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BackgroundSeveral protein markers, including vimentin, have been used to diagnose human melanoma. Because melanoma often has metastasized by the time of diagnosis, early markers prognostic for metastatic potential need to be identified. Commonly, vimentin is found in mesenchymal cells, and keratins are present in epithelial cells, but recent studies report coexpression of vimentin and keratin(s) in epithelial and nonepithelial neoplasms, including some melanomas.PurposeOur purpose was to determine whether coexpression of vimentin and keratin(s) is correlated with tumor cell invasion and metastatic behavior.MethodsWe evaluated nine human melanoma cell lines expressing vimentin and other markers of aggressive tumor behavior (HMB-45, S-100, HLA-ABC class I and HLA-DR class II histocompatibility antigens, and K8 and K18 keratins). Levels of K8 and K18 keratins were determined in the highly metastatic C8161 cell line, the poorly metastatic A375P line, and the moderately metastatic A375M line. To determine whether the presence of keratin affects migratory ability, we altered the conformational structure of keratin filaments in C8161 cells by transfection with a mutant K18 complementary DNA. We also determined messenger RNA levels of human type IV collagenase, an enzyme marker for invasion and metastasis.ResultsIn A375P cells, two-dimensional electrophoresis with Coomassie-stained gels, immunoblotting, and immunofluorescence staining showed no detectable levels of K8 or K18. A375M cells showed low levels of K8 and K18 by Western and Northern blotting, with a distinctive fluorescent subpopulation of cells. In comparison, K8 and K18 levels in C8161 cells were high in all cells. Type IV collagenase messenger RNA levels were lowest in A375P cells and highest in C8161 cells, correlating with invasive ability in vitro and metastatic potential in athymic nude mice. The transfectant clones C1070-10 and C1070-14 derived from the C8161 parent line showed dramatic morphological changes, disrupted keratin filaments, and decreased invasive and metastatic potential directly correlated with a reduction in migratory activity.ConclusionThese findings show a correlation between the coexpression of vimentin with K8 and K18 keratins and the invasive and metastatic behavior of three representative human melanoma cell lines.

Purpose: Trastuzumab emtansine (T-DM1) is an antibody-drug conjugate comprising trastuzumab and DM1, a microtubule polymerization inhibitor, covalently bound via a stable thioether linker. To characterize the pharmacokinetics (PK) of T-DM1 in patients with human epidermal growth factor receptor 2 (HER2)-positive metastatic breast cancer, data from four studies (TDM3569g, TDM4258g, TDM4374g, and TDM4688g) of single-agent T-DM1 administered at 3.6 mg/kg every 3 weeks (q3w) were assessed in aggregate.Methods: Multiple analytes-T-DM1, total trastuzumab (TT), DM1, and key metabolites-were quantified using enzyme-linked immunosorbent assays or liquid chromatography tandem mass spectrometry. PK parameters of T-DM1, TT, and DM1 exposure were calculated using standard noncompartmental approaches and correlated to efficacy (objective response rate) and safety (platelet counts, hepatic transaminase concentrations). Immunogenicity was evaluated by measuring anti-therapeutic antibodies (ATA) to T-DM1 after repeated dosing using validated bridging antibody electrochemiluminescence or enzyme-linked immunosorbent assays.Results: PK parameters for T-DM1, TT, and DM1 were consistent across studies at cycle 1 and steady state. T-DM1 PK was not affected by residual trastuzumab from prior therapy or circulating extracellular domain of HER2. No significant correlations were observed between T-DM1 exposure and efficacy, thrombocytopenia, or increased concentrations of transaminases. Across the studies, ATA formation was detected in 4.5% (13/286) of evaluable patients receiving T-DM1 q3w.Conclusions: The PK profile of single-agent T-DM1 (3.6 mg/kg q3w) is predictable, well characterized, and unaffected by circulating levels of HER2 extracellular domain or residual trastuzumab. T-DM1 exposure does not correlate with clinical responses or key adverse events. [ABSTRACT FROM AUTHOR]

Urinary tract stones are a common and frequently recurring medical issue. Accurately predicting the success rate after surgery can help avoid ineffective medical procedures and reduce unnecessary healthcare costs. This study collected data from patients with upper ureter stones who underwent extracorporeal shock wave lithotripsy, including cases of successful as well as unsuccessful stone removal after the first and second lithotripsy procedures, and constructed prediction systems for the outcomes of the first and second lithotripsy procedures. Features were extracted from three categories of information: patient characteristics, stone characteristics, and extracorporeal shock wave lithotripsy machine data, and additional features were created using Feature Creation. Finally, the impact of features on the models was analyzed using six methods to calculate feature importance. Our prediction model for the first lithotripsy, selected from among 43 methods and seven ensemble learning techniques, achieves an AUC of 0.91. For the second lithotripsy, the AUC reaches 0.76. The results indicate that the detailed and binary information provided by patients regarding their history of stone experiences contributes differently to the predictive accuracy of the first and second lithotripsy procedures. The prediction tool is available at https://predictor.isu.edu.tw/ks., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Purpose: Locomotor experiences in upright postures are essential for developing toddlers' mobility and social functions. This pilot randomised controlled trial aimed to examine the effectiveness of using a modified ride-on car (ROC) with postural combinations of standing and sitting on mobility and social function in toddlers with motor delays., Materials and Methods: Nineteen participants aged 1-3 years with mild, moderate or severe motor delays were randomly assigned to four ROC groups. The ROC groups had different combinations of standing and sitting, namely standing for 70 min (ROC-Stand 70 , five participants), standing for 45 min (ROC-Stand 45 , four participants), standing for 25 min (ROC-Stand 25 , five participants) and sitting for 70 min (ROC-Sit 70 , five participants). All participants participated in 2-h sessions twice a week for 12 weeks. The Pediatric Evaluation of Disability Inventory, Goal Attainment Scaling and Bayley-III tests were administered before and after the intervention, and after 12 weeks of follow-up. A mixed-model analysis of variance was used to compare inter- and intra-group differences. This trial was registered at ClinicalTrials.gov (NCT03707405)., Results: All groups showed significantly improved mobility, social function and goal achievement at the post-test ( p  < .001). However, no significant changes were observed in Bayley scores., Conclusions: Combining physical and social environmental modifications with active exploration is crucial for early power mobility training in toddlers with motor delays. To enhance the robustness and generalisability of our findings, future studies should include larger sample sizes, consider variations in motor delays, and measure energy expenditure during the intervention.Implications for rehabilitationProviding active exploratory experience using ride-on cars (ROCs) with various postural combinations can improve a child's mobility.The ROC training with various postural combinations can improve social function, and the degree of improvement may depend on the severity of motor delays.Setting goals with caregivers and incorporating their roles in the training process can empower them to interact with children more frequently and actively.

Atopic dermatitis (AD) is a complex, chronic inflammatory skin disease. An estimated 57.5% of asthmatic patients and 50.7% of rhinitis patients are allergic to cockroaches in Taiwan. However, the role of cockroaches in the pathogenesis of AD is undetermined. Oral tolerance might be another strategy for protecting against AD and allergic inflammation by regulating T helper 2 (Th2) immune responses. Aim to examine the underlying immunologic mechanism, we developed an AD-like murine model by skin-brushing with cockroach Per a 2. We also investigated whether the systemic inflammation of AD in this murine model could be improved by specific tolerance to Lactococcus lactis-expressing Per a 2, which was administered orally. Repeated painting of Per a 2 without adjuvant to the skin of mice resulted in increased total IgE, Per a 2-specific IgE, and IgG1, but not IgG2a. In addition, epidermal thickening was significantly increased, there were more scratch episodes, and there were increases in total white blood cells (eosinophil, neutrophil, and lymphocyte) and Th2 cytokines (Interleukin (IL)-4, IL-5, IL-9, and IL-13) in a dose-dependent manner. The results revealed that oral administration of L. lactis-Per a 2 ameliorated Per a 2-induced scratch behavior and decreased the production of total IgE, Per a 2-specific IgE, and IgG1. Furthermore, L. lactis-Per a 2 treatment also suppressed inflammatory infiltration, expressions of thymic stromal lymphopoietin (TSLP) and IL-31 in skin lesions, and downregulated splenic IL-4 and IL-13 in Per a 2-induced AD mice. This study provides evidence supporting that repeated brushing of aeroallergens to the skin leads to atopic dermatitis phenotypes and oral allergen-specific immune tolerance can ameliorate AD-like symptoms and systemic inflammation and prevent progression of atopic march., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2023 Lee et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Background: The Invasive Respiratory Infection Surveillance (IRIS) Consortium was established to assess the impact of the COVID-19 pandemic on invasive diseases caused by Streptococcus pneumoniae, Haemophilus influenzae, Neisseria meningitidis, and Streptococcus agalactiae. We aimed to analyse the incidence and distribution of these diseases during the first 2 years of the COVID-19 pandemic compared to the 2 years preceding the pandemic., Methods: For this prospective analysis, laboratories in 30 countries and territories representing five continents submitted surveillance data from Jan 1, 2018, to Jan 2, 2022, to private projects within databases in PubMLST. The impact of COVID-19 containment measures on the overall number of cases was analysed, and changes in disease distributions by patient age and serotype or group were examined. Interrupted time-series analyses were done to quantify the impact of pandemic response measures and their relaxation on disease rates, and autoregressive integrated moving average models were used to estimate effect sizes and forecast counterfactual trends by hemisphere., Findings: Overall, 116 841 cases were analysed: 76 481 in 2018-19, before the pandemic, and 40 360 in 2020-21, during the pandemic. During the pandemic there was a significant reduction in the risk of disease caused by S pneumoniae (risk ratio 0·47; 95% CI 0·40-0·55), H influenzae (0·51; 0·40-0·66) and N meningitidis (0·26; 0·21-0·31), while no significant changes were observed for S agalactiae (1·02; 0·75-1·40), which is not transmitted via the respiratory route. No major changes in the distribution of cases were observed when stratified by patient age or serotype or group. An estimated 36 289 (95% prediction interval 17 145-55 434) cases of invasive bacterial disease were averted during the first 2 years of the pandemic among IRIS-participating countries and territories., Interpretation: COVID-19 containment measures were associated with a sustained decrease in the incidence of invasive disease caused by S pneumoniae, H influenzae, and N meningitidis during the first 2 years of the pandemic, but cases began to increase in some countries towards the end of 2021 as pandemic restrictions were lifted. These IRIS data provide a better understanding of microbial transmission, will inform vaccine development and implementation, and can contribute to health-care service planning and provision of policies., Funding: Wellcome Trust, NIHR Oxford Biomedical Research Centre, Spanish Ministry of Science and Innovation, Korea Disease Control and Prevention Agency, Torsten Söderberg Foundation, Stockholm County Council, Swedish Research Council, German Federal Ministry of Health, Robert Koch Institute, Pfizer, Merck, and the Greek National Public Health Organization., Competing Interests: Declaration of interests The UK Health Security Agency's Immunisation and Vaccine Preventable Diseases Division has provided vaccine manufacturers (GSK, Pfizer, and Sanofi) with post-marketing surveillance reports, which the Marketing Authorization Holders are required to submit to the UK Licensing authority in compliance with their Risk Management Strategy. A cost recovery charge is made for these reports. The UK Health Security Agency's Respiratory and Vaccine Preventable Bacteria Reference Unit has received unrestricted research grants from Pfizer to participate in pneumococcal surveillance projects. CHI de Créteil (France) received research grants from the French Public Health Agency, Pfizer, and MSD. University Hospitals Leuven (Belgium) received research grants from Merck-MSD and Pfizer, and consulting fees from Merck-MSD. SD received personal payments or honoraria from Pfizer. The Swiss National Reference Center for Invasive Pneumococci received funding from the Federal Office of Public Health. MH has received grants from Pfizer and personal fees (for being on an advisory board) from Pfizer and Merck Sharp & Dohme. The National Medicines Institute (Warsaw, Poland) received funding from the Polish Ministry of Health, the Polish Ministry of Science and Higher Education, Pfizer, and MSD. AS received payments from MSD and Pfizer for lectures, and from MSD, Pfizer, and Sanofi Pasteur for participation in advisory boards. AS is the unpaid Vice President of the European Meningococcal and Haemophilus Disease Society. The Finnish Institute for Health and Welfare (Finland) received research funding from Pfizer. ABB is an unpaid adviser to WHO, providing expertise related to vaccines and antimicrobial resistance. ABB is an unpaid General Assembly member (2022 onwards), and has been a board member (2016–22) and Secretary (2018–22), of the International Society of Pneumonia and Pneumococcal Diseases (ISPPD). MD has received financial support from Pfizer to attend national scientific meetings. MdP received grant funding from the National Research Foundation (South Africa) and the Bill & Melinda Gates Foundation to support the International Pathogenic Neisseria Conference (IPNC) 2022 meeting. MdP received personal support from the ISPPD to participate in the ISPPD conference in 2022, and was a member of the organising and scientific committee for the IPNC meeting in 2022. HH and MC received a grant from Pfizer (W1243730) to investigate Irish pneumococcal serotypes by whole-genome sequencing. HH received payment from Scottish Hospitals Enquiry for expert testimony. HH was the President of the Healthcare Infection Society (2018–22). KAJ received personal royalties from GlaxoSmithKline, and personal honoraria from the Wellcome Trust. SL performs contract research on behalf of St George's University of London for pharmaceutical companies (GlaxoSmithKline, Pfizer, and Sanofi), including vaccine manufacturers, but does not receive any personal remuneration. T-TL received consulting fees from the Trond Mohn Foundation. T-TL is an unpaid board member of the European Meningococcal and Haemophilus Disease Society and the German Society for Hygiene and Microbiology, committee for microbial systematics, population genetics and infection epidemiology. SM participated on an unpaid advisory board for Pfizer for the meningococcal type B vaccine in South Africa in 2020. WM received funding from GlaxoSmithKline and Pfizer for investigator-initiated research on meningitis B (MenB) strain vaccine coverage. CS received financial support for flights, accommodation, and registration to attend the 2022 ISPPD meeting in Canada. H-CS received funding from Pfizer for a pneumococcal carriage project. H-CS received funding for participation on a data safety monitoring board or advisory board for MSD. LS received personal support from the European Centre for Disease Prevention and Control for attending the European Scientific Conference on Applied Infectious Disease Epidemiology in 2022. MPGvdL received consulting fees from Pfizer, Merck, and GlaxoSmithKline; payment or honoraria from Pfizer and Merck; and support for attending meetings or travel, or both, from Pfizer. AvG is the chairperson for the National Advisory Group on Immunization of South Africa. NMvS received fees for services and consulting fees from MSD and GlaxoSmithKline, and research funding from the Dutch Health Counsel, US National Institutes of Health, and Amsterdam University Medical Centers, and from MSD and GlaxoSmithKline, which are all directly paid to the institution. NMvS holds a patent (WO 2013/020090 A3) on vaccine development against Streptococcus pyogenes. NMvS is an unpaid scientific adviser to the ItsME foundation, and a scientific adviser to the StrepAotearoa New Zealand project but fees are paid to the University of Amsterdam. NMvS holds personal stocks in Genmab. JY received payments for lectures given at scientific meetings organised by MSD and Pfizer; received support from MSD and Pfizer to attend national and international scientific meetings; and participated in advisory boards for MSD and Pfizer. DS is supported by an Oxford Clarendon Scholarship. All other authors declare no competing interests., (Copyright © 2023 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 license. Published by Elsevier Ltd.. All rights reserved.)

The progression of allergic diseases from atopic dermatitis in childhood to other allergic conditions such as asthma in later life is often referred to as the atopic march. In order to study the relationship between cutaneous sensitization by aeroallergen and atopic march, we established a mouse model to test the hypothesis using American cockroaches and house dust mites as the model allergens. Mice were sensitized via skin with native cockroach extract (CraA) or recombinant Per a 2 and Der p 2 proteins without adjuvant. Each mouse was subjected to a total of three 1-week patching sensitizations with a 2-week interval in between each application. The resulting immunological variables in sera, scratching behavior, airway hyperresponsiveness (AHR), and pathology of skin lesions and nasal mucosa were evaluated. In mice, application of CraA, rPer a 2, and rDer p 2 aeroallergens through skin patching induced significantly high levels of both total IgE and specific IgEs. The epicutaneous sensitization after a subsequent allergen challenge showed a significant increase in scratch bouts, AHR, epidermal thickness, and eosinophil counts in the skin compared with the control mice. In addition, stimulation of murine splenocytes with allergens increased higher levels of Th2 cytokines, anti-inflammatory cytokines, and chemokines excretion. Our study provides evidence supporting that epicutaneous sensitization to aeroallergens also led to nasal and airway symptoms comparable to atopic march as described in humans. We hope this new allergy model will be useful in the development of new preventive and therapeutic strategies aimed at stopping the atopic march., Competing Interests: The authors declare that they have no competing interests., (Copyright: © 2023 Lee et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

(1) Background: Breast cancer is the second leading cause of cancer death among women. The accurate prediction of survival intervals will help physicians make informed decisions about treatment strategies or the use of palliative care. (2) Methods: Gene expression is predictive and correlates to patient prognosis. To establish a reliable prediction tool, we collected a total of 1187 RNA-seq data points from breast cancer patients (median age 58 years) in Fragments Per Kilobase Million (FPKM) format from the TCGA database. Among them, we selected 144 patients with date of death information to establish the SaBrcada-AD dataset. We first normalized the SaBrcada-AD dataset to TPM to build the survival prediction model SaBrcada. After normalization and dimension raising, we used the differential gene expression data to test eight different deep learning architectures. Considering the effect of age on prognosis, we also performed a stratified random sampling test on all ages between the lower and upper quartiles of patient age, 48 and 69 years; (3) Results: Stratifying by age 61, the performance of SaBrcada built by GoogLeNet was improved to a highest accuracy of 0.798. We also built a free website tool to provide five predicted survival periods: within six months, six months to one year, one to three years, three to five years, or over five years, for clinician reference. (4) Conclusions: We built the prediction model, SaBrcada, and the website tool of the same name for breast cancer survival analysis. Through these models and tools, clinicians will be provided with survival interval information as a basis for formulating precision medicine.

CVM-1118 (foslinanib) is a phosphoric ester compound selected from 2-phenyl-4-quinolone derivatives. The NCI 60 cancer panel screening showed CVM-1125, the major active metabolite of CVM-1118, to exhibit growth inhibitory and cytotoxic effects at nanomolar range. CVM-1118 possesses multiple bioactivities, including inducing cellular apoptosis, cell cycle arrest at G 2 /M, as well as inhibiting vasculogenic mimicry (VM) formation. The TNF receptor associated protein 1 (TRAP1) was identified as the binding target of CVM-1125 using nematic protein organization technique (NPOT) interactome analysis. Further studies demonstrated CVM-1125 reduced the protein level of TRAP1 and impeded its downstream signaling by reduction of cellular succinate levels and destabilization of HIF-1α. The pharmacogenomic biomarkers associated with CVM-1118 were also examined by Whole Genome CRISPR Knock-Out Screening. Two hits ( STK11 and NF2 ) were confirmed with higher sensitivity to the drug in cell knock-down experiments. Biological assays indicate that the mechanism of action of CVM-1118 is via targeting TRAP1 to induce mitochondrial apoptosis, suppress tumor cell growth, and inhibit vasculogenic mimicry formation. Most importantly, the loss-of-function mutations of STK11 and NF2 are potential biomarkers of CVM-1118 which can be applied in the selection of cancer patients for CVM-1118 treatment. CVM-1118 is currently in its Phase 2a clinical development., Competing Interests: Authors LS, Y-LC, C-CH, D-SC, and Y-WC were employed by the company TaiRx, Inc. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. The authors declare Inoviem Scientific performed the target identification using their own proprietary technology NPOT, and provided valuable discussion on the evaluation and confirmation of the identified target., (Copyright © 2023 Shen, Chen, Huang, Shyu, Seftor, Seftor, Hendrix, Chien and Chu.)

Importance: A child's independent mobility, environments, and mastery motivation are critical factors during early development., Objective: To examine the effectiveness of ride-on car (ROC) training with a standing (ROC-Stand) or a sitting posture (ROC-Sit) in enhancing children's mastery motivation and decreasing parenting stress levels., Design: Randomized controlled trial (RCT) with a multiple pretest-posttest design., Setting: Hospital-based environment in northern Taiwan., Participants: Thirty-nine children with disabilities ages 1 to 3 yr were randomly assigned to ROC-Stand (n = 16), ROC-Sit (n = 12), or conventional therapy (control; n = 11). All groups received 2-hr training sessions two times a week for 12 wk and then a 12-wk follow-up period that involved only regular therapy., Measures: Assessments included the Revised Dimensions of Mastery Questionnaire-Chinese version and the Parenting Stress Index., Results: All groups showed significant changes in social persistence with adults, mastery pleasure, and general competence after the intervention. The two ROC training groups showed a significantly greater decrease in parenting stress than the control group. In addition, increased general competence of the ROC-Stand group also strongly correlated with decreased parent-child dysfunctional interaction., Conclusions and Relevance: This RCT verifies the effectiveness of ROC training and offers a novel approach to increase children's mastery motivation and decrease parenting stress. What This Article Adds: Providing a large amount of active, exploratory experiences with goal-directed, moderately challenging tasks and cooperation with caregivers may result in the greatest benefits to young children with motor disabilities., (Copyright © 2022 by the American Occupational Therapy Association, Inc.)

Purposes: This study discussed the accommodative response and pupil size of myopic adults using a double-mirror system (DMS). The viewing distance could be extended to 2.285 m by using a DMS, which resulted in a reduction and increase in the accommodative response and pupil size, respectively. By using a DMS, the reduction of the accommodative response could improve eye fatigue with near work. Method: Sixty subjects aged between 18 and 22 years old were recruited in this study, and the average age was 20.67 ± 1.09. There were two main steps in the experimental process. In the first step, we examined the subjects’ refraction state and visual function, and then fitted disposable contact lenses with a corresponding refractive error. In the second step, the subjects gazed at an object from a viewing distance of 0.4 m and at a virtual image through a DMS, respectively, and the accommodative response and pupil size were measured using an open field autorefractor. Results: When the subjects gazed at the object from a distance of 0.4 m, or gazed at the virtual image through a DMS, the mean value of the accommodative response was 1.74 ± 0.43 or 0.16 ± 0.47 D, and the pupil size was 3.98 ± 0.06 mm or 4.18 ± 0.58 mm, respectively. With an increase in the viewing distance from 0.4 m to 2.285 m, the accommodative response and pupil size were significantly reduced about 1.58 D and enlarged about 0.2 mm, respectively. For three asterisk targets of different sizes (1 cm × 1 cm, 2 cm × 2 cm, and 3 cm × 3 cm), the mean accommodative response and pupil size through the DMS was 0.19 ± 0.16, 0.27 ± 0.24, 0.26 ± 0.19 D; and 4.20 ± 1.02, 3.94 ± 0.73, 4.21 ± 0.57 mm, respectively. The changes of the accommodative response and pupil size were not significant with the size of the targets (p > 0.05). In the low or high myopia group, the accommodative response of 0.4 m and 2.285 m was 1.68 ± 0.42 D and 0.21 ± 0.48 D; and 1.88 ± 0.25 D and 0.05 ± 0.40 D, respectively. The accommodative response was significantly reduced by 1.47 D and 1.83 D for these two groups. The accommodative microfluctuations (AMFs) were stable when a DMS was used; on the contrary, the AMFs were unstable at a viewing distance of 0.4 m. Conclusions: In this study, the imaging through a DMS extended the viewing distance and enlarged the image, and resulted in a reduction in the accommodative response and an increase in the pupil size. For the low myopia group and the high myopia group, the accommodative response and pupil size were statistically significantly different before and after the use of the DMS. The reduction of the accommodative response could be applied for the improvement of asthenopia.

There are many factors that affect vitamin D supplementation, including those from the theory of planned behaviour (TPB); however, how the perceived benefit acts in the model remains unknown. In the current study, we tested the efficacy of the TPB and the impacts of the perceived benefit (PBE) in the model. The subjects were 287 customers who purchased vitD from pharmacies in major cities in Taiwan. A structured questionnaire was used to collect the data. t -tests, analysis of variance (ANOVA), regression analyses, and path analysis via SPSS and AMOS were used to analyse the data. The original TPB model explained 47.5% of the variance of intention with the three variables of attitude (β = 0.261), perceived behavioural control (β = 0.183), and subjective norms (β = 0.169). The model that incorporated PBE increased the explained variance to 59.7%, and PBE became the strongest predictor (β = 0.310) and a significant mediator linking attitude, subjective norms, perceived control (ANC) with supplementation intention. PBE and attitude were the two most important variables in predicting vitD supplementation intention. We suggest that updated information regarding dietary sources of vitD and its benefits should be included in health- or nutrition-related courses in education programs for the overall health of the nation.

Background: Fractures are a great health issue associated with morbidity, quality of life, life span, and health care expenditure. Fractures are correlated with cardiovascular disease, type 2 diabetes mellitus, cerebrovascular disease, and some psychiatric disorders. However, representative national data are few, and longitudinal cohort studies on the association between schizophrenia and the subsequent fracture risk are scant. We designed a nationwide population-based cohort study to investigate the association of schizophrenia with hip, vertebral, and wrist fractures over a 10-year follow-up., Methods: Data of patients with schizophrenia (International Classification of Diseases, Ninth Revision, Clinical Modification code 295) and matched over January 2000-December 2009) were extracted from Taiwan National Health Insurance Research Database. A Cox proportional-hazards regression model was constructed to calculate hazard ratios (HRs) for fractures between the schizophrenia and control cohorts., Results: Of 2028 people with schizophrenia (mean age: 36.3 years, 49.4% female), 89 (4.4%) reported newly diagnosed fractures-significantly higher than the proportion in the control population (257, 3.2%; P = 0.007). The incidences of hip (1.2%, P = 0.009) and vertebral (2.6%, P = 0.011) fractures were significantly higher in the schizophrenia cohort than in the control cohort. In Cox regression analysis, hip (adjusted HR: 1.78, 95% confidence interval [CI]: 1.08-2.93) and vertebral (adjusted HR: 1.40, 95% CI: 1.01-1.95) fracture risks were significantly higher in patients with schizophrenia. Furthermore, a sex-based subgroup analysis revealed that the risk of hip fracture remained significantly higher in female patients with schizophrenia (HR: 2.68, 95% CI: 1.32-5.44) than in female controls. On the other hand, there was no significant interaction between effects of sex and schizophrenia on the risk of fractures., Conclusions: Over a 10-year follow-up, hip and vertebral fracture risks were higher in the people with schizophrenia than in the controls. The risk of fractures in patients with schizophrenia does not differ between female and male., (© 2022. The Author(s).)

To change the expression of the flanking genes by inserting T-DNA into the genome is commonly used in rice functional gene research. However, whether the expression of a gene of interest is enhanced must be validated experimentally. Consequently, to improve the efficiency of screening activated genes, we established a model to predict gene expression in T-DNA mutants through machine learning methods. We gathered experimental datasets consisting of gene expression data in T-DNA mutants and captured the PROMOTER and MIDDLE sequences for encoding. In first-layer models, support vector machine (SVM) models were constructed with nine features consisting of information about biological function and local and global sequences. Feature encoding based on the PROMOTER sequence was weighted by logistic regression. The second-layer models integrated 16 first-layer models with minimum redundancy maximum relevance (mRMR) feature selection and the LADTree algorithm, which were selected from nine feature selection methods and 65 classified methods, respectively. The accuracy of the final two-layer machine learning model, referred to as TIMgo, was 99.3% based on fivefold cross-validation, and 85.6% based on independent testing. We discovered that the information within the local sequence had a greater contribution than the global sequence with respect to classification. TIMgo had a good predictive ability for target genes within 20 kb from the 35S enhancer. Based on the analysis of significant sequences, the G-box regulatory sequence may also play an important role in the activation mechanism of the 35S enhancer., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2021 Chien, Huang, Lo, Chen, Liao, Chen and Chu.)

How cortical circuits build representations of complex objects is poorly understood. Individual neurons must integrate broadly over space, yet simultaneously obtain sharp tuning to specific global stimulus features. Groups of neurons identifying different global features must then assemble into a population that forms a comprehensive code for these global stimulus properties. Although the logic for how single neurons summate over their spatial inputs has been well explored in anesthetized animals, how large groups of neurons compose a flexible population code of higher-order features in awake animals is not known. To address this question, we probed the integration and population coding of higher-order stimuli in the somatosensory and visual cortices of awake mice using two-photon calcium imaging across cortical layers. We developed a novel tactile stimulator that allowed the precise measurement of spatial summation even in actively whisking mice. Using this system, we found a sparse but comprehensive population code for higher-order tactile features that depends on a heterogeneous and neuron-specific logic of spatial summation beyond the receptive field. Different somatosensory cortical neurons summed specific combinations of sensory inputs supra-linearly, but integrated other inputs sub-linearly, leading to selective responses to higher-order features. Visual cortical populations employed a nearly identical scheme to generate a comprehensive population code for contextual stimuli. These results suggest that a heterogeneous logic of input-specific supra-linear summation may represent a widespread cortical mechanism for the synthesis of sparse higher-order feature codes in neural populations. This may explain how the brain exploits the thalamocortical expansion of dimensionality to encode arbitrary complex features of sensory stimuli., Competing Interests: EL, DM, SP, YC, AD, HA No competing interests declared, (© 2021, Lyall et al.)

Upon invasion by foreign pathogens, specific antibodies can identify specific foreign antigens and disable them. As a result of this ability, antibodies can help with vaccine production and food allergen detection in patients. Many studies have focused on predicting linear B-cell epitopes, but only two prediction tools are currently available to predict the sub-type of an epitope. NIgPred was developed as a prediction tool for IgA, IgE, and IgG. NIgPred integrates various heterologous features with machine-learning approaches. Differently from previous studies, our study considered peptide-characteristic correlation and autocorrelation features. Sixty kinds of classifier were applied to construct the best prediction model. Furthermore, the genetic algorithm and hill-climbing algorithm were used to select the most suitable features for improving the accuracy and reducing the time complexity of the training model. NIgPred was found to be superior to the currently available tools for predicting IgE epitopes and IgG epitopes on independent test sets. Moreover, NIgPred achieved a prediction accuracy of 100% for the IgG epitopes of a coronavirus data set. NIgPred is publicly available at our website.

Background: Streptococcus pneumoniae, Haemophilus influenzae, and Neisseria meningitidis, which are typically transmitted via respiratory droplets, are leading causes of invasive diseases, including bacteraemic pneumonia and meningitis, and of secondary infections subsequent to post-viral respiratory disease. The aim of this study was to investigate the incidence of invasive disease due to these pathogens during the early months of the COVID-19 pandemic., Methods: In this prospective analysis of surveillance data, laboratories in 26 countries and territories across six continents submitted data on cases of invasive disease due to S pneumoniae, H influenzae, and N meningitidis from Jan 1, 2018, to May, 31, 2020, as part of the Invasive Respiratory Infection Surveillance (IRIS) Initiative. Numbers of weekly cases in 2020 were compared with corresponding data for 2018 and 2019. Data for invasive disease due to Streptococcus agalactiae, a non-respiratory pathogen, were collected from nine laboratories for comparison. The stringency of COVID-19 containment measures was quantified using the Oxford COVID-19 Government Response Tracker. Changes in population movements were assessed using Google COVID-19 Community Mobility Reports. Interrupted time-series modelling quantified changes in the incidence of invasive disease due to S pneumoniae, H influenzae, and N meningitidis in 2020 relative to when containment measures were imposed., Findings: 27 laboratories from 26 countries and territories submitted data to the IRIS Initiative for S pneumoniae (62 837 total cases), 24 laboratories from 24 countries submitted data for H influenzae (7796 total cases), and 21 laboratories from 21 countries submitted data for N meningitidis (5877 total cases). All countries and territories had experienced a significant and sustained reduction in invasive diseases due to S pneumoniae, H influenzae, and N meningitidis in early 2020 (Jan 1 to May 31, 2020), coinciding with the introduction of COVID-19 containment measures in each country. By contrast, no significant changes in the incidence of invasive S agalactiae infections were observed. Similar trends were observed across most countries and territories despite differing stringency in COVID-19 control policies. The incidence of reported S pneumoniae infections decreased by 68% at 4 weeks (incidence rate ratio 0·32 [95% CI 0·27-0·37]) and 82% at 8 weeks (0·18 [0·14-0·23]) following the week in which significant changes in population movements were recorded., Interpretation: The introduction of COVID-19 containment policies and public information campaigns likely reduced transmission of S pneumoniae, H influenzae, and N meningitidis, leading to a significant reduction in life-threatening invasive diseases in many countries worldwide., Funding: Wellcome Trust (UK), Robert Koch Institute (Germany), Federal Ministry of Health (Germany), Pfizer, Merck, Health Protection Surveillance Centre (Ireland), SpID-Net project (Ireland), European Centre for Disease Prevention and Control (European Union), Horizon 2020 (European Commission), Ministry of Health (Poland), National Programme of Antibiotic Protection (Poland), Ministry of Science and Higher Education (Poland), Agencia de Salut Pública de Catalunya (Spain), Sant Joan de Deu Foundation (Spain), Knut and Alice Wallenberg Foundation (Sweden), Swedish Research Council (Sweden), Region Stockholm (Sweden), Federal Office of Public Health of Switzerland (Switzerland), and French Public Health Agency (France)., Competing Interests: Declaration of interests The following authors received support for work unrelated to this study: MPGvdL has received grants from Pfizer, Merck, and the Robert Koch Institut; RB has done contract research on behalf of Public Health England for GlaxoSmithKline, Pfizer, and Sanofi Pasteur, but received no personal remuneration; MC has received grants from Pfizer; SAC has done contract research on behalf of Public Health England for GlaxoSmithKline, Pfizer, and Sanofi Pasteur, but received no personal remuneration; SD has received a grant from Pfizer; SJG did contract research (carriage studies) for vaccine manufacturers (GlaxoSmithKline and Pfizer) on behalf of Public Health England, but received no personal remuneration; MH has received grants from Pfizer and the Federal Office of Public Health, and personal fees (for being on an advisory board) from Pfizer and Merck Sharp & Dohme; HH has received grants from Astellas and Pfizer; KAJ has received a grant from Wellcome Trust and personal fees from GlaxoSmithKline; SNL has done contract research for vaccine manufacturers (GlaxoSmithKline, Pfizer, and Sanofi Pasteur) on behalf of St. George's University of London, but received no personal remuneration; DJL has received grants from GlaxoSmithKline and Pfizer; SM has received a grant from Sanofi Pasteur; CM-A has received grants from Quiastat, Roche, Pfizer, and Genomica, and personal fees from Roche, Pfizer, and Qiagen; LS has received a grant from GlaxoSmithKline; H-CS has received a grant from Pfizer; MI has received non-financial support from GlaxoSmithKline and Pfizer, personal fees from Pfizer (speaker fees) and Merck Sharp & Dohme (speaker fees), and grants from Merck Sharp & Dohme; M-KT has received grants from GlaxoSmithKline, Pfizer, and Sanofi Pasteur; ASk has received grants and non-financial support from Pfizer, and personal fees from Pfizer, Merck Sharp & Dohme, and Sanofi Pasteur; CLS has received grants from Pfizer and GlaxoSmithKline for investigator-led research; EV has received grants on behalf of her institution (Intercommunal Hospital of Créteil) from Pfizer and Merck Sharp & Dohme; MT has received grants from GlaxoSmithKline and Pfizer; NKF's institution (Public Health England) has received funding for investigator-initiated research from GlaxoSmithKline, Pfizer, and other vaccine manufacturers (GlaxoSmithKline, Pfizer, and Affinivax), but NKF received no personal remuneration; AvG has received a grant from Sanofi Pasteur; NMvS has received a grant from Pfizer, a fee for service paid to their institution from Merck Sharp & Dohme and GlaxoSmithKline, and also has a patent (WO 2013/020090 A3) on vaccine development against Streptococcus pyogenes, unrelated to this study, with royalties paid to University of California San Diego, CA, USA; and MKT has a patent (630133) for a vaccine for serogroup X meningococcus with GlaxoSmithKline. All other authors declare no competing interests., (Copyright © 2021 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 license. Published by Elsevier Ltd.. All rights reserved.)

Candida auris is an emerging human pathogen associated with multidrug resistance and nosocomial outbreaks. We report the draft genome sequences of 19 C. auris isolates that were associated with a cluster of cases in a hospital in Hong Kong., (Copyright © 2021 Tse et al.)

Objective: Chronic inflammatory processes involving the vascular wall may induce atherosclerosis. Immune-inflammatory processes proceed throughout all stages of acute stroke. We investigated the association of three immune-inflammatory markers, namely systemic immune-inflammation index (SII), neutrophil-to-lymphocyte ratio (NLR), and neutrophil count (NC), with prehospital delay and clinical features in patients with acute ischemic stroke., Methods: We retrospectively enrolled 2543 inpatients admitted within 4 days of symptom onset from May 2010 to February 2020. Patients were stratified into three groups: Group A, comprising 161 patients with tissue plasminogen activator (tPA) treatment; Group B, comprising 415 patients who were eligible for tPA treatment; and Group C, comprising all 2543 patients., Results: The levels of all three immune-inflammatory markers had positive linear correlations with onsetto- emergency room time, initial National Institutes of Health Stroke Scale (NIHSS) scores, and discharge modified Rankin Scale scores. In Group B, levels of follow-up, but not initial, immuneinflammatory markers were higher in patients with unfavorable outcomes. Common significant predictors of in-hospital complications and unfavorable outcomes were age > 72 years, female sex, NIHSS > 4, diabetes mellitus, and all three immune-inflammatory markers. When combined with other predictors, NC > 7.2 × 103/mL achieved optimal predictive performance (0.794) for in-hospital complications, and SII > 651, NLR > 2.9, and NC > 7.2 × 103/mL had equal predictive performance up to 0.859 for unfavorable outcomes., Conclusions: Immune-inflammatory markers dynamically increased from symptom onset of acute ischemic stroke in patients eligible for thrombolytic therapy. Higher levels of immune-inflammatory markers suggest more in-hospital complications and unfavorable short-term outcomes.

Protein phosphorylation is one of the most important post-translational modifications, and many biological processes are related to phosphorylation, such as DNA repair, transcriptional regulation and signal transduction and, therefore, abnormal regulation of phosphorylation usually causes diseases. If we can accurately predict human phosphorylation sites, this could help to solve human diseases. Therefore, we developed a kinase-specific phosphorylation prediction system, GasPhos, and proposed a new feature selection approach, called Gas, based on the ant colony system and a genetic algorithm and used performance evaluation strategies focused on different kinases to choose the best learning model. Gas uses the mean decrease Gini index (MDGI) as a heuristic value for path selection and adopts binary transformation strategies and new state transition rules. GasPhos can predict phosphorylation sites for six kinases and showed better performance than other phosphorylation prediction tools. The disease-related phosphorylated proteins that were predicted with GasPhos are also discussed. Finally, Gas can be applied to other issues that require feature selection, which could help to improve prediction performance. GasPhos is available at http://predictor.nchu.edu.tw/GasPhos.

Mosunetuzumab, a T-cell dependent bispecific antibody that binds CD3 and CD20 to drive T-cell mediated B-cell killing, is currently being tested in non-Hodgkin lymphoma. However, potent immune stimulation with T-cell directed therapies poses the risk of cytokine release syndrome, potentially limiting dose and utility. To understand mechanisms behind safety and efficacy and explore safety mitigation strategies, we developed a novel mechanistic model of immune and antitumor responses to the T-cell bispecifics (mosunetuzumab and blinatumomab), including the dynamics of B- and T-lymphocytes in circulation, lymphoid tissues, and tumor. The model was developed and validated using mosunetuzumab nonclinical and blinatumomab clinical data. Simulations delineated mechanisms contributing to observed cell and cytokine (IL6) dynamics and predicted that initial step-fractionated dosing limits systemic T-cell activation and cytokine release without compromising tumor response. These results supported a change to a step-fractionated treatment schedule of mosunetuzumab in the ongoing Phase I clinical trial, enabling safer administration of higher doses.

It remains difficult to control the morphology of two-dimensional (2D) materials via direct chemical vapor deposition (CVD) growth. In particular, off-equilibrium (kinetic) growth may produce flakes with non-Wulff shapes (e.g., high-index edges, symmetrical shapes, etc.), which are potentially useful; however, a general controllable approach for the kinetic growth of 2D materials is currently lacking. In this work, we pushed the CVD growth of 2D MoS 2 into deep kinetic regime, by using potassium chloride (KCl) as catalyst and plasma pretreatment on growth substrates. The unprecedented nonequilibrium high-index faceting and unusual high-symmetry shapes in 2D materials have been realized. The growth mechanism of high-index facets is rationalized based on the theory of kinetic instability on crystal surfaces. This new vapor-liquid-adatom-solid (VLAS) growth mechanism-synergistic capture of multiple vapor phase molecules by the catalyst particles on corners and the oversaturated adatom diffusion along adjacent edges can offer great opportunities for shape engineering on 2D materials. The high-quality, rapid, and controllable synthesis of high-index facets (edges) and other non-Wulff shapes of 2D transition metal dichalcogenides will benefit the developments in 2D materials.

Hepatocellular carcinoma (HCC), which is associated with an absence of obvious symptoms and poor prognosis, is the second leading cause of cancer death worldwide. Genome-wide molecular biology studies should provide biological insights into HCC development. Based on the importance of phosphorylation for signal transduction, several protein kinase inhibitors have been developed that improve the survival of cancer patients. However, a comprehensive database of HCC-related phosphorylated biomarkers (HCCPMs) and novel HCCPMs prediction platform has been lacking. We have thus constructed the dBMHCC databases to provide expression profiles, phosphorylation and drug information, and evidence type; gathered information on HCC-related pathways and their involved genes as candidate HCC biomarkers; and established a system for evaluating protein phosphorylation and HCC-related biomarkers to improve the reliability of biomarker prediction. The resulting dBMHCC contains 611 notable HCC-related genes, 234 HCC-related pathways, 17 phosphorylation-related motifs and their 255 corresponding protein kinases, 5955 HCC biomarkers, and 1077 predicted HCCPMs. Methionine adenosyltransferase 2B (MAT2B) and acireductone dioxygenase 1 (ADI1), which regulate HCC development and hepatitis C virus infection, respectively, were among the top 10 HCCPMs predicted by dBMHCC. Platelet-derived growth factor receptor alpha (PDGFRA), which had the highest evaluation score, was identified as the target of one HCC drug (Regorafenib), five cancer drugs, and four non-cancer drugs. dBMHCC is an open resource for HCC phosphorylated biomarkers, which supports researchers investigating the development of HCC and designing novel diagnosis methods and drug treatments. Database URL: http://predictor.nchu.edu.tw/dBMHCC., Competing Interests: No authors have competing interests.

Information about the expression status of hormone receptors such as estrogen receptor (ER), progesterone receptor (PR), and Her-2 is crucial in the management and prognosis of breast cancer. Therefore, the retrieval and analysis of hormone receptor expression characteristics in metastatic breast cancer may be valuable in breast cancer study. Herein, we report a text mining tool based on word/phrase matching that retrieves hormone receptor expression data of regional or distant metastatic breast cancer from pathology reports. It was tested on pathology reports at the China Medical University Hospital from 2013 to 2018. The tool showed specificities of 91.6% and 63.3% for the detection of regional lymph node metastasis and distant metastasis, respectively. Sensitivity in immunohistochemical study result extraction in these cases was 98.6% for distant metastasis and 78.3% for regional lymph node metastasis. Statistical analysis on these retrieved data showed significant difference s in PR and Her-2 expressions between regional and metastatic breast cancer, which is compatible with previous studies. In conclusion, our study shows that metastatic breast cancer hormone receptor expression characteristics can be retrieved by text mining. The algorithm designed in this study may be useful in future studies about text mining in pathology reports., Competing Interests: The authors report no conflict of interest., (Copyright © 2020 Kai-Po Chang et al.)

Many proteins exist in natures as oligomers with various quaternary structural attributes rather than as single chains. Predicting these attributes is an essential task in computational biology for the advancement of proteomics. However, the existing methods do not consider the integration of heterogeneous coding and the accuracy of subunit categories with limited data. To this end, we proposed a tool that can predict more than 12 subunit protein oligomers, QUATgo. Meanwhile, three kinds of sequence coding were used, including dipeptide composition, which was used for the first time to predict protein quaternary structural attributes, and protein half-life characteristics, and we modified the coding method of the functional domain composition proposed by predecessors to solve the problem of large feature vectors. QUATgo solves the problem of insufficient data for a single subunit using a two-stage architecture and uses 10-fold cross-validation to test the predictive accuracy of the classifier. QUATgo has 49.0% cross-validation accuracy and 31.1% independent test accuracy. In the case study, the accuracy of QUATgo can reach 61.5% for predicting the quaternary structure of influenza virus hemagglutinin proteins. Finally, QUATgo is freely accessible to the public as a web server via the site http://predictor.nchu.edu.tw/QUATgo., Competing Interests: The authors have declared that no competing interests exist.

Protein mutations can lead to structural changes that affect protein function and result in disease occurrence. In protein engineering, drug design or and optimization industries, mutations are often used to improve protein stability or to change protein properties while maintaining stability. To provide possible candidates for novel protein design, several computational tools for predicting protein stability changes have been developed. Although many prediction tools are available, each tool employs different algorithms and features. This can produce conflicting prediction results that make it difficult for users to decide upon the correct protein design. Therefore, this study proposes an integrated prediction tool, iStable 2.0, which integrates 11 sequence-based and structure-based prediction tools by machine learning and adds protein sequence information as features. Three coding modules are designed for the system, an Online Server Module, a Stand-alone Module and a Sequence Coding Module, to improve the prediction performance of the previous version of the system. The final integrated structure-based classification model has a higher Matthews correlation coefficient than that of the single prediction tool (0.708 vs 0.547, respectively), and the Pearson correlation coefficient of the regression model likewise improves from 0.669 to 0.714. The sequence-based model not only successfully integrates off-the-shelf predictors but also improves the Matthews correlation coefficient of the best single prediction tool by at least 0.161, which is better than the individual structure-based prediction tools. In addition, both the Sequence Coding Module and the Stand-alone Module maintain performance with only a 5% decrease of the Matthews correlation coefficient when the integrated online tools are unavailable. iStable 2.0 is available at http://ncblab.nchu.edu.tw/iStable2., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2020 The Authors.)

MicroRNAs (miRNAs) are short non-coding RNAs that regulate gene expression and biological processes through binding to messenger RNAs. Predicting the relationship between miRNAs and their targets is crucial for research and clinical applications. Many tools have been developed to predict miRNA-target interactions, but variable results among the different prediction tools have caused confusion for users. To solve this problem, we developed miRgo, an application that integrates many of these tools. To train the prediction model, extreme values and median values from four different data combinations, which were obtained via an energy distribution function, were used to find the most representative dataset. Support vector machines were used to integrate 11 prediction tools, and numerous feature types used in these tools were classified into six categories-binding energy, scoring function, evolution evidence, binding type, sequence property, and structure-to simplify feature selection. In addition, a novel evaluation indicator, the Chu-Hsieh-Liang (CHL) index, was developed to improve the prediction power in positive data for feature selection. miRgo achieved better results than all other prediction tools in evaluation by an independent testing set and by its subset of functionally important genes. The tool is available at http://predictor.nchu.edu.tw/miRgo.

Influenza type A, a serious infectious disease of the human respiratory tract, poses an enormous threat to human health worldwide. It leads to high mortality rates in poultry, pigs, and humans. The primary target identity regions for the human immune system are hemagglutinin (HA) and neuraminidase (NA), two surface proteins of the influenza A virus. Research and development of vaccines is highly complex because the influenza A virus evolves rapidly. This study focused on three genetic features of viral surface proteins: ribonucleic acid (RNA) sequence conservation, linear B-cell epitopes, and N-linked glycosylation. On the basis of these three properties, we analyzed 12,832 HA and 9487 NA protein sequences, which we retrieved from the influenza virus database. We classified the viral surface protein sequences into the 18 HA and 11 NA subtypes that have been identified thus far. Using available analytic tools, we searched for the representative strain of each virus subtype. Furthermore, using machine learning methods, we looked for conservation regions with sequences showing linear B-cell epitopes and N-linked glycosylation. Compared to the prediction of the Immune Epitope Database (IEDB) antibody neutralization response (i.e., screening of antibody sequence regions), in this study, the virus sequence coverage was large and accurate and contained N-linked glycosylation sites. The results of this study proved that we can use the machine learning-based prediction method to solve the problem of vaccine invalidation that occurred during the rapid evolution of the influenza A virus and also as a prevaccine assessment. In addition, the screening fragments can be used as a universal influenza vaccine design reference in the future., (Copyright © 2019 Elsevier Inc. All rights reserved.)

T-DNA activation-tagging technology is widely used to study rice gene functions. When T-DNA inserts into genome, the flanking gene expression may be altered using CaMV 35S enhancer, but the affected genes still need to be validated by biological experiment. We have developed the EAT-Rice platform to predict the flanking gene expression of T-DNA insertion site in rice mutants. The three kinds of DNA sequences including UPS1K, DISTANCE, and MIDDLE were retrieved to encode and build a forecast model of two-layer machine learning. In the first-layer models, the features nucleotide context (N-gram), cis-regulatory elements (Motif), nucleotide physicochemical properties (NPC), and CG-island (CGI) were used to build SVM models by analysing the concealed information embedded within the three kinds of sequences. Logistic regression was used to estimate the probability of gene activation which as feature-encoding weighting within first-layer model. In the second-layer models, the NaiveBayesUpdateable algorithm was used to integrate these first layer-models, and the system performance was 88.33% on 5-fold cross-validation, and 79.17% on independent-testing finally. In the three kinds of sequences, the model constructed by Middle had the best contribution to the system for identifying the activated genes. The EAT-Rice system provided better performance and gene expression prediction at further distances when compared to the TRIM database. An online server based on EAT-rice is available at http://predictor.nchu.edu.tw/EAT-Rice., Competing Interests: The authors have declared that no competing interests exist.

Background: Antibody-drug conjugates (ADCs) polatuzumab vedotin (pola) and pinatuzumab vedotin (pina) showed clinical activity and tolerability in phase 1 trials. The aim of this multicentre, open-label, phase 2 study was to compare rituximab plus pola (R-pola) or pina (R-pina) in patients with relapsed or refractory diffuse large B-cell lymphoma and follicular lymphoma., Methods: In this phase 2 randomised study at 39 investigational sites in six countries, patients were randomly assigned (1:1), by use of a dynamic hierarchical randomisation scheme, to receive R-pola or R-pina (375 mg/m 2 rituximab plus 2·4 mg/kg ADCs) every 21 days until disease progression or unacceptable toxicity up to 1 year. Treatment allocations were not masked to the investigator, patients or sponsor after the patients were enrolled and randomly assigned. The primary objectives were safety and tolerability, and antitumour response. The study is registered with ClinicalTrials.gov, number NCT01691898, and is closed to accrual., Findings: 81 patients with diffuse large B-cell lymphoma and 42 with follicular lymphoma were recruited between Sept 27, 2012, and Oct 10, 2013, and were assigned to treatment. 81 patients with diffuse large B-cell lymphoma and 41 patients with follicular lymphoma were eligible for analysis. Of the 42 patients with diffuse large B-cell lymphoma who received R-pina, 25 (60%, 95% CI 43-74) achieved an objective response and 11 (26%, 95% CI 14-42) achieved a complete response. Of the 39 patients in this cohort who received R-pola, 21 (54%, 95% CI 37-70) achieved an objective response, and eight (21%, 95% CI 9-36) achieved a complete response. Of the 21 patients in the follicular lymphoma cohort who received R-pina, 13 (62%, 95% CI 38-82) achieved an objective response, and one (5%, 95% CI 0·1-24) achieved a complete response. Of the 20 patients in this cohort who received R-pola, 14 (70%, 95% CI 46-88) achieved an objective response, and nine (45%, 95% CI 23-68) achieved a complete response. In the diffuse large B-cell lymphoma cohort, grade 3-5 adverse events occurred in 33 (79%) of 42 patients receiving R-pina (most common were neutropenia [29%] and hyperglycaemia [10%]; nine [21%] grade 5 adverse events, five of which were infection-related), and in 30 (77%) of 39 patients receiving R-pola (most common were neutropenia [23%], anaemia [8%] and diarrhoea [8%]; no grade 5 adverse events). In the follicular lymphoma cohort, grade 3-5 adverse events occurred in 13 (62%) of 21 patients receiving R-pina (most common were neutropenia [29%] and hyperglycaemia [14%]; no grade 5 adverse events) and in ten (50%) of 20 patients receiving R-pola (most common were neutropenia [15%] and diarrhoea [10%]; one grade 5 adverse event)., Interpretation: R-pina and R-pola are potential treatment options in patients with relapsed or refractory diffuse large B-cell lymphoma and follicular lymphoma. Pola was selected by the study funder for further development in non-Hodgkin lymphoma, partly because of longer durations of response than pina, and an overall benefit-risk favouring R-pola., Funding: F Hoffmann-La Roche., (Copyright © 2019 Elsevier Ltd. All rights reserved.)

Multidrug resistant bacterial infection remains a significant public concern. In this report, photosensitizer Chlorin e6 doped silica was synthesized. This hybrid structure exhibits enhanced photostability and high antibacterial efficiency towards Staphylococcus aureus (S. aureus) and methicillin-resistant S. aureus (MRSA). In summary, this work demonstrates an effective platform to improve the efficiency of antibiotics for better treatment of wound infections.

Background: Hormone receptors of breast cancer, such as estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor 2 (Her-2), are important prognostic factors for breast cancer., Objective: The current study aimed to develop a method to retrieve the statistics of hormone receptor expression status, documented in pathology reports, given their importance in research for primary and recurrent breast cancer, and quality management of pathology laboratories., Method: A two-stage text mining approach via regular expression-based word/phrase matching, was developed to retrieve the data., Results: The method achieved a sensitivity of 98.8%, 98.7% and 98.4% for extraction of ER, PR, and Her-2 results. The hormone expression status from 3679 primary and 44 recurrent breast cancer cases was successfully retrieved with the method. Statistical analysis of these data showed that the recurrent disease had a significantly lower positivity rate for ER (54.5% vs 76.5%, p=0.001278) than primary breast cancer and a higher positivity rate for Her-2 (48.8% vs 16.2%, p=9.79e-8). These results corroborated the previous literature., Conclusion: Text mining on pathology reports using the developed method may benefit research of primary and recurrent breast cancer., Competing Interests: Conflict of interest: The authors declare no conflict of interest.

Background: Serum glutathione peroxidase-3 (GPx-3) is known as a key selenoprotein with antioxidant properties. GPx-3 deficiency has been associated with sepsis. The objectives of this study are (1) to compare the GPx-3 protein concentrations and GPx-3 bioactivity in normal healthy subjects and septic patients, and (2) to evaluate the relationship between GPx-3 bioactivity and its protein concentration., Methods: Serum samples were collected from 50 normal healthy subjects and 70 septic patients. The reliable bioanalytical methods for GPx-3 protein concentration and bioactivity in human serum were developed and validated. Analyses of GPx-3 bioactivity and GPx-3 protein concentration were then performed., Results: Geometric mean GPx-3 bioactivity was 78.13U/l for patients with sepsis, significantly lower than normal subjects with 108.21U/l (p<0.0001). Similarly, the GPx-3 protein concentration was significantly lower in patients with sepsis than in normal subjects, with the mean GPx-3 value of 0.78 vs 3.10μg/ml, respectively (p<0.0001). A positive correlation was observed between the GPx-3 bioactivity and its corresponding protein concentration in septic serum samples (R=0.74, p<0.0001), regardless of gender or age difference., Conclusion: These findings suggest that the decrease in GPx-3 bioactivity observed in the septic patients was resulted from the significant sepsis-related decline of GPx-3 protein concentrations., (Copyright © 2017 Elsevier B.V. All rights reserved.)

The MADS-box gene family is an important transcription factor family involved in floral organogenesis. The previously proposed ABCDE model suggests that different floral organ identities are controlled by various combinations of classes of MADS-box genes. The five-class ABCDE model cannot cover all the species of angiosperms, especially the orchid. Thus, we developed a two-stage approach for MADS-box gene classification to advance the study of floral organogenesis of angiosperms. First, eight classes of reference datasets (A, AGL6, B12, B34, BPI, C, D, and E) were curated and clustered by phylogenetic analysis and unsupervised learning, and they were confirmed by the literature. Second, feature selection and multiple prediction models were curated according to sequence similarity and the characteristics of the MADS-box gene domain using support vector machines. Compared with the BindN and COILS features, the local BLAST model yielded the best accuracy. For performance evaluation, the accuracy of Phalaenopsis aphrodite MADS-box gene classification was 93.3%, which is higher than 86.7% of our previous classification prediction tool, iMADS. Phylogenetic tree construction - the most common method for gene classification yields classification errors and is time-consuming for analysis of massive, multi-species, or incomplete sequences. In this regard, our new system can also confirm the classification errors of all the random selection that were incorrectly classified by phylogenetic tree analysis. Our model constitutes a reliable and efficient MADS-box gene classification system for angiosperms.

Thermostability is a protein property that impacts many types of studies, including protein activity enhancement, protein structure determination, and drug development. However, most computational tools designed to predict protein thermostability require tertiary structure data as input. The few tools that are dependent only on the primary structure of a protein to predict its thermostability have one or more of the following problems: a slow execution speed, an inability to make large-scale mutation predictions, and the absence of temperature and pH as input parameters. Therefore, we developed a computational tool, named KStable, that is sequence-based, computationally rapid, and includes temperature and pH values to predict changes in the thermostability of a protein upon the introduction of a mutation at a single site. KStable was trained using basis features and minimal redundancy-maximal relevance (mRMR) features, and 58 classifiers were subsequently tested. To find the representative features, a regular-mRMR method was developed. When KStable was evaluated with an independent test set, it achieved an accuracy of 0.708.

Diagnosing nasopharyngeal carcinoma (NPC) is a significant challenge because of the highly complex process. We proposed an approach to diagnose NPC serum using a combination of hyperspectral imaging and weight-based principal component analysis. Samples were prepared by pressing boric acid into pellets for use as the sera substrate. The sera, collected from 100 healthy volunteers and 60 NPC patients, was dripped onto the surface of the substrate for hyperspectral imaging. The characteristic spectral bands were selected based on the variable weight obtained from a support vector machine (SVM) model, using principal component analysis (PCA) to reduce the dimension in the extracted bands. Obtained results show that the accuracy rate, sensitivity, and specificity between the NPC sera and the sera of the healthy controls reached extremely high levels of 99.15%, 98.79%, and 99.36%, respectively. For the model's consistency evaluation, we found that the Kappa and area under the curve (AUC) of the receiver operating characteristic (ROC) curve were 0.99 and 0.98, respectively. These results suggest that the developed approach could serve as a noninvasive diagnostic and screening tool for highly accurate and consistent detection of NPC. Hence, a combination of hyperspectral imaging (HSI) and a weighted principal component analysis (WPCA)-SVM model represents a powerful and promising tool for NPC diagnosis.

Most modern tools used to predict sites of small ubiquitin-like modifier (SUMO) binding (referred to as SUMOylation) use algorithms, chemical features of the protein, and consensus motifs. However, these tools rarely consider the influence of post-translational modification (PTM) information for other sites within the same protein on the accuracy of prediction results. This study applied the Random Forest machine learning method, as well as motif screening models and a feature selection combination mechanism, to develop a SUMOylation prediction system, referred to as SUMOgo. With regard to prediction method, PTM sites were coded as new functional features in addition to structural features, such as sequence-based binary coding, encoded chemical features of proteins, and encoded secondary structure information that is important for PTM. Twenty cycles of prediction were conducted with a 1:1 combination of positive test data and random negative data. Matthew's correlation coefficient of SUMOgo reached 0.511, which is higher than that of current commonly used tools. This study further verified the important role of PTM in SUMOgo and includes a case study on CREB binding protein (CREBBP). The website for the final tool is http://predictor.nchu.edu.tw/SUMOgo .

Objective: The aim of this study was to investigate the role of microRNA-222 (miR-222) in osteosarcoma (OS), and to further explore the potential molecular mechanism., Patients and Methods: We measured the level of miR-222 in OS tissues and cell lines using quantitative Real-time polymerase chain reaction. Synthesized miR-222 mimics or inhibitors were obtained to up-regulate or down-regulate the expression of miR-222 in U2OS or Saos2 cells. Cell counting kit-8 (CCK8) and colony formation assay were employed to detect the ability of cell proliferation, and transwell assay was used to confirm the ability of cell invasion. Furthermore, luciferase assay and Western blot were applied to verify the target of miR-222 in OS., Results: The level of miR-222 in OS tumor tissue samples was significantly lower than that in normal group. Over-expression of miR-222 decreased cell proliferation and invasion in U2OS cells while knockdown of miR-222 promoted cell growth and metastasis in Saos2 cells. Furthermore, YWHAG was found to be a candidate target of miR-222 using several databases. Elevated level of miR-222 inhibited YWHAG expression while reduced miR-222 promoted YWHAG expression. Also, up-regulation of YWHAG restored the inhibiting effect of miR-222 mimics., Conclusions: We identified for the first time that the expression level of miR-222 was reduced in OS tissues as well as in OS cell lines. miR-222 could inhibit cell proliferation and invasion via down-regulating YWHAG. These data could provide a potential target for the biological treatment of OS.

An efficient method has been developed to identify meat species by using laser-induced breakdown spectroscopy (LIBS). To improve the accuracy and stability of meat species identification, multiplicative scatter correction (MSC) was adopted to first pretreat the spectrum for correction of spectrum scatter. Then the corrected spectra were identified by using the K-nearest neighbor (KNN) model. The results showed that the identification rate improved from 94.17% to 100% and the prediction coefficient of variance (CV) decreased from 5.16% to 0.56%. This means that the accuracy and stability of meat species identification using MSC and LIBS simultaneously improved. In light of the findings, the proposed method can be a valuable tool for meat species identification using LIBS.