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BioRxiv : the Preprint Server For... Jun 2024Both endogenous antibodies and a subset of antibody therapeutics engage Fc gamma receptor (FcγR)IIIa / CD16a to stimulate a protective immune response. Increasing the...
Both endogenous antibodies and a subset of antibody therapeutics engage Fc gamma receptor (FcγR)IIIa / CD16a to stimulate a protective immune response. Increasing the FcγRIIIa/IgG1 interaction improves the immune response and thus represents a strategy to improve therapeutic efficacy. FcγRIIIa is a heavily glycosylated receptor and glycan composition affects antibody-binding affinity. Though our laboratory previously demonstrated that natural killer (NK) cell N-glycan composition affected the potency of one key protective mechanism, antibody-dependent cell-mediated cytotoxicity (ADCC), it was unclear if this effect was due to FcγRIIIa glycosylation. Furthermore, the structural mechanism linking glycan composition to affinity and cellular activation remained undescribed. To define the role of individual amino acid and N-glycan residues we measured affinity using multiple FcγRIIIa glycoforms. We observed stepwise affinity increases with each glycan truncation step with the most severely truncated glycoform displaying the highest affinity. Removing the N162 glycan demonstrated its predominant role in regulating antibody-binding affinity, in contrast to four other FcγRIIIa N-glycans. We next evaluated the impact of the N162 glycan on NK cell ADCC. NK cells expressing the FcγRIIIa V158 allotype exhibited increased ADCC following kifunensine treatment to limit N-glycan processing. Notably, an increase was not observed with cells expressing the FcγRIIIa V158 S164A variant that lacks N162 glycosylation, indicating the N162 glycan is required for increased NK cell ADCC. To gain structural insight into the mechanisms of N162 regulation, we applied a novel protein isotope labeling approach in combination with solution NMR spectroscopy. FG loop residues proximal to the N162 glycosylation site showed large chemical shift perturbations following glycan truncation. These data support a model for the regulation of FcγRIIIa affinity and NK cell ADCC whereby composition of the N162 glycan stabilizes the FG loop and thus the antibody-binding site.
PubMed: 38948809
DOI: 10.1101/2024.06.17.599285 -
BioRxiv : the Preprint Server For... Jun 2024Cochlear hair cell stereocilia bundles are key organelles required for normal hearing. Often, deafness mutations cause aberrant stereocilia heights or morphology that...
Cochlear hair cell stereocilia bundles are key organelles required for normal hearing. Often, deafness mutations cause aberrant stereocilia heights or morphology that are visually apparent but challenging to quantify. Actin-based structures, stereocilia are easily and most often labeled with phalloidin then imaged with 3D confocal microscopy. Unfortunately, phalloidin non-specifically labels all the actin in the tissue and cells and therefore results in a challenging segmentation task wherein the stereocilia phalloidin signal must be separated from the rest of the tissue. This can require many hours of manual human effort for each 3D confocal image stack. Currently, there are no existing software pipelines that provide an end-to-end automated solution for 3D stereocilia bundle instance segmentation. Here we introduce VASCilia, a Napari plugin designed to automatically generate 3D instance segmentation and analysis of 3D confocal images of cochlear hair cell stereocilia bundles stained with phalloidin. This plugin combines user-friendly manual controls with advanced deep learning-based features to streamline analyses. With VASCilia, users can begin their analysis by loading image stacks. The software automatically preprocesses these samples and displays them in Napari. At this stage, users can select their desired range of z-slices, adjust their orientation, and initiate 3D instance segmentation. After segmentation, users can remove any undesired regions and obtain measurements including volume, centroids, and surface area. VASCilia introduces unique features that measures bundle heights, determines their orientation with respect to planar polarity axis, and quantifies the fluorescence intensity within each bundle. The plugin is also equipped with trained deep learning models that differentiate between inner hair cells and outer hair cells and predicts their tonotopic position within the cochlea spiral. Additionally, the plugin includes a training section that allows other laboratories to fine-tune our model with their own data, provides responsive mechanisms for manual corrections through event-handlers that check user actions, and allows users to share their analyses by uploading a pickle file containing all intermediate results. We believe this software will become a valuable resource for the cochlea research community, which has traditionally lacked specialized deep learning-based tools for obtaining high-throughput image quantitation. Furthermore, we plan to release our code along with a manually annotated dataset that includes approximately 55 3D stacks featuring instance segmentation. This dataset comprises a total of 1,870 instances of hair cells, distributed between 410 inner hair cells and 1,460 outer hair cells, all annotated in 3D. As the first open-source dataset of its kind, we aim to establish a foundational resource for constructing a comprehensive atlas of cochlea hair cell images. Together, this open-source tool will greatly accelerate the analysis of stereocilia bundles and demonstrates the power of deep learning-based algorithms for challenging segmentation tasks in biological imaging research. Ultimately, this initiative will support the development of foundational models adaptable to various species, markers, and imaging scales to advance and accelerate research within the cochlea research community.
PubMed: 38948743
DOI: 10.1101/2024.06.17.599381 -
Several common methods of making vesicles (except an emulsion method) capture intended lipid ratios.BioRxiv : the Preprint Server For... Jun 2024Researchers choose different methods of making giant unilamellar vesicles in order to satisfy different constraints of their experimental designs. A challenge of using a...
UNLABELLED
Researchers choose different methods of making giant unilamellar vesicles in order to satisfy different constraints of their experimental designs. A challenge of using a variety of methods is that each may produce vesicles of different lipid compositions, even if all vesicles are made from a common stock mixture. Here, we use mass spectrometry to investigate ratios of lipids in vesicles made by five common methods: electroformation on indium tin oxide slides, electroformation on platinum wires, gentle hydration, emulsion transfer, and extrusion. We made vesicles from either 5-component or binary mixtures of lipids chosen to span a wide range of physical properties: di(18:1)PC, di(16:0)PC, di(18:1)PG, di(12:0)PE, and cholesterol. For a mixture of all five of these lipids, ITO electroformation, Pt electroformation, gentle hydration, and extrusion methods result in only minor shifts (≤ 5 mol%) in lipid ratios of vesicles relative to a common stock solution. In contrast, emulsion transfer results in ∼80% less cholesterol than expected from the stock solution, which is counterbalanced by a surprising overabundance of saturated PC-lipid relative to all other phospholipids. Experiments using binary mixtures of some of the lipids largely support results from the 5-component mixture. Exact values of lipid ratios variations likely depend on the details of each method, so a broader conclusion is that experiments that increment lipid ratios in small steps will be highly sensitive to the method of lipid formation and to sample-to-sample variations, which are low (roughly ±2 mol% in the 5-component mixture and either scale proportionally with increasing mole fraction or remain low). Experiments that increment lipid ratios in larger steps or that seek to explain general trends or new phenomena will be less sensitive to the method used.
SIGNIFICANCE STATEMENT
Small changes to the amounts and types of lipids in membranes can drastically affect the membrane's behavior. Unfortunately, it is unknown whether (or to what extent) different methods of making vesicles alter the ratios of lipids in membranes, even when identical stock solutions are used. This presents challenges for researchers when comparing data with colleagues who use different methods. Here, we measure ratios of lipid types in vesicle membranes produced by five methods. We assess each method's reproducibility and compare resulting vesicle compositions across methods. In doing so, we provide a quantitative basis that the scientific community can use to estimate whether differences between their results can be simply attributed to differences between methods or to sample-to-sample variations.
PubMed: 38948736
DOI: 10.1101/2024.02.21.581444 -
Cleaner Water Jun 2024Environmental sustainability has gained acceptance to achieving the goal of a secure ecosystem with a reliable management system. Heavy metal remediation of aqueous...
Environmental sustainability has gained acceptance to achieving the goal of a secure ecosystem with a reliable management system. Heavy metal remediation of aqueous streams is of special concern due to the intractability and persistence in the environment. Adsorption is a potential alternative to the existing inefficient conventional technologies for the removal and recovery of metal ions from aqueous solutions and becomes vital to align with the Sustainable Development Goals (SDGs) and mitigate the adverse environmental and social impacts. Calcium Alginate-Graphene oxide (CA-GO) composite has been synthesized for the adsorption of heavy metals including Cr, Cu, and Cd ions from tannery effluents. Graphene oxide is prepared from commercial graphite powder and reacted with sodium alginate and calcium chloride to form the beads of CA-GO composite. The developed composite was characterized by FTIR, elemental analysis, SEM, XRD analysis, and Raman spectroscopy. Moreover, the effect of pH, adsorbent dosage, contact time, and initial concentration of metal ions on the adsorption capacity were investigated through batch experiments. At a pH>3.0 (pHzpc), the carboxyl group of CA-GO was deprotonated to make the surface negatively charged and facilitate metal adsorption. The optimum pH and maximum adsorption capacity of CA-GO for removal of Cr(III), Cu(II), and Cd(II) were 4.5, 6.0, and 7.0, and 90.58, 108.57, and 134.77 mg g, respectively. The kinetics, adsorption isotherms, and thermodynamics were studied to determine the adsorption mechanism. The kinetic of adsorption adopted the second-order model. Thermodynamic parameter were calculated and the adsorption process was determined to be exothermic and spontaneous at room temperature. The developed composite has been efficaciously applied for the removal of metal ions and pollution from real tannery effluents.
PubMed: 38948691
DOI: 10.1016/j.clwat.2024.100016 -
The Journal of Innovations in Cardiac... Jun 2024
PubMed: 38948663
DOI: 10.19102/icrm.2024.15067 -
Turkish Journal of Physical Medicine... Jun 2024This study aimed to compare the effectiveness of local ozone (O) injection versus corticosteroid injection in the treatment of mild to moderate carpal tunnel syndrome...
OBJECTIVES
This study aimed to compare the effectiveness of local ozone (O) injection versus corticosteroid injection in the treatment of mild to moderate carpal tunnel syndrome (CTS).
PATIENTS AND METHODS
This double-blind randomized controlled trial was performed on 42 patients (9 males, 33 females; mean age: 46.7±2.1 years; range, 18 to 70 years) with mild to moderate CTS between May 2021 and June 2021. The corticosteroid group (n=21) was injected with 40 mg triamcinolone, and in the O group B (n=21), 4 mL of a 10 mcg/mL oxygen (O)-O mixture was injected. Symptom severity and functional impairments were assessed using a Visual Analog Scale and Boston Carpal Tunnel Questionnaire. Electrodiagnostic and ultrasonographic parameters were obtained at baseline and eight weeks after the procedure.
RESULTS
The O-O solution improved pain and Boston Carpal Tunnel Questionnaire score after eight weeks (p<0.001); however, the change was nonsignificant compared to the corticosteroid group (p>0.05). Sensory nerve and compound muscle action potential latencies were not significantly changed eight weeks after O-O injection (p>0.05), while both were significantly decreased in the steroid injection group (p<0.001). Volar bulging and median nerve cross-section surface area were not improved after O-O injection, while the improvement was significant in the corticosteroid arm (p=0.02).
CONCLUSION
Symptoms in patients with mild to moderate CTS may be alleviated by local O-O injection; however, electrodiagnostic and ultrasonographic indices may be unchanged. Corticosteroid local injection may alleviate patient symptoms along with electrodiagnostic and ultrasonographic parameters.
PubMed: 38948651
DOI: 10.5606/tftrd.2024.12590 -
Journal of Family Medicine and Primary... May 2024Artificial intelligence (AI) has led to the development of various opportunities during the COVID-19 pandemic. An abundant number of applications have surfaced...
BACKGROUND
Artificial intelligence (AI) has led to the development of various opportunities during the COVID-19 pandemic. An abundant number of applications have surfaced responding to the pandemic, while some other applications were futile.
OBJECTIVES
The present study aimed to assess the perception and opportunities of AI used during the COVID-19 pandemic and to explore the perception of medical data analysts about the inclusion of AI in medical education.
MATERIAL AND METHODS
This study adopted a mixed-method research design conducted among medical doctors for the quantitative part while including medical data analysts for the qualitative interview.
RESULTS
The study reveals that nearly 64.8% of professionals were working in high COVID-19 patient-load settings and had significantly more acceptance of AI tools compared to others ( < 0.05). The learning barrier like engaging in new skills and working under a non-medical hierarchy led to dissatisfaction among medical data analysts. There was widespread recognition of their work after the COVID-19 pandemic.
CONCLUSION
Notwithstanding that the majority of professionals are aware that public health emergency creates a significant strain on doctors, the majority still have to work in extremely high case load setting to demand solutions. AI applications are still not being integrated into medicine as fast as technology has been advancing. Sensitization workshops can be conducted among specialists to develop interest which will encourage them to identify problem statements in their fields, and along with AI experts, they can create AI-enabled algorithms to address the problems. A lack of educational opportunities about AI in formal medical curriculum was identified.
PubMed: 38948570
DOI: 10.4103/jfmpc.jfmpc_1543_23 -
Journal of Family Medicine and Primary... May 2024Historically, it takes an average of 17 years to move new treatments from clinical evidence to daily practice. Given the highly effective treatments now available to...
Historically, it takes an average of 17 years to move new treatments from clinical evidence to daily practice. Given the highly effective treatments now available to prevent or delay kidney disease onset and progression, this is far too long. The time is now to narrow the gap between what we know and what we do. Clear guidelines exist for the prevention and management of common risk factors for kidney disease, such as hypertension and diabetes, but only a fraction of people with these conditions worldwide are diagnosed, and even fewer are treated to target. Similarly, the vast majority of people living with kidney disease are unaware of their condition because in the early stages, it is often silent. Even among patients who have been diagnosed, many do not receive appropriate treatment for kidney disease. Considering the serious consequences of kidney disease progression, kidney failure, or death, it is imperative that treatments are initiated early and appropriately. Opportunities to diagnose and treat kidney disease early must be maximized beginning at the primary care level. Many systematic barriers exist, ranging from patient to clinician to health systems to societal factors. To preserve and improve kidney health for everyone everywhere, each of these barriers must be acknowledged so that sustainable solutions are developed and implemented without further delay.
PubMed: 38948565
DOI: 10.4103/jfmpc.jfmpc_518_24 -
Cancer Innovation Feb 2024In recent years, the three-dimensional (3D) culture system has emerged as a promising preclinical model for tumor research owing to its ability to replicate the tissue... (Review)
Review
In recent years, the three-dimensional (3D) culture system has emerged as a promising preclinical model for tumor research owing to its ability to replicate the tissue structure and molecular characteristics of solid tumors in vivo. This system offers several advantages, including high throughput, efficiency, and retention of tumor heterogeneity. Traditional Matrigel-submerged organoid cultures primarily support the long-term proliferation of epithelial cells. One solution for the exploration of the tumor microenvironment is a reconstitution approach involving the introduction of exogenous cell types, either in dual, triple or even multiple combinations. Another solution is a holistic approach including patient-derived tumor fragments, air-liquid interface, suspension 3D culture, and microfluidic tumor-on-chip models. Organoid co-culture models have also gained popularity for studying the tumor microenvironment, evaluating tumor immunotherapy, identifying predictive biomarkers, screening for effective drugs, and modeling infections. By leveraging these 3D culture systems, it is hoped to advance the clinical application of therapeutic approaches and improve patient outcomes.
PubMed: 38948532
DOI: 10.1002/cai2.101 -
Frontiers in Bioengineering and... 2024The simulation-to-reality (sim2real) problem is a common issue when deploying simulation-trained models to real-world scenarios, especially given the extremely high...
The simulation-to-reality (sim2real) problem is a common issue when deploying simulation-trained models to real-world scenarios, especially given the extremely high imbalance between simulation and real-world data (scarce real-world data). Although the cycle-consistent generative adversarial network (CycleGAN) has demonstrated promise in addressing some sim2real issues, it encounters limitations in situations of data imbalance due to the lower capacity of the discriminator and the indeterminacy of learned sim2real mapping. To overcome such problems, we proposed the imbalanced Sim2Real scheme (ImbalSim2Real). Differing from CycleGAN, the ImbalSim2Real scheme segments the dataset into paired and unpaired data for two-fold training. The unpaired data incorporated discriminator-enhanced samples to further squash the solution space of the discriminator, for enhancing the discriminator's ability. For paired data, a term targeted regression loss was integrated to ensure specific and quantitative mapping and further minimize the solution space of the generator. The ImbalSim2Real scheme was validated through numerical experiments, demonstrating its superiority over conventional sim2real methods. In addition, as an application of the proposed ImbalSim2Real scheme, we designed a finger joint stiffness self-sensing framework, where the validation loss for estimating real-world finger joint stiffness was reduced by roughly 41% compared to the supervised learning method that was trained with scarce real-world data and by 56% relative to the CycleGAN trained with the imbalanced dataset. Our proposed scheme and framework have potential applicability to bio-signal estimation when facing an imbalanced sim2real problem.
PubMed: 38948382
DOI: 10.3389/fbioe.2024.1334643