Creating 3D Nanoparticle Structural Space via Data Augmentation to Bidirectionally Predict Nanoparticle Mixture's Purity, Size, and Shape from Extinction Spectra.

Nanoparticle (NP) characterization is essential because diverse shapes, sizes, and morphologies inevitably occur in as-synthesized NP mixtures, profoundly impacting their properties and applications. Currently, the only technique to concurrently determine these structural parameters is electron microscopy, but it is time-intensive and tedious. Here, we create a three-dimensional (3D) NP structural space to concurrently determine the purity, size, and shape of 1000 sets of as-synthesized Ag nanocubes mixtures containing interfering nanospheres and nanowires from their extinction spectra, attaining low predictive errors at 2.7-7.9 %. We first use plasmonically-driven feature enrichment to extract localized surface plasmon resonance attributes from spectra and establish a lasso regressor (LR) model to predict purity, size, and shape. Leveraging the learned LR, we artificially generate 425,592 augmented extinction spectra to overcome data scarcity and create a comprehensive NP structural space to bidirectionally predict extinction spectra from structural parameters with <4 % error. Our interpretable NP structural space further elucidates the two higher-order combined electric dipole, quadrupole, and magnetic dipole as the critical structural parameter predictors. By incorporating other NP shapes and mixtures' extinction spectra, we anticipate our approach, especially the data augmentation, can create a fully generalizable NP structural space to drive on-demand, autonomous synthesis-characterization platforms.

Case of human rabies despite post-exposure prophylaxis (PEP) and complete recovery after intrathecal rabies immunoglobulin (RIG).

Rabies, a fatal viral zoonotic disease, has become a public health concern in Sarawak, Malaysia. Despite pre-exposure and post-exposure prophylaxis being available, there has been limited progress in developing treatments for rabies, emphasising the pressing need for productive solutions. We present a laboratory-confirmed human rabies case in which the patient survived without neurological sequelae after receiving intrathecal rabies immunoglobulin.

Achieving Molecular Recognition of Structural Analogues in Surface-Enhanced Raman Spectroscopy: Inducing Charge and Geometry Complementarity to Mimic Molecular Docking.

Molecular recognition of complex isomeric biomolecules remains challenging in surface-enhanced Raman scattering (SERS) spectroscopy due to their small Raman cross-sections and/or poor surface affinities. To date, the use of molecular probes has achieved excellent molecular sensitivities but still suffers from poor spectral specificity. Here, we induce "charge and geometry complementarity" between probe and analyte as a key strategy to achieve high spectral specificity for effective SERS molecular recognition of structural analogues. We employ 4-mercaptopyridine (MPY) as the probe, and chondroitin sulfate (CS) disaccharides with isomeric sulfation patterns as our proof-of-concept study. Our experimental and in silico studies reveal that "charge and geometry complementarity" between MPY's binding pocket and the CS sulfation patterns drives the formation of site-specific, multidentate interactions at the respective CS isomerism sites, which "locks" each CS in its analogue-specific complex geometry, akin to molecular docking events. Leveraging the resultant spectral fingerprints, we achieve > 97 % classification accuracy for 4 CSs and 5 potential structural interferences, as well as attain multiplex CS quantification with < 3 % prediction error. These insights could enable practical SERS differentiation of biologically important isomers to meet the burgeoning demand for fast-responding applications across various fields such as biodiagnostics, food and environmental surveillance.

Secondary hemophagocytic lymphohistiocytosis: an unusual complication in disseminated Mycobacterium tuberculosis.

Tuberculosis-associated hemophagocytic lymphohistiocytosis (TB-HLH) is a rare and life-threatening complication of tuberculosis infection. Early recognition and treatment of TB-HLH is crucial for improving outcomes. Treatment typically involves a combination of antituberculosis therapy and immunosuppressive therapy to control the immune system's overreaction. In this report, we present the case of a 53-year-old ambulance driver who was diagnosed with TB-HLH. His CT scan revealed splenic abscesses, hepatomegaly and bilateral lung consolidation. He subsequently developed multiorgan failure, including acute respiratory distress syndrome (ARDS), transaminitis and bone marrow dysfunction. The clinical course and simultaneous increase in serum ferritin raised the suspicion of HLH. His Hscore was 254, indicating a high probability of hemophagocytic syndrome. TB diagnosis was confirmed by positive endotracheal TB GeneXpert and bone marrow aspiration (BMA) which detected acid-fast bacilli organisms. The patient was promptly started on anti-TB, dexamethasone and IVIG. The patient responded well to treatment and made a full recovery without any lasting complications. This case highlights the importance of promptly recognising HLH and identifying the underlying cause. In critically ill patients, it is crucial not to delay HLH-specific treatment while working up for differential diagnosis.

Incorporating Chaotropic/Kosmotropic Chemistries onto Plasmonic Nanoheater to Boost Steam Generation Beyond its Photothermal Property.

Photothermal steam generation promises decentralized water purification, but current methods suffer from slow water evaporation even at high photothermal efficiency of ≈98%. This drawback arises from the high latent heat of vaporization that is required to overcome the strong and extensive hydrogen bonding network in water for steam generation. Here, light-to-vapor conversion is boosted by incorporating chaotropic/kosmotropic chemistries onto plasmonic nanoheater to manipulate water intermolecular network at the point-of-heating. The chaotropic-plasmonic nanoheater affords rapid light-to-vapor conversion (2.79 kg m-2  h-1  kW-1 ) at ≈83% efficiency, with the steam generation rate up to 6-fold better than kosmotropic platforms or emerging photothermal designs. Notably, the chaotropic-plasmonic nanoheater also lowers the enthalpy of water vaporization by 1.6-fold when compared to bulk water, signifying that a correspondingly higher amount of steam can be generated with the same energy input. Simulation studies unveil chaotropic surface chemistry is crucial to disrupt water hydrogen bonding network and suppress the energy barrier for water evaporation. Using the chaotropic-plasmonic nanoheater, organic-polluted water is purified at ≈100% efficiency, a feat otherwise challenging in conventional treatments. This study offers a unique chemistry approach to boost light-driven steam generation beyond a material photothermal property.

Recent advances in the production of recombinant factor IX: bioprocessing and cell engineering.

Appropriate treatment of Hemophilia B is vital for patients' quality of life. Historically, the treatment used was the administration of coagulation Factor IX derived from human plasma. Advancements in recombinant technologies allowed Factor IX to be produced recombinantly. Successful recombinant production has triggered a gradual shift from the plasma derived origins of Factor IX, as it provides extended half-life and expanded production capacity. However, the complex post-translational modifications of Factor IX have made recombinant production at scale difficult. Considerable research has therefore been invested into understanding and optimizing the recombinant production of Factor IX. Here, we review the evolution of recombinant Factor IX production, focusing on recent developments in bioprocessing and cell engineering to control its post-translational modifications in its expression from Chinese Hamster Ovary (CHO) cells.

Incorporating plasmonic featurization with machine learning to achieve accurate and bidirectional prediction of nanoparticle size and size distribution.

Determination of nanoparticle size and size distribution is important because these key parameters dictate nanomaterials' properties and applications. Yet, it is only accomplishable using low-throughput electron microscopy. Herein, we incorporate plasmonic-domain-driven feature engineering with machine learning (ML) for accurate and bidirectional prediction of both parameters for complete characterization of nanoparticle ensembles. Using gold nanospheres as our model system, our ML approach achieves the lowest prediction errors of 2.3% and ±1.0 nm for ensemble size and size distribution respectively, which is 3-6 times lower than previously reported ML or Mie approaches. Knowledge elicitation from the plasmonic domain and concomitant translation into featurization allow us to mitigate noise and boost data interpretability. This enables us to overcome challenges arising from size anisotropy and small sample size limitations to achieve highly generalizable ML models. We further showcase inverse prediction capabilities, using size and size distribution as inputs to generate spectra with LSPRs that closely match experimental data. This work illustrates a ML-empowered total nanocharacterization strategy that is rapid (<30 s), versatile, and applicable over a wide size range of 200 nm.

Downregulated Calcium-Binding Protein S100A16 and HSP27 in Placenta-Derived Multipotent Cells Induce Functional Astrocyte Differentiation.

Little is known about genes that induce stem cells differentiation into astrocytes. We previously described that heat shock protein 27 (HSP27) downregulation is directly related to neural differentiation under chemical induction in placenta-derived multipotent stem cells (PDMCs). Using this neural differentiation cell model, we cross-compared transcriptomic and proteomic data and selected 26 candidate genes with the same expression trends in both omics analyses. Those genes were further compared with a transcriptomic database derived from Alzheimer's disease (AD). Eighteen out of 26 candidates showed opposite expression trends between our data and the AD database. The mRNA and protein expression levels of those candidates showed downregulation of HSP27, S100 calcium-binding protein A16 (S100A16) and two other genes in our neural differentiation cell model. Silencing these four genes with various combinations showed that co-silencing HSP27 and S100A16 has stronger effects than other combinations for astrocyte differentiation. The induced astrocyte showed typical astrocytic star-shape and developed with ramified, stringy and filamentous processes as well as differentiated endfoot structures. Also, some of them connected with each other and formed continuous network. Immunofluorescence quantification of various neural markers indicated that HSP27 and S100A16 downregulation mainly drive PDMCs differentiation into astrocytes. Immunofluorescence and confocal microscopic images showed the classical star-like shape morphology and co-expression of crucial astrocyte markers in induced astrocytes, while electrophysiology and Ca2+ influx examination further confirmed their functional characteristics. In conclusion, co-silencing of S100A16 and HSP27 without chemical induction leads to PDMCs differentiation into functional astrocytes.

Noninvasive and Point-of-Care Surface-Enhanced Raman Scattering (SERS)-Based Breathalyzer for Mass Screening of Coronavirus Disease 2019 (COVID-19) under 5 min.

Population-wide surveillance of COVID-19 requires tests to be quick and accurate to minimize community transmissions. The detection of breath volatile organic compounds presents a promising option for COVID-19 surveillance but is currently limited by bulky instrumentation and inflexible analysis protocol. Here, we design a hand-held surface-enhanced Raman scattering-based breathalyzer to identify COVID-19 infected individuals in under 5 min, achieving >95% sensitivity and specificity across 501 participants regardless of their displayed symptoms. Our SERS-based breathalyzer harnesses key variations in vibrational fingerprints arising from interactions between breath metabolites and multiple molecular receptors to establish a robust partial least-squares discriminant analysis model for high throughput classifications. Crucially, spectral regions influencing classification show strong corroboration with reported potential COVID-19 breath biomarkers, both through experiment and in silico. Our strategy strives to spur the development of next-generation, noninvasive human breath diagnostic toolkits tailored for mass screening purposes.

Promotion of the Differentiation of Dental Pulp Stem Cells into Oligodendrocytes by Knockdown of Heat Shock Protein 27.

Stem cell-based therapy has been evaluated in many different clinical trials for various diseases. This capability was applied in various neurodegenerative diseases, such as multiple sclerosis, which is characterized by demyelination, axonal injury, and neuronal loss. Dental pulp stem cells (DPSCs) are mesenchymal stem cells from the oral cavity that have been studied with potential application for the regeneration of different tissues. Heat shock protein 27 (HSP27) regulates neurogenesis in the process of neural differentiation of placenta multipotent stem cells. Here, we hypothesize that HSP27 expression is also critical for the neural differentiation of DPSCs. An evaluation of the possible role of HSP27 in the differentiation of DPSCs was performed using gene knockdown and neural immunofluorescent staining. We found that HSP27 played a role in the differentiation of DPSCs and that knockdown of HSP27 in DPSCs rendered cells to oligodendrocyte progenitors; i.e., small hairpin specific for HSP27 DPSCs exhibited NG2-positive immunoreactivity and gave rise to oligodendrocytes or type-2 astrocytes. This neural differentiation of DPSCs may have clinical significance in the treatment of patients with neurodegenerative diseases. In conclusion, our data provide an example of the oligodendrocyte differentiation of a DPSC model, which may be applied in human regenerative medicine.

Perfusion culture of Chinese Hamster Ovary cells for bioprocessing applications.

Much of the biopharmaceutical industry's success over the past 30 years has relied on products derived from Chinese Hamster Ovary (CHO) cell lines. During this time, improvements in mammalian cell cultures have come from cell line development and process optimization suited for large-scale fed-batch processes. Originally developed for high cell densities and sensitive products, perfusion processes have a long history. Driven by high volumetric titers and a small footprint, perfusion-based bioprocess research has regained an interest from academia and industry. The recent pandemic has further highlighted the need for such intensified biomanufacturing options. In this review, we outline the technical history of research in this field as it applies to biologics production in CHO cells. We demonstrate a number of emerging trends in the literature and corroborate these with underlying drivers in the commercial space. From these trends, we speculate that the future of perfusion bioprocesses is bright and that the fields of media optimization, continuous processing, and cell line engineering hold the greatest potential. Aligning in its continuous setup with the demands for Industry 4.0, perfusion biomanufacturing is likely to be a hot topic in the years to come.

Classification of Activated Microglia by Convolutional Neural Networks.

Microglia are the resident macrophages in the central nervous system. Brain injuries, such as traumatic brain injury, hypoxia, and stroke, can induce inflammatory responses accompanying microglial activation. The morphology of microglia is notably diverse and is one of the prominent manifestations during activation. In this study, we proposed to detect the activated microglia in immunohistochemistry images by convolutional neural networks (CNN). 2D Iba1 images (40µm) were acquired from a control and a cardiac arrest treated Sprague-Dawley rat brain by a scanning microscope using a 20X objective. The training data were a collection of 54,333 single-cell images obtained from the cortex and midbrain areas, and curated by experienced neuroscientists. Results were compared between CNNs with different architectures, including Resnet18, Resnet50, Resnet101, and support vector machine (SVM) classifiers. The highest model performance was found by Resnet18, trained after 120 epochs with a classification accuracy of 95.5%. The findings indicate a potential application for using CNN in quantitative analysis of microglial morphology over regional difference in a large brain section.

Developing a scale of community capacity: testing community organizations in Taiwan.

This study focused on the development of a scale to assess community capacity. The concept of community capacity has become a core concept in governmental community-based programs in recent years. Community capacity is also considered to be the foundation for promoting community health service programs. Although some scholars have engaged in the study of community capacity issues, the discussion pertaining to a community capacity scale remains nascent. Thus, in order to develop a community capacity scale, this research followed a methodology consisting of reviewing relevant literature, conducting expert focus groups and employing the Delphi technique. Finally, the six-dimensional modified draft scale, which consisted of 24 indicators in total was tested in 97 community organizations across seven Taiwanese counties in July and August 2016. The developed community capacity scale includes six dimensions, namely leadership and organization, administrative management, resource mobilization, residents' participation, collaborative work and network and public relations and initiatives. Each dimension includes four indicators, and each indicator has clear descriptions to aid assessment and evaluation. The tested data was evaluated for its reliability, content validity, criterion validity and examined by factor analysis. The results show that the developed scale is highly reliable, valid and is suitable for professional community work. The scale could be used as a reference tool in developing community service plans and reviewing the effects of community programs. Undeniable, this scale still has limitations in Taiwan context, and the test with a limitation for its sample size and characteristics.

Self-assembled magnetic heterostructure of Co/DLC films.

In order to adapt to the quick and large amount of necessity in data flow for 5G cloud generation, it is necessary to develop a technology of warm storage device in market which takes a great balance between the reading/writing performance and the price per storage capacity. The technologies of warm storage devices are assumed to adopt phase change memory (PCM), resistive random access memory or magnetoresistive random access memory which have the highest possibilities to 5G structures and magnetic properties of Co on non-hydrogenated diamond like carbon (DLC)/Si(100) films and Co/DLC interface are investigated. The self-assembled magnetic heterostructure is firstly reported in hexagonal close packing Co layers perpendicular magnetic anisotropy (PMA) on Co carbide layers (in-plane) during Co deposited on DLC/Si(100). A PMA/in-plane magnetic heterostructure is expected to have the highest switching current to the energy barrier ratio of near 4 in previous report, which has great potential for developing warm memory devices. Based on these unique characteristics, we provide a novel design called magnetic anisotropy-phase change memory (Mani-PCM) which can impact the developing blueprint of memory. The working process of Mani-PCM includes in set, reset and read states as a universal PCM. This brand new technology is highly promising as warm memory devices including high reading/writing performance and economical price per storage capacity.

Macular Ischemia Quantification Using Deep-Learning Denoised Optical Coherence Tomography Angiography in Branch Retinal Vein Occlusion.

Purpose: To examine whether deep-learning denoised optical coherence tomography angiography (OCTA) images could enhance automated macular ischemia quantification in branch retinal vein occlusion (BRVO). Methods: This retrospective, single-center, cross-sectional study enrolled 74 patients with BRVO and 46 age-matched healthy subjects. The severity of macular ischemia was graded as mild, moderate, or severe. Denoised OCTA images were produced using a neural network model. Quantitative parameters derived from denoised images, including vessel density and nonperfusion area, were compared with those derived from the OCTA machine. The main outcome measures were correlations between quantitative parameters, and areas under receiver operating characteristic curves (AUCs) in classifying the severity of the macular ischemia. Results: The vessel density and nonperfusion area from denoised images were correlated strongly with the corresponding parameters from machine-derived images in control eyes and BRVO eyes with mild or moderate macular ischemia (all P < 0.001). However, no such correlation was found in eyes with severe macular ischemia. The vessel density and nonperfusion area from denoised images had significantly larger area under receiver operating characteristic curve than those derived from the original images in classifying moderate versus severe macular ischemia (0.927 vs 0.802 [P = 0.042] and 0.946 vs 0.797, [P = 0.022], respectively). There were no significant differences in the areas under receiver operating characteristic curve between the denoised images and the machine-derived parameters in classifying control versus BRVO, and mild versus moderate macular ischemia. Conclusions: A neural network model is useful for removing speckle noise on OCTA images and facilitating the automated grading of macular ischemia in eyes with BRVO. Translational Relevance: Deep-learning denoised optical coherence tomography angiography images could enhance automated macular ischemia quantification.

Coagulation factor IX analysis in bioreactor cell culture supernatant predicts quality of the purified product.

Coagulation factor IX (FIX) is a complex post-translationally modified human serum glycoprotein and high-value biopharmaceutical. The quality of recombinant FIX (rFIX), especially complete γ-carboxylation, is critical for rFIX clinical efficacy. Bioreactor operating conditions can impact rFIX production and post-translational modifications (PTMs). With the goal of optimizing rFIX production, we developed a suite of Data Independent Acquisition Mass Spectrometry (DIA-MS) proteomics methods and used these to investigate rFIX yield, γ-carboxylation, other PTMs, and host cell proteins during bioreactor culture and after purification. We detail the dynamics of site-specific PTM occupancy and structure on rFIX during production, which correlated with the efficiency of purification and the quality of the purified product. We identified new PTMs in rFIX near the GLA domain which could impact rFIX GLA-dependent purification and function. Our workflows are applicable to other biologics and expression systems, and should aid in the optimization and quality control of upstream and downstream bioprocesses.

Surface-Enhanced Raman Scattering (SERS) Taster: A Machine-Learning-Driven Multireceptor Platform for Multiplex Profiling of Wine Flavors.

Integrating machine learning with surface-enhanced Raman scattering (SERS) accelerates the development of practical sensing devices. Such integration, in combination with direct detection or indirect analyte capturing strategies, is key to achieving high predictive accuracies even in complex matrices. However, in-depth understanding of spectral variations arising from specific chemical interactions is essential to prevent model overfit. Herein, we design a machine-learning-driven "SERS taster" to simultaneously harness useful vibrational information from multiple receptors for enhanced multiplex profiling of five wine flavor molecules at parts-per-million levels. Our receptors employ numerous noncovalent interactions to capture chemical functionalities within flavor molecules. By strategically combining all receptor-flavor SERS spectra, we construct comprehensive "SERS superprofiles" for predictive analytics using chemometrics. We elucidate crucial molecular-level interactions in flavor identification and further demonstrate the differentiation of primary, secondary, and tertiary alcohol functionalities. Our SERS taster also achieves perfect accuracies in multiplex flavor quantification in an artificial wine matrix.

Enantiospecific Molecular Fingerprinting Using Potential-Modulated Surface-Enhanced Raman Scattering to Achieve Label-Free Chiral Differentiation.

Chiral differentiation is critical in diverse fields ranging from pharmaceutics to chiral synthesis. While surface-enhanced Raman scattering (SERS) offers molecule-specific vibrational information with high detection sensitivity, current strategies rely on indirect detection using additional selectors and cannot exploit SERS' key advantages for univocal and generic chiral differentiation. Here, we achieve direct, label-free SERS sensing of biologically important enantiomers by synergizing asymmetric nanoporous gold (NPG) nanoparticles with electrochemical-SERS to generate enantiospecific molecular fingerprints. Experimental and in silico studies reveal that chiral recognition is two pronged. First, the numerous surface atomic defects in NPG provide the necessary localized asymmetric environment to induce enantiospecific molecular adsorptions and interaction affinities. Concurrently, the applied potential drives and orients the enantiomers close to the NPG surface for maximal analyte-surface interactions. Notably, our strategy is versatile and can be readily extended to detect various enantiomers. Furthermore, we can achieve multiplex quantification of enantiomeric ratios with excellent predictive performance. Our combinatorial approach thus offers an important paradigm shift from current approaches to achieve label-free chiral SERS sensing of various enantiomers.