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It has been more than three decades since the first monoclonal antibody was approved by the United States Food and Drug Administration (US FDA) in 1986, and during this time, antibody engineering has dramatically evolved. Current antibody drugs have increasingly fewer adverse effects due to their high specificity. As a result, therapeutic antibodies have become the predominant class of new drugs developed in recent years. Over the past five years, antibodies have become the best-selling drugs in the pharmaceutical market, and in 2018, eight of the top ten bestselling drugs worldwide were biologics. The global therapeutic monoclonal antibody market was valued at approximately US$115.2 billion in 2018 and is expected to generate revenue of $150 billion by the end of 2019 and $300 billion by 2025. Thus, the market for therapeutic antibody drugs has experienced explosive growth as new drugs have been approved for treating various human diseases, including many cancers, autoimmune, metabolic and infectious diseases. As of December 2019, 79 therapeutic mAbs have been approved by the US FDA, but there is still significant growth potential. This review summarizes the latest market trends and outlines the preeminent antibody engineering technologies used in the development of therapeutic antibody drugs, such as humanization of monoclonal antibodies, phage display, the human antibody mouse, single B cell antibody technology, and affinity maturation. Finally, future applications and perspectives are also discussed.

Taiwan has strictly followed infection control measures to prevent spread of coronavirus disease. Meanwhile, nationwide surveillance data revealed drastic decreases in influenza diagnoses in outpatient departments, positivity rates of clinical specimens, and confirmed severe cases during the first 12 weeks of 2020 compared with the same period of 2019.

Decision confidence reflects our ability to evaluate the quality of decisions and guides subsequent behavior. Experiments on confidence reports have almost exclusively focused on two-alternative decision-making. In this realm, the leading theory is that confidence reflects the probability that a decision is correct (the posterior probability of the chosen option). There is, however, another possibility, namely that people are less confident if the best two options are closer to each other in posterior probability, regardless of how probable they are in absolute terms. This possibility has not previously been considered because in two-alternative decisions, it reduces to the leading theory. Here, we test this alternative theory in a three-alternative visual categorization task. We found that confidence reports are best explained by the difference between the posterior probabilities of the best and the next-best options, rather than by the posterior probability of the chosen (best) option alone, or by the overall uncertainty (entropy) of the posterior distribution. Our results upend the leading notion of decision confidence and instead suggest that confidence reflects the observer’s subjective probability that they made the best possible decision. Conventional theory suggests that people’s confidence about a decision reflects their subjective probability that the decision was correct. By studying decisions with multiple alternatives, the authors show that confidence reports instead reflect the difference in probabilities between the chosen and the next-best alternative.

To curb viral epidemics and pandemics, antiviral drugs are needed with activity against entire genera or families of viruses. Here, we develop a cell-based multiplex antiviral assay for high-throughput screening against multiple viruses at once, as demonstrated by using three distantly related orthoflaviviruses: dengue, Japanese encephalitis and yellow fever virus. Each virus is tagged with a distinct fluorescent protein, enabling individual monitoring in cell culture through high-content imaging. Specific antisera and small-molecule inhibitors are employed to validate that multiplexing approach yields comparable inhibition profiles to single-virus infection assays. To facilitate downstream analysis, a kernel is developed to deconvolute and reduce the multidimensional quantitative data to three cartesian coordinates. The methodology is applicable to viruses from different families as exemplified by co-infections with chikungunya, parainfluenza and Bunyamwera viruses. The multiplex approach is expected to facilitate the discovery of broader-spectrum antivirals, as shown in a pilot screen of approximately 1200 drug-like small-molecules. Antiviral approaches against entire genera or families of viruses need to be constantly developed and innovated. Focusing on several distantly-related orthoflaviviruses, the authors develop a cell-based multiplex antiviral assay for high-throughput screening against multiple viruses at once.

The expanding pandemic of coronavirus disease 2019 (COVID-19) requires the development of safe, efficacious and fast-acting vaccines. Several vaccine platforms are being leveraged for a rapid emergency response 1 . Here we describe the development of a candidate vaccine (YF-S0) for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) that uses live-attenuated yellow fever 17D (YF17D) vaccine as a vector to express a noncleavable prefusion form of the SARS-CoV-2 spike antigen. We assess vaccine safety, immunogenicity and efficacy in several animal models. YF-S0 has an excellent safety profile and induces high levels of SARS-CoV-2 neutralizing antibodies in hamsters ( Mesocricetus auratus ), mice ( Mus musculus ) and cynomolgus macaques ( Macaca fascicularis ), and—concomitantly—protective immunity against yellow fever virus. Humoral immunity is complemented by a cellular immune response with favourable T helper 1 polarization, as profiled in mice. In a hamster model 2 and in macaques, YF-S0 prevents infection with SARS-CoV-2. Moreover, a single dose conferred protection from lung disease in most of the vaccinated hamsters within as little as 10 days. Taken together, the quality of the immune responses triggered and the rapid kinetics by which protective immunity can be attained after a single dose warrant further development of this potent SARS-CoV-2 vaccine candidate. A candidate vaccine against SARS-CoV-2 that uses the yellow fever 17D live-virus vector is highly efficacious and displays a favourable safety profile in Syrian hamster, mouse and cynomolgus macaque models.

Breast cancer is a heterogeneous disease. To guide proper treatment decisions for each patient, robust prognostic biomarkers, which allow reliable prognosis prediction, are necessary. Gene feature selection based on microarray data is an approach to discover potential biomarkers systematically. However, standard pure-statistical feature selection approaches often fail to incorporate prior biological knowledge and select genes that lack biological insights. Besides, due to the high dimensionality and low sample size properties of microarray data, selecting robust gene features is an intrinsically challenging problem. We hence combined systems biology feature selection with ensemble learning in this study, aiming to select genes with biological insights and robust prognostic predictive power. Moreover, to capture breast cancer's complex molecular processes, we adopted a multi-gene approach to predict the prognosis status using deep learning classifiers. We found that all ensemble approaches could improve feature selection robustness, wherein the hybrid ensemble approach led to the most robust result. Among all prognosis prediction models, the bimodal deep neural network (DNN) achieved the highest test performance, further verified by survival analysis. In summary, this study demonstrated the potential of combining ensemble learning and bimodal DNN in guiding precision medicine.

Purpose: Our audio--vocal system involves a negative feedback system that functions to correct for fundamental frequency (f[subscript 0]) errors in production. Therefore, automatic and opposing responses appear when an unexpected change in voice pitch is present in auditory feedback. This study explores following responses to pitch perturbation in auditory feedback in tonal language speakers, which have been commonly overlooked or discarded by past research. We examine whether the number of response types (opposing vs. following) and their dynamic f[subscript 0] contours in tone word production vary as a function of instruction (involuntary [\"to ignore\"] vs. volitional [\"to compensate\"]). Method: Twenty-four native speakers of Taiwanese Southern Min (TSM) produced three disyllabic TSM words while receiving pitch perturbation through headphones. The three disyllabic words were \"tsau\"[superscript 55]-\"im\"[subscript 55] (\"out of tune\"; HH), \"kau\"[superscript 33]-\"uann\"[superscript 33] (\"exchange\"; MM), and \"pan\"[superscript 11]-\"an\"[superscript 11] (\"handle a case\"; LL) that carry an identical high-, mid-, or low-level tone. The participants were instructed either \"to ignore\" or \"to compensate\" for the pitch shifts. Results: Results from a Bayesian Poisson regression show that the number of opposing and following responses were split nearly 50-50 for the \"ignore\" condition and 55%-35% for the \"compensate\" condition. The simulation results indicate that the speakers were able to switch between the feedback and the feedforward mode during the testing. On the other hand, f[subscript 0] contour analyses using generalized additive models show that pitch-increasing responses (i.e., oppose to downshifts or follow upshifts) were significantly larger than pitch-decreasing responses (i.e., oppose to upshifts or follow downshifts) for the MM and LL words, but not for the HH word. Conclusions: Our results provide support for the view that, for tone speakers, following responses are not uncommon. The magnitudes of pitch shift response trajectories have to do with the available pitch range for moving up or down in tone word production.

Hydrogels (HGs) are attractive matrices for cell-based cartilage tissue regeneration given their injectability and ability to fill defects with irregular shapes. However, most HGs developed to date often lack cell scale macroporosity, which restrains the encapsulated cells, leading to delayed new extracellular matrix deposition restricted to pericellular regions. Furthermore, tissue-engineered cartilage using conventional HGs generally suffers from poor mechanical property and fails to restore the load-bearing property of articular cartilage. The goal of this study was to evaluate the potential of macroporous gelatin-based microribbon (μRB) HGs as novel 3D matrices for accelerating chondrogenesis and new cartilage formation by human mesenchymal stem cells (MSCs) in 3D with improved mechanical properties. Unlike conventional HGs, these μRB HGs are inherently macroporous and exhibit cartilage-mimicking shock-absorbing mechanical property. After 21 days of culture, MSC-seeded μRB scaffolds exhibit a 20-fold increase in compressive modulus to 225 kPa, a range that is approaching the level of native cartilage. In contrast, HGs only resulted in a modest increase in compressive modulus of 65 kPa. Compared with conventional HGs, macroporous μRB scaffolds significantly increased the total amount of neocartilage produced by MSCs in 3D, with improved interconnectivity and mechanical strength. Altogether, these results validate gelatin-based μRBs as promising scaffolds for enhancing and accelerating MSC-based cartilage regeneration and may be used to enhance cartilage regeneration using other cell types as well.

The role of fibronectin (FN) in tumorigenesis and malignant progression has been highly controversial. Cancerous FN plays a tumor-suppressive role, whereas it is pro-metastatic and associated with poor prognosis. Interestingly, FN matrix deposited in the tumor microenvironments (TMEs) promotes tumor progression but is paradoxically related to a better prognosis. Here, we justify how FN impacts tumor transformation and subsequently metastatic progression. Next, we try to reconcile and rationalize the seemingly conflicting roles of FN in cancer and TMEs. Finally, we propose future perspectives for potential FN-based therapeutic strategies.

Glaucoma is a major global health concern and the leading cause of irreversible blindness worldwide, characterized by the progressive degeneration of retinal ganglion cells (RGCs) and their axons. This review focuses on the need for neuroprotective strategies in glaucoma management, addressing the limitations of current treatments that primarily target intraocular pressure (IOP) reduction. Despite effective IOP management, many patients continue to experience RGC degeneration, leading to irreversible blindness. This review provides an overview of both pharmacological interventions and emerging technologies aimed at directly protecting RGCs and the optic nerve, independent of IOP reduction. Pharmacological agents such as brimonidine, neurotrophic factors, memantine, Ginkgo biloba extract, citicoline, nicotinamide, insulin, and resveratrol show promise in preclinical and early clinical studies for their neuroprotective properties. Emerging technologies, including stem cell therapy, gene therapy, mitochondrial-targeted therapies, and nanotechnologies, offer innovative approaches for neuroprotection and regeneration of damaged RGCs. While these interventions hold significant potential, further research and clinical trials are necessary to confirm their efficacy and establish their role in clinical practice. This review highlights the multifaceted nature of neuroprotection in glaucoma, aiming to guide future research and clinical practice toward more effective management of glaucoma-induced neurodegeneration.