Miracle-dz/UniFAR-FTUs huggingface dataset:
Applying machine learning to the pharmacokinetic modeling of cyclosporine in adult renal transplant recipients: a multi-method comparison

Objective: The aim of this study was to identify the important factors affecting cyclosporine (CsA) blood concentration and estimate CsA concentration using seven different machine learning (ML) algorithms. We also assessed the predictability of established ML models and previously built population pharmacokinetic (popPK) model. Finally, the most suitable ML model and popPK model to guide precision dosing were determined. Methods: In total, 3,407 whole-blood trough and peak concentrations of CsA were obtained from 183 patients who underwent initial renal transplantation. These samples were divided into model-building and evaluation sets. The model-building set was analyzed using seven different ML algorithms. The effects of potential covariates were evaluated using the least absolute shrinkage and selection operator algorithms. A separate evaluation set was used to assess the ability of all models to predict CsA blood concentration. R squared (R 2) scores, median prediction error (MDPE), median absolute prediction error (MAPE), and the percentages of PE within 20% (F20) and 30% (F30) were calculated to assess the predictive performance of these models. In addition, previously built popPK model was included for comparison. Results: Sixteen variables were selected as important covariates. Among ML models, the predictive performance of nonlinear-based ML models was superior to that of linear regression (MDPE: 3.27%, MAPE: 34.21%, F20: 30.63%, F30: 45.03%, R 2 score: 0.68). The ML model built with the artificial neural network algorithm was considered the most suitable (MDPE: −0.039%, MAPE: 25.60%, F20: 39.35%, F30: 56.46%, R 2 score: 0.75). Its performance was superior to that of the previously built popPK model (MDPE: 5.26%, MAPE: 29.22%, F20: 33.94%, F30: 51.22%, R 2 score: 0.68). Furthermore, the application of the most suitable model and the popPK model in clinic showed that most dose regimen recommendations were reasonable. Conclusion: The performance of these ML models indicate that a nonlinear relationship for covariates may help to improve model predictability. These results might facilitate the application of ML models in clinic, especially for patients with unstable status or during initial dose optimization.
Neoadjuvant immunotherapy combined with chemotherapy significantly improved patients’ overall survival when compared with neoadjuvant chemotherapy in non-small cell lung cancer: A cohort study

Background Programmed death-1 (PD-1)/programmed death ligand-1 (PD-L1) inhibitors displayed considerable advantages in neoadjuvant therapy of non-small cell lung cancer (NSCLC), but the specific application of neoadjuvant immunotherapy has not been well determined, and the long-term prognostic data of neoadjuvant immunochemotherapy combined with surgical resection of NSCLC remains limited. In this study, we intended to assess the efficacy of the neoadjuvant therapy of the PD-1 inhibitor and long-term prognosis in patients with resectable NSCLC. Methods We retrospectively analyzed NSCLC surgical patients treated with neoadjuvant therapy in our hospital, and divided them into a neoadjuvant chemotherapy group and a neoadjuvant immunotherapy combined with chemotherapy group. The propensity score matching method was used to evaluate the effectiveness of immunotherapy combined with chemotherapy in the treatment of resectable lung cancer, and the long-term prognosis of these two groups was compared. Results A total of 62 cases were enrolled, including 20 patients (20/62, 32.26%) in the immunotherapy group and 42 patients (42/62, 67.74%) in the chemotherapy group. The clinical baseline data of these two groups were balanced. In the immunotherapy group, all patients had tumor regression in imaging finding (tumor regression ratio: 11.88% - 75.00%). In the chemotherapy group, 30 patients had tumor regression (tumor regression ratio: 2.70% - 58.97%). The R0 removal rates of cancers were comparable between the immunotherapy group and chemotherapy group (19/20, 95.00% vs. 39/42, 92.86%, P=1.000). The two groups were balanced in complete minimally invasive surgery, pneumonectomy, operative duration, blood loss, postoperative complications, and hospital stay. The immunotherapy group had more sleeve resection (36.84% vs. 10.26%, p=0.039) including bronchial sleeve and vascular sleeve, higher pathological complete response (pCR) rate (57.89% vs. 5.13%, P<0.001) and major pathologic response (MPR) rate (78.95% vs. 10.26%, P<0.001). There were no differences in survival curves for: smoker and non-smoker, squamous cell carcinoma and adenocarcinoma, or right lung cancer and left lung cancer. Moreover, patients who achieved MPR (including pCR) had significantly better overall survival (OS) and disease-free survival (DFS). Patients in immunotherapy group had significantly better OS and longer DFS than those in chemotherapy group. Conclusions In conclusion, neoadjuvant immunotherapy combined with chemotherapy can provide better OS and DFS and improving pCR and MPR rates by shrinking tumors. This study has been registered in the Chinese Clinical Trial Registry, number ChiCTR2200060433. http://www.chictr. org.cn/edit.aspx? pid=170157&htm=4.
Joint Modeling of 
Longitudinal Ordinal Data on 
Quality of Life and Survival

Joint models for longitudinal and survival data have been widely discussed in the literature. This thesis proposes a joint model using a stereotype model for the longitudinal ordinal responses and a Cox proportional hazards model for survival time. Our current joint model has a new feature since no literature has examined the joint model under the stereotype model. The stereotype model can improve the fit by adding extra score parameters, but it still has the advantage of requiring only a single parameter to describe the effect of a predictor on the item response levels. We give an example to model longitudinal ordinal data and survival data for patients being followed up after treatments. The main focus is on modeling both the quality of life data and the survival data simultaneously with a goal of understanding the association between the two processes over time. These two models are linked through a latent variable that characterizes the quality of life of an individual and is assumed to underlie the hazard rate. In other words, the latent variable serves as a shared variable in the joint model. We present the joint model in two different aspects: one based on a Bayesian approach and the other one a semiparametric approach using the EM algorithm. For the Bayesian approach, the latent variable is treated as a continuous variable and is assumed to have a multivariate normal distribution. The partial survival likelihood function is used in the survival component of the Bayesian joint model, while the full likelihood function is considered in the semiparametric joint model. In the latter approach the baseline hazard is assumed to be a step function and has no parametric form. The latent variable in the semiparametric joint model is then treated as a discrete variable. We illustrate our methodologies by analyzing data from the Staccato study, a randomized trial to compare two treatment methods, for Human Immunodeficiency Virus (HIV) infection of Thai patients on Highly Active Antiretroviral Therapy (HAART), in which the quality of life was assessed with a HIV Medical Outcome Study (MOS-HIV) questionnaire. Furthermore, we extend the study further to the case of multiple failure types in the survival component. Thus, the extension of the joint model consists of the stereotype model and the competing risks model. The Bayesian method is employed to estimate all unknown parameters in this extended joint model. The results we obtained are consistent for both the Bayesian joint model and the semiparametric joint model. Both models show that patients who had a better quality of life were associated with a lower hazard of HIV progression. Patients on continuous treatment also had a lower hazard of HIV progression compared with patients on CD4-guided interruption treatment.
Second-harmonic generation in dielectric nanoparticles with different symmetries

In this work we study second-harmonic generation of a monocrystalline nanoparticle with a noncentrosymmetric crystalline lattice. It was shown that breaking the symmetry of the nanoparticle’s shape can significantly affect the second-harmonic radiation pattern. We propose a method for explaining and predicting the generated field for arbitrary nanoparticles and provide selection rules for nanoparticles with several different symmetries.
Electrorheological polishing performance of cerium-doped titanium dioxide particles

Electrorheological (ER) polishing as a new type of polishing technique has flexibility and controllability. However, ER polishing cannot be widely used in actual processing because the present ER polishing fluid is usually prepared by simple mixing abrasive materials into ER fluid, which is easy to occur phase separation and decreased ER performance. This paper develops a new type of ER polishing material based on cerium-doped titanium dioxide. As a classic abrasive, the cerium oxide not only has high polishing performance but also can enhance ER performance of titanium dioxide by doping effect, which endows the cerium-doped titanium dioxide with high ER polishing efficiency compared to ER polishing fluid prepared by a simple mixing method. Cerium-doped titanium dioxide particles were prepared by the sol-gel method. The size, surface morphology and elemental distribution were characterized. The ER properties were tested. The effects of different concentrations, machining gap, voltage and rotation speed on polishing performance were studied. Under the conditions of a 0.2 mm machining gap, 3 kV voltage and 200 r/min rotation speed, the surface roughness (Ra) of the copper workpiece decrease from 136 nm to 14.4 nm after 0.5 h of polishing, which is far higher than the polishing efficiency of simple mixed ER polishing fluid.
Effectiveness of a peer group-based online intervention program in empowering families of children with disabilities at home

Background The empowerment of families raising children with disabilities (CWD) is crucial in maintaining their health. We developed an evidence-based, family empowerment intervention program focusing on social resource utilization and reducing care burden. Objective This study aimed to determine the program's effectiveness in promoting family empowerment. Methods We compared an intervention group that started the online intervention program a week after initial evaluation and a group that received delayed intervention (waitlist-controlled group) at three time points: initial (T1), post-course (T2), and follow-up (T3). The required sample size was 52. Results There were 60 participants who applied to the program. One participant dropped out due to scheduling issues, and the others were assigned to either the intervention group (n = 29) or the waitlist-controlled group (n = 30). Those who responded to the baseline questionnaire (T1: 26 from the intervention group; 29 from the waitlist-controlled group) comprised the final sample. Among them, 20 members of the intervention group and 20 of the waitlist-controlled group attended all four sessions (completion rates of 77% and 69%, respectively). The attendance rate for sessions 1–4 was 94%, 89%, 81%, and 83%, respectively. The participant numbers in each session ranged from 5 to 18 per month. The baseline outcome score did not differ between the groups. The primary outcome, family empowerment, measured using the family empowerment scale (FES), was significantly higher at T2 for the intervention group than in the waitlist-controlled group and was sustained in the sensitivity analysis. The intervention group's FES, in the family relationships (FA) and relationships with service systems (SS) subdomains, increased significantly, unlike involvement with the community (SP). The intervention group experienced lower care burden and higher self-compassion, especially in the isolation and over-identification items of the self-compassion scale-short form (SCS-SF). The intervention group's FES (total, FA, SS) and SCS-SF (total, common humanity, isolation) changed significantly between T1 and T2, and all, except common humanity, were sustained up to T3; this group's FES (SP) and SCS (negative score, over-identification) changed significantly between T1 and T3. The waitlist-controlled group's FES (total, FA) and SCS (total) changed significantly and were sustained between T2 and T3. Conclusions The developed intervention program promotes family empowerment in families of CWD. Clinical Trial Registration This study is registered as a clinical trial in the UMIN Clinical Trials Registry (https://center6.umin.ac.jp/cgi-open-bin/ctr/ctr_view.cgi? recptno=R000050422, UMIN000044172).
Finding ikigai: How robots can support meaning in later life

Previous research in human-robot interaction has explored using robots to increase objective and hedonic aspects of well-being and quality of life, but there is no literature on how robots might be used to support eudaimonic aspects of well-being (such as meaning in life). A sense of meaning has been shown to positively affect health and longevity. We frame our study around the Japanese concept of ikigai, which is widely used with Japanese older adults to enhance their everyday lives, and is closely related to the concept of eudaimonic well-being (EWB) known in Western countries. Using a mixed-methods and exploratory approach, including interviews with 17 older adults and the collection of 100 survey responses, we explored how older adults in the US experience a sense of meaning, and if and how a social robot could be used to help foster this sense. We find that meaning for older adults is often obtained by helping others, through family connections, and/or through activities of daily life, and that sources of meaning often differ based on the older adults’ living situation. Assessing how meaning compares to happiness and social connection, we highlight general similarities and differences, and also find that living situation influences older adults’ sources of happiness, desire for social connection, and barriers to well-being. , in addition to companionship and happiness having a weaker correlation with meaning for those who live alone than for those who live with others. Additionally, we evaluated initial perceptions of a social robot (QT) meant to enhance ikigai and overall well-being. Finding mostly positive perceptions, though those who live alone also reported being less willing to adopt a social robot into their homes. Using both data collected on older adults’ meaning and the potential use of QT to support meaning, we make several design recommendations with regards to using robots to enhance ikigai, such as by prompting daily reflecting, enhancing family bonds, and suggesting new experiences and volunteer opportunities.
Changes in the geometry and robustness of diffusion tensor imaging networks: Secondary analysis from a randomized controlled trial of young autistic children receiving an umbilical cord blood infusion

Diffusion tensor imaging (DTI) has been used as an outcome measure in clinical trials for several psychiatric disorders but has rarely been explored in autism clinical trials. This is despite a large body of research suggesting altered white matter structure in autistic individuals. The current study is a secondary analysis of changes in white matter connectivity from a double-blind placebo-control trial of a single intravenous cord blood infusion in 2–7-year-old autistic children (1). Both clinical assessments and DTI were collected at baseline and 6 months after infusion. This study used two measures of white matter connectivity: change in node-to-node connectivity as measured through DTI streamlines and a novel measure of feedback network connectivity, Ollivier-Ricci curvature (ORC). ORC is a network measure which considers both local and global connectivity to assess the robustness of any given pathway. Using both the streamline and ORC analyses, we found reorganization of white matter pathways in predominantly frontal and temporal brain networks in autistic children who received umbilical cord blood treatment versus those who received a placebo. By looking at changes in network robustness, this study examined not only the direct, physical changes in connectivity, but changes with respect to the whole brain network. Together, these results suggest the use of DTI and ORC should be further explored as a potential biomarker in future autism clinical trials. These results, however, should not be interpreted as evidence for the efficacy of cord blood for improving clinical outcomes in autism. This paper presents a secondary analysis using data from a clinical trial that was prospectively registered with ClinicalTrials.gov(NCT02847182).
Circulating syndecan-1 and glypican-4 predict 12-month survival in metastatic colorectal cancer patients

Cell surface syndecans and glypicans play important roles in the development and prognosis of colorectal cancer (CRC). Their soluble forms from proteoglycan shedding can be detected in blood and have been proposed as new prognostic biomarkers in several cancer entities. However, studies on circulating syndecan-1 (SDC1) and glypican-4 (GPC4) in CRC are limited. We, therefore, evaluated the impact of plasma SDC1 and GPC4 on the prognosis of metastatic (m)CRC patients. The present study included 93 patients with mCRC. The endpoints were progression-free survival (PFS) and overall survival (OS) at 12 months. SDC1 and GPC4 levels were measured in plasma using enzyme-linked immunosorbent assays. Plasma levels of SDC1 and GPC4 were significantly correlated. Significant correlations of these two markers were also found with carcinoembryonic antigen (CEA). Kaplan-Meier curve analyses indicated that PFS and OS probabilities significantly decreased with increasing levels of SDC1 and GPC4, respectively. Multivariable Cox regression analyses showed that both markers were significantly associated with PFS and OS independently from clinicopathological characteristics including CEA. Respective adjusted hazard ratios (HR) together with corresponding 95% confidence intervals for one standard deviation change of SDC1 were 1.32 [1.02-1.84] for PFS and 1.48 [1.01-2.15] for OS. Adjusted HRs [95% confidence intervals] of GPC4 were 1.42 [1.07-1.89] for PFS and 2.40 [1.51-3.81] for OS. Results from area under the receiver operating characteristic curve analyses suggest that GPC4 and SDC1 add additional prognostic values to CEA for OS. In conclusion, we showed significant associations of circulating SDC1 and GPC4 with poor survival of mCRC patients.
Structure formation with suppressed small-scale perturbations

All commonly considered dark matter scenarios are based on hypothetical particles with small but non-zero thermal velocities and tiny interaction cross-sections. A generic consequence of these attributes is the suppression of small-scale matter perturbations either due to free-streaming or due to interactions with the primordial plasma. The suppression scale can vary over many orders of magnitude depending on particle candidate and production mechanism in the early Universe. While nonlinear structure formation has been explored in great detail well above the suppression scale, the range around suppressed perturbations is still poorly understood. In this paper we study structure formation in the regime of suppressed perturbations using both analytical techniques and numerical simulations. We develop simple and theoretically motivated recipes for the halo mass function, the expected number of satellites, and the halo concentrations, which are designed to work for power spectra with suppression at arbitrary scale and of arbitrary shape. As case studies, we explore warm and mixed dark matter scenarios where effects are most distinctive. Additionally, we examine the standard dark matter scenario based on weakly interacting massive particles (WIMP) and compare it to pure cold dark matter with zero primordial temperature. We find that our analytically motivated recipes are in good agreement with simulations for all investigated dark matter scenarios, and we therefore conclude that they can be used for generic cases with arbitrarily suppressed small-scale perturbations.
Antimicrobial use in cattle and poultry production on occurrence of multidrug resistant Escherichia coli. A systematic review with focus on sub-Saharan Africa

Antimicrobial use in livestock production has been linked to antimicrobial resistance (AMR) worldwide; however, optimization of their use has been considered an important strategy in dealing with it. The aims of this study were as follows: (a) to assess the literature on antimicrobial usage (practices, frequency, class, type) in cattle and poultry production with regard to resistance in Escherichia coli (E. coli) including multidrug resistance (MDR) (b) summarize evidence for quantitative (volumes of active antimicrobial ingredients) and quality (identify and quantify active ingredient) and (c) to identify data gaps. Peer reviewed literature search was conducted by querying two online databases: PubMed and Google scholar from November 15, 2018 to February 2019. The inclusion criteria for eligibility were articles: published in English between 2008 and 2018, including poultry (chicken) or cattle or both, E. coli bacteria of choice, antimicrobial use on farms, quantitative data and quality of antimicrobial used. Microsoft Excel was used for data extraction and Rayyan software for eligibility studies. The search retrieved 1,446 probable articles including those from the reference list of significant papers, of which twenty-four articles remained on full text review with more than a third of the studies being conducted in Nigeria. Farm surveys and antimicrobial sales were identified as the main sources of data and the mean quantities of antimicrobials based on sales data were 23,234, 41,280.87, and 1,538,443 kg of the active ingredient in Nigeria, Zambia and South Africa, respectively. One study from Cameroon determined the quantities of active ingredients based on dose metrics while another study still from Cameroon mentioned the quality of antimicrobials. Tetracyclines, beta-lactams/aminoglycosides and fluoroquinolones were the most common classes of antimicrobials (antibiotics) used. Our review reveals a dearth of information in Sub- Saharan Africa on the quantity and quality of veterinary drugs and yet they play a role in the overall picture of antimicrobial resistance. This finding gives an opportunity in the area of focus for future research as far as resistance and multidrug resistance are concerned in food producing animals.
Reexamining the calculations of exercise energy expenditure in the energy availability equation of free-living athletes

COPYRIGHT © 2022 Taguchi and Manore. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. Reexamining the calculations of exercise energy expenditure in the energy availability equation of free-living athletes