Oriented Antibody Coupling to an Antifouling Polymer Using Glycan Remodeling for Biosensing by Particle Motion.

Biosensors based on immobilized antibodies require molecular strategies that (i) couple the antibodies in a stable fashion while maintaining the conformation and functionality, (ii) give outward orientation of the paratope regions of the antibodies for good accessibility to analyte molecules in the biofluid, and (iii) surround the antibodies by antibiofouling molecules. Here, we demonstrate a method to achieve oriented coupling of antibodies to an antifouling poly(l-lysine)-grafted-poly(ethylene glycol) (PLL-g-PEG) substrate, using glycan remodeling to create antibody-DNA conjugates. The coupling, orientation, and functionality of the antibodies were studied using two analysis methods with single-molecule resolution, namely single-molecule localization microscopy and continuous biosensing by particle motion. The biosensing functionality of the glycan-remodeled antibodies was demonstrated in a sandwich immunosensor for procalcitonin. The results show that glycan-remodeled antibodies enable oriented immobilization and biosensing functionality with low nonspecific binding on antifouling polymer substrates.

Exploring keratin composition variability for sustainable thermal insulator design.

In pursuing sustainable thermal insulation solutions, this study explores the integration of human hair and feather keratin with alginate. The aim is to assess its potential in thermal insulation materials, focusing on the resultant composites' thermal and mechanical characteristics. The investigation uncovers that the type and proportion of keratin significantly influence the composites' porosity and thermal conductivity. Specifically, higher feather keratin content is associated with lesser sulfur and reduced crosslinking due to shorter amino acids, leading to increased porosity and pore sizes. This, in turn, results in a decrease in ß-structured hydrogen bond networks, raising non-ordered protein structures and diminishing thermal conductivity from 0.044 W/(m·K) for pure alginate matrices to between 0.033 and 0.038 W/(m·K) for keratin-alginate composites, contingent upon the specific ratio of feather to hair keratin used. Mechanical evaluations further indicate that composites with a higher ratio of hair keratin exhibit an enhanced compressive modulus, ranging from 60 to 77 kPa, demonstrating the potential for tailored mechanical properties to suit various applications. The research underscores the critical role of sulfur content and the crosslinking index within keratin's structures, significantly impacting the thermal and mechanical properties of the matrices. The findings position keratin-based composites as environmentally friendly alternatives to traditional insulation materials.

A novel treatment planning method via scissor beams for uniform-target-dose proton GRID with peak-valley-dose-ratio optimization.

BACKGROUND: Proton spatially fractionated RT (SFRT) can potentially synergize the unique advantages of using proton Bragg peak and SFRT peak-valley dose ratio (PVDR) to reduce the radiation-induced damage for normal tissues. Uniform-target-dose (UTD) proton GRID is a proton SFRT modality that can be clinically desirable and conveniently adopted since its UTD resembles target dose distribution in conventional proton RT (CONV). However, UTD proton GRID is not used clinically, which is likely due to the lack of an effective treatment planning method. PURPOSE: This work will develop a novel treatment planning method using scissor beams (SB) for UTD proton GRID, with the joint optimization of PVDR and dose objectives. METHODS: The SB method for spatial dose modulation in normal tissues with UTD has two steps: (1) a primary beam (PB) is halved with interleaved beamlets, to generate spatial dose modulation in normal tissues; (2) a complementary beam (CB) is added to fill in previously valley-dose positions in the target to generate UTD, while the CB is angled slightly from the PB, to maintain spatial dose modulation in normal tissues. A treatment planning method with PVDR optimization via the joint total variation and L1 (TVL1) regularization is developed to jointly optimize PVDR and dose objectives. The plan optimization solution is obtained using an iterative convex relaxation algorithm. RESULTS: The new methods SB and SB-TVL1 were validated in comparison with CONV. Compared to CONV of relatively homogeneous dose distribution, SB had modulated spatial dose pattern in normal tissues with UTD and comparable plan quality. Compared to SB, SB-TVL1 further maximized PVDR, with comparable dose-volume parameters. CONCLUSIONS: A novel SB method is proposed that can generate modulated spatial dose pattern in normal tissues to achieve UTD proton GRID. A treatment planning method with PVDR optimization capability via TVL1 regularization is developed that can jointly optimize PVDR and dose objectives for proton GRID.

Dynamic assemblies of parvalbumin interneurons in brain oscillations.

Brain oscillations are crucial for perception, memory, and behavior. Parvalbumin-expressing (PV) interneurons are critical for these oscillations, but their population dynamics remain unclear. Using voltage imaging, we simultaneously recorded membrane potentials in up to 26 PV interneurons in vivo during hippocampal ripple oscillations in mice. We found that PV cells generate ripple-frequency rhythms by forming highly dynamic cell assemblies. These assemblies exhibit rapid and significant changes from cycle to cycle, varying greatly in both size and membership. Importantly, this variability is not just random spiking failures of individual neurons. Rather, the activities of other PV cells contain significant information about whether a PV cell spikes or not in a given cycle. This coordination persists without network oscillations, and it exists in subthreshold potentials even when the cells are not spiking. Dynamic assemblies of interneurons may provide a new mechanism to modulate postsynaptic dynamics and impact cognitive functions flexibly and rapidly.

Exogenous putrescine plays a switch-like influence on the pH stress adaptability of biofilm-based activated sludge.

Microbial community adaptability to pH stress plays a crucial role in biofilm formation. This study aims to investigate the regulatory mechanisms of exogenous putrescine on pH stress, as well as enhance understanding and application for the technical measures and molecular mechanisms of biofilm regulation. Findings demonstrated that exogenous putrescine acted as a switch-like distributor affecting microorganism pH stress, thus promoting biofilm formation under acid conditions while inhibiting it under alkaline conditions. As pH decreases, the protonation degree of putrescine increases, making putrescine more readily adsorbed. Protonated exogenous putrescine could increase cell membrane permeability, facilitating its entry into the cell. Subsequently, putrescine consumed intracellular H+ by enhancing the glutamate-based acid resistance strategy and the γ-aminobutyric acid metabolic pathway to reduce acid stress on cells. Furthermore, putrescine stimulated ATPase expression, allowing for better utilization of energy in H+ transmembrane transport and enhancing oxidative phosphorylation activity. However, putrescine protonation was limited under alkaline conditions, and the intracellular H+ consumption further exacerbated alkali stress and inhibits cellular metabolic activity. Exogenous putrescine promoted the proportion of fungi and acidophilic bacteria under acidic stress and alkaliphilic bacteria under alkali stress while having a limited impact on fungi in alkaline biofilms. Increasing Bdellovibrio under alkali conditions with putrescine further aggravated the biofilm decomposition. This research shed light on the unclear relationship between exogenous putrescine, environmental pH, and pH stress adaptability of biofilm. By judiciously employing putrescine, biofilm formation could be controlled to meet the needs of engineering applications with different characteristics.IMPORTANCEThe objective of this study is to unravel the regulatory mechanism by which exogenous putrescine influences biofilm pH stress adaptability and understand the role of environmental pH in this intricate process. Our findings revealed that exogenous putrescine functioned as a switch-like distributor affecting the pH stress adaptability of biofilm-based activated sludge, which promoted energy utilization for growth and reproduction processes under acidic conditions while limiting biofilm development to conserve energy under alkaline conditions. This study not only clarified the previously ambiguous relationship between exogenous putrescine, environmental pH, and biofilm pH stress adaptability but also offered fresh insights into enhancing biofilm stability within extreme environments. Through the modulation of energy utilization, exerting control over biofilm growth and achieving more effective engineering goals could be possible.

Simultaneous dose and dose rate optimization via dose modifying factor modeling for FLASH effective dose.

BACKGROUND: Although the FLASH radiotherapy (FLASH) can improve the sparing of organs-at-risk (OAR) via the FLASH effect, it is generally a tradeoff between the physical dose coverage and the biological FLASH coverage, for which the concept of FLASH effective dose (FED) is needed to quantify the net improvement of FLASH, compared to the conventional radiotherapy (CONV). PURPOSE: This work will develop the first-of-its-kind treatment planning method called simultaneous dose and dose rate optimization via dose modifying factor modeling (SDDRO-DMF) for proton FLASH that directly optimizes FED. METHODS: SDDRO-DMF models and optimizes FED using FLASH dose modifying factor (DMF) models, which can be classified into two categories: (1) the phenomenological model of the FLASH effect, such as the FLASH effectiveness model (FEM); (2) the mechanistic model of the FLASH radiobiology, such as the radiolytic oxygen depletion (ROD) model. The general framework of SDDRO-DMF will be developed, with specific DMF models using FEM and ROD, as a demonstration of general applicability of SDDRO-DMF for proton FLASH via transmission beams (TB) or Bragg peaks (BP) with single-field or multi-field irradiation. The FLASH dose rate is modeled as pencil beam scanning dose rate. The solution algorithm for solving the inverse optimization problem of SDDRO-DMF is based on iterative convex relaxation method. RESULTS: SDDRO-DMF is validated in comparison with IMPT and a state-of-the-art method called SDDRO, with demonstrated efficacy and improvement for reducing the high dose and the high-dose volume for OAR in terms of FED. For example, in a SBRT lung case of the dose-limiting factor that the max dose of brachial plexus should be no more than 26 Gy, only SDDRO-DMF met this max dose constraint; moreover, SDDRO-DMF completely eliminated the high-dose (V70%) volume to zero for CTV10mm (a high-dose region as a 10 mm ring expansion of CTV). CONCLUSION: We have proposed a new proton FLASH optimization method called SDDRO-DMF that directly optimizes FED using phenomenological or mechanistic models of DMF, and have demonstrated the efficacy of SDDO-DMF in reducing the high-dose volume or/and the high-dose value for OAR, compared to IMPT and a state-of-the-art method SDDRO.

Cardinality-constrained plan-quality and delivery-time optimization method for proton therapy.

BACKGROUND: While minimizing plan delivery time is beneficial for proton therapy in terms of motion management, patient comfort, and treatment throughput, it often poses a tradeoff with optimizing plan quality. A key component of plan delivery time is the energy switching time, which is approximately proportional to the number of energy layers, that is, the cardinality. PURPOSE: This work aims to develop a novel optimization method that can efficiently compute the pareto surface between plan quality and energy layer cardinality, for the planner to navigate through this quality-and-efficiency tradeoff and select the appropriate plan of a balanced tradeoff. METHODS: A new IMPT method CARD is proposed that (1) explicitly incorporates the minimization of energy layer cardinality as an optimization objective, and (2) automatically generates a set of plans sequentially with a descending order in number of energy layers. The energy layer cardinality is penalized through the l1,0-norm regularization with an upper bound, and the upper bound is monotonically decreased to compute a series of treatment plans with gradually decreased energy layer cardinality on the quality-and-efficiency pareto surface. For any given treatment plan, the plan optimality is enforced using dose-volume planning objectives and the plan deliverability is imposed through minimum-monitor-unit (MMU) constraints, with optimization solution algorithm based on iterative convex relaxation. RESULTS: The new method CARD was validated in comparison with the benchmark plan of all energy layers (P0), and a state-of-the-art method called MMSEL, using prostate, head-and-neck (HN), lung, pancreas, liver and brain cases. While labor-intensive and time-consuming manual parameter tuning was needed for MMSEL to generate plans of predefined energy layer cardinality, CARD automatically and efficiently computed all plans with sequentially decreasing predefined energy layer cardinality all at once. With the acceptable plan quality (i.e., no more than 110% of total optimization objective value from P0), CARD achieved the reduction of number of energy layers to 52% (from 77 to 40), 48% (from 135 to 65), 59% (from 85 to 50), 67% (from 52 to 35), 80% (from 50 to 40), and 30% (from 66 to 20), for prostate, HN, lung, pancreas, liver, and brain cases, respectively, compared to P0, with overall better plan quality than MMSEL. Moreover, due to the nonconvexity of the MMU constraint, CARD provided the similar or even smaller optimization objective than P0, at the same time with fewer number of energy layers, that is, 55 versus 77, 85 versus 135, 45 versus 52, and 25 versus 66 for prostate, HN, pancreas, and brain cases, respectively. CONCLUSIONS: We have developed a novel optimization algorithm CARD that can efficiently and automatically compute a series of treatment plans of any given energy layer sequentially, which allows the planner to navigate through the plan-quality and energy-layer-cardinality tradeoff and select the appropriate plan of a balanced tradeoff.

[Factors influencing cognitive function among the older adults in Beijing].

OBJECTIVE: To explore the current status of cognitive function of the older adults in Beijing, and to analyze the factors affecting their cognitive function. METHODS: It was a cross-sectional study. A questionnaire survy was conducted in 2023 among the older adults in Beijing. The cognitive function of the older adults was assessed with the Hong Kong brief cognitive test (HKBC) scale, a simple cognitive assessment tool. Using SPSS 27.0 to perform the descriptive analysis and multiple linear regression analysis of factors, which affect cognitive function among the older adults. RESULTS: Totally 349 older adults were recruited, with the highest percentage of respondents aged 60-69 years (41.3%), of whom 58.7% were female, 88.0% of the respondents had a junior high school or above education level. Most of the older adults (68.8%) worked 35-48 h/week before they retired, and 14.0% of the older adults had a family history of dementia. After controlling age and gender, the linear regression analysis showed that marital status married (ß=0.501, 95%CI: 0.144-0.859) and 3-4 times physical activity per week (ß=0.617, 95%CI: 0.087-1.148) were protective factors of cognitive function in the older adults, and depressive symptoms were a risk factor (ß= -0.723, 95%CI: -1.198 to -0.247) of cognitive function for the older adults. CONCLUSION: In this study, the factors influencing cognitive function among the older adults was analyzed based on a life-cycle perspective. Lack of physical activity and depressive symptoms were risk factors for cognitive function among the older adults. It was suggested that strengthening physical activity, improving mental health of the older adults, as well as conducting preventive intervention in early stages of the life-cycle will be benefit for preventing and slowing cognitive decline in the older adults.

Biological optimization for hybrid proton-photon radiotherapy.

Objective.Hybrid proton-photon radiotherapy (RT) is a cancer treatment option to broaden access to proton RT. Additionally, with a refined treatment planning method, hybrid RT has the potential to offer superior plan quality compared to proton-only or photon-only RT, particularly in terms of target coverage and sparing organs-at-risk (OARs), when considering robustness to setup and range uncertainties. However, there is a concern regarding the underestimation of the biological effect of protons on OARs, especially those in close proximity to targets. This study seeks to develop a hybrid treatment planning method with biological dose optimization, suitable for clinical implementation on existing proton and photon machines, with each photon or proton treatment fraction delivering a uniform target dose.Approach.The proposed hybrid biological dose optimization method optimized proton and photon plan variables, along with the number of fractions for each modality, minimizing biological dose to the OARs and surrounding normal tissues. To mitigate underestimation of hot biological dose spots, proton biological dose was minimized within a ring structure surrounding the target. Hybrid plans were designed to be deliverable separately and robustly on existing proton and photon machines, with enforced uniform target dose constraints for the proton and photon fraction doses. A probabilistic formulation was utilized for robust optimization of setup and range uncertainties for protons and photons. The nonconvex optimization problem, arising from minimum monitor unit constraint and dose-volume histogram constraints, was solved using an iterative convex relaxation method.Main results.Hybrid planning with biological dose optimization effectively eliminated hot spots of biological dose, particularly in normal tissues surrounding the target, outperforming proton-only planning. It also provided superior overall plan quality and OAR sparing compared to proton-only or photon-only planning strategies.Significance.This study presents a novel hybrid biological treatment planning method capable of generating plans with reduced biological hot spots, superior plan quality to proton-only or photon-only plans, and clinical deliverability on existing proton and photon machines, separately and robustly.

Comprehensive clinical application analysis of artificial intelligence-enabled electrocardiograms for screening multiple valvular heart diseases.

BACKGROUND: Valvular heart disease (VHD) is becoming increasingly important to manage the risk of future complications. Electrocardiographic (ECG) changes may be related to multiple VHDs, and (AI)-enabled ECG has been able to detect some VHDs. We aimed to develop five deep learning models (DLMs) to identify aortic stenosis, aortic regurgitation, pulmonary regurgitation, tricuspid regurgitation, and mitral regurgitation. METHODS: Between 2010 and 2021, 77,047 patients with echocardiography and 12-lead ECG performed within 7 days were identified from an academic medical center to provide DLM development (122,728 ECGs), and internal validation (7,637 ECGs). Additional 11,800 patients from a community hospital were identified to external validation. The ECGs were classified as with or without moderate-to-severe VHDs according to transthoracic echocardiography (TTE) records, and we also collected the other echocardiographic data and follow-up TTE records to identify new-onset valvular heart diseases. RESULTS: AI-ECG adjusted for age and sex achieved areas under the curves (AUCs) of >0.84, >0.80, >0.77, >0.83, and >0.81 for detecting aortic stenosis, aortic regurgitation, pulmonary regurgitation, tricuspid regurgitation, and mitral regurgitation, respectively. Since predictions of each DLM shared similar components of ECG rhythms, the positive findings of each DLM were highly correlated with other valvular heart diseases. Of note, a total of 37.5-51.7% of false-positive predictions had at least one significant echocardiographic finding, which may lead to a significantly higher risk of future moderate-to-severe VHDs in patients with initially minimal-to-mild VHDs. CONCLUSION: AI-ECG may be used as a large-scale screening tool for detecting VHDs and a basis to undergo an echocardiography.

Drivers of coastal benthic communities in a complex environmental setting.

Analyzing the environmental factors affecting benthic communities in coastal areas is crucial for uncovering key factors that require conservation action. Here, we collected benthic and environmental (physical-chemical-historical and land-based) data for 433 transects in Taiwan. Using a k-means approach, five communities dominated by crustose coralline algae, turfs, stony corals, digitate, or bushy octocorals were first delineated. Conditional random forest models then identified physical, chemical, and land-based factors (e.g., light intensity, nitrite, and population density) relevant to community delineation and occurrence. Historical factors, including typhoons and temperature anomalies, had only little effect. The prevalent turf community correlated positively with chemical and land-based drivers, which suggests that anthropogenic impacts are causing a benthic homogenization. This mechanism may mask the effects of climate disturbances and regional differentiation of benthic assemblages. Consequently, management of nutrient enrichment and terrestrial runoff is urgently needed to improve community resilience in Taiwan amidst increasing challenges of climate change.

A peripheral system disease-Pulmonary hypertension.

Pulmonary hypertension (PH) is a cardiovascular disorder characterized by substantial morbidity and mortality rates. It is a chronic condition characterized by intricate pathogenesis and uncontrollable factors. We summarized the pathological effects of estrogen, genetics, neuroinflammation, intestinal microbiota, metabolic reorganization, and histone modification on PH. PH is not only a pulmonary vascular disease, but also a systemic disease. The findings emphasize that the onset of PH is not exclusively confined to the pulmonary vasculature, consequently necessitating treatment approaches that extend beyond targeting pulmonary blood vessels. Hence, the research on the pathological mechanism of PH is not limited to target organs such as pulmonary vessels, but also focuses on exploring other fields (such as estrogen, genetics, neuroinflammation, intestinal microbiota, metabolic reorganization, and histone modification).

Fabrication of an Fe-Doped ZIF-67 Derived Magnetic Fe/Co/C Composite for Effective Removal of Congo Red.

The dyes in printing and dyeing wastewater are harmful to the human body and the environment. It is essential to develop practical and effective adsorbents to deal with them. In this study, an Fe-doped, ZIF-67 derived Fe/Co/C composite material with strong magnetism was successfully synthesized. The effects of pH, initial concentration, and adsorption time on the properties of the adsorbent were investigated. To further improve the removal efficiency and enhance the practicality, potassium peroxymonosulfate (PMS) was added to the system due to its Fenton-like effect. Then, an Fe/Co/C composite was used with PMS to remove Congo red (CR) with a 98% removal of 250 mg·L-1. Moreover, for its high saturation magnetization of 85.4 emu·g-1, the Fe/Co/C composite can be easily recovered by applying a magnetic field, solving the problem that powdery functional materials are difficult to recover and, thus, avoiding secondary pollution. Furthermore, since the composite material was doped before carbonization, this synthetic strategy is flexible and the required metal elements can be added at will to achieve different purposes. This study demonstrates that this Fe-doped, ZIF-67 derived magnetic material has potential application prospects for dye adsorption.

Evidence that a Novel Chalcone Derivative, Compound 27, Acts on the Epithelium Via the PI3K/AKT/Nrf2-Keap1 Signaling Pathway, to Mitigate LPS-Induced Acute Lung Injury in Mice.

Acute lung injury (ALI) is a highly heterogeneous clinical syndrome and an important cause of mortality in critically ill patients, with limited treatment options currently available. Chalcone, an essential secondary metabolite found in edible or medicinal plants, exhibits good antioxidant activity and simple structure for easy synthesis. In our study, we synthesized a novel chalcone derivative, compound 27 (C27). We hypothesized that C27 could be a potential treatment for acute respiratory distress syndrome (ARDS). Therefore, the protective effects of C27 on lung epithelial cells during ALI and the underlying molecular mechanisms were investigated. In vivo, Intratracheal instillation of LPS (10 mg/kg) was used to induce acute lung injury in mice. In vitro, the bronchial epithelial cell line (Beas-2b) was treated with 30 µM tert-butyl hydroperoxide (t-BHP) to simulate oxidative stress. Our findings demonstrate that pretreatment with C27 reduces LPS-induced oxidative destruction and cellular apoptosis in lung tissues of mice. Furthermore, it significantly attenuates t-BHP-induced cellular reactive oxygen species (ROS) generation, mitochondrial damage, and apoptosis in vitro. Mechanistically, the signaling pathway involving Nrf2-Keap1 and the downstream antioxidative proteins were activated by C27 in vivo. Additionally, PI3K inhibitor LY294002 and Nrf2 inhibitor ML385 abolished the effect of C27 in vitro, indicating that the protective effect of C27 is mediated via the PI3K/AKT/Nrf2-Keap1 pathway. Our study provides evidence that C27 protects against LPS-induced ALI by mitigating oxidative stress via activation of the PI3K/AKT/Nrf2-Keap1 signaling pathway. Therefore, we hypothesize that C27 represents a viable alternative for ALI therapy.

Uterine mesothelial cysts mimicking ovarian cysts in a primipara patient with a history of Cesarean section: A case report and review of the literature.

OBJECTIVE: We describe a rare case of uterine mesothelial cysts mimicking ovarian cysts in a primipara patient with a history of Cesarean section. CASE REPORT: A 39-year-old female patient with history of Cesarean section presented with dysmenorrhea. Sonography revealed that a hypoechoic and anechoic multicystic complex, which was located on the right side of the pelvic cavity, had infiltrated the adjacent posterior wall of the uterus, and it was preoperatively misdiagnosed as ovarian cysts with suspected endometrioma. Laparoscopic surgery revealed multiple cystic lesions filled with clear yellow fluid on the posterior uterine wall instead of the adnexa. Laparoscopic uterine cystectomy was performed, and the patient's recovery was uneventful. Pathohistological and immunohistochemical examinations confirmed the diagnosis of uterine mesothelial cysts. CONCLUSION: Uterine mesothelial cysts should be considered in the differential diagnosis of pelvic lesions. Increasing the awareness of this rare disease can contribute to improved evaluation, decision-making, and disease management.

Stochastic processes shape microbial community assembly in biofilters: Hidden role of rare taxa.

Stochastic and deterministic processes are the major themes governing microbial community assembly; however, their roles in bioreactors are poorly understood. Herein, the mechanisms underlying microbial assembly and the effect of rare taxa were studied in biofilters. Phylogenetic tree analysis revealed differences in microbial communities at various stages. Null model analysis showed that stochastic processes shaped the community assembly, and deterministic processes emerged only in the inoculated activated sludge after domestication. This finding indicates the dominant role of stochastic factors (biofilm formation, accumulation, and aging). The Sloan neutral model corroborated the advantages of stochastic processes and mainly attributed these advantages to rare taxa. Cooccurrence networks revealed the importance of rare taxa, which accounted for more than 85% of the keystones. Overall, these results provide good foundations for understanding community assembly, especially the role of rare taxa, and offer theoretical support for future community design and reactor regulation.

CCR5 and inflammatory storm.

Chemokines and their corresponding receptors play crucial roles in orchestrating inflammatory and immune responses, particularly in the context of pathological conditions disrupting the internal environment. Among these receptors, CCR5 has garnered considerable attention due to its significant involvement in the inflammatory cascade, serving as a pivotal mediator of neuroinflammation and other inflammatory pathways associated with various diseases. However, a notable gap persists in comprehending the intricate mechanisms governing the interplay between CCR5 and its ligands across diverse and intricate inflammatory pathologies. Further exploration is warranted, especially concerning the inflammatory cascade instigated by immune cell infiltration and the precise binding sites within signaling pathways. This study aims to illuminate the regulatory axes modulating signaling pathways in inflammatory cells by providing a comprehensive overview of the pathogenic processes associated with CCR5 and its ligands across various disorders. The primary focus lies on investigating the pathomechanisms associated with CCR5 in disorders related to neuroinflammation, alongside the potential impact of aging on these processes and therapeutic interventions. The discourse culminates in addressing current challenges and envisaging potential future applications, advocating for innovative research endeavors to advance our comprehension of this realm.

Development and characterization of the first proton minibeam system for single-gantry proton facility.

BACKGROUND: Minibeam represents a preclinical spatially fractionated radiotherapy modality with great translational potential. The advantage lies in its high therapeutic index (compared to GRID and LATTICE) and ability to treat at greater depth (compared to microbeam). Proton minibeam radiotherapy (pMBRT) is a synergy of proton and minibeam. While the single-gantry proton facility has gained popularity due to its affordability and compact design, it often has limited beam time available for research purposes. Conversely, given the current requirement of pMBRT on specific minibeam hardware collimators, necessitates a reproducible and fast setup to minimize pMBRT treatment time and streamline the switching time between pMBRT and conventional treatment for clinically translation. PURPOSE: The contribution of this work is the development and characterization of the first pMBRT system tailored for single-gantry proton facility. The system allows for efficient and reproducible plug-and-play setup, achievable within minutes. METHODS: The single room pMBRT system is constructed based on IBA ProteusONE proton machine. The end of nozzle is attached with beam modifying accessories though an accessory drawer. A small snout is attached to the accessory drawer and used to hold apertures and range shifters. The minibeam aperture consists of two components: a fitting ring and an aperture body. Three minibeam apertures were manufactured. The first-generation apertures underwent qualitatively analysis with film, and the second generation aperture underwent more comprehensive quantitative measurement. The reproducibility of the setup is accessed, and the film measurements are performed to characterize the pMBRT system in cross validation with Monte Carlo (MC) simulations. RESULTS: We presented initial results of large field pMBRT aperture and the film measurements indicates the effect of source-to-isocenter distance = 930 cm in Y proton scanning direction. Consistent with TOPAS MC simulation, the dose uniformity of pMBRT field <2 cm is demonstrated to be better than 2%, rendering its suitability for pre-clinical studies. Subsequently, we developed the second generation of aperture with five slits and characterized the aperture with film dosimetry studies and compared the results to the benchmark MC. Comprehensive film measurements were also performed to evaluate the effect of divergence, air gap and gantry-angle dependency and repeatability and revealing a consistent performance within 5%. Furthermore, the 2D gamma analysis indicated a passing rate exceeding 99% using 3% dose difference and 0.2 mm distance agreement criteria. We also establish the peak valley dose ratio and the depth dose profile measurements, and the results are within 10% from MC simulation. CONCLUSIONS: We have developed the first pMBRT system tailored for a single-gantry proton facility, which has demonstrated accuracy in benchmark with MC simulations, and allows for efficient plug-and-play setup, emphasizing efficiency.

Interpretable Deep Learning System for Identifying Critical Patients Through the Prediction of Triage Level, Hospitalization, and Length of Stay: Prospective Study.

BACKGROUND: Triage is the process of accurately assessing patients' symptoms and providing them with proper clinical treatment in the emergency department (ED). While many countries have developed their triage process to stratify patients' clinical severity and thus distribute medical resources, there are still some limitations of the current triage process. Since the triage level is mainly identified by experienced nurses based on a mix of subjective and objective criteria, mis-triage often occurs in the ED. It can not only cause adverse effects on patients, but also impose an undue burden on the health care delivery system. OBJECTIVE: Our study aimed to design a prediction system based on triage information, including demographics, vital signs, and chief complaints. The proposed system can not only handle heterogeneous data, including tabular data and free-text data, but also provide interpretability for better acceptance by the ED staff in the hospital. METHODS: In this study, we proposed a system comprising 3 subsystems, with each of them handling a single task, including triage level prediction, hospitalization prediction, and length of stay prediction. We used a large amount of retrospective data to pretrain the model, and then, we fine-tuned the model on a prospective data set with a golden label. The proposed deep learning framework was built with TabNet and MacBERT (Chinese version of bidirectional encoder representations from transformers [BERT]). RESULTS: The performance of our proposed model was evaluated on data collected from the National Taiwan University Hospital (901 patients were included). The model achieved promising results on the collected data set, with accuracy values of 63%, 82%, and 71% for triage level prediction, hospitalization prediction, and length of stay prediction, respectively. CONCLUSIONS: Our system improved the prediction of 3 different medical outcomes when compared with other machine learning methods. With the pretrained vital sign encoder and repretrained mask language modeling MacBERT encoder, our multimodality model can provide a deeper insight into the characteristics of electronic health records. Additionally, by providing interpretability, we believe that the proposed system can assist nursing staff and physicians in taking appropriate medical decisions.