were randomly selected as the testing set, while the remaining patients were used for training with 10-fold cross-validation. RESULTS: For 10-fold cross-validation on 14,957 samples, we reached an accuracy of 84.1%±0.3% and an area under the receiver operating characteristic (ROC) curve of 0.92±0.02. The model achieved similar performance on the testing set of 1,662 patients. SHAP values showed that the five most important features were acuity, arrival transportation, age, shock index, and respiratory rate. CONCLUSION: Machine learning models such as XGBoost may be used for sepsis prediction using only a small amount of data conveniently collected in the ED triage stage. This may help reduce workload in the ED and warn medical workers against the risk of sepsis in advance.

Engineered nanoparticles for precise targeted drug delivery and enhanced therapeutic efficacy in cancer immunotherapy.

The advent of cancer immunotherapy has imparted a transformative impact on cancer treatment paradigms by harnessing the power of the immune system. However, the challenge of practical and precise targeting of malignant cells persists. To address this, engineered nanoparticles (NPs) have emerged as a promising solution for enhancing targeted drug delivery in immunotherapeutic interventions, owing to their small size, low immunogenicity, and ease of surface modification. This comprehensive review delves into contemporary research at the nexus of NP engineering and immunotherapy, encompassing an extensive spectrum of NP morphologies and strategies tailored toward optimizing tumor targeting and augmenting therapeutic effectiveness. Moreover, it underscores the mechanisms that NPs leverage to bypass the numerous obstacles encountered in immunotherapeutic regimens and probes into the combined potential of NPs when co-administered with both established and novel immunotherapeutic modalities. Finally, the review evaluates the existing limitations of NPs as drug delivery platforms in immunotherapy, which could shape the path for future advancements in this promising field.

Improved Dementia Prediction in Cerebral Small Vessel Disease Using Deep Learning-Derived Diffusion Scalar Maps From T1.

BACKGROUND: Cerebral small vessel disease is the most common pathology underlying vascular dementia. In small vessel disease, diffusion tensor imaging is more sensitive to white matter damage and better predicts dementia risk than conventional magnetic resonance imaging sequences, such as T1 and fluid attenuation inversion recovery, but diffusion tensor imaging takes longer to acquire and is not routinely available in clinical practice. As diffusion tensor imaging-derived scalar maps-fractional anisotropy (FA) and mean diffusivity (MD)-are frequently used in clinical settings, one solution is to synthesize FA/MD from T1 images. METHODS: We developed a deep learning model to synthesize FA/MD from T1. The training data set consisted of 4998 participants with the highest white matter hyperintensity volumes in the UK Biobank. Four external validations data sets with small vessel disease were included: SCANS (St George's Cognition and Neuroimaging in Stroke; n=120), RUN DMC (Radboud University Nijmegen Diffusion Tensor and Magnetic Resonance Imaging Cohort; n=502), PRESERVE (Blood Pressure in Established Cerebral Small Vessel Disease; n=105), and NETWORKS (n=26), along with 1000 normal controls from the UK Biobank. RESULTS: The synthetic maps resembled ground-truth maps (structural similarity index >0.89 for MD maps and >0.80 for FA maps across all external validation data sets except for SCANS). The prediction accuracy of dementia using whole-brain median MD from the synthetic maps is comparable to the ground truth (SCANS ground-truth c-index, 0.822 and synthetic, 0.821; RUN DMC ground truth, 0.816 and synthetic, 0.812) and better than white matter hyperintensity volume (SCANS, 0.534; RUN DMC, 0.710). CONCLUSIONS: We have developed a fast and generalizable method to synthesize FA/MD maps from T1 to improve the prediction accuracy of dementia in small vessel disease when diffusion tensor imaging data have not been acquired.

Pharmacological effects and the related mechanism of scutellarin on inflammation-related diseases: a review.

Inflammation is a biological response of multicellular organisms caused by injuries, pathogens or irritants. An excessive inflammatory response can lead to tissue damage and various chronic diseases. Chronic inflammation is a common feature of many diseases, making the search for drugs to treat inflammation-related diseases urgent. Scutellarin, a natural flavonoid metabolite, is widely used in the treatment of various inflammation-related diseases for its anti-inflammatory, anti-oxidant and anti-cancer activities. Scutellarin can inhibit key inflammatory pathways (PI3K/Akt, MAPK, and NF-κB, etc.) and activate the anti-oxidant related pathways (Nrf2, ARE, ect.), thereby protecting tissues from inflammation and oxidative stress. Modern extraction technologies, such as microwave-assisted, ultrasound assisted, and supercritical fluid extraction, have been utilized to extract scutellarin from Scutellaria and Erigeron genera. These technologies improve efficiency and retain biological activity, making scutellarin suitable for large-scale production. Scutellarin has significant therapeutic effects in treating osteoarthritis, pulmonary fibrosis, kidney injury, and cardiovascular diseases. However, due to its low bioavailability and short half-life, its clinical application is limited. Researchers are exploring innovative formulations (ß-cyclodextrin polymers, triglyceride mimetic active ingredients, and liposome precursors, etc.) to improve stability and absorption rates. Despite these challenges, the potential of scutellarin in anti-inflammatory, anti-oxidant, and anti-cancer applications remains enormous. By optimizing formulations, exploring combination therapies, and conducting in-depth mechanistic research, scutellarin can play an important role in treating various inflammatory diseases, providing patients with more and effective treatment options.

Dynamic analysis of the relationship between systemic lupus erythematosus disease activity and psychosocial support.

Background: Systemic lupus erythematosus (SLE) is a multi-system autoimmune disease that significantly affects both the physical and mental health of patients. Psychosocial support systems play a crucial role in managing chronic diseases, yet their specific impact on the disease activity of SLE patients remains unclear. This study aims to explore the dynamic relationship between disease activity in SLE patients and various types of psychosocial support systems. Methods: We conducted a retrospective longitudinal observational study, including 150 SLE patients who received treatment at our hospital from January 2022 to January 2023. Emotional support, tangible support, social interaction support, and informational support were assessed using the revised Social Support Rating Scale. Disease activity was quantified using the European Consensus Lupus Activity Measurement. The relationship between psychosocial support and disease activity was analyzed using Spearman's rank correlation coefficient and multiple linear regression models, with Bootstrap resampling employed to test the robustness of the results. Results: We found a significant negative correlation between psychosocial support and SLE disease activity, with emotional support, social interaction support, and informational support showing stronger negative correlations. Multiple regression analysis revealed that the inhibitory effects of emotional support, social interaction support, and informational support on disease activity increased over time. Although the impact of tangible support was not statistically significant, it gradually became more apparent over time. Conclusion: Our findings indicate a significant negative correlation between psychosocial support and SLE disease activity, particularly with emotional support, social interaction support, and informational support. Over time, the impact of tangible support also becomes evident. These findings provide important references for the comprehensive treatment and management of SLE patients. However, due to the observational nature of the study, the causality of this relationship requires further exploration.

Myocardium-targeted liposomal delivery of the antioxidant peptide 8P against doxorubicin-induced myocardial injury.

Doxorubicin (Dox) is a broad-spectrum antineoplastic chemotherapeutic agent used in clinical settings, yet it exhibits significant cardiotoxicity, which in severe cases can lead to heart failure. Research indicates that oxidative stress plays a pivotal role in Dox -induced cardiomyocyte injury. Therefore, the application of antioxidants represents an effective strategy to mitigate the cardiotoxic effects of doxorubicin. In preliminary studies, we isolated an antioxidative peptide, PHWWEYRR (8P). This study utilizes a PCM cardiomyocyte-targeting peptide-modified liposome as a carrier to deliver 8P into cardiomyocytes, aiming to prevent Dox-induced cardiac injury through its antioxidative mechanism. The results demonstrated that we prepared the 8P-loaded and PCM-targeting peptide-modified liposome (P-P-8P), which exhibited good dispersibility, encapsulation efficiency, drug loading capacity, and in vitro release, along with myocardial targeting capability. In vitro experiments showed that P-P-8P could prevent oxidative stress injury in H9C2 cells, protect mitochondrial functions, and inhibit cell apoptosis through a mitochondria-dependent pathway. In vivo experiments indicated that P-P-8P could prevent abnormalities in serum biochemical indicators, cardiac dysfunction, and myocardial pathological changes in mice. In conclusion, P-P-8P effectively delivers 8P to cardiomyocytes, offering protection against the cardiotoxic effects of Dox, and holds potential as a future preventative or therapeutic agent for drug-induced cardiomyopathy.

Research progress on the protection and mechanism of active peptides in Alzheimer's disease and Parkinson's disease.

Neurodegenerative diseases are the main causes of death and morbidity among elderly people worldwide. From the pathological point of view, oxidative stress, neuroinflammation, mitochondrial damage and apoptosis are the causes of neuronal diseases, and play a harmful role in the process of neuronal cell death and neurodegeneration. The most common neurodegenerative diseases are Alzheimer's disease(AD) and Parkinson's disease(PD), and there is no effective treatment. The physiological role of active peptides in the human body is significant. Modern medical research has found that animal and plant peptides, natural peptides in human body, can act on the central nervous system, and their active components can improve learning and memory ability, and play the roles of antioxidation, anti-inflammation, anti-apoptosis and maintaining the structure and function of mitochondria. This review reviews the reports on neurodegenerative diseases such as AD and PD by active peptides from animals and plants and natural peptides from the human body, and summarizes the neuroprotective mechanism of peptides. A theoretical basis for further research and development of active peptides was provided by examining the research and application of peptides, which provided a theoretical basis for further research and development.

GDNF-induced phosphorylation of MUC21 promotes pancreatic cancer perineural invasion and metastasis by activating RAC2 GTPase.

Perineural invasion (PNI) is an adverse prognostic feature of pancreatic ductal adenocarcinoma (PDAC). However, the understanding of the interactions between tumors and neural signaling within the tumor microenvironment is limited. In the present study, we found that MUC21 servers as an independent risk factor for poor prognosis in PDAC. Furthermore, we demonstrated that MUC21 promoted the metastasis and PNI of PDAC cells by activating JNK and inducing epithelial-mesenchymal transition (EMT). Mechanistically, glial cell-derived neurotrophic factor, secreted by Schwann cells, phosphorylates the intracellular domain S543 of MUC21 via CDK1 in PDAC cells, facilitating the interaction between MUC21 and RAC2. This interaction leads to membrane anchoring and activation of RAC2, which in turn activates the JNK/ZEB1/EMT axis, ultimately enhancing the metastasis and PNI of PDAC cells. Our results present a novel mechanism of PNI, suggesting that MUC21 is a potential prognostic marker and therapeutic target for PDAC.

Magnesium and Cognitive Health in Adults: A Systematic Review and Meta-Analysis.

Magnesium (Mg) plays a key role in neurological functioning and manifestations. However, the evidence from randomized controlled trials (RCTs) and cohorts on Mg and cognitive health among adults has not been systematically reviewed. We aimed to examine the associations of various Mg forms (supplements, dietary intake, and biomarkers) with cognitive outcomes by summarizing evidence from RCTs and cohorts. PubMed, Embase, PsycINFO, and the Cochrane Central Register of Controlled Trials were searched for relevant peer-reviewed articles published up to May 3, 2024. Three random-effects models were performed, when appropriate, to evaluate the relationship between Mg and cognitive outcomes: 1) linear meta-regression, 2) nonlinear (quadratic) meta-regression, and 3) meta-analysis using Mg variables categorized based on pre-existing recommendations. Three RCTs and 12 cohort studies were included in this systematic review. Evidence from the limited number of RCTs was insufficient to draw conclusions on the effects of Mg supplements. Cohort studies showed inconsistent dose-response relationships between dietary Mg and cognitive disorders, with high heterogeneity across populations. However, consistent U-shape associations of serum Mg with all-cause dementia and cognitive impairment were found in cohorts, suggesting an optimal serum Mg concentration of ∼0.85 mmol/L. This nonlinear association was detected in meta-regression (Pquadratic = 0.003) and in meta-analysis based on the reference interval of serum Mg (0.75-0.95 mmol/L) [<0.75 compared with 0.85 mmol/L: pooled hazard ratio (HR) = 1.43; 95% confidence interval (CI) = 1.05, 1.93; >0.95 compared with 0.85 mmol/L: pooled HR = 1.30; 95% CI = 1.03, 1.64]. More evidence from RCTs and cohorts is warranted. Future cohort studies should evaluate various Mg biomarkers and collect repeated measurements of Mg intake over time, considering different sources (diet or supplements) and factors affecting absorption (for example, calcium-to-Mg intake ratio). This systematic review was preregistered in PROSPERO (CRD42023423663).

Therapeutic effect of the metaverse on mental health.

López del Hoyo et al collections reported the meta verse based on the virtual reality, augmented reality and artificial intelligence could be used in the therapy of mental health, although there were still some challenges. This manuscript reported that the meta verse is a prospective method to improve the prognosis of mental health problems.

Risk factors for intensive-care-unit-acquired weakness.

Wang et al reported 1063 cases from the initial 14 d of intensive care unit (ICU) stay, and analyzed relevant data such as age, comorbidities, recent dosages, vapor pressure dosages, duration of mechanical ventilation, length of ICU stay, and rehabilitation therapy, which are closely related to ICU-acquired weakness (ICU-AW). It is suggested that the length of ICU stay and the duration of mechanical ventilation are the main factors. ICU-AW is the most common neuromuscular injury in the ICU, which affects clinical progression and outcomes of patients. This manuscript helps to improve the early recognition of ICU-AW, thereby reducing mortality and improving prognosis.

Exploring the impact of m6A modification on immune diseases: mechanisms and therapeutic implication.

N6-methyladenosine (m6A) is a chemical modification of RNA and has become a widely discussed topic among scientific researchers in recent years. It is distributed in various organisms, including eukaryotes and bacteria. It has been found that m6A is composed of writers, erasers and readers and is involved in biological functions such as splicing, transport and translation of RNA. The balance of the human immune microenvironment is important for human health abnormalities. Increasing studies have found that m6A affects the development of immune diseases such as inflammatory enteritis and systemic lupus erythematosus (SLE) by participating in the homeostatic regulation of the immune microenvironment in vivo. In this manuscript, we introduce the composition, biological function, regulation of m6A in the immune microenvironment and its progression in various immune diseases, providing new targets and directions for the treatment of immune diseases in clinical practice.

Next-Generation Green Hydrogen: Progress and Perspective from Electricity, Catalyst to Electrolyte in Electrocatalytic Water Splitting.

Green hydrogen from electrolysis of water has attracted widespread attention as a renewable power source. Among several hydrogen production methods, it has become the most promising technology. However, there is no large-scale renewable hydrogen production system currently that can compete with conventional fossil fuel hydrogen production. Renewable energy electrocatalytic water splitting is an ideal production technology with environmental cleanliness protection and good hydrogen purity, which meet the requirements of future development. This review summarizes and introduces the current status of hydrogen production by water splitting from three aspects: electricity, catalyst and electrolyte. In particular, the present situation and the latest progress of the key sources of power, catalytic materials and electrolyzers for electrocatalytic water splitting are introduced. Finally, the problems of hydrogen generation from electrolytic water splitting and directions of next-generation green hydrogen in the future are discussed and outlooked. It is expected that this review will have an important impact on the field of hydrogen production from water.

Cerebrospinal fluid-based spatial statistics: towards quantitative analysis of cerebrospinal fluid pseudodiffusivity.

BACKGROUND: Cerebrospinal fluid (CSF) circulation is essential in removing metabolic wastes from the brain and is an integral component of the glymphatic system. Abnormal CSF circulation is implicated in neurodegenerative diseases. Low b-value magnetic resonance imaging quantifies the variance of CSF motion, or pseudodiffusivity. However, few studies have investigated the relationship between the spatial patterns of CSF pseudodiffusivity and cognition. METHODS: We introduced a novel technique, CSF-based spatial statistics (CBSS), to automatically quantify CSF pseudodiffusivity in each sulcus, cistern and ventricle. Using cortical regions as landmarks, we segmented each CSF region. We retrospectively analyzed a cohort of 93 participants with varying degrees of cognitive impairment. RESULTS: We identified two groups of CSF regions whose pseudodiffusivity profiles were correlated with each other: one group displaying higher pseudodiffusivity and near large arteries and the other group displaying lower pseudodiffusivity and away from the large arteries. The pseudodiffusivity in the third ventricle positively correlated with short-term memory (standardized slope of linear regression = 0.38, adjusted p < 0.001) and long-term memory (slope = 0.37, adjusted p = 0.005). Fine mapping along the ventricles revealed that the pseudodiffusivity in the region closest to the start of the third ventricle demonstrated the highest correlation with cognitive performance. CONCLUSIONS: CBSS enabled quantitative spatial analysis of CSF pseudodiffusivity and suggested the third ventricle pseudodiffusivity as a potential biomarker of cognitive impairment.

Effect of guar gum on the physicochemical properties and in vitro digestive characteristics of extruded starches.

The present study assessed the impact of guar gum (GG) on the physical and chemical attributes and the in vitro digestibility of maize starch (MS), pea starch (PS), and sweet potato starch (SPS) subjected to extrusion treatment. Starch with 25 % moisture content and combined with GG in a 9:1 ratio was selected for extrusion. Scanning electron microscopy and differential scanning calorimetry reveal that extrusion disrupts the ordered structure of starch and induces aggregation of starch granules, resulting in a more cohesive structure, and GG addition led to the further evolution of this structure into a more intricate and irregular form. Rheological assessments demonstrated a remarkable enhancement in the gelatinization characteristics of starch with GG addition, which led to elevated flow resistance and increased viscosity. On evaluating the in vitro digestive characteristics, we noted that adding GG to starch augmented the levels of slow-digestible starch and resistant starch. Consequently, this resulted in diminished digestibility and a lowered glycemic index. In summary, GG synergistically interacts with starch, forming intricately assimilable components. Moreover, the effects of extrusion vary across different starches, which proves advantageous for SPS and GG amalgamation, thereby enhancing their resistant components. Conversely, extrusion manifests contrasting outcomes for MS and PS.

Michael addition-activated alkylation of G-quadruplex DNA with methylamine-protected vinyl-quinazolinone derivatives.

The role of G-quadruplex (G4) in cellular processes can be investigated by the covalent modification of G4-DNA using alkylating reagents. Controllable alkylating reagents activated by external stimuli can react elegantly and selectively. Herein, we report a chemical activation system that can significantly boost the reaction rate of methylamine-protected vinyl-quinazolinone (VQ) derivative for the alkylation of G4-DNA. The two screened activators can transform low-reactive VQ-NHR' to highly reactive intermediates following the Michael addition mechanism. This approach expands the toolbox of activable G4 alkylating reagents.

Deep learning survival model predicts outcome after intracerebral hemorrhage from initial CT scan.

BACKGROUND: Predicting functional impairment after intracerebral hemorrhage (ICH) provides valuable information for planning of patient care and rehabilitation strategies. Current prognostic tools are limited in making long term predictions and require multiple expert-defined inputs and interpretation that make their clinical implementation challenging. This study aimed to predict long term functional impairment of ICH patients from admission non-contrast CT scans, leveraging deep learning models in a survival analysis framework. METHODS: We used the admission non-contrast CT scans from 882 patients from the Massachusetts General Hospital ICH Study for training, hyperparameter optimization, and model selection, and 146 patients from the Yale New Haven ICH Study for external validation of a deep learning model predicting functional outcome. Disability (modified Rankin scale [mRS] > 2), severe disability (mRS > 4), and dependent living status were assessed via telephone interviews after 6, 12, and 24 months. The prediction methods were evaluated by the c-index and compared with ICH score and FUNC score. RESULTS: Using non-contrast CT, our deep learning model achieved higher prediction accuracy of post-ICH dependent living, disability, and severe disability by 6, 12, and 24 months (c-index 0.742 [95% CI -0.700 to 0.778], 0.712 [95% CI -0.674 to 0.752], 0.779 [95% CI -0.733 to 0.832] respectively) compared with the ICH score (c-index 0.673 [95% CI -0.662 to 0.688], 0.647 [95% CI -0.637 to 0.661] and 0.697 [95% CI -0.675 to 0.717]) and FUNC score (c-index 0.701 [95% CI- 0.698 to 0.723], 0.668 [95% CI -0.657 to 0.680] and 0.727 [95% CI -0.708 to 0.753]). In the external independent Yale-ICH cohort, similar performance metrics were obtained for disability and severe disability (c-index 0.725 [95% CI -0.673 to 0.781] and 0.747 [95% CI -0.676 to 0.807], respectively). Similar AUC of predicting each outcome at 6 months, 1 and 2 years after ICH was achieved compared with ICH score and FUNC score. CONCLUSION: We developed a generalizable deep learning model to predict onset of dependent living and disability after ICH, which could help to guide treatment decisions, advise relatives in the acute setting, optimize rehabilitation strategies, and anticipate long-term care needs.

Sensitivity, precision, and accuracy of fs-LIBS for heavy metal detection in flowing aqueous solutions.

This investigation employs femtosecond laser-induced breakdown spectroscopy (fs-LIBS) to measure the concentrations of chromium (Cr), lead (Pb), and copper (Cu) in flowing aqueous solutions. The fs pulsed laser excites the water, generating plasma in a dynamic setting that prevents liquid splashing-a notable advantage over static methods. The flowing water column maintains a stable liquid level, circumventing the laser focus irregularities due to liquid-level fluctuations. Calibration curves, based on a linear function, reveal limits of detection (LODs) as low as 0.0179 µg/mL for Cr, 0.1301 µg/mL for Pb, and 0.0120 µg/mL for Cu. The reliability of the experiment is confirmed by R2 values exceeding 0.99. These findings offer valuable insights for the analysis of trace heavy metals in flowing aqueous solutions using fs-LIBS, demonstrating the technique's potential for environmental monitoring.