Hunting imaging biomarkers in pulmonary fibrosis: Benchmarks of the AIIB23 challenge.

Airway-related quantitative imaging biomarkers are crucial for examination, diagnosis, and prognosis in pulmonary diseases. However, the manual delineation of airway structures remains prohibitively time-consuming. While significant efforts have been made towards enhancing automatic airway modelling, current public-available datasets predominantly concentrate on lung diseases with moderate morphological variations. The intricate honeycombing patterns present in the lung tissues of fibrotic lung disease patients exacerbate the challenges, often leading to various prediction errors. To address this issue, the 'Airway-Informed Quantitative CT Imaging Biomarker for Fibrotic Lung Disease 2023' (AIIB23) competition was organized in conjunction with the official 2023 International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI). The airway structures were meticulously annotated by three experienced radiologists. Competitors were encouraged to develop automatic airway segmentation models with high robustness and generalization abilities, followed by exploring the most correlated QIB of mortality prediction. A training set of 120 high-resolution computerised tomography (HRCT) scans were publicly released with expert annotations and mortality status. The online validation set incorporated 52 HRCT scans from patients with fibrotic lung disease and the offline test set included 140 cases from fibrosis and COVID-19 patients. The results have shown that the capacity of extracting airway trees from patients with fibrotic lung disease could be enhanced by introducing voxel-wise weighted general union loss and continuity loss. In addition to the competitive image biomarkers for mortality prediction, a strong airway-derived biomarker (Hazard ratio>1.5, p < 0.0001) was revealed for survival prognostication compared with existing clinical measurements, clinician assessment and AI-based biomarkers.

The ARH score, a practical guide to decision-making for retreatment with hepatic arterial infusion chemotherapy in hepatocellular carcinoma patients.

BACKGROUND: Hepatic arterial infusionchemotherapy (HAIC) is a promising option for large unresectable hepatocellular carcinoma (HCC). Identifying patients who could benefit from continuous HAIC remains a challenge. We aimed to establish an objective model to guide the decision for retreatment with HAIC. METHODS: Between 2015 and 2020, the data of patients with large unresectable HCC without macrovascular invasion or extrahepatic spread undergoing multiple HAIC cycles from 3 different centers were retrieved. We investigated the basic tumor parameters and the effect of HAIC on liver function and tumor response, and their impact on overall survival (OS). A point score (ARH, Assessment for Retreatment with HAIC) was built by using a stepwise Cox regression model in the training cohort (n = 112) and was validated in an independent validation cohort (n = 71). RESULTS: The high α-fetoprotein before the second cycle of HAIC, an increase in Child-Pugh score, and undesirable radiologic tumor responses remained independent negative prognostic factors and were used to create the ARH score. The prognosis of HCC patients deteriorated significantly with the increase in ARH score. The median OS of patients with ARH score 0-2 points and ≥ 2.5 points were 19.37 months and 11.60 months (P < 0.001). All of these results had been confirmed in the external validation cohort and demonstrated significance across multiple subgroups. CONCLUSIONS: The ARH score makes an excellent prediction of the prognosis of HCC patients who received retreatment of HAIC. Patients with an ARH score ≥ 2.5 prior to the second cycle of HAIC may not profit from further sessions.

Carotid Vessel Wall Segmentation Through Domain Aligner, Topological Learning, and Segment Anything Model for Sparse Annotation in MR Images.

Medical image analysis poses significant challenges due to limited availability of clinical data, which is crucial for training accurate models. This limitation is further compounded by the specialized and labor-intensive nature of the data annotation process. For example, despite the popularity of computed tomography angiography (CTA) in diagnosing atherosclerosis with an abundance of annotated datasets, magnetic resonance (MR) images stand out with better visualization for soft plaque and vessel wall characterization. However, the higher cost and limited accessibility of MR, as well as time-consuming nature of manual labeling, contribute to fewer annotated datasets. To address these issues, we formulate a multi-modal transfer learning network, named MT-Net, designed to learn from unpaired CTA and sparsely-annotated MR data. Additionally, we harness the Segment Anything Model (SAM) to synthesize additional MR annotations, enriching the training process. Specifically, our method first segments vessel lumen regions followed by precise characterization of carotid artery vessel walls, thereby ensuring both segmentation accuracy and clinical relevance. Validation of our method involved rigorous experimentation on publicly available datasets from COSMOS and CARE-II challenge, demonstrating its superior performance compared to existing state-of-the-art techniques.

Breaking the barrier: Nanoparticle-enhanced radiotherapy as the new vanguard in brain tumor treatment.

The pursuit of effective treatments for brain tumors has increasingly focused on the promising area of nanoparticle-enhanced radiotherapy (NERT). This review elucidates the context and significance of NERT, with a particular emphasis on its application in brain tumor therapy-a field where traditional treatments often encounter obstacles due to the blood-brain barrier (BBB) and tumor cells' inherent resistance. The aims of this review include synthesizing recent advancements, analyzing action mechanisms, and assessing the clinical potential and challenges associated with nanoparticle (NP) use in radiotherapy enhancement. Preliminary preclinical studies have established a foundation for NERT, demonstrating that nanoparticles (NPs) can serve as radiosensitizers, thereby intensifying radiotherapy's efficacy. Investigations into various NP types, such as metallic, magnetic, and polymeric, have each unveiled distinct interactions with ionizing radiation, leading to an augmented destruction of tumor cells. These interactions, encompassing physical dose enhancement and biological and chemical radio sensitization, are crucial to the NERT strategy. Although clinical studies are in their early phases, initial trials have shown promising results in terms of tumor response rates and survival, albeit with mindful consideration of toxicity profiles. This review examines pivotal studies affirming NERT's efficacy and safety. NPs have the potential to revolutionize radiotherapy by overcoming challenges in targeted delivery, reducing off-target effects, and harmonizing with other modalities. Future directions include refining NP formulations, personalizing therapies, and navigating regulatory pathways. NERT holds promise to transform brain tumor treatment and provide hope for patients.

Matrine-loaded Nano-liposome Induces Apoptosis in Human Esophageal-squamous Carcinoma KYSE-150 Cells.

INTRODUCTION: Esophageal-Squamous Cell Carcinoma (ESCC) is often diagnosed at the middle or late stage, thus requiring more effective therapeutic strategies. Pharmacologically, the anti-tumor activity of the principal active constituent of Sophora flavescens, matrine (MA), has been explored widely. Notwithstanding, it is significant to nanotechnologically enhance the anti-tumor activity of MA in view of its potential to distribute non-tumor cells. METHODS: Herein, MA-loaded Nano-Liposomes (MNLs) were prepared to enhance the effect of anti-ESCC. The MNL showed a smaller sized particle (25.95 ± 1.02 nm) with a low polydispersed index (PDI = 0.130 ± 0.054), uniform spherical morphology, good solution stability, and encapsulated efficiency (65.55% ± 2.47). Furthermore, we determined the characteristics of KYSE-150 cells by cell viability assay, IC50, Mitochondrial Membrane Potential (MMP), Western blot, and apoptotic analysis, which indicated that MNLs down-regulated the cell viability and IC50 in a concentration-dependent manner and induced a significant change in JC-1 fluorescence from red to green. RESULTS: The above observations resulted in increased Bax and Caspase-3 levels, coupled with a substantial decrease in Bcl-2 and apoptotic promotion at the advanced stage compared with MA. CONCLUSION: Based on these results, MNLs may serve as a more effective and promising therapeutic option for ESCC.

Lethal Immune-Related Pneumonitis after Durvalumab Therapy for Small Cell Lung Cancer: A First Case in China.

Introduction: Although programmed death ligand 1 (PD-L1) inhibitor plus chemotherapy regimen is a promising strategy for malignant tumors, it can induce significant immune-related adverse events, such as immune-related pneumonitis. Here, we report the first case of lethal immune-related pneumonitis in an Asian patient receiving anti-PD-L1 treatment. Case Presentation: A 68-year-old man was diagnosed with small cell lung cancer and interstitial pneumonia. After his pulmonary infection was relieved by comprehensive treatment, the patient received first-line treatment with durvalumab plus etoposide and carboplatin. Two weeks after starting durvalumab treatment, the patient had chest pain and shortness of breath. He was diagnosed with immune-induced pneumonia and treated with methylprednisolone, cefoperazone, and sulbactam, followed by oxygen and pirfenidone. Oxygen partial pressure decreased to 58 mm Hg within next the 4 days and laboratory assessment suggested cytokine storm. The patient underwent 2 plasma exchanges, one double filtration plasmapheresis and oxygen saturation decreased continuously. The patient died 1 month after durvalumab treatment. Conclusion: Immune-related pneumonitis induced by PD-L1 inhibitors is rare but life-threatening. Infection should be ruled out before starting immunotherapy.

Cooperative Catalysis-Enabled (4 + 3) Cycloaddition of 2-Indolylmethanols with In Situ-Generated ortho-Naphthoquinone Methides.

A cooperative catalysis-enabled (4 + 3) cycloaddition of 2-indolylmethanols with ortho-naphthoquinone methides (o-NQMs), which were in situ-generated from enynones, has been established in the presence of silver/Brønsted acid cocatalysts. In the reaction pathway, the key o-NQM intermediates were formed through Ag(I)-catalyzed cyclization of enynones, while the indole-based carbocation intermediates were generated via Brønsted acid-catalyzed dehydration of 2-indolylmethanols. By this approach, a wide range of seven-membered cyclohepta[b]indoles were synthesized in good yields with high efficiency under mild reaction conditions, which serves as a useful strategy toward constructing indole-fused seven-membered rings. Moreover, the catalytic asymmetric version of this (4 + 3) cycloaddition has been realized under the cooperative catalysis of Ag(I) with chiral phosphoric acid, which offered chiral cyclohepta[b]indole with a good enantioselectivity (75% ee). This work not only represents the first cooperative catalysis-enabled (4 + 3) cycloaddition of 2-indolylmethanols but also provides a good example for o-NQM-involved cycloadditions, which will contribute to the chemistry of 2-indolylmethanols and enrich the research contents of cooperative catalysis.

Intermolecular α-Functionalization of Benzylamides with N-Nucleophiles via Oxidative Umpolung: Synthesis of Tetrasubstituted 3,3'-Oxindoles.

A hypervalent iodine-mediated intermolecular α-umpolung reaction between α-aryl- or alkyl-substituted amides and benzotriazoles or purine derivatives as N-centered nucleophiles has been established. The reaction involves sequential intra/intermolecular oxidative C-N couplings in a controlled manner, affording tetrasubstituted 3,3'-oxindoles in moderate to good yields. This approach efficiently addresses the challenges in constructing tetrasubstituted carbon centers via α-umpolung functionalizations of carbonyl compounds and serves as a new strategy for synthesizing biologically important 3,3'-disubstituted oxindoles.

Identification of Prolactinoma in Pituitary Neuroendocrine Tumors Using Radiomics Analysis Based on Multiparameter MRI.

This study aims to investigate the feasibility of preoperatively predicting histological subtypes of pituitary neuroendocrine tumors (PitNETs) using machine learning and radiomics based on multiparameter MRI. Patients with PitNETs from January 2016 to May 2022 were retrospectively enrolled from four medical centers. A cfVB-Net network was used to automatically segment PitNET multiparameter MRI. Radiomics features were extracted from the MRI, and the radiomics score (Radscore) of each patient was calculated. To predict histological subtypes, the Gaussian process (GP) machine learning classifier based on radiomics features was performed. Multi-classification (six-class histological subtype) and binary classification (PRL vs. non-PRL) GP model was constructed. Then, a clinical-radiomics nomogram combining clinical factors and Radscores was constructed using the multivariate logistic regression analysis. The performance of the models was evaluated using receiver operating characteristic (ROC) curves. The PitNET auto-segmentation model eventually achieved the mean Dice similarity coefficient of 0.888 in 1206 patients (mean age 49.3 ± SD years, 52% female). In the multi-classification model, the GP of T2WI got the best area under the ROC curve (AUC), with 0.791, 0.801, and 0.711 in the training, validation, and external testing set, respectively. In the binary classification model, the GP of T2WI combined with CE T1WI demonstrated good performance, with AUC of 0.936, 0.882, and 0.791 in training, validation, and external testing sets, respectively. In the clinical-radiomics nomogram, Radscores and Hardy' grade were identified as predictors for PRL expression. Machine learning and radiomics analysis based on multiparameter MRI exhibited high efficiency and clinical application value in predicting the PitNET histological subtypes.

Parallel CNN-Deep Learning Clinical-Imaging Signature for Assessing Pathologic Grade and Prognosis of Soft Tissue Sarcoma Patients.

BACKGROUND: Traditional biopsies pose risks and may not accurately reflect soft tissue sarcoma (STS) heterogeneity. MRI provides a noninvasive, comprehensive alternative. PURPOSE: To assess the diagnostic accuracy of histological grading and prognosis in STS patients when integrating clinical-imaging parameters with deep learning (DL) features from preoperative MR images. STUDY TYPE: Retrospective/prospective. POPULATION: 354 pathologically confirmed STS patients (226 low-grade, 128 high-grade) from three hospitals and the Cancer Imaging Archive (TCIA), divided into training (n = 185), external test (n = 125), and TCIA cohorts (n = 44). 12 patients (6 low-grade, 6 high-grade) were enrolled into prospective validation cohort. FIELD STRENGTH/SEQUENCE: 1.5 T and 3.0 T/Unenhanced T1-weighted and fat-suppressed-T2-weighted. ASSESSMENT: DL features were extracted from MR images using a parallel ResNet-18 model to construct DL signature. Clinical-imaging characteristics included age, gender, tumor-node-metastasis stage and MRI semantic features (depth, number, heterogeneity at T1WI/FS-T2WI, necrosis, and peritumoral edema). Logistic regression analysis identified significant risk factors for the clinical model. A DL clinical-imaging signature (DLCS) was constructed by incorporating DL signature with risk factors, evaluated for risk stratification, and assessed for progression-free survival (PFS) in retrospective cohorts, with an average follow-up of 23 ± 22 months. STATISTICAL TESTS: Logistic regression, Cox regression, Kaplan-Meier curves, log-rank test, area under the receiver operating characteristic curve (AUC)，and decision curve analysis. A P-value <0.05 was considered significant. RESULTS: The AUC values for DLCS in the external test, TCIA, and prospective test cohorts (0.834, 0.838, 0.819) were superior to clinical model (0.662, 0.685, 0.694). Decision curve analysis showed that the DLCS model provided greater clinical net benefit over the DL and clinical models. Also, the DLCS model was able to risk-stratify patients and assess PFS. DATA CONCLUSION: The DLCS exhibited strong capabilities in histological grading and prognosis assessment for STS patients, and may have potential to aid in the formulation of personalized treatment plans. TECHNICAL EFFICACY: Stage 2.

[Response of radial growth of Pinus wallichiana to climate change in Mount Qomolangma, Tibet, China]. / è¥¿è—ç å³°åœ°åŒºä¹”æ¾å¾„å‘ç”Ÿé•¿å¯¹æ°”å€™å˜åŒ–çš„å“åº”.

Global warming would significantly impact tree growth in the Tibetan Plateau. However, the specific effects of climate change on the radial growth of Pinus wallichiana in Mount Qomolangma are still uncertain. To investigate the responses of radial growth of P. wallichiana to climate change, we analyzed tree-ring samples in Mount Qomolangma. We removed the age-related growth trends and established three chronologies by using the modified negative exponential curve, basal area index, and regional curve standardization, and conducted Pearson correlation and moving correlation analyses to examine the association between radial growth of P. wallichiana and climatic factors. The results showed that this region had experienced a significant upward trend in temperature and that the Palmer drought severity index (PDSI) indicated a decreasing trend since 1980s, while the relative humi-dity changed from a significant upward to a downward trend around 2004, implying the climate shifted toward warmer and drier. Results of Pearson correlation analysis indicated a significant and positive relationship between the radial growth of P. wallichiana and the minimum temperature of April-June and July-September, and precipitation of January-April in the current year. The radial growth of P. wallichiana was significantly and negatively associated with the relative humidity of June, July, and August in the current year. As temperature rose after 1983, the relationship between radial growth of P. wallichiana and the minimum temperature in July and September of the current year increased from a non-significant association to a significant and positive association, while the relationship between radial growth of P. wallichiana and relative humidity in August and precipitation in September of the current year changed from non-significant correlation to a significant and negative correlation. Results of the moving correlation analysis suggested that the radial growth of P. wallichiana showed a significant and stable correlation with the July-September minimum temperature of the current year. Under the background of climate warming, the rapid increases of temperature would accelerate the radial growth of P. wallichiana in Mount Qomolangma.

The relationship between contrast-enhanced computed tomography radiomics features and mitosis karyorrhexis index in neuroblastoma.

OBJECTIVE: Mitosis karyorrhexis index (MKI) can reflect the proliferation status of neuroblastoma cells. This study aimed to investigate the contrast-enhanced computed tomography (CECT) radiomics features associated with the MKI status in neuroblastoma. MATERIALS AND METHODS: 246 neuroblastoma patients were retrospectively included and divided into three groups: low-MKI, intermediate-MKI, and high-MKI. They were randomly stratified into a training set and a testing set at a ratio of 8:2. Tumor regions of interest were delineated on arterial-phase CECT images, and radiomics features were extracted. After reducing the dimensionality of the radiomics features, a random forest algorithm was employed to establish a three-class classification model to predict MKI status. RESULTS: The classification model consisted of 5 radiomics features. The mean area under the curve (AUC) of the classification model was 0.916 (95% confidence interval (CI) 0.913-0.921) in the training set and 0.858 (95% CI 0.841-0.864) in the testing set. Specifically, the classification model achieved AUCs of 0.928 (95% CI 0.927-0.934), 0.915 (95% CI 0.912-0.919), and 0.901 (95% CI 0.900-0.909) for predicting low-MKI, intermediate-MKI, and high-MKI, respectively, in the training set. In the testing set, the classification model achieved AUCs of 0.873 (95% CI 0.859-0.882), 0.860 (95% CI 0.852-0.872), and 0.820 (95% CI 0.813-0.839) for predicting low-MKI, intermediate-MKI, and high-MKI, respectively. CONCLUSIONS: CECT radiomics features were found to be correlated with MKI status and are helpful for reflecting the proliferation status of neuroblastoma cells.

Rhizophagus irregularis and biochar can synergistically improve the physiological characteristics of saline-alkali resistance of switchgrass.

Inoculation of arbuscular mycorrhizal fungi (AMF) or biochar (BC) application can improve photosynthesis and promote plant growth under saline-alkali stress. However, little is known about the effects of the two combined on growth and physiological characteristics of switchgrass under saline-alkali stress. This study examined the effects of four treatments: (1) no AMF inoculation and no biochar addition (control), (2) biochar (BC) alone, (3) AMF (Rhizophagus irregularis, Ri) alone, and (4) the combination of both (BC+Ri) on the plant biomass, antioxidant enzymes, chlorophyll, and photosynthetic parameters of switchgrass under saline-alkali stress. The results showed that the above-ground, belowground and total biomass of switchgrass in the BC+Ri treatment group was significantly higher (+136.7%, 120.2% and 132.4%, respectively) than in other treatments compared with Control. BC+Ri treatment significantly increased plant leaves' relative chlorophyll content, antioxidant enzyme activity, and photosynthesis parameters. It is worth noting that the transpiration rate, stomatal conductance, net photosynthetic rate, PSII efficiency and other photosynthetic-related indexes of the BC+Ri treatment group were the highest (38% to 54% higher than other treatments). The fitting results of light response and CO2 response curves showed that the light saturation point, light compensation point, maximum carboxylation rate and maximum electron transfer rate of switchgrass in the Ri+BC treatment group were the highest. In conclusion, biochar combined with Ri has potential beneficial effects on promoting switchgrass growth under saline-alkali stress and improving the activity of antioxidant enzymes and photosynthetic characteristics of plants.

Automatic detection of cognitive impairment in patients with white matter hyperintensity and causal analysis of related factors using artificial intelligence of MRI.

PURPOSE: White matter hyperintensity (WMH) is a common feature of brain aging, often linked with cognitive decline and dementia. This study aimed to employ deep learning and radiomics to develop models for detecting cognitive impairment in WMH patients and to analyze the causal relationships among cognitive impairment and related factors. MATERIALS AND METHODS: A total of 79 WMH patients from hospital 1 were randomly divided into a training set (62 patients) and a testing set (17 patients). Additionally, 29 patients from hospital 2 were included as an independent testing set. All participants underwent formal neuropsychological assessments to determine cognitive status. Automated identification and segmentation of WMH were conducted using VB-net, with extraction of radiomics features from cortex, white matter, and nuclei. Four machine learning classifiers were trained on the training set and validated on the testing set to detect cognitive impairment. Model performances were evaluated and compared. Causal analyses were conducted among cortex, white matter, nuclei alterations, and cognitive impairment. RESULTS: Among the models, the logistic regression (LR) model based on white matter features demonstrated the highest performance, achieving an AUC of 0.819 in the external test dataset. Causal analyses indicated that age, education level, alterations in cortex, white matter, and nuclei were causal factors of cognitive impairment. CONCLUSION: The LR model based on white matter features exhibited high accuracy in detecting cognitive impairment in WMH patients. Furthermore, the possible causal relationships among alterations in cortex, white matter, nuclei, and cognitive impairment were elucidated.

Evolving Tumor Characteristics and Smart Nanodrugs for Tumor Immunotherapy.

Typical physiological characteristics of tumors, such as weak acidity, low oxygen content, and upregulation of certain enzymes in the tumor microenvironment (TME), provide survival advantages when exposed to targeted attacks by drugs and responsive nanomedicines. Consequently, cancer treatment has significantly progressed in recent years. However, the evolution and adaptation of tumor characteristics still pose many challenges for current treatment methods. Therefore, efficient and precise cancer treatments require an understanding of the heterogeneity degree of various factors in cancer cells during tumor evolution to exploit the typical TME characteristics and manage the mutation process. The highly heterogeneous tumor and infiltrating stromal cells, immune cells, and extracellular components collectively form a unique TME, which plays a crucial role in tumor malignancy, including proliferation, invasion, metastasis, and immune escape. Therefore, the development of new treatment methods that can adapt to the evolutionary characteristics of tumors has become an intense focus in current cancer treatment research. This paper explores the latest understanding of cancer evolution, focusing on how tumors use new antigens to shape their "new faces"; how immune system cells, such as cytotoxic T cells, regulatory T cells, macrophages, and natural killer cells, help tumors become "invisible", that is, immune escape; whether the diverse cancer-associated fibroblasts provide support and coordination for tumors; and whether it is possible to attack tumors in reverse. This paper discusses the limitations of targeted therapy driven by tumor evolution factors and explores future strategies and the potential of intelligent nanomedicines, including the systematic coordination of tumor evolution factors and adaptive methods, to meet this therapeutic challenge.

ON-OFF Control of Marangoni Self-propulsion via A Supra-amphiphile Fuel and Switch.

Marangoni self-propulsion refers to motion of liquid or solid driven by a surface tension gradient, and has applications in soft robots/devices, cargo delivery, self-assembly etc. However, two problems remain to be addressed for motion control (e.g., ON-OFF) with conventional surfactants as Marangoni fuel: (1) limited motion lifetime due to saturated interfacial adsorption of surfactants; (2) in- situ motion stop is difficult once Marangoni flows are triggered. Instead of covalent surfactants, supra-amphiphiles with hydrophilic and hydrophobic parts linked noncovalently, hold promise to solve these problems owing to its dynamic and reversible surface activity responsively. Here, we propose a new concept of 'supra-amphiphile fuel and switch' based on the facile synthesis of disodium-4-azobenzene-amino-1,3-benzenedisulfonate (DABS) linked by a Schiff base, which has amphiphilicity for self-propulsion, hydrolyzes timely to avoid saturated adsorption, and provides pH-responsive control over ON-OFF motion. The self-propulsion lifetime is extended by 50-fold with DABS and motion control is achieved. The mechanism is revealed with coupled interface chemistry involving two competitive processes of interfacial adsorption and hydrolysis of DABS based on both experiments and simulation. The concept of 'supra-amphiphile fuel and switch' provides an active solution to prolong and control Marangoni self-propulsive devices for the advance of intelligent material systems.

An Au bipyramids@CuZn MOF core-shell nanozyme enables universal SERS and a colorimetric dual-model bioassay.

In this study, a new type of gold nano-bipyramids@CuZn bimetallic organic framework (AuNBPs@CuZn MOF) nanozyme with high peroxidase (POD)-like activity and surface enhanced Raman scattering (SERS) activity was constructed with a special core-shell structure, which can catalyze the oxidation of TMB (colourless and Raman-inactive) into ox-TMB (blue and Raman-active). An AuNBPs@CuZn MOF-enabling universal SERS and colorimetric dual-model bioassay was thus developed for biomolecules with excellent performance, and has promising application prospects in the biosensing fields.

Settling velocity of atmospheric particles in seawater: Based on hydrostatic sedimentation method using video imaging techniques.

When atmospheric particles deposit to the ocean, their settling velocities and residence times associated are critical for their effects on oceanic ecosystems. We developed a hydrostatic sedimentation method using video imaging techniques to track particles of 5-20 µm in diameter falling into seawater and determine the particle settling velocities in relation to their diameter, shape, organic matter contained, and seawater salinity. The measured settling velocities varied from 0.025 to 0.41 mm/s. Irregular particle shape and organic matter contained in particles also, however, reduced the values. The settling velocities were decelerated by the dissolution process of particle in seawater. Combined with the experimental results, a formula for calculating the settling velocity formulae for atmospheric particles was estimated. Using this equation, the residence time of particles is estimated to be less than one month in continental shelf sea and more than 100 days in the oceans.