Highly sensitive spatial transcriptomics using FISHnCHIPs of multiple co-expressed genes.

High-dimensional, spatially resolved analysis of intact tissue samples promises to transform biomedical research and diagnostics, but existing spatial omics technologies are costly and labor-intensive. We present Fluorescence In Situ Hybridization of Cellular HeterogeneIty and gene expression Programs (FISHnCHIPs) for highly sensitive in situ profiling of cell types and gene expression programs. FISHnCHIPs achieves this by simultaneously imaging ~2-35 co-expressed genes (clustered into modules) that are spatially co-localized in tissues, resulting in similar spatial information as single-gene Fluorescence In Situ Hybridization (FISH), but with ~2-20-fold higher sensitivity. Using FISHnCHIPs, we image up to 53 modules from the mouse kidney and mouse brain, and demonstrate high-speed, large field-of-view profiling of a whole tissue section. FISHnCHIPs also reveals spatially restricted localizations of cancer-associated fibroblasts in a human colorectal cancer biopsy. Overall, FISHnCHIPs enables fast, robust, and scalable cell typing of tissues with normal physiology or undergoing pathogenesis.

Segment anything model for medical images?

The Segment Anything Model (SAM) is the first foundation model for general image segmentation. It has achieved impressive results on various natural image segmentation tasks. However, medical image segmentation (MIS) is more challenging because of the complex modalities, fine anatomical structures, uncertain and complex object boundaries, and wide-range object scales. To fully validate SAM's performance on medical data, we collected and sorted 53 open-source datasets and built a large medical segmentation dataset with 18 modalities, 84 objects, 125 object-modality paired targets, 1050K 2D images, and 6033K masks. We comprehensively analyzed different models and strategies on the so-called COSMOS 1050K dataset. Our findings mainly include the following: (1) SAM showed remarkable performance in some specific objects but was unstable, imperfect, or even totally failed in other situations. (2) SAM with the large ViT-H showed better overall performance than that with the small ViT-B. (3) SAM performed better with manual hints, especially box, than the Everything mode. (4) SAM could help human annotation with high labeling quality and less time. (5) SAM was sensitive to the randomness in the center point and tight box prompts, and may suffer from a serious performance drop. (6) SAM performed better than interactive methods with one or a few points, but will be outpaced as the number of points increases. (7) SAM's performance correlated to different factors, including boundary complexity, intensity differences, etc. (8) Finetuning the SAM on specific medical tasks could improve its average DICE performance by 4.39% and 6.68% for ViT-B and ViT-H, respectively. Codes and models are available at: https://github.com/yuhoo0302/Segment-Anything-Model-for-Medical-Images. We hope that this comprehensive report can help researchers explore the potential of SAM applications in MIS, and guide how to appropriately use and develop SAM.

Accurate prediction of biliary atresia with an integrated model using MMP-7 levels and bile acids.

BACKGROUND: Biliary atresia (BA) is a rare fatal liver disease in children, and the aim of this study was to develop a method to diagnose BA early. METHODS: We determined serum levels of matrix metalloproteinase-7 (MMP-7), the results of 13 liver tests, and the levels of 20 bile acids, and integrated computational models were constructed to diagnose BA. RESULTS: Our findings demonstrated that MMP-7 expression levels, as well as the results of four liver tests and levels of ten bile acids, were significantly different between 86 BA and 59 non-BA patients (P < 0.05). The computational prediction model revealed that MMP-7 levels alone had a higher predictive accuracy [area under the receiver operating characteristic curve (AUC) = 0.966, 95% confidence interval (CI): 0.942, 0.989] than liver test results and bile acid levels. The AUC was 0.890 (95% CI 0.837, 0.943) for liver test results and 0.825 (95% CI 0.758, 0.892) for bile acid levels. Furthermore, bile levels had a higher contribution to enhancing the predictive accuracy of MMP-7 levels (AUC = 0.976, 95% CI 0.953, 1.000) than liver test results. The AUC was 0.983 (95% CI 0.962, 1.000) for MMP-7 levels combined with liver test results and bile acid levels. In addition, we found that MMP-7 levels were highly correlated with gamma-glutamyl transferase levels and the liver fibrosis score. CONCLUSION: The innovative integrated models based on a large number of indicators provide a noninvasive and cost-effective approach for accurately diagnosing BA in children. Video Abstract (MP4 142103 KB).

The predictive value of [TIMP-2]*[IGFBP7] in adverse outcomes for acute kidney injury: a systematic review and meta-analysis.

MATERIALS AND METHODS: Relevant articles published up to 17 June 2023 were retrieved from five databases (Cochrane Library/Embase/PubMed/SinoMed/Web of Science). The pre-established inclusion and exclusion criteria determined the selection of publications. Pooled sensitivity (SEN), specificity (SPE), diagnostic odds ratio, likelihood ratio, and summary receiver operating characteristic curve were employed to assess the predictive value. The presence or potential sources of heterogeneity were investigated via subgroup and SEN analyses. RESULTS: Ten published and eligible studies (1559 cases) were included in the evaluation for the capability of [TIMP-2]*[IGFBP7] to predict the poor prognosis of AKI through the random effect model. Pooled SEN, SPE, diagnostic odds ratio, and positive and negative likelihood ratios were 0.82 (95% CI: 0.77-0.86, I2 = 53.4%), 0.64 (95% CI: 0.61-0.67, I2 = 88.3%), 14.06 (95% CI: 7.31-27.05, I2 = 55.0%), 2.859 (95% CI: 2.15-3.77, I2 = 80.7%), and 0.28 (95% CI: 0.20-0.40, I2 = 35.0%), respectively. The estimated area under the curve was 0.8864 (standard error: 0.0306), and the Q* was 0.7970 (standard error: 0.0299). The endpoints and cutoff values were the main causes of heterogeneity. CONCLUSIONS: [TIMP-2]*[IGFBP7] is possible in predicting poor prognosis of AKI, but it is better to be applied along with other indicators or clinical risk factors.

Glucagon-modified Liposomes Delivering Thyroid Hormone for Anti-obesity Therapy.

BACKGROUND: Thyroid hormones (active form T3) are naturally potent compounds that influence energy expenditure, cholesterol metabolism, and fat oxidation. T3 would be an effective anti-obesity drug if it would not be delivered to the heart and bones, which leads to serious side effects, such as cardiovascular and bone thyrotoxicity, muscle wasting, and so on. METHODS: In this study, we designed a targeted drug delivery system that is a glucagon-modified liposome to deliver T3 to the liver and adipose tissues. RESULTS: The liposomes exhibited excellent properties, including uniform nanoscale particle size, good physicochemical stability, and adequate drug release behavior. More importantly, the glucagon-modified liposomes were enriched in the liver, which minimized the undesired bone and cardiovascular thyrotoxicity of T3. Compared to the control group, T3-loading glucagon-modified liposomes could effectively decrease body weight, reverse hepatic steatosis, and correct hyperlipidemia and hyperglycemia in ob/ob mice, without the undesired cardiovascular and bone thyrotoxicity. CONCLUSION: These findings indicate that delivery of thyroid hormone by glucagon-modified liposomes may provide an effective strategy for anti-obesity therapy.

Deep learning based on carotid transverse B-mode scan videos for the diagnosis of carotid plaque: a prospective multicenter study.

OBJECTIVES: Accurate detection of carotid plaque using ultrasound (US) is essential for preventing stroke. However, the diagnostic performance of junior radiologists (with approximately 1 year of experience in carotid US evaluation) is relatively poor. We thus aim to develop a deep learning (DL) model based on US videos to improve junior radiologists' performance in plaque detection. METHODS: This multicenter prospective study was conducted at five hospitals. CaroNet-Dynamic automatically detected carotid plaque from carotid transverse US videos allowing clinical detection. Model performance was evaluated using expert annotations (with more than 10 years of experience in carotid US evaluation) as the ground truth. Model robustness was investigated on different plaque characteristics and US scanning systems. Furthermore, its clinical applicability was evaluated by comparing the junior radiologists' diagnoses with and without DL-model assistance. RESULTS: A total of 1647 videos from 825 patients were evaluated. The DL model yielded high performance with sensitivities of 87.03% and 94.17%, specificities of 82.07% and 74.04%, and areas under the receiver operating characteristic curve of 0.845 and 0.841 on the internal and multicenter external test sets, respectively. Moreover, no significant difference in performance was noted among different plaque characteristics and scanning systems. Using the DL model, the performance of the junior radiologists improved significantly, especially in terms of sensitivity (largest increase from 46.3 to 94.44%). CONCLUSIONS: The DL model based on US videos corresponding to real examinations showed robust performance for plaque detection and significantly improved the diagnostic performance of junior radiologists. KEY POINTS: • The deep learning model based on US videos conforming to real examinations showed robust performance for plaque detection. • Computer-aided diagnosis can significantly improve the diagnostic performance of junior radiologists in clinical practice.

Artificial Intelligence-Assisted Ultrasound Diagnosis on Infant Developmental Dysplasia of the Hip Under Constrained Computational Resources.

OBJECTIVES: Ultrasound (US) is important for diagnosing infant developmental dysplasia of the hip (DDH). However, the accuracy of the diagnosis depends heavily on expertise. We aimed to develop a novel automatic system (DDHnet) for accurate, fast, and robust diagnosis of DDH. METHODS: An automatic system, DDHnet, was proposed to diagnose DDH by analyzing static ultrasound images. A five-fold cross-validation experiment was conducted using a dataset containing 881 patients to verify the performance of DDHnet. In addition, a blind test was conducted on 209 patients (158 normal and 51 abnormal cases). The feasibility and performance of DDHnet were investigated by embedding it into ultrasound machines at low computational cost. RESULTS: DDHnet obtained reliable measurements and accurate diagnosis predictions. It reported an intra-class correlation coefficient (ICC) on α angle of 0.96 (95% CI: 0.93-0.97), ß angle of 0.97 (95% CI: 0.95-0.98), FHC of 0.98 (95% CI: 0.96-0.99) and PFD of 0.94 (95% CI: 0.90-0.96) in abnormal cases. DDHnet achieved a sensitivity of 90.56%, specificity of 100%, accuracy of 98.64%, positive predictive value (PPV) of 100%, and negative predictive value (NPV) of 98.44% for the diagnosis of DDH. For the measurement task on the US device, DDHnet took only 1.1 seconds to operate and complete, whereas the experienced senior expert required an average 41.4 seconds. CONCLUSIONS: The proposed DDHnet demonstrate state-of-the-art performance for all four indicators of DDH diagnosis. Fast and highly accurate DDH diagnosis is achievable through DDHnet, and is accessible under constrained computational resources.

Synthesis and reproductive toxicity of bisphenol A analogs with cyclic side chains in Caenorhabditis elegans.

Accumulating evidence has shown that bisphenol A (BPA) affects not only the growth and development of reproductive tissues but also disrupts meiosis. Meiotic disturbances lead to the formation of aneuploid gametes, resulting in the inability to conceive, pregnancy loss, and developmental disabilities in offspring. In recent years, increasing health concerns led manufacturers to seek BPA alternatives. In response, BPA analogs have been prepared and investigated in a variety of toxicity-related studies. Despite hopes that these analogs would prove less harmful than BPA, published data show that these alternatives continue to pose a significant risk to human health. In this study, we synthesized two less investigated BPA analogs with cyclic side chains, bisphenol Y (BPY) and bisphenol Z (BPZ), and evaluated their reprotoxic potential using Caenorhabditis elegans. C. elegans were cultured on nematode growth medium plates containing a 1 mM concentration of the dimethyl sulfoxide-dissolved bisphenols. The uptake of the chemicals was via two major routes: ingestion and cuticle diffusion. Following exposure, we evaluated fertilized egg count, germline apoptosis, and embryonic lethality-three parameters previously shown to reliably predict the reprotoxic potential of bisphenols in mammals. Our results indicated that both BPY and BPZ had a significant impact on fertility, resulting in increased germline apoptosis and a reduced number of progeny, without affecting the embryonic viability. After comparison with commercially relevant BPA and bisphenol S, our findings imply that BPA analogs with cyclic side chains, BPY and BPZ, adversely affect meiotic fidelity, resulting in diminished reproductive capacity.

Therapeutic potential and deleterious effect of glucocorticoids on azoxymethane/dextran sulfate sodium-induced colorectal cancer in mice.

Glucocorticoids (GCs) are widely used in the treatment of various autoimmune and inflammatory diseases, including inflammatory bowel disease (IBD). However, the effect of GCs on the progression of colitis-associated colorectal cancer (CAC) has not been well explored. In this study, we first established a colorectal cancer model induced by azoxymethane and dextran sulfate sodium (AOM/DSS) and a colitis model induced by DSS in mice. Dexamethasone (DEX) was then administered at different periods of time to determine its effect on tumorigenesis and tumor progression. Meanwhile, body weight, stool property and fecal blood of mice were recorded. At the end of this study, the number and load of tumors were evaluated, and the expression of proteins associated with cell proliferation was analyzed. To evaluate the inflammation in colon, we detected the level of pro-inflammatory cytokine TNFα, and the mucosal infiltration of inflammatory cells. Our results revealed that AOM injection followed by three cycles of drinking water containing 1.5% DSS successfully induced multiple tumor formation in mouse colon and rectum. Both early and late DEX intervention suppressed tumor growth in mouse colorectum, and downregulated the expression of PCNA and cyclin D1. Moreover, DEX treatment significantly inhibited TNFα production, mucosal infiltration of inflammatory cells, and the activity of MAPK/JNK pathway, particularly early DEX intervention. However, we also found that DEX treatment deteriorated the general state of mouse manifested by greater loss of body weight and rectal bleeding. In summary, both early and late DEX interventions significantly ameliorate colonic inflammation and inhibit the progression of AOM/DSS-induced colorectal cancer, at least partly due to the inhibition of MAPK/JNK pathway. It is noteworthy that the deleterious effect on the general condition of mouse may limit the duration of GCs treatment.

Atovaquone-HSA nano-drugs enhance the efficacy of PD-1 blockade immunotherapy by alleviating hypoxic tumor microenvironment.

BACKGROUND: Hypoxia is inherent character of most solid malignancies, leading to the failure of chemotherapy, radiotherapy and immunotherapy. Atovaquone, an anti-malaria drug, can alleviate tumor hypoxia by inhibiting mitochondrial complex III activity. The present study exploits atovaquone/albumin nanoparticles to improve bioavailability and tumor targeting of atovaquone, enhancing the efficacy of anti-PD-1 therapy by normalizing tumor hypoxia. METHODS: We prepared atovaquone-loaded human serum albumin (HSA) nanoparticles stabilized by intramolecular disulfide bonds, termed HSA-ATO NPs. The average size and zeta potential of HSA-ATO NPs were measured by particle size analyzer. The morphology of HSA-ATO NPs was characterized by transmission electron microscope (TEM). The bioavailability and safety of HSA-ATO NPs were assessed by animal experiments. Flow cytometry and ELISA assays were used to evaluate tumor immune microenvironment. RESULTS: Our data first verified that atovaquone effectively alleviated tumor hypoxia by inhibiting mitochondrial activity both in vitro and in vivo, and successfully encapsulated atovaquone in vesicle with albumin, forming HSA-ATO NPs of approximately 164 nm in diameter. We then demonstrated that the HSA-ATO NPs possessed excellent bioavailability, tumor targeting and a highly favorable biosafety profile. When combined with anti-PD-1 antibody, we observed that HSA-ATO NPs strongly enhanced the response of mice bearing tumor xenografts to immunotherapy. Mechanistically, HSA-ATO NPs promoted intratumoral CD8+ T cell recruitment by alleviating tumor hypoxia microenvironment, thereby enhancing the efficacy of anti-PD-1 immunotherapy. CONCLUSIONS: Our data provide strong evidences showing that HSA-ATO NPs can serve as safe and effective nano-drugs to enhance cancer immunotherapy by alleviating hypoxic tumor microenvironment.

Catalytic Urea Synthesis from Ammonium Carbamate Using a Copper(II) Complex: A Combined Experimental and Theoretical Study.

The synthesis of urea fertilizer is currently the largest CO2 conversion process by volume in the industry. In this process, ammonium carbamate is an intermediate en route to urea formation. We determined that the tetraammineaquacopper(II) sulfate complex, [Cu(NH3)4(OH2)]SO4, catalyzed the formation of urea from ammonium carbamate in an aqueous solution. A urea yield of up to 18 ± 6% was obtained at 120 °C after 15 h and in a high-pressure metal reactor. No significant urea formed without the catalyst. The urea product was characterized by Fourier transform infrared (FT-IR), powder X-ray diffraction (PXRD), and quantitative 1H{13C} NMR analyses. The [Cu(NH3)4(OH2)]SO4 catalyst was then recovered at the end of the reaction in a 29% recovery yield, as verified by FT-IR, PXRD, and quantitative UV-vis spectroscopy. A precipitation method using CO2 was developed to recover and reuse 66 ± 3% of Cu(II). The catalysis mechanism was investigated by the density functional theory at the B3LYP/6-31G** level with an SMD continuum solvent model. We determined that the [Cu(NH3)4]2+ complex is likely an effective catalyst structure. The study of the catalysis mechanism suggests that the coordinated carbamate with [Cu(NH3)4]2+ is likely the starting point of the catalyzed reaction, and carbamic acid can be involved as a transient intermediate that facilitates the removal of an OH group. Our work has paved the way for the rational design of catalysts for urea synthesis from the greenhouse gas CO2.

Optimized Biocatalytic Synthesis of 2-Selenopyrimidine Nucleosides by Transglycosylation*.

Selenium-modified nucleosides are powerful tools to study the structure and function of nucleic acids and their protein interactions. The widespread application of 2-selenopyrimidine nucleosides is currently limited by low yields in established synthetic routes. Herein, we describe the optimization of the synthesis of 2-Se-uridine and 2-Se-thymidine derivatives by thermostable nucleoside phosphorylases in transglycosylation reactions using natural uridine or thymidine as sugar donors. Reactions were performed at 60 or 80 °C and at pH 9 under hypoxic conditions to improve the solubility and stability of the 2-Se-nucleobases in aqueous media. To optimize the conversion, the reaction equilibria in analytical transglycosylation reactions were studied. The equilibrium constants of phosphorolysis of the 2-Se-pyrimidines were between 5 and 10, and therefore differ by an order of magnitude from the equilibrium constants of any other known case. Hence, the thermodynamic properties of the target nucleosides are inherently unfavorable, and this complicates their synthesis significantly. A tenfold excess of sugar donor was needed to achieve 40-48 % conversion to the target nucleoside. Scale-up of the optimized conditions provided four Se-containing nucleosides in 6-40 % isolated yield, which compares favorably to established chemical routes.

Lenvatinib Promotes the Antitumor Effect of Doxorubicin in Anaplastic Thyroid Cancer.

PURPOSE: Anaplastic thyroid cancer (ATC) is a kind of rare thyroid cancer with very poor prognosis. Doxorubicin has been approved in ATC treatment as a single agent, but monotherapy still shows no improvement of the total survival in advanced ATC. Lenvatinib was investigated with encouraging results in treating patients with radioiodine-refractory differentiated thyroid cancer (DTC). However, antitumor efficacy of combination therapy with lenvatinib and doxorubicin remains largely unclear. MATERIALS AND METHODS: The antitumor efficacy of combination therapy with lenvatinib and doxorubicin on ATC cell proliferation was assessed by the MTT assay and colony formation. Flow cytometry was employed to assess ATC cells' apoptosis and cell cycle arrest in response to combination therapy. Transwell assay was used to test the migration and invasion in response to combination therapy. Xenograft models were used to test its in vivo antitumor activity. RESULTS: Lenvatinib monotherapy was less effective than doxorubicin in treating ATC cell lines and xenograft model. The combination therapy of lenvatinib and doxorubicin significantly inhibited ATC cell proliferation and tumor growth in nude mice, and induced cell apoptosis and cell cycle arrest as compared to lenvatinib or doxorubicin monotherapy. CONCLUSION: Lenvatinib promotes the antitumor effect of doxorubicin in ATC cell and xenograft model. The lenvatinib/doxorubicin combination may be a potential candidate therapeutic approach for anaplastic thyroid cancer.

Time series computational prediction of vaccines for influenza A H3N2 with recurrent neural networks.

Influenza viruses are persistently threatening public health, causing annual epidemics and sporadic pandemics due to rapid viral evolution. Vaccines are used to prevent influenza infections but the composition of the influenza vaccines have to be updated regularly to ensure its efficacy. Computational tools and analyses have become increasingly important in guiding the process of vaccine selection. By constructing time-series training samples with splittings and embeddings, we develop a computational method for predicting suitable strains as the recommendation of the influenza vaccines using recurrent neural networks (RNNs). The Encoder-decoder architecture of RNN model enables us to perform sequence-to-sequence prediction. We employ this model to predict the prevalent sequence of the H3N2 viruses sampled from 2006 to 2017. The identity between our predicted sequence and recommended vaccines is greater than 98% and the Pepitope<0.2 indicates their antigenic similarity. The multi-step vaccine prediction further demonstrates the robustness of our method which achieves comparable results in contrast to single step prediction. The results show significant matches of the recommended vaccine strains to the circulating strains. We believe it would facilitate the process of vaccine selection and surveillance of seasonal influenza epidemics.

HopPER: an adaptive model for probability estimation of influenza reassortment through host prediction.

BACKGROUND: Influenza reassortment, a mechanism where influenza viruses exchange their RNA segments by co-infecting a single cell, has been implicated in several major pandemics since 19th century. Owing to the significant impact on public health and social stability, great attention has been received on the identification of influenza reassortment. METHODS: We proposed a novel computational method named HopPER (Host-prediction-based Probability Estimation of Reassortment), that sturdily estimates reassortment probabilities through host tropism prediction using 147 new features generated from seven physicochemical properties of amino acids. We conducted the experiments on a range of real and synthetic datasets and compared HopPER with several state-of-the-art methods. RESULTS: It is shown that 280 out of 318 candidate reassortants have been successfully identified. Additionally, not only can HopPER be applied to complete genomes but its effectiveness on incomplete genomes is also demonstrated. The analysis of evolutionary success of avian, human and swine viruses generated through reassortment across different years using HopPER further revealed the reassortment history of the influenza viruses. CONCLUSIONS: Our study presents a novel method for the prediction of influenza reassortment. We hope this method could facilitate rapid reassortment detection and provide novel insights into the evolutionary patterns of influenza viruses.

Unique Thia-Baeyer-Villiger-Type Oxidation of Dibenzothiophene Sulfoxides Derivatives.

The present research has demonstrated that selective C-S bond cleavages of dibenzothiophene and its derivatives are feasible by thia-Baeyer-Villiger type oxidation, i. e. the oxygen insertion process within a sulfoxide-carbon linkage, in the presence of porphyrin iron (III) and by ultraviolet irradiation originating from sunlight, high pressure Hg-lamp or residentially germicidal ultraviolet lamp under very mild conditions. This reaction with tert-butylhydroperoxide at 30.0 °C leads to dibenzo[1,2]oxathiin-6-oxide (PBS) in 83.2 % isolated yield or its hydrated products, 2-(2-hydroxyphenyl)-benzenesulfinic derivatives (HPBS) in near 100 % yield based HPLC data. PBS and HPBS are a type of biological products detected on the C-S bond cleavage step through various oxidative biodesulfurization (OBDS) pathways, and are useful synthetic intermediates and fine chemicals. These observations may contribute on understanding delicately molecular aspect of OBDS in the photosynthesis system, expanding the C-S cleavage chemistry of S-heterocyclic compounds and approaching toward biomemic desulfurization with respect to converting sulfur contaminants to chemically beneficial blocks as needed and performing under the ambient conditions.

Molecular insights into evolution, mutations and receptor-binding specificity of influenza A and B viruses from outpatients and hospitalized patients in Singapore.

BACKGROUND: This study compared the genomes of influenza viruses that caused mild infections among outpatients and severe infections among hospitalized patients in Singapore, and characterized their molecular evolution and receptor-binding specificity. METHODS: The complete genomes of influenza A/H1N1, A/H3N2 and B viruses that caused mild infections among outpatients and severe infections among inpatients in Singapore during 2012-2015 were sequenced and characterized. Using various bioinformatics approaches, we elucidated their evolutionary, mutational and structural patterns against the background of global and vaccine strains. RESULTS: The phylogenetic trees of the 8 gene segments revealed that the outpatient and inpatient strains overlapped with representative global and vaccine strains. We observed a cluster of inpatients with A/H3N2 strains that were closely related to vaccine strain A/Texas/50/2012(H3N2). Several protein sites could accurately discriminate between outpatient versus inpatient strains, with site 221 in neuraminidase (NA) achieving the highest accuracy for A/H3N2. Interestingly, amino acid residues of inpatient but not outpatient isolates at those sites generally matched the corresponding residues in vaccine strains, except at site 145 of hemagglutinin (HA). This would be especially relevant for future surveillance of A/H3N2 strains in relation to their antigenicity and virulence. Furthermore, we observed a trend in which the HA proteins of influenza A/H3N2 and A/H1N1 exhibited enhanced ability to bind both avian and human host cell receptors. In contrast, the binding ability to each receptor was relatively stable for the HA of influenza B. CONCLUSIONS: Overall, our findings extend our understanding of the molecular and structural evolution of influenza virus strains in Singapore within the global context of these dynamic viruses.

Lysozyme Aptamer-Functionalized Magnetic Nanoparticles for the Purification of Lysozyme from Chicken Egg White.

Lysozyme is in high demand due to its many favorable characteristics such as being naturally occurring, non-toxic, and easy to digest and absorb. Recently, superparamagnetic nanoparticles with strong magnetic responsiveness have attracted significant interest for enzyme purification. The aptamer of the enzyme can be chemically synthesized rapidly at a large scale using simple and low-cost preparation methods. Therefore, Fe3O4 nanoparticles (Fe3O4 NPs) were prepared by chemical co-precipitation and were then functionalized with amino groups to produce NH2-Fe3O4 NPs. The specific reaction of aldehyde and amino groups was used to attach lysozyme aptamers with specific sequences to NH2-Fe3O4 NPs to produce Apt-NH2-Fe3O4 NPs. The synthesized materials were characterized using transmission electron microscopy (TEM), X-ray diffraction (XRD), Fourier transform infrared spectroscopy (FTIR), hysteresis loop analysis, and thermogravimetric analysis (TGA). The optimal experimental conditions for adsorption of lysozyme were investigated. The effects of initial lysozyme concentration, adsorption time, pH, reaction temperature, and ionic strength were determined. The maximum adsorption capacity and relevant activity of Apt-NH2-Fe3O4 NPs was 460 mg·g-¹ and 16,412 ± 55 U·mg-¹ in an aqueous lysozyme solution. In addition, as demonstrated by sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE) electrophoresis analysis, lysozyme could be separated from crude fresh egg white using Apt-NH2-Fe3O4 NPs with an amount up to 113 ± 4.2 mg·g-¹ and an activity up to 16,370 46 U·mg-¹.

Computational identification of physicochemical signatures for host tropism of influenza A virus.

Avian influenza viruses from migratory birds have managed to cross host species barriers and infected various hosts like human and swine. Epidemics and pandemics might occur when influenza viruses are adapted to humans, causing deaths and enormous economic loss. Receptor-binding specificity of the virus is one of the key factors for the transmission of influenza viruses across species. The determination of host tropism and understanding of molecular properties would help identify the mechanism why zoonotic influenza viruses can cross species barrier and infect humans. In this study, we have constructed computational models for host tropism prediction on human-adapted subtypes of influenza HA proteins using random forest. The feature vectors of the prediction models were generated based on seven physicochemical properties of amino acids from influenza sequences of three major hosts. Feature aggregation and associative rules were further applied to select top 20 features and extract host-associated physicochemical signatures on the combined model of nonspecific subtypes. The prediction model achieved high performance ( Accuracy=0.948 , Precision=0.954 , MCC=0.922 ). Support and confidence rates were calculated for the host class-association rules. The results indicated that secondary structure and normalized Van der Waals volume were identified as more important physicochemical signatures in determining the host tropism.

Predicting antigenic variants of H1N1 influenza virus based on epidemics and pandemics using a stacking model.

H1N1 is the earliest emerging subtype of influenza A viruses with available genomic sequences, has caused several pandemics and seasonal epidemics, resulting in millions of deaths and enormous economic losses. Timely determination of new antigenic variants is crucial for the vaccine selection and flu prevention. In this study, we chronologically divided the H1N1 strains into several periods in terms of the epidemics and pandemics. Computational models have been constructed to predict antigenic variants based on epidemic and pandemic periods. By sequence analysis, we demonstrated the diverse mutation patterns of HA1 protein on different periods and that an individual model built upon each period can not represent the variations of H1N1 virus. A stacking model was established for the prediction of antigenic variants, combining all the variation patterns across periods, which would help assess a new influenza strain's antigenicity. Three different feature extraction methods, i.e. residue-based, regional band-based and epitope region-based, were applied on the stacking model to verify its feasibility and robustness. The results showed the capability of determining antigenic variants prediction with accuracy as high as 0.908 which performed better than any of the single models. The prediction performance using the stacking model indicates clear distinctions of mutation patterns and antigenicity between epidemic and pandemic strains. It would also facilitate rapid determination of antigenic variants and influenza surveillance.