bluuebunny/crossref_metadata_2025_split huggingface dataset:
COVID-19 Lockdown Increased the Risk of Preterm Birth
Authors: Lin Ting-ting , Zhang Chen , Chen Lei , Jin Li , Lin Xian-hua , Pan Jie-xue , Dennis Cindy-Lee , Mol Ben W. , Huang He-feng , Wu Yan-ting 
Published: September 2021

Purpose: To estimate whether the city-specific lockdown in Shanghai induced by the COVID-19 pandemic affected preterm birth rates among uninfected pregnant women in different trimesters.Methods: The population-based retrospective cohort study was conducted in the International Peace Maternity and Child Health Hospital (IPMCH) in Shanghai, China. Pregnant women without COVID-19 received perinatal healthcare during lockdown (from January 24, 2020 to March 24, 2020) and non-lockdown (from January 24, 2019 to March 24, 2019) period and giving birth to a live infant at IPMCH were enrolled. 1:1 propensity score matching and Inverse probability of treatment weighting were used to evaluate preterm birth (&lt;37 weeks), very preterm birth (&lt;34 weeks), preterm birth with premature rupture of membranes (PROM-PTB), spontaneous preterm birth with intact membranes (S-PTB), and medically induced preterm birth (MI-PTB) between two groups.Results: 8,270 pregnant women were in the lockdown group, and 9,815 were in the non-lockdown group. Pregnant women in second trimester during lockdown had a higher risk of PTB than those during the non-lockdown period [OR: 1.43 (CI 1.01–2.02), ARD: 1.7% (CI 0.04–3.4%), p = 0.045]. Furthermore, pregnant women in third trimester during lockdown had a higher risk of PROM-PTB than those during the non-lockdown period [OR: 1.64 (CI 1.09–2.47), ARD: 0.9% (CI 0.2–1.6%), p = 0.02]; no group differences were found related to rates of VPTB, S-PTB or MI-PTB.Conclusion: In this cohort study in China, we found that there was an increased risk in preterm birth for non-infected women in COVID-19 lockdown who were in their second trimester.
A Prospective Observational Study on the Efficacy and Safety of Infliximab-Biosimilar (CT-P13) in Patients With Takayasu Arteritis (TAKASIM)
Authors: Campochiaro Corrado , Tomelleri Alessandro , Sartorelli Silvia , Sembenini Camilla , Papa Maurizio , Fallanca Federico , Picchio Maria , Cavalli Giulio , De Cobelli Francesco , Baldissera Elena , Dagna Lorenzo 
Published: September 2021

Objectives: Infliximab (IFX) is widely used in patients with refractory Takayasu arteritis (TAK). Recently, the IFX-biosimilar CT-P13 has been introduced for the treatment of inflammatory diseases. The aim of this study was to assess the efficacy and safety of CT-P13 in patients with refractory TAK.Methods: In this prospective, open-label, single-center trial, TAK patients either already on treatment with IFX-originator (switch group) or never treated with IFX (naïve group) received CT-P13 for 52 weeks. The primary outcomes of the study were: (i) number of patients with active disease at month 6; (ii) incidence of treatment-emergent adverse events at month 12. Disease activity was assessed at month 6 and month 12 by clinical evaluation (ITAS-2020, ITAS-ESR, and ITAS-CRP scores) and imaging assessment [magnetic resonance angiography (MRA) and (18F)-FDG-PET].Results: 23 patients were recruited (21 switch, 2 naïve). At baseline, 7 patients (32%) were classified as active. At month 6, one patient voluntarily dropped out and 7 patients were still active (30%), including one patient started on a different bDMARD at month 2 due to poor disease control. Mean daily dose of prednisone equivalent was significantly lower than baseline (4.2 ± 1.9 mg vs. 4.8 ± 2.1 mg, p = 0.009). At month 12, another patient was excluded because of pregnancy desire. Five patients were classified as active (24%), including two patients started on a different bDMARD at month 2 and month 6. Mean daily dose of prednisone equivalent was significantly lower than baseline (3.3 ± 2.6, p = 0.034). No patient experienced side effects during CT-P13 infusion. Overall, one patient experienced grade 1 adverse event and 9 patients experienced grade 2 adverse events. In no case hospitalization was required. CT-P13 retention rate was 90.9% at month 6 and 90.4% at month 12.Conclusion: In this study, the use of IFX-biosimilar CT-P13 in patients with refractory TAK showed satisfying efficacy and safety profile.
Molecular Characterisation of Vancomycin-Resistant Enterococcus faecium Isolates Belonging to the Lineage ST117/CT24 Causing Hospital Outbreaks
Authors: Lisotto Paola , Couto Natacha , Rosema Sigrid , Lokate Mariëtte , Zhou Xuewei , Bathoorn Erik , Harmsen Hermie J. M. , Friedrich Alexander W. , Rossen John W. A. , Chlebowicz-Fliss Monika A. 
Published: September 2021

Background: Vancomycin-resistant Enterococcus faecium (VREfm) is a successful nosocomial pathogen. The current molecular method recommended in the Netherlands for VREfm typing is based on core genome Multilocus sequence typing (cgMLST), however, the rapid emergence of specific VREfm lineages challenges distinguishing outbreak isolates solely based on their core genome. Here, we explored if a detailed molecular characterisation of mobile genetic elements (MGEs) and accessory genes could support and expand the current molecular typing of VREfm isolates sharing the same genetic background, enhancing the discriminatory power of the analysis.Materials/Methods: The genomes of 39 VREfm and three vancomycin-susceptible E. faecium (VSEfm) isolates belonging to ST117/CT24, as assessed by cgMLST, were retrospectively analysed. The isolates were collected from patients and environmental samples from 2011 to 2017, and their genomes were analysed using short-read sequencing. Pangenome analysis was performed on de novo assemblies, which were also screened for known predicted virulence factors, antimicrobial resistance genes, bacteriocins, and prophages. Two representative isolates were also sequenced using long-read sequencing, which allowed a detailed analysis of their plasmid content.Results: The cgMLST analysis showed that the isolates were closely related, with a minimal allelic difference of 10 between each cluster’s closest related isolates. The vanB-carrying transposon Tn1549 was present in all VREfm isolates. However, in our data, we observed independent acquisitions of this transposon. The pangenome analysis revealed differences in the accessory genes related to prophages and bacteriocins content, whilst a similar profile was observed for known predicted virulence and resistance genes.Conclusion: In the case of closely related isolates sharing a similar genetic background, a detailed analysis of MGEs and the integration point of the vanB-carrying transposon allow to increase the discriminatory power compared to the use of cgMLST alone. Thus, enabling the identification of epidemiological links amongst hospitalised patients.
Application of Machine Learning Techniques to Identify Data Reliability and Factors Affecting Outcome After Stroke Using Electronic Administrative Records
Authors: Rana Santu , Luo Wei , Tran Truyen , Venkatesh Svetha , Talman Paul , Phan Thanh , Phung Dinh , Clissold Benjamin 
Published: September 2021

Aim: To use available electronic administrative records to identify data reliability, predict discharge destination, and identify risk factors associated with specific outcomes following hospital admission with stroke, compared to stroke specific clinical factors, using machine learning techniques.Method: The study included 2,531 patients having at least one admission with a confirmed diagnosis of stroke, collected from a regional hospital in Australia within 2009–2013. Using machine learning (penalized regression with Lasso) techniques, patients having their index admission between June 2009 and July 2012 were used to derive predictive models, and patients having their index admission between July 2012 and June 2013 were used for validation. Three different stroke types [intracerebral hemorrhage (ICH), ischemic stroke, transient ischemic attack (TIA)] were considered and five different comparison outcome settings were considered. Our electronic administrative record based predictive model was compared with a predictive model composed of “baseline” clinical features, more specific for stroke, such as age, gender, smoking habits, co-morbidities (high cholesterol, hypertension, atrial fibrillation, and ischemic heart disease), types of imaging done (CT scan, MRI, etc.), and occurrence of in-hospital pneumonia. Risk factors associated with likelihood of negative outcomes were identified.Results: The data was highly reliable at predicting discharge to rehabilitation and all other outcomes vs. death for ICH (AUC 0.85 and 0.825, respectively), all discharge outcomes except home vs. rehabilitation for ischemic stroke, and discharge home vs. others and home vs. rehabilitation for TIA (AUC 0.948 and 0.873, respectively). Electronic health record data appeared to provide improved prediction of outcomes over stroke specific clinical factors from the machine learning models. Common risk factors associated with a negative impact on expected outcomes appeared clinically intuitive, and included older age groups, prior ventilatory support, urinary incontinence, need for imaging, and need for allied health input.Conclusion: Electronic administrative records from this cohort produced reliable outcome prediction and identified clinically appropriate factors negatively impacting most outcome variables following hospital admission with stroke. This presents a means of future identification of modifiable factors associated with patient discharge destination. This may potentially aid in patient selection for certain interventions and aid in better patient and clinician education regarding expected discharge outcomes.
Online Statistical Learning in Developmental Language Disorder
Authors: Lukács Ágnes , Lukics Krisztina Sára , Dobó Dorottya 
Published: September 2021

Purpose: The vulnerability of statistical learning (SL) in developmental language disorder (DLD) has mainly been demonstrated with metacognitive offline measures which give little insight into the more specific nature and timing of learning. Our aims in this study were to test SL in children with and without DLD with both online and offline measures and to compare the efficiency of SL in the visual and acoustic modalities in DLD.Method: We explored SL in school-age children with and without DLD matched on age and sex (n = 36). SL was investigated with the use of acoustic verbal and visual nonverbal segmentation tasks relying on online (reaction times and accuracy) and offline (two-alternative forced choice, 2AFC and production) measures.Results: In online measures, learning was evident in both groups in both the visual and acoustic modalities, while offline measures showed difficulties in DLD. The visual production task showed a significant learning effect in both groups, while the visual two-alternative forced choice (2AFC) and the two acoustic offline tasks only showed evidence of learning in the control group. The comparison of learning indices revealed an SL impairment in DLD, which is present in both modalities.Conclusions: Our findings suggest that children with DLD are comparable to typically developing (TD) children in their ability to extract acoustic verbal and visual nonverbal patterns that are cued only by transitional probabilities in online tasks, but they show impairments on metacognitive measures of learning. The pattern of online and offline measures implies that online tests can be more sensitive and valid indices of SL than offline tasks, and the combined use of different measures provides a better picture of learning efficiency, especially in groups where metacognitive tasks are challenging.
Integrative Analysis of Histopathological Images and Genomic Data in Colon Adenocarcinoma
Authors: Li Hui , Chen Linyan , Zeng Hao , Liao Qimeng , Ji Jianrui , Ma Xuelei 
Published: September 2021

BackgroundColon adenocarcinoma (COAD) is one of the most common malignant tumors in the world. The histopathological features are crucial for the diagnosis, prognosis, and therapy of COAD.MethodsWe downloaded 719 whole-slide histopathological images from TCIA, and 459 corresponding HTSeq-counts mRNA expression and clinical data were obtained from TCGA. Histopathological image features were extracted by CellProfiler. Prognostic image features were selected by the least absolute shrinkage and selection operator (LASSO) and support vector machine (SVM) algorithms. The co-expression gene module correlated with prognostic image features was identified by weighted gene co-expression network analysis (WGCNA). Random forest was employed to construct an integrative prognostic model and calculate the histopathological-genomic prognosis factor (HGPF).ResultsThere were five prognostic image features and one co-expression gene module involved in the model construction. The time-dependent receiver operating curve showed that the prognostic model had a significant prognostic value. Patients were divided into high-risk group and low-risk group based on the HGPF. Kaplan-Meier analysis indicated that the overall survival of the low-risk group was significantly better than the high-risk group.ConclusionsThese results suggested that the histopathological image features had a certain ability to predict the survival of COAD patients. The integrative prognostic model based on the histopathological images and genomic features could further improve the prognosis prediction in COAD, which may assist the clinical decision in the future.
Dose-Response Reduction in Risk of Nasopharyngeal Carcinoma From Smoking Cessation: A Multicenter Case-Control Study in Hong Kong, China
Authors: Wang Lijun , Mai Zhi-Ming , Ngan Roger Kai-Cheong , Ng Wai-Tong , Lin Jia-Huang , Kwong Dora Lai-Wan , Chiang Shing-Chun , Yuen Kam-Tong , Ng Alice Wan-Ying , Ip Dennis Kai-Ming , Chan Yap-Hang , Lee Anne Wing-Mui , Lung Maria Li , Ho Sai Yin , Lam Tai-Hing 
Published: September 2021

BackgroundCigarette smoking is associated with nasopharyngeal cancer (NPC) risk. Whether quitting reduces the risk is unclear. We investigated the associations of NPC with duration of and age at quitting in an endemic region.MethodsWe investigated the associations between NPC and quitting in a multicenter case-control study in Hong Kong with 676 newly diagnosed NPC cases and 1,285 hospital controls between 2014 and 2017, using a computer-assisted self-administered questionnaire. Multivariable unconditional logistic regression yielded adjusted odds ratios (AORs) of NPC by quitting status, duration and age of quitting, combinations of duration and age of quitting, and quitting to smoking duration ratio, compared with current smoking.ResultsQuitting (AOR: 0.72; 95% CI: 0.53–0.98) and never smoking (0.73, 0.56–0.95) were associated with lower NPC risk. NPC risk decreased with (i) longer quitting duration (p &lt; 0.01), reaching significance after 11–20 (0.62, 0.39–0.99) and 21+ years (0.54, 0.31–0.92) of quitting; (ii) younger quitting age (p = 0.01), reaching significance for quitting at &lt;25 years (0.49, 0.24–0.97); and (iii) higher quitting to smoking duration ratio (p &lt; 0.01), reaching significance when the ratio reached 1 (0.60, 0.39–0.93). Quitting younger (age &lt;25) appeared to confer larger reductions (49% for ≤10 years of quitting, 50% for 11+ years) in NPC risk than quitting at older ages (25+) regardless of quitting duration (16% for ≤10 years, 39% for 11+ years).ConclusionsWe have shown longer duration and younger age of quitting were associated with lower NPC risk, with dose-response relations. Our findings support including smoking as a cause of NPC. Stronger tobacco control measures and quitting services are needed to prevent NPC.
Computed Tomography-Based Radiomics for Preoperative Prediction of Tumor Deposits in Rectal Cancer
Authors: Jin Yumei , Li Mou , Zhao Yali , Huang Chencui , Liu Siyun , Liu Shengmei , Wu Min , Song Bin 
Published: September 2021

ObjectiveTo develop and validate a computed tomography (CT)-based radiomics model for predicting tumor deposits (TDs) preoperatively in patients with rectal cancer (RC).MethodsThis retrospective study enrolled 254 patients with pathologically confirmed RC between December 2017 and December 2019. Patients were divided into a training set (n = 203) and a validation set (n = 51). A large number of radiomics features were extracted from the portal venous phase images of CT. After selecting features with L1-based method, we established Rad-score by using the logistic regression analysis. Furthermore, a combined model incorporating Rad-score and clinical factors was developed and visualized as the nomogram. The models were evaluated by the receiver operating characteristic curve (ROC) analysis and area under the ROC curve (AUC).ResultsOne hundred and seventeen of 254 patients were eventually found to be TDs+. Rad-score and clinical factors including carbohydrate antigen (CA) 19-9, CT-reported T stage (cT), and CT-reported peritumoral nodules (+/-) were significantly different between the TDs+ and TDs- groups (all P &lt; 0.001). These factors were all included in the combined model by the logistic regression analysis (odds ratio = 2.378 for Rad-score, 2.253 for CA19-9, 2.281 for cT, and 4.485 for peritumoral nodules). This model showed good performance to predict TDs in the training and validation cohorts (AUC = 0.830 and 0.832, respectively). Furthermore, the combined model outperformed the clinical model incorporating CA19-9, cT, and peritumoral nodules (+/-) in both training and validation cohorts for predicting TDs preoperatively (AUC = 0.773 and 0.718, P = 0.008 and 0.039).ConclusionsThe combined model incorporating Rad-score and clinical factors could provide a preoperative prediction of TDs and help clinicians guide individualized treatment for RC patients.
Detection of Metastatic Tumor Cells in the Bone Marrow Aspirate Smears by Artificial Intelligence (AI)-Based Morphogo System
Authors: Chen Pu , Chen Xu Run , Chen Nan , Zhang Lan , Zhang Li , Zhu Jianfeng , Pan Baishen , Wang Beili , Guo Wei 
Published: September 2021

IntroductionMetastatic carcinomas of bone marrow (MCBM) are characterized as tumors of non-hematopoietic origin spreading to the bone marrow through blood or lymphatic circulation. The diagnosis is critical for tumor staging, treatment selection and prognostic risk stratification. However, the identification of metastatic carcinoma cells on bone marrow aspiration smears is technically challenging by conventional microscopic screening.ObjectiveThe aim of this study is to develop an automatic recognition system using deep learning algorithms applied to bone marrow cells image analysis. The system takes advantage of an artificial intelligence (AI)-based method in recognizing metastatic atypical cancer clusters and promoting rapid diagnosis.MethodsWe retrospectively reviewed metastatic non-hematopoietic malignancies in bone marrow aspirate smears collected from 60 cases of patients admitted to Zhongshan Hospital. High resolution digital bone marrow aspirate smear images were generated and automatically analyzed by Morphogo AI based system. Morphogo system was trained and validated using 20748 cell cluster images from randomly selected 50 MCBM patients. 5469 pre-classified cell cluster images from the remaining 10 MCBM patients were used to test the recognition performance between Morphogo and experienced pathologists.ResultsMorphogo exhibited a sensitivity of 56.6%, a specificity of 91.3%, and an accuracy of 82.2% in the recognition of metastatic cancer cells. Morphogo’s classification result was in general agreement with the conventional standard in the diagnosis of metastatic cancer clusters, with a Kappa value of 0.513. The test results between Morphogo and pathologists H1, H2 and H3 agreement demonstrated a reliability coefficient of 0.827. The area under the curve (AUC) for Morphogo to diagnose the cancer cell clusters was 0.865.ConclusionIn patients with clinical history of cancer, the Morphogo system was validated as a useful screening tool in the identification of metastatic cancer cells in the bone marrow aspirate smears. It has potential clinical application in the diagnostic assessment of metastatic cancers for staging and in screening MCBM during morphology examination when the symptoms of the primary site are indolent.
Parents' Views to Strengthen Partnerships in Newborn Intensive Care
Authors: Ferreira Alexie , Ferretti Emanuela , Curtis Krista , Joly Cynthia , Sivanthan Myuri , Major Nathalie , Daboval Thierry 
Published: September 2021

Background:Parental involvement in their newborn's neonatal intensive care reduces stress and helps with the parent-child attachment, transition to home, and future development. However, parents' perspectives are not often sought or considered when adapting family-centered care in neonatal intensive care units (NICUs).Aim:To identify what parents believe helps or hinders their involvement in their newborn's care when admitted to our Level 3B NICU.Methods:Between August and October 2018, nine mothers and one father were interviewed during three 60- to 90-min audiotaped focus groups using a semi-structured interview tool. From the content analysis of the verbatims, three reviewers identified key themes that affected how involved parents could be in their newborn's care.Results:Parents provided examples of factors that facilitated or restricted their involvement. The analysis identified themes: (1) parent-staff interactions, (2) supportive/trustworthy healthcare professionals, (3) consistency in care and caring staff, (4) family, couple, and peer support, (5) newborn status, (6) resources and education for parents, (7) the NICU environment, and (8) academic and research participation.Conclusion:We identified a conceptual framework to allow our NICU team to prioritize working strategies to strengthen parental involvement in newborn care. In addition to implementing ways to involve parents, we need to address parents' satisfaction with their participation. These findings may help other investigators explore parents' expectations toward their NICU experience.