Dynamic Remodeling of Membrane Composition Drives Cell Cycle through Primary Cilia Excision.

The life cycle of a primary cilium begins in quiescence and ends prior to mitosis. In quiescent cells, the primary cilium insulates itself from contiguous dynamic membrane processes on the cell surface to function as a stable signaling apparatus. Here, we demonstrate that basal restriction of ciliary structure dynamics is established by the cilia-enriched phosphoinositide 5-phosphatase, Inpp5e. Growth induction displaces ciliary Inpp5e and accumulates phosphatidylinositol 4,5-bisphosphate in distal cilia. This change triggers otherwise-forbidden actin polymerization in primary cilia, which excises cilia tips in a process we call cilia decapitation. While cilia disassembly is traditionally thought to occur solely through resorption, we show that an acute loss of IFT-B through cilia decapitation precedes resorption. Finally, we propose that cilia decapitation induces mitogenic signaling and constitutes a molecular link between the cilia life cycle and cell-division cycle. This newly defined ciliary mechanism may find significance in cell proliferation control during normal development and cancer.

Treatment for Mild Chronic Hypertension during Pregnancy.

BACKGROUND: The benefits and safety of the treatment of mild chronic hypertension (blood pressure, <160/100 mm Hg) during pregnancy are uncertain. Data are needed on whether a strategy of targeting a blood pressure of less than 140/90 mm Hg reduces the incidence of adverse pregnancy outcomes without compromising fetal growth. METHODS: In this open-label, multicenter, randomized trial, we assigned pregnant women with mild chronic hypertension and singleton fetuses at a gestational age of less than 23 weeks to receive antihypertensive medications recommended for use in pregnancy (active-treatment group) or to receive no such treatment unless severe hypertension (systolic pressure, ≥160 mm Hg; or diastolic pressure, ≥105 mm Hg) developed (control group). The primary outcome was a composite of preeclampsia with severe features, medically indicated preterm birth at less than 35 weeks' gestation, placental abruption, or fetal or neonatal death. The safety outcome was small-for-gestational-age birth weight below the 10th percentile for gestational age. Secondary outcomes included composites of serious neonatal or maternal complications, preeclampsia, and preterm birth. RESULTS: A total of 2408 women were enrolled in the trial. The incidence of a primary-outcome event was lower in the active-treatment group than in the control group (30.2% vs. 37.0%), for an adjusted risk ratio of 0.82 (95% confidence interval [CI], 0.74 to 0.92; P<0.001). The percentage of small-for-gestational-age birth weights below the 10th percentile was 11.2% in the active-treatment group and 10.4% in the control group (adjusted risk ratio, 1.04; 95% CI, 0.82 to 1.31; P = 0.76). The incidence of serious maternal complications was 2.1% and 2.8%, respectively (risk ratio, 0.75; 95% CI, 0.45 to 1.26), and the incidence of severe neonatal complications was 2.0% and 2.6% (risk ratio, 0.77; 95% CI, 0.45 to 1.30). The incidence of any preeclampsia in the two groups was 24.4% and 31.1%, respectively (risk ratio, 0.79; 95% CI, 0.69 to 0.89), and the incidence of preterm birth was 27.5% and 31.4% (risk ratio, 0.87; 95% CI, 0.77 to 0.99). CONCLUSIONS: In pregnant women with mild chronic hypertension, a strategy of targeting a blood pressure of less than 140/90 mm Hg was associated with better pregnancy outcomes than a strategy of reserving treatment only for severe hypertension, with no increase in the risk of small-for-gestational-age birth weight. (Funded by the National Heart, Lung, and Blood Institute; CHAP ClinicalTrials.gov number, NCT02299414.).

Dysregulation of integrin αvß3 and α5ß1 impedes migration of placental endothelial cells in fetal growth restriction.

Placentas from pregnancies complicated by severe early-onset fetal growth restriction (FGR) exhibit diminished vascular development mediated by impaired angiogenesis, but underlying mechanisms remain unknown. In this study, we show that FGR endothelial cells demonstrate inherently reduced migratory capacity despite the presence of fibronectin, a matrix protein abundant in placental stroma that displays abnormal organization in FGR placentas. Thus, we hypothesized that aberrant endothelial-fibronectin interactions in FGR are a key mechanism underlying impaired FGR endothelial migration. Using human fetoplacental endothelial cells isolated from uncomplicated term control and FGR pregnancies, we assessed integrin α5ß1 and αvß3 regulation during cell migration. We show that endothelial integrin α5ß1 and αvß3 interactions with fibronectin are required for migration and that FGR endothelial cells responded differentially to integrin inhibition, indicating integrin dysregulation in FGR. Whole-cell expression was not different between groups. However, there were significantly more integrins in focal adhesions and reduced intracellular trafficking in FGR. These newly identified changes in FGR endothelial cellular processes represent previously unidentified mechanisms contributing to persistent angiogenic deficiencies in FGR.

Placental differences between severe fetal growth restriction and hypertensive disorders of pregnancy requiring early preterm delivery: morphometric analysis of the villous tree supported by artificial intelligence.

BACKGROUND: The great obstetrical syndromes of fetal growth restriction and hypertensive disorders of pregnancy can occur individually or be interrelated. Placental pathologic findings often overlap between these conditions, regardless of whether 1 or both diagnoses are present. Quantification of placental villous structures in each of these settings may identify distinct differences in developmental pathways. OBJECTIVE: This study aimed to determine how the quantity and surface area of placental villi and vessels differ between severe, early-onset fetal growth restriction with absent or reversed umbilical artery Doppler indices and hypertensive disorders of pregnancy or the 2 conditions combined among subjects with disease severity that warrant early preterm delivery. We hypothesized that the trajectories of placental morphogenesis diverge after a common initiating insult of deep defective placentation. Specifically, we postulated that only villi are affected in pregnancy-related hypertension, whereas both villous and vascular structures are proportionally diminished in severe fetal growth restriction with no additional effect when hypertension is concomitantly present. STUDY DESIGN: In this retrospective cohort study, paraffin-embedded placental tissue was obtained from 4 groups, namely (1) patients with severe fetal growth restriction with absent or reversed umbilical artery end-diastolic velocities and hypertensive disorders of pregnancy, (2) patients with severe fetal growth restriction with absent or reversed umbilical artery Doppler indices and no hypertension, (3) gestational age-matched, appropriately grown pregnancies with hypertensive disease, and (4) gestational age-matched, appropriately grown pregnancies without hypertension. Dual immunohistochemistry for cytokeratin-7 (trophoblast) and CD34 (endothelial cells) was performed, followed by artificial intelligence-driven morphometric analyses. The number of villi, total villous area, number of fetoplacental vessels, and total vascular area across villi within a uniform region of interest were quantified. Quantitative analyses of placental structures were modeled using linear regression. RESULTS: Placentas from pregnancies complicated by hypertensive disorders of pregnancy exhibited significantly fewer stem villi (-282 stem villi; 95% confidence interval, -467 to -98; P<.01), a smaller stem villous area (-4.3 mm2; 95% confidence interval, -7.3 to -1.2; P<.01), and fewer stem villous vessels (-4967 stem villous vessels; 95% confidence interval, -8501 to -1433; P<.01) with no difference in the total vascular area. In contrast, placental abnormalities in cases with severe growth restriction were limited to terminal villi with global decreases in the number of villi (-873 terminal villi; 95% confidence interval, -1501 to -246; P<.01), the villous area (-1.5 mm2; 95% confidence interval, -2.7 to -0.4; P<.01), the number of blood vessels (-5165 terminal villous vessels; 95% confidence interval, -8201 to -2128; P<.01), and the vascular area (-0.6 mm2; 95% confidence interval, -1.1 to -0.1; P=.02). The combination of hypertension and growth restriction had no additional effect beyond the individual impact of each state. CONCLUSION: Pregnancies complicated by hypertensive disorders of pregnancy exhibited defects in the stem villi only, whereas placental abnormalities in severely growth restricted pregnancies with absent or reversed umbilical artery end-diastolic velocities were limited to the terminal villi. There were no significant statistical interactions in the combination of growth restriction and hypertension, suggesting that distinct pathophysiological pathways downstream of the initial insult of defective placentation are involved in each entity and do not synergize to lead to more severe pathologic consequences. Delineating mechanisms that underly the divergence in placental development after a common inciting event of defective deep placentation may shed light on new targets for prevention or treatment.

Improving dengue fever predictions in Taiwan based on feature selection and random forests.

BACKGROUND: Dengue fever is a well-studied vector-borne disease in tropical and subtropical areas of the world. Several methods for predicting the occurrence of dengue fever in Taiwan have been proposed. However, to the best of our knowledge, no study has investigated the relationship between air quality indices (AQIs) and dengue fever in Taiwan. RESULTS: This study aimed to develop a dengue fever prediction model in which meteorological factors, a vector index, and AQIs were incorporated into different machine learning algorithms. A total of 805 meteorological records from 2013 to 2015 were collected from government open-source data after preprocessing. In addition to well-known dengue-related factors, we investigated the effects of novel variables, including particulate matter with an aerodynamic diameter < 10 µm (PM10), PM2.5, and an ultraviolet index, for predicting dengue fever occurrence. The collected dataset was randomly divided into an 80% training set and a 20% test set. The experimental results showed that the random forests achieved an area under the receiver operating characteristic curve of 0.9547 for the test set, which was the best compared with the other machine learning algorithms. In addition, the temperature was the most important factor in our variable importance analysis, and it showed a positive effect on dengue fever at < 30 °C but had less of an effect at > 30 °C. The AQIs were not as important as temperature, but one was selected in the process of filtering the variables and showed a certain influence on the final results. CONCLUSIONS: Our study is the first to demonstrate that AQI negatively affects dengue fever occurrence in Taiwan. The proposed prediction model can be used as an early warning system for public health to prevent dengue fever outbreaks.

A single cell transcriptional atlas of early synovial joint development.

Synovial joint development begins with the formation of the interzone, a region of condensed mesenchymal cells at the site of the prospective joint. Recently, lineage-tracing strategies have revealed that Gdf5-lineage cells native to and from outside the interzone contribute to most, if not all, of the major joint components. However, there is limited knowledge of the specific transcriptional and signaling programs that regulate interzone formation and fate diversification of synovial joint constituents. To address this, we have performed single cell RNA-Seq analysis of 7329 synovial joint progenitor cells from the developing murine knee joint from E12.5 to E15.5. By using a combination of computational analytics, in situ hybridization and in vitro characterization of prospectively isolated populations, we have identified the transcriptional profiles of the major developmental paths for joint progenitors. Our freely available single cell transcriptional atlas will serve as a resource for the community to uncover transcriptional programs and cell interactions that regulate synovial joint development.

Mechanistic insights into the development of severe fetal growth restriction.

Fetal growth restriction (FGR), which most commonly results from suboptimal placental function, substantially increases risks for adverse perinatal and long-term outcomes. The only "treatment" that exists is delivery, which averts stillbirth but does not improve outcomes in survivors. Furthermore, the potential long-term consequences of FGR to the fetus, including cardiometabolic disorders, predispose these individuals to developing FGR in their future pregnancies. This creates a multi-generational cascade of adverse effects stemming from a single dysfunctional placenta, and understanding the mechanisms underlying placental-mediated FGR is critically important if we are to improve outcomes and overall health. The mechanisms behind FGR remain unknown. However, placental insufficiency derived from maldevelopment of the placental vascular systems is the most common etiology. To highlight important mechanistic interactions within the placenta, we focus on placental vascular development in the setting of FGR. We delve into fetoplacental angiogenesis, a robust and ongoing process in normal pregnancies that is impaired in severe FGR. We review cellular models of FGR, with special attention to fetoplacental angiogenesis, and we highlight novel integrin-extracellular matrix interactions that regulate placental angiogenesis in severe FGR. In total, this review focuses on key developmental processes, with specific focus on the human placenta, an underexplored area of research.

Uteroplacental insufficiency decreases leptin expression and impairs lung development in growth-restricted newborn rats.

BACKGROUND: The study aimed to analyze the effect of uteroplacental insufficiency (UPI) on leptin expression and lung development of intrauterine growth restriction (IUGR) rats. METHODS: On day 17 of pregnancy, time-dated Sprague-Dawley rats were randomly divided into either an IUGR group or a control group. Uteroplacental insufficiency surgery (IUGR) and sham surgery (control) were conducted. Offspring rats were spontaneously delivered on day 22 of pregnancy. On postnatal days 0 and 7, rats' pups were selected at random from the control and IUGR groups. Blood was withdrawn from the heart to determine leptin levels. The right lung was obtained for leptin and leptin receptor levels, immunohistochemistry, proliferating cell nuclear antigen (PCNA), western blot, and metabolomic analyses. RESULTS: UPI-induced IUGR decreased leptin expression and impaired lung development, causing decreased surface area and volume in offspring. This results in lower body weight, decreased serum leptin levels, lung leptin and leptin receptor levels, alveolar space, PCNA, and increased alveolar wall volume fraction in IUGR offspring rats. The IUGR group found significant relationships between serum leptin, radial alveolar count, von Willebrand Factor, and metabolites. CONCLUSION: Leptin may contribute to UPI-induced lung development during the postnatal period, suggesting supplementation as a potential treatment. IMPACT: The neonatal rats with intrauterine growth restriction (IUGR) caused by uteroplacental insufficiency (UPI) showed decreased leptin expression and impaired lung development. UPI-induced IUGR significantly decreased surface area and volume in lung offspring. This is a novel study that investigates leptin expression and lung development in neonatal rats with IUGR caused by UPI. If our findings translate to IUGR infants, leptin may contribute to UPI-induced lung development during the postnatal period, suggesting supplementation as a potential treatment.

Symptom-based scoring technique by machine learning to predict COVID-19: a validation study.

BACKGROUND: Coronavirus disease 2019 (COVID-19) surges, such as that which occurred when omicron variants emerged, may overwhelm healthcare systems. To function properly, such systems should balance detection and workloads by improving referrals using simple yet precise and sensitive diagnostic predictions. A symptom-based scoring system was developed using machine learning for the general population, but no validation has occurred in healthcare settings. We aimed to validate a COVID-19 scoring system using self-reported symptoms, including loss of smell and taste as major indicators. METHODS: A cross-sectional study was conducted to evaluate medical records of patients admitted to Dr. Sardjito Hospital, Yogyakarta, Indonesia, from March 2020 to December 2021. Outcomes were defined by a reverse-transcription polymerase chain reaction (RT-PCR). We compared the symptom-based scoring system, as the index test, with antigen tests, antibody tests, and clinical judgements by primary care physicians. To validate use of the index test to improve referral, we evaluated positive predictive value (PPV) and sensitivity. RESULTS: After clinical judgement with a PPV of 61% (n = 327/530, 95% confidence interval [CI]: 60% to 62%), confirmation with the index test resulted in the highest PPV of 85% (n = 30/35, 95% CI: 83% to 87%) but the lowest sensitivity (n = 30/180, 17%, 95% CI: 15% to 19%). If this confirmation was defined by either positive predictive scoring or antigen tests, the PPV was 92% (n = 55/60, 95% CI: 90% to 94%). Meanwhile, the sensitivity was 88% (n = 55/62, 95% CI: 87% to 89%), which was higher than that when using only antigen tests (n = 29/41, 71%, 95% CI: 69% to 73%). CONCLUSIONS: The symptom-based COVID-19 predictive score was validated in healthcare settings for its precision and sensitivity. However, an impact study is needed to confirm if this can balance detection and workload for the next COVID-19 surge.

Expression of the AHPND Toxins PirAvp and PirBvp Is Regulated by Components of the Vibrio parahaemolyticus Quorum Sensing (QS) System.

Acute hepatopancreatic necrosis disease (AHPND) in shrimp is caused by Vibrio strains that harbor a pVA1-like plasmid containing the pirA and pirB genes. It is also known that the production of the PirA and PirB proteins, which are the key factors that drive the observed symptoms of AHPND, can be influenced by environmental conditions and that this leads to changes in the virulence of the bacteria. However, to our knowledge, the mechanisms involved in regulating the expression of the pirA/pirB genes have not previously been investigated. In this study, we show that in the AHPND-causing Vibrio parahaemolyticus 3HP strain, the pirAvp and pirBvp genes are highly expressed in the early log phase of the growth curve. Subsequently, the expression of the PirAvp and PirBvp proteins continues throughout the log phase. When we compared mutant strains with a deletion or substitution in two of the quorum sensing (QS) master regulators, luxO and/or opaR (luxOD47E, ΔopaR, ΔluxO, and ΔopaRΔluxO), our results suggested that expression of the pirAvp and pirBvp genes was related to the QS system, with luxO acting as a negative regulator of pirAvp and pirBvp without any mediation by opaRvp. In the promoter region of the pirAvp/pirBvp operon, we also identified a putative consensus binding site for the QS transcriptional regulator AphB. Real-time PCR further showed that aphBvp was negatively controlled by LuxOvp, and that its expression paralleled the expression patterns of pirAvp and pirBvp. An electrophoretic mobility shift assay (EMSA) showed that AphBvp could bind to this predicted region, even though another QS transcriptional regulator, AphAvp, could not. Taken together, these findings suggest that the QS system may regulate pirAvp/pirBvp expression through AphBvp.

Co-AMPpred for in silico-aided predictions of antimicrobial peptides by integrating composition-based features.

BACKGROUND: Antimicrobial peptides (AMPs) are oligopeptides that act as crucial components of innate immunity, naturally occur in all multicellular organisms, and are involved in the first line of defense function. Recent studies showed that AMPs perpetuate great potential that is not limited to antimicrobial activity. They are also crucial regulators of host immune responses that can modulate a wide range of activities, such as immune regulation, wound healing, and apoptosis. However, a microorganism's ability to adapt and to resist existing antibiotics triggered the scientific community to develop alternatives to conventional antibiotics. Therefore, to address this issue, we proposed Co-AMPpred, an in silico-aided AMP prediction method based on compositional features of amino acid residues to classify AMPs and non-AMPs. RESULTS: In our study, we developed a prediction method that incorporates composition-based sequence and physicochemical features into various machine-learning algorithms. Then, the boruta feature-selection algorithm was used to identify discriminative biological features. Furthermore, we only used discriminative biological features to develop our model. Additionally, we performed a stratified tenfold cross-validation technique to validate the predictive performance of our AMP prediction model and evaluated on the independent holdout test dataset. A benchmark dataset was collected from previous studies to evaluate the predictive performance of our model. CONCLUSIONS: Experimental results show that combining composition-based and physicochemical features outperformed existing methods on both the benchmark training dataset and a reduced training dataset. Finally, our proposed method achieved 80.8% accuracies and 0.871 area under the receiver operating characteristic curve by evaluating on independent test set. Our code and datasets are available at https://github.com/onkarS23/CoAMPpred .

Human placental villous stromal extracellular matrix regulates fetoplacental angiogenesis in severe fetal growth restriction.

Pregnancies complicated by severe, early-onset fetal growth restriction with abnormal Doppler velocimetry (FGRadv) have a sparse villous vascular tree secondary to impaired angiogenesis. As endothelial cell (EC) and stromal matrix interactions are key regulators of angiogenesis, we investigated the role of placental stromal villous matrix on fetoplacental EC angiogenesis. We have developed a novel model of generating placental fibroblast (FB) cell-derived matrices (CDMs), allowing us to interrogate placenta-specific human EC and stromal matrix interactions and their effects on fetoplacental angiogenesis. We found that as compared with control ECs plated on control matrix, FGRadv ECs plated on FGRadv matrix exhibited severe migrational defects, as measured by velocity, directionality, accumulated distance, and Euclidean distance in conjunction with less proliferation. However, control ECs, when interacting with FGRadv CDM, also demonstrated significant impairment in proliferation and migratory properties. Conversely several angiogenic attributes were rescued in FGRadv ECs subjected to control matrix, demonstrating the importance of placental villous stromal matrix and EC-stromal matrix interactions in regulation of fetoplacental angiogenesis.

Predicting New Daily COVID-19 Cases and Deaths Using Search Engine Query Data in South Korea From 2020 to 2021: Infodemiology Study.

BACKGROUND: Given the ongoing COVID-19 pandemic situation, accurate predictions could greatly help in the health resource management for future waves. However, as a new entity, COVID-19's disease dynamics seemed difficult to predict. External factors, such as internet search data, need to be included in the models to increase their accuracy. However, it remains unclear whether incorporating online search volumes into models leads to better predictive performances for long-term prediction. OBJECTIVE: The aim of this study was to analyze whether search engine query data are important variables that should be included in the models predicting new daily COVID-19 cases and deaths in short- and long-term periods. METHODS: We used country-level case-related data, NAVER search volumes, and mobility data obtained from Google and Apple for the period of January 20, 2020, to July 31, 2021, in South Korea. Data were aggregated into four subsets: 3, 6, 12, and 18 months after the first case was reported. The first 80% of the data in all subsets were used as the training set, and the remaining data served as the test set. Generalized linear models (GLMs) with normal, Poisson, and negative binomial distribution were developed, along with linear regression (LR) models with lasso, adaptive lasso, and elastic net regularization. Root mean square error values were defined as a loss function and were used to assess the performance of the models. All analyses and visualizations were conducted in SAS Studio, which is part of the SAS OnDemand for Academics. RESULTS: GLMs with different types of distribution functions may have been beneficial in predicting new daily COVID-19 cases and deaths in the early stages of the outbreak. Over longer periods, as the distribution of cases and deaths became more normally distributed, LR models with regularization may have outperformed the GLMs. This study also found that models performed better when predicting new daily deaths compared to new daily cases. In addition, an evaluation of feature effects in the models showed that NAVER search volumes were useful variables in predicting new daily COVID-19 cases, particularly in the first 6 months of the outbreak. Searches related to logistical needs, particularly for "thermometer" and "mask strap," showed higher feature effects in that period. For longer prediction periods, NAVER search volumes were still found to constitute an important variable, although with a lower feature effect. This finding suggests that search term use should be considered to maintain the predictive performance of models. CONCLUSIONS: NAVER search volumes were important variables in short- and long-term prediction, with higher feature effects for predicting new daily COVID-19 cases in the first 6 months of the outbreak. Similar results were also found for death predictions.

Isotypes of autoantibodies against novel differential 4-hydroxy-2-nonenal-modified peptide adducts in serum is associated with rheumatoid arthritis in Taiwanese women.

BACKGROUND: Rheumatoid arthritis (RA) is an autoimmune disorder with systemic inflammation and may be induced by oxidative stress that affects an inflamed joint. Our objectives were to examine isotypes of autoantibodies against 4-hydroxy-2-nonenal (HNE) modifications in RA and associate them with increased levels of autoantibodies in RA patients. METHODS: Serum samples from 155 female patients [60 with RA, 35 with osteoarthritis (OA), and 60 healthy controls (HCs)] were obtained. Four novel differential HNE-modified peptide adducts, complement factor H (CFAH)1211-1230, haptoglobin (HPT)78-108, immunoglobulin (Ig) kappa chain C region (IGKC)2-19, and prothrombin (THRB)328-345, were re-analyzed using tandem mass spectrometric (MS/MS) spectra (ProteomeXchange: PXD004546) from RA patients vs. HCs. Further, we determined serum protein levels of CFAH, HPT, IGKC and THRB, HNE-protein adducts, and autoantibodies against unmodified and HNE-modified peptides. Significant correlations and odds ratios (ORs) were calculated. RESULTS: Levels of HPT in RA patients were greatly higher than the levels in HCs. Levels of HNE-protein adducts and autoantibodies in RA patients were significantly greater than those of HCs. IgM anti-HPT78-108 HNE, IgM anti-IGKC2-19, and IgM anti-IGKC2-19 HNE may be considered as diagnostic biomarkers for RA. Importantly, elevated levels of IgM anti-HPT78-108 HNE, IgM anti-IGKC2-19, and IgG anti-THRB328-345 were positively correlated with the disease activity score in 28 joints for C-reactive protein (DAS28-CRP). Further, the ORs of RA development through IgM anti-HPT78-108 HNE (OR 5.235, p < 0.001), IgM anti-IGKC2-19 (OR 12.655, p < 0.001), and IgG anti-THRB328-345 (OR 5.761, p < 0.001) showed an increased risk. Lastly, we incorporated three machine learning models to differentiate RA from HC and OA, and performed feature selection to determine discriminative features. Experimental results showed that our proposed method achieved an area under the receiver operating characteristic curve of 0.92, which demonstrated that our selected autoantibodies combined with machine learning can efficiently detect RA. CONCLUSIONS: This study discovered that some IgG- and IgM-NAAs and anti-HNE M-NAAs may be correlated with inflammation and disease activity in RA. Moreover, our findings suggested that IgM anti-HPT78-108 HNE, IgM anti-IGKC2-19, and IgG anti-THRB328-345 may play heavy roles in RA development.

Prediction of robotic neurorehabilitation functional ambulatory outcome in patients with neurological disorders.

INTRODUCTION: Conflicting results persist regarding the effectiveness of robotic-assisted gait training (RAGT) for functional gait recovery in post-stroke survivors. We used several machine learning algorithms to construct prediction models for the functional outcomes of robotic neurorehabilitation in adult patients. METHODS AND MATERIALS: Data of 139 patients who underwent Lokomat training at Taipei Medical University Hospital were retrospectively collected. After screening for data completeness, records of 91 adult patients with acute or chronic neurological disorders were included in this study. Patient characteristics and quantitative data from Lokomat were incorporated as features to construct prediction models to explore early responses and factors associated with patient recovery. RESULTS: Experimental results using the random forest algorithm achieved the best area under the receiver operating characteristic curve of 0.9813 with data extracted from all sessions. Body weight (BW) support played a key role in influencing the progress of functional ambulation categories. The analysis identified negative correlations of BW support, guidance force, and days required to complete 12 Lokomat sessions with the occurrence of progress, while a positive correlation was observed with regard to speed. CONCLUSIONS: We developed a predictive model for ambulatory outcomes based on patient characteristics and quantitative data on impairment reduction with early-stage robotic neurorehabilitation. RAGT is a customized approach for patients with different conditions to regain walking ability. To obtain a more-precise and clearer predictive model, collecting more RAGT training parameters and analyzing them for each individual disorder is a possible approach to help clinicians achieve a better understanding of the most efficient RAGT parameters for different patients. TRIAL REGISTRATION: Retrospectively registered.

Hiltonol Cocktail Kills Lung Cancer Cells by Activating Cancer-Suppressors, PKR/OAS, and Restraining the Tumor Microenvironment.

NSCLC (non-small cell lung cancer) is a leading cause of cancer-related deaths worldwide. Clinical trials showed that Hiltonol, a stable dsRNA representing an advanced form of polyI:C (polyinosinic-polycytidilic acid), is an adjuvant cancer-immunomodulator. However, its mechanisms of action and effect on lung cancer have not been explored pre-clinically. Here, we examined, for the first time, how a novel Hiltonol cocktail kills NSCLC cells. By retrospective analysis of NSCLC patient tissues obtained from the tumor biobank; pre-clinical studies with Hiltonol alone or Hiltonol+++ cocktail [Hiltonol+anti-IL6+AG490 (JAK2 inhibitor)+Stattic (STAT3 inhibitor)]; cytokine analysis; gene knockdown and gain/loss-of-function studies, we uncovered the mechanisms of action of Hiltonol+++. We demonstrated that Hiltonol+++ kills the cancer cells and suppresses the metastatic potential of NSCLC through: (i) upregulation of pro-apoptotic Caspase-9 and Caspase-3, (ii) induction of cytosolic cytochrome c, (iii) modulation of pro-inflammatory cytokines (GRO, MCP-1, IL-8, and IL-6) and anticancer IL-24 in NSCLC subtypes, and (iv) upregulation of tumor suppressors, PKR (protein kinase R) and OAS (2'5' oligoadenylate synthetase). In silico analysis showed that Lys296 of PKR and Lys66 of OAS interact with Hiltonol. These Lys residues are purportedly involved in the catalytic/signaling activity of the tumor suppressors. Furthermore, knockdown of PKR/OAS abrogated the anticancer action of Hiltonol, provoking survival of cancer cells. Ex vivo analysis of NSCLC patient tissues corroborated that loss of PKR and OAS is associated with cancer advancement. Altogether, our findings unraveled the significance of studying tumor biobank tissues, which suggests PKR and OAS as precision oncological suppressor candidates to be targeted by this novel Hiltonol+++ cocktail which represents a prospective drug for development into a potent and tailored therapy for NSCLC subtypes.

Ovatodiolide, isolated from Anisomeles indica, suppresses bladder carcinogenesis through suppression of mTOR/ß-catenin/CDK6 and exosomal miR-21 derived from M2 tumor-associated macrophages.

Bladder cancer (BCa) is the fourth leading cause of cancer deaths worldwide due to its aggressiveness and resistance against therapies. Intricate interactions between cancer cells and the tumor microenvironment (TME) are essential for both disease progression and regression. Thus, interrupting molecular communications within the TME could potentially provide improved therapeutic efficacies. M2-polarized tumor-associated macrophages (M2 TAMs) were shown to contribute to BCa progression and drug resistance. We attempted to provide evidence for ovatodiolide (OV) as a potential therapeutic agent that targets both TME and BCa cells. First, tumor-suppressing functions of OV were determined by cell viability, colony, and tumor-sphere formation assays using a coculture system composed of M2 TAMs/BCa cells. Subsequently, we demonstrated that extracellular vesicles (EVs) isolated from M2 TAMs containing oncomiR-21 and mRNAs, including Akt, STAT3, mTOR, and ß-catenin, promoted cisplatin (CDDP) resistance, migration, and tumor-sphere generation in BCa cells, through increasing CDK6, mTOR, STAT3, and ß-catenin expression. OV treatment also prevented M2 polarization and reduced EV cargos from M2 TAMs. Finally, in vivo data demonstrated that OV treatment overcame CDDP resistance. OV only and the OV + CDDP combination both resulted in significant reductions in mTOR, ß-catenin, CDK6, and miR-21 expression in tumor samples and EVs isolated from serum. Collectively, we demonstrated that M2 TAMs induced malignant properties in BCa cells, in part via oncogenic EVs. OV treatment prevented M2 TAM polarization, reduced EV cargos derived from M2 TAMs, and suppressed ß-catenin/mTOR/CDK6 signaling. These findings provide preclinical evidence for OV as a single or adjuvant agent for treating drug-resistant BCa.

Understanding the Community Risk Perceptions of the COVID-19 Outbreak in South Korea: Infodemiology Study.

BACKGROUND: South Korea is among the best-performing countries in tackling the coronavirus pandemic by using mass drive-through testing, face mask use, and extensive social distancing. However, understanding the patterns of risk perception could also facilitate effective risk communication to minimize the impacts of disease spread during this crisis. OBJECTIVE: We attempt to explore patterns of community health risk perceptions of COVID-19 in South Korea using internet search data. METHODS: Google Trends (GT) and NAVER relative search volumes (RSVs) data were collected using COVID-19-related terms in the Korean language and were retrieved according to time, gender, age groups, types of device, and location. Online queries were compared to the number of daily new COVID-19 cases and tests reported in the Kaggle open-access data set for the time period of December 5, 2019, to May 31, 2020. Time-lag correlations calculated by Spearman rank correlation coefficients were employed to assess whether correlations between new COVID-19 cases and internet searches were affected by time. We also constructed a prediction model of new COVID-19 cases using the number of COVID-19 cases, tests, and GT and NAVER RSVs in lag periods (of 1-3 days). Single and multiple regressions were employed using backward elimination and a variance inflation factor of <5. RESULTS: The numbers of COVID-19-related queries in South Korea increased during local events including local transmission, approval of coronavirus test kits, implementation of coronavirus drive-through tests, a face mask shortage, and a widespread campaign for social distancing as well as during international events such as the announcement of a Public Health Emergency of International Concern by the World Health Organization. Online queries were also stronger in women (r=0.763-0.823; P<.001) and age groups ≤29 years (r=0.726-0.821; P<.001), 30-44 years (r=0.701-0.826; P<.001), and ≥50 years (r=0.706-0.725; P<.001). In terms of spatial distribution, internet search data were higher in affected areas. Moreover, greater correlations were found in mobile searches (r=0.704-0.804; P<.001) compared to those of desktop searches (r=0.705-0.717; P<.001), indicating changing behaviors in searching for online health information during the outbreak. These varied internet searches related to COVID-19 represented community health risk perceptions. In addition, as a country with a high number of coronavirus tests, results showed that adults perceived coronavirus test-related information as being more important than disease-related knowledge. Meanwhile, younger, and older age groups had different perceptions. Moreover, NAVER RSVs can potentially be used for health risk perception assessments and disease predictions. Adding COVID-19-related searches provided by NAVER could increase the performance of the model compared to that of the COVID-19 case-based model and potentially be used to predict epidemic curves. CONCLUSIONS: The use of both GT and NAVER RSVs to explore patterns of community health risk perceptions could be beneficial for targeting risk communication from several perspectives, including time, population characteristics, and location.

RNA editing-based classification of diffuse gliomas: predicting isocitrate dehydrogenase mutation and chromosome 1p/19q codeletion.

BACKGROUND: Accurate classification of diffuse gliomas, the most common tumors of the central nervous system in adults, is important for appropriate treatment. However, detection of isocitrate dehydrogenase (IDH) mutation and chromosome1p/19q codeletion, biomarkers to classify gliomas, is time- and cost-intensive and diagnostic discordance remains an issue. Adenosine to inosine (A-to-I) RNA editing has emerged as a novel cancer prognostic marker, but its value for glioma classification remains largely unexplored. We aim to (1) unravel the relationship between RNA editing and IDH mutation and 1p/19q codeletion and (2) predict IDH mutation and 1p/19q codeletion status using machine learning algorithms. RESULTS: By characterizing genome-wide A-to-I RNA editing signatures of 638 gliomas, we found that tumors without IDH mutation exhibited higher total editing level compared with those carrying it (Kolmogorov-Smirnov test, p < 0.0001). When tumor grade was considered, however, only grade IV tumors without IDH mutation exhibited higher total editing level. According to 10-fold cross-validation, support vector machines (SVM) outperformed random forest and AdaBoost (DeLong test, p < 0.05). The area under the receiver operating characteristic curve (AUC) of SVM in predicting IDH mutation and 1p/19q codeletion were 0.989 and 0.990, respectively. After performing feature selection, AUCs of SVM and AdaBoost in predicting IDH mutation were higher than that of random forest (0.985 and 0.983 vs. 0.977; DeLong test, p < 0.05), but AUCs of the three algorithms in predicting 1p/19q codeletion were similar (0.976-0.982). Furthermore, 67% of the six continuously misclassified samples by our 1p/19q codeletion prediction models were misclassifications in the original labelling after inspection of 1p/19q status and/or pathology report, highlighting the accuracy and clinical utility of our models. CONCLUSIONS: The study represents the first genome-wide analysis of glioma editome and identifies RNA editing as a novel prognostic biomarker for glioma. Our prediction models provide standardized, accurate, reproducible and objective classification of gliomas. Our models are not only useful in clinical decision-making, but also able to identify editing events that have the potential to serve as biomarkers and therapeutic targets in glioma management and treatment.