Selection Of Time Points Research Articles

Understanding primary care provider’s knowledge and perceptions of diabetes self-management education and support

ObjectiveThe American Diabetes Association Standards of Care recommends that individuals with diabetes receive self-management education, but the utilization of these services remains low. This study explores primary care providers’ knowledge and perceptions of diabetes self-management education and support (DSMES). Study designA convergent mixed methods study design grounded in the Theoretical Domains Framework was conducted with an initial provider survey, followed by semi-structured interview of a purposeful sub-sample of providers. The survey was distributed via REDCap and interviews were recorded and transcribed verbatim. ResultsA total of 140 PCP’s responded to the survey (response rate 37.63 %) and 17 participated in interviews. The average knowledge score of the correct selection of DSMES referral time points was 70.35 %. Five themes emerged in provider interviews: procedural knowledge, ability, beliefs, reinforcement, and cultural norms. Providers wanted bidirectional communication with the DSMES program and were more likely to refer if they had a cultural norm of referring. ConclusionProviders have limited knowledge of the appropriate time to refer to DSMES but expressed a willingness to refer. They emphasized the importance of providing their patients with appropriate self-management education and support.

Comparative Evaluation of Neural Network Models for Optimizing ECG Signal in Non-Uniform Sampling Domain

Electrocardiographic signals (ECG) are ubiquitous, which justifies the research of their optimal storage and transmission. However, proposals for non-uniform signal sampling must take into account the priority of diagnostic data accuracy and record integrity, as well as robustness to noise and interference. In this study, two novel methods are introduced, each utilizing a distinct neural network architecture for optimizing non-uniform sampling of ECG signal. A transformer model refines each time point selection through an iterative process using gradient descent optimization, with the goal of minimizing the mean squared error between the original and resampled signals. It adaptively modifies time points, which improves the alignment between both signals. In contrast, the Temporal Convolutional Network model trains on the original signal, and gradient descent optimization is utilized to improve the selection of time points. Evaluation of both strategies’ efficacy is performed by calculating signal distances at lower and higher sampling rates. First, a collection of synthetic data points that resembled the P-QRS-T wave was used to train the model. Then, the ECG-ID database for real data analysis was used. Filtering to remove baseline wander followed by evaluation and testing were carried out in the real patient data. The results, in particular MSE = 0.0005, RMSE = 0.0216, and Pearson’s CC = 0.9904 for 120 sps in the case of the transformer patient data model, provide viable paths for maintaining the precision and dependability of ECG-based diagnostic systems at much lower sampling rate. Outcomes indicate that both techniques are effective at improving the fidelity between the original and modified ECG signals.

Investigating the contribution of image time series observations to cauliflower harvest-readiness prediction.

Cauliflower cultivation is subject to high-quality control criteria during sales, which underlines the importance of accurate harvest timing. Using time series data for plant phenotyping can provide insights into the dynamic development of cauliflower and allow more accurate predictions of when the crop is ready for harvest than single-time observations. However, data acquisition on a daily or weekly basis is resource-intensive, making selection of acquisition days highly important. We investigate which data acquisition days and development stages positively affect the model accuracy to get insights into prediction-relevant observation days and aid future data acquisition planning. We analyze harvest-readiness using the cauliflower image time series of the GrowliFlower dataset. We use an adjusted ResNet18 classification model, including positional encoding of the data acquisition dates to add implicit information about development. The explainable machine learning approach GroupSHAP analyzes time points' contributions. Time points with the lowest mean absolute contribution are excluded from the time series to determine their effect on model accuracy. Using image time series rather than single time points, we achieve an increase in accuracy of 4%. GroupSHAP allows the selection of time points that positively affect the model accuracy. By using seven selected time points instead of all 11 ones, the accuracy improves by an additional 4%, resulting in an overall accuracy of 89.3%. The selection of time points may therefore lead to a reduction in data collection in the future.

Transforming the Discovery of Targeted Protein Degraders: The Translational Power of Predictive PK/PD Modeling.

Targeted protein degraders (TPDs), an emerging therapeutic modality, are attracting considerable interest with the promise to address disease-related proteins that are not druggable with conventional small molecule inhibitors. Despite their novel mechanism of action, the PK/PD relationship of degraders is still approached with a mindset deeply rooted in inhibitor drugs. Here, we establish how predictive mechanistic modeling specifically tailored to TPDs can significantly enhance the value of the available information during lead generation and optimization. By integrating the results from in vitro assays with routinely collected PK data, modeling accurately predicts degradation in vivo. These predictions transform the prioritization of compounds for in vivo studies as well as the selection of optimal dose schedules and most informative measurement time points with the least number of animals. Moreover, the comprehensive modeling framework (1) identifies the PK/PD driver of targeted protein degradation and subsequent downstream pharmacodynamic effects, and (2) uncovers the fundamental difference between degrader and inhibitor PK/PD relationships. The practical utility of our predictive modeling is demonstrated with relevant use cases. This framework will allow researchers to transition from current, mostly serendipity-based approaches to more sound model-informed decision making. Going forward, the presented predictive PK/PD modeling framework lays out a rational path to incorporate inter-species differences in the pharmacology and thus promises to help with getting the dose right in clinical trials.

Opioid vaccine clinical testing: lessons learned.

Opioid use disorder (OUD) presents a serious public health concern, with dramatic increases in opioid-overdose mortality in recent years and a small percentage of those with OUD accessing or remaining engaged with available treatments. Efforts are currently underway to identify vaccines targeting opioids, which could provide a novel and complimentary approach. The current review provides an overview of existing literature, practical considerations for designing and conducting clinical trials with vaccines for opioids, and future directions. This review covers the following themes: clinical trial design and selection of endpoints, timepoint selection, practical considerations and lessons learned from the first (ongoing) trial of a vaccine targeting opioids, and future directions. Efforts to develop and test vaccines targeting OUD are based on a foundation of preclinical work and close collaboration between preclinical and clinical researchers. Efforts to learn from shortcomings of prior clinical trials of vaccines for other substances are essential in designing and testing effective vaccines for OUD. Design and implementation of clinical trials for a vaccine for OUD requires careful balance of participant safety and strategies for retention and efforts to gather viable data to inform future work.

Osteocyte RANKL Drives Bone Resorption in Mouse Ligature-Induced Periodontitis.

Mouse ligature-induced periodontitis (LIP) has been used to study bone loss in periodontitis. However, the role of osteocytes in LIP remains unclear. Furthermore, there is no consensus on the choice of alveolar bone parameters and time points to evaluate LIP. Here, we investigated the dynamics of changes in osteoclastogenesis and bone volume (BV) loss in LIP over 14 days. Time-course analysis revealed that osteoclast induction peaked on days 3 and 5, followed by the peak of BV loss on day 7. Notably, BV was restored by day 14. The bone formation phase after the bone resorption phase was suggested to be responsible for the recovery of bone loss. Electron microscopy identified bacteria in the osteocyte lacunar space beyond the periodontal ligament (PDL) tissue. We investigated how osteocytes affect bone resorption of LIP and found that mice lacking receptor activator of NF-κB ligand (RANKL), predominantly in osteocytes, protected against bone loss in LIP, whereas recombination activating 1 (RAG1)-deficient mice failed to resist it. These results indicate that T/B cells are dispensable for osteoclast induction in LIP and that RANKL from osteocytes and mature osteoblasts regulates bone resorption by LIP. Remarkably, mice lacking the myeloid differentiation primary response gene 88 (MYD88) did not show protection against LIP-induced bone loss. Instead, osteocytic cells expressed nucleotide-binding oligomerization domain containing 1 (NOD1), and primary osteocytes induced significantly higher Rankl than primary osteoblasts when stimulated with a NOD1 agonist. Taken together, LIP induced both bone resorption and bone formation in a stage-dependent manner, suggesting that the selection of time points is critical for quantifying bone loss in mouse LIP. Pathogenetically, the current study suggests that bacterial activation of osteocytes via NOD1 is involved in the mechanism of osteoclastogenesis in LIP. The NOD1-RANKL axis in osteocytes may be a therapeutic target for bone resorption in periodontitis. © 2023 The Authors. Journal of Bone and Mineral Research published by Wiley Periodicals LLC on behalf of American Society for Bone and Mineral Research (ASBMR).

Communicating and understanding statistical measures when quantifying the between-group difference in competing risks.

Competing risks issues are common in clinical trials and epidemiological studies for patients in follow-up who may experience a variety of possible outcomes. Under such competing risks, two hazard-based statistical methods, cause-specific hazard (CSH) and subdistribution hazard (SDH), are frequently used to assess treatment effects among groups. However, the outcomes of the CSH-based and SDH-based methods have a close connection with the proportional hazards (CSH or SDH) assumption and may have an non-intuitive interpretation. Recently, restricted mean time lost (RMTL) has been used as an alternative summary measure for analysing competing risks, due to its clinical interpretability and robustness to the proportional hazards assumption. Considering the above approaches, we summarize the differences between hazard-based and RMTL-based methods from the aspects of practical interpretation, proportional hazards model assumption and the selection of restricted time points, and propose corresponding suggestions for the analysis of between-group differences under competing risks. Moreover, an R package 'cRMTL' and corresponding step-by-step guidance are available to help users for applying these approaches.

Long-Term Observation of Patients With Cancer - an Entity-Independent Registry for Healthcare and Translational Research at the University Medicine Dresden (Cancer-Reg-VT).

Translational research is important, especially in medicine where decisions affect people's lives. Clinical registries and the studies embedded in them allow the depiction of actual care practice under routine conditions. Translating the findings of health services research back into clinical research through prospective cohort studies has the potential to drive medical innovations faster, more effectively and, above all, in a more targeted manner. These must therefore be a central component of cutting-edge oncological research. The aim of the registry is the establishment of clinical cohorts and the provision of a comprehensive, high-quality data set for oncological diseases. The registry will prospectively record all patients treated for cancer at Dresden University Hospital (UKD). In addition to the data from the hospital information systems (ORBIS, TDS, GEPADO, etc.), monitoring of health-related quality of life (HRQOL) is to be carried out at regular intervals at the beginning and during the course of treatment. In addition, individual linkage with data from clinical cancer registries and health insurance companies (including AOK PLUS) is planned for a period of five years before and after inclusion. All these data will be merged in a registry database. The selection of variables and measurement time points is closely based on the guidelines for colorectal carcinoma of the international initiative ICHOM (International Consortium for Health Outcomes Measurement). The study management software (STeVe) separates personal identification characteristics (IDAT) and medical data (MDAT) at an early stage. The independent trust centre of the TU Dresden (Treuhandstelle) ensures that no personal data enter the registry database. It is thereby also ensured that the data owners involved (UKD, biobank, health insurance company, cancer registry, patient) only receive the personal data they need for allocation. The MOSAIC software tools recommended by the TMF (Technologie- und Methodenplattform für die vernetzte medizinische Forschung e.V.) are used to manage the pseudonyms. With the registry, previously missing evidence on the effectiveness, safety and costs of diagnostic and therapeutic measures can be made, taking into account long-term and patient-reported outcomes of routine care. The data potentially allow for the identification of barriers to and facilitators of innovative promising cancer diagnostics and therapies. They also enable generation of scientifically relevant hypotheses in the field of translational and outcomes research.

Single-Time-Point Imaging for Dosimetry After [177Lu]Lu-DOTATATE: Accuracy of Existing Methods and Novel Data-Driven Models for Reducing Sensitivity to Time-Point Selection.

Estimation of the time-integrated activity (TIA) for dosimetry from imaging at a single time point (STP) facilitates the clinical translation of dosimetry-guided radiopharmaceutical therapy. However, the accuracy of the STP methods for TIA estimation varies on the basis of time-point selection. We constructed patient data-driven regression models to reduce the sensitivity to time-point selection and to compare these new models with commonly used STP methods. Methods: SPECT/CT performed at time period (TP) 1 (3-5 h), TP2 (days 1-2), TP3 (days 3-5), and TP4 (days 6-8) after cycle 1 of [177Lu]Lu-DOTATATE therapy involved 27 patients with 100 segmented tumors and 54 kidneys. Influenced by the previous physics-based STP models of Madsen etal. and Hänscheid etal., we constructed an STP prediction expression, TIA = A(t) × g(t), in a SPECT data-driven way (model 1), in which A(t) is the observed activity at imaging time t, and the curve, g(t), is estimated with a nonparametric generalized additive model by minimizing the normalized mean square error relative to the TIA derived from 4-time-point SPECT (reference TIA). Furthermore, we fit a generalized additive model that incorporates baseline biomarkers as auxiliary data in addition to the single activity measurement (model 2). Leave-one-out cross validation was performed to evaluate STP models using mean absolute error (MAE) and mean square error between the predicted and reference TIA. Results: At days 3-5, all evaluated STP methods performed very well, with an MAE of less than 7% (between-patient SD of <10%) for both kidneys and tumors. At other TPs, the Madsen method and data-driven models 1 and 2 performed reasonably well (MAEs < 17% for kidneys and < 32% for tumors), whereas the error with the Hänscheid method was substantially higher. The proof of concept of adding baseline biomarkers to the prediction model was demonstrated and showed a moderate enhancement at TP1, especially for estimating kidney TIA (MAE ± SD from 15.6% ± 1.3% to 11.8% ± 1.0%). Evaluations on 500 virtual patients using clinically relevant time-activity simulations showed a similar performance. Conclusion: The performance of the Madsen method and proposed data-driven models is less sensitive to TP selection than is the Hänscheid method. At the earliest TP, which is the most practical, the model incorporating baseline biomarkers outperforms other methods that rely only on the single activity measurement.

Late-stage MC38 tumours recapitulate features of human colorectal cancer - implications for appropriate timepoint selection in preclinical studies.

Anti-tumour T cell responses play a crucial role in controlling the progression of colorectal cancer (CRC), making this disease a promising candidate for immunotherapy. However, responses to immune-targeted therapies are currently limited to subpopulations of patients and specific types of cancer. Clinical studies have therefore focussed on identifying biomarkers that predict immunotherapy responses and elucidating the immunological landscapes of different cancers. Meanwhile, our understanding of how preclinical tumour models resemble human disease has fallen behind, despite their crucial role in immune-targeted drug development. A deeper understanding of these models is therefore needed to improve the development of immunotherapies and the translation of findings made in these systems. MC38 colon adenocarcinoma is a widely used preclinical model, yet how it recapitulates human colorectal cancer remains poorly defined. This study investigated the tumour-T cell immune landscape of MC38 tumours using histology, immunohistochemistry, and flow cytometry. We demonstrate that early-stage tumours exhibit a nascent TME, lacking important immune-resistance mechanisms of clinical interest, while late-stage tumours exhibit a mature TME resembling human tumours, with desmoplasia, T cell exhaustion, and T cell exclusion. Consequently, these findings clarify appropriate timepoint selection in the MC38 model when investigating both immunotherapies and mechanisms that contribute to immunotherapy resistance. Overall, this study provides a valuable resource that will enable appropriate application of the MC38 model and expedite the development and clinical translation of new immunotherapies.

In Situ Degradation Kinetics of 25 Feedstuffs and the Selection of Time Points in Mathematical Statistics

Rumen degradation kinetics of 25 feedstuffs (six protein feeds, nine energy feeds and ten roughages) were first determined using the nylon bag technique in situ and the differences of degradation characteristics fitted with five or seven time points measuring data were evaluated with the goodness of fit (R2) of degradation curves. Protein and energy feeds were incubated for 2, 4, 8, 16, 24, 36, 48 h, roughages were incubated for 4, 8, 16, 24, 36, 48, 72 h, where three and six data sets of five time points were screened out, respectively. Only the degradation parameters a (rapidly degraded proportion), b (slowly degraded proportion) and c (degradation rate of slowly degraded proportion) of several feeds at five time points were significantly different from those at seven time points (p < 0.05), and the others were not significant (p > 0.05). The R2 of the degradation curves obtained at five time points was closer to 1, indicating that the fitting obtained at five time points was more accurate in predicting the real-time rumen degradation rate of feed. These results indicate that it is feasible to determine the rumen degradation characteristics of feedstuffs by only setting five measuring time points.

Optimization of hydrogen-deuterium exchange ensemble reweighting (HDXer) to aid the biophysical characterization of proteins.

Hydrogen-deuterium exchange mass spectrometry (HDX-MS) experiments provide unique insights into native protein structure and dynamics in solution but have limited spatial resolution. Molecular dynamics (MD) simulations, in contrast, allow for the exploration of protein conformational landscapes with an atomistic level of resolution. However, forcefield inaccuracies, crystallographic biases, and timescale limitations may compromise the validity of the final structural ensemble. Hydrogen Deuterium eXchange Ensemble Reweighting (HDXer) is a maximum entropy approach that was recently developed to (1) quantitatively predict hydrogen-deuterium exchange from a modeled conformational ensemble and (2) refine the candidate ensemble to best fit an experimental HDX-MS dataset. Here, we compare how various computational and experimental parameters impact the performance of HDXer, using extracellular regulated kinase 2 (ERK2) and hen egg white lysozyme (HEWL) as model proteins. Conventional molecular dynamics simulations of ERK2 and HEWL were performed in triplicate using the CHARMM36m, AMBER FF19SB, and Drude forcefields. After predicting the HDX rates of each candidate ensemble, HDXer was used to fit the simulated structures to the corresponding HDX-MS datasets. To assess the influence of simulation length, truncated and strided copies of the full-length simulations were generated. Other factors, including peptide charge state selection, ion mobility mass spectrometry, and independent HDX-MS data acquisitions, were also tested. We find that while some variables (e.g., high vs. low peptide charge states) do not have a significant effect, other parameters (e.g., early vs. late timepoint selection) directly correlate with the disagreement between calculated and experimental deuterium uptake. The results from this benchmarking inform the development of a recommended HDXer workflow and best practices for optimal data acquisition.

Micro-CT analysis of osteomyelitis of rabbit tibial for model establishment and biomaterials application in tissue engineering

Osteomyelitis is one of the most difficult diseases to treat in orthopedics field. The construction of animal models of osteomyelitis is now more mature but still lacks a deeper criterion other than “successful infection”. In this work, rabbit tibial osteomyelitis model with S. aureus infection was established. Whole tibia, cortical bone around bone window, and tibial condyle were characterized in considerable detail using micro-CT and other means at 2/4/6 weeks, respectively. The results show that in addition to the obvious inflammatory response and bone destruction, osteomyelitis caused some other effects on compact and cancellous bone, and in particular, changes in bone mineral density after infection were of interest. Although the modeling groups all exhibited osteolysis and bone loss, their overall bone mineral density averages and those of the control groups were mostly in the range of 870 mg/cm3 to 920 mg/cm3, without statistical difference. The results suggest that overall bone mineral density is determined by both bone destruction conditions and the amount of dead bone deposition. This work provides a reference basis for the selection of time points for the subsequent animal models establishment and some valuable reference indicators of the application of biomaterials in tissue engineering.

Opioid antagonism in humans: a primer on optimal dose and timing for central mu-opioid receptor blockade

Non-human animal studies outline precise mechanisms of central mu-opioid regulation of pain, stress, affiliation and reward processing. In humans, pharmacological blockade with non-selective opioid antagonists such as naloxone and naltrexone is typically used to assess involvement of the mu-opioid system in such processing. However, robust estimates of the opioid receptor blockade achieved by opioid antagonists are missing. Dose and timing schedules are highly variable and often based on single studies. Here, we provide a detailed analysis of central opioid receptor blockade after opioid antagonism based on existing positron emission tomography data. We also create models for estimating opioid receptor blockade with intravenous naloxone and oral naltrexone. We find that common doses of intravenous naloxone (0.10–0.15 mg/kg) and oral naltrexone (50 mg) are more than sufficient to produce full blockade of central MOR (>90% receptor occupancy) for the duration of a typical experimental session (~60 min), presumably due to initial super saturation of receptors. Simulations indicate that these doses also produce high KOR blockade (78–100%) and some DOR blockade (10% with naltrexone and 48–74% with naloxone). Lower doses (e.g., 0.01 mg/kg intravenous naloxone) are estimated to produce less DOR and KOR blockade while still achieving a high level of MOR blockade for ~30 min. The models and simulations form the basis of two novel web applications for detailed planning and evaluation of experiments with opioid antagonists. These tools and recommendations enable selection of appropriate antagonists, doses and assessment time points, and determination of the achieved receptor blockade in previous studies.

Dynamic Delayed-Decision Task Assignment Under Spatial-Temporal Constraints in Mobile Crowdsensing

Task assignment is a research focus of Mobile Crowd Sensing(MCS). On the arrival of a sensing task, most researches use the way of immediate recruitment to assign the task, ignoring the impact of the task assignment time point. However, the arrival time and start time of a task are often different, hence an earlier assignment decision leads to rough predictions of users reaching the destination of task, which reduces task completion ratio and budget utilization ratio. In order to solve this problem, in this paper we propose a dynamic delayed-decision task assignment method. Firstly, we formalize a new task assignment problem considering the time point of task assignment, in which each selected user can complete as many tasks as possible within the given spatial-temporal constraints, rather than only one task, and propose a method of decision time point selection using a delayed-decision strategy to deal with the tasks of dynamically arriving at the MCS platform. Secondly, we propose two mobility prediction methods to efficiently compute the probabilities of users reaching the destinations before the end time of the sensing tasks, and then propose the related task assignment algorithms, namely with TDMar and TDMeta, which use the proposed semi-Markov prediction and meta-path prediction, respectively. Finally, by using large-scale real data sets, we evaluate the two algorithms and compare them with the baseline methods. The results show that using delayed-decision strategy could evidently improve the task completion ratio and budget utilization ratio, and also decrease the user singleness.

Applications, Challenges, and Outlook for PBPK Modeling and Simulation: A Regulatory, Industrial and Academic Perspective.

Several regulatory guidances on the use of physiologically based pharmacokinetic (PBPK) analyses and physiologically based biopharmaceutics model(s) (PBBM(s)) have been issued. Workshops are routinely held, demonstrating substantial interest in applying these modeling approaches to address scientific questions in drug development. PBPK models and PBBMs have remarkably contributed to model-informed drug development (MIDD) such as anticipating clinical PK outcomes affected by extrinsic and intrinsic factors in general and specific populations. In this review, we proposed practical considerations for a "base" PBPK model construction and development, summarized current status, challenges including model validation and gaps in system models, and future perspectives in PBPK evaluation to assess a) drug metabolizing enzyme(s)- or drug transporter(s)- mediated drug-drug interactions b) dosing regimen prediction, sampling timepoint selection and dose validation in pediatric patients from newborns to adolescents, c) drug exposure in patients with renal and/or and hepatic organ impairment, d) maternal-fetal drug disposition during pregnancy, and e) pH-mediated drug-drug interactions in patients treated with proton pump inhibitors/acid-reducing agents (PPIs/ARAs) intended for gastric protection. Since PBPK can simulate outcomes in clinical studies with enrollment challenges or ethical issues, the impact of PBPK models on waivers and how to strengthen study waiver is discussed.

Current Approaches for Dissolution Similarity Assessment, Requirements, and Global Expectations.

This report summarizes podium presentations and breakout sessions from the second day of the 2019 M-CERSI workshop on In Vitro Dissolution Similarity Assessment in Support of Drug Product Quality: What, How, and When? Presenters from the U.S. Food and Drug Administration (FDA), Health Canada (HC), European Medicines Agency (EMA), Brazilian Health Surveillance Agency (ANVISA), and the pharmaceutical industry shared experiences/concerns with dissolution profile similarity assessment supporting minor/moderate Chemistry, Manufacturing and Control (CMC) changes. Members from regulatory agencies explained that dissolution profile similarity testing is only part of the overall assessment of the acceptability of the proposed changes; decisions are usually made based on aggregate weight of evidence. Scientific shortcomings of f2 were highlighted but no proposal on how to replace it was made. Controlling dissolution timepoint variability and application of pairwise batch-to-batch comparisons (PBC) of dissolution profiles caused considerable debate. Several industry participants suggested increased sample sizes to raise confidence in decision-making and to avoid PBC. They proposed identification of a single mathematical method with predefined acceptance criteria and suggested that dissolution timepoint selection should follow EMA and HC guidance. A majority of meeting attendees favored applying clinically relevant dissolution specifications (CRDS) and dissolution safe space to determine the impact of minor/moderate CMC changes as opposed to dissolution profile similarity assessment via statistical methods. Day 2 of the workshop highlighted the need and opportunities for global harmonization including variability, timepoint selection, role of CRDS, and statistical methods to address the ambiguity globally operating pharmaceutical companies are currently facing.

Electroacupuncture in promoting neural repair after spinal cord injury: Inhibiting the Notch signaling pathway and regulating downstream proteins expression.

Spinal cord injury (SCI) is one of the serious central nervous system injuries and the incidence of SCI continues to increase. Previous studies have indicated that electroacupuncture (EA) is beneficial for promoting recovery after SCI. In the present study, we attempted to evaluate how EA can promote the neural repair in SCI model rats by observing changes in the Notch signaling pathway. Experimental rats were randomly divided into four groups. Each group had its own intervention period: 1 day, 7 days, 14 days, and 28 days, and five randomized subgroups: blank control (B) group, blank electroacupuncture (BE) group, sham operation (S) group, model control (M) group and EA group. Animals in the EA group and the BE group were treated with EA at Dazhui (GV14) and Mingmen (GV4) acupoints for 20 min. After the intervention period, the Basso-Beattie-Bresnahan (BBB) score was used to evaluate the neurological function. We found that BBB score increased in EA-treated groups. Hematoxylin and eosin staining was used to observe pathological changes in the injured spinal cord and the results showed that EA therapy could promote the repair of injured spinal cord tissue. Immunohistochemistry and Western blot methods were used to detect the expression of proteins Delta1, Presenilin1, Hes1, and Hes5 in the injured spinal cord. The results showed that the expression levels of Delta1, Presenilin1, Hes1, and Hes5 increased significantly after SCI and decreased after EA treatment. Our study suggested that the possible mechanism by which EA could benefit the recovery after SCI in rats may include inhibiting the Notch signaling pathway and regulating the downstream proteins expression. In addition, our study can provide reference for selecting acupoints and treatment cycle in the treatment of SCI.

Optimizing similarity factor of in vitro drug release profile for development of early stage formulation of drug using linear regression model

The objective of this article is to optimize the similarity factor within immediate release (IR) and modified release (MR) of in vitro drug release profiles. The least square method is used to minimize the difference between empirical and regression curve fitting data of in vitro IR/MR drug release profiles. An estimation of percentage drug release at intermediate timepoints has been done to improve the similarity factor f_{2} using linear curve fit method. In this study linear regression model is used to analyze the similarity factor f_{2} for Nitrofurantoin MR Capsules, Venlafaxine HCl MR Tablets and Lurasidone IR Tablets in order to exhibit the significance as well as similarity owing to the consideration of extra intervening timepoints. This linear regression model may help pharmaceutical industries to examine the inside comparison of IR/MR in vitro drug release profile with few modifications in timepoint selection to improve similarity factor f_{2}.

COVID-19 combined with liver injury: Current challenges and management.

Coronavirus disease 2019 (COVID-19) combined with liver injury has become a very prominent clinical problem. Due to the lack of a clear definition of liver injury in patients with COVID-19, the different selection of evaluation parameters and statistical time points, there are the conflicting conclusions about the incidence rate in different studies. The mechanism of COVID-19 combined with liver injury is complicated, including the direct injury of liver cells caused by severe acute respiratory syndrome coronavirus 2 replication and liver injury caused by cytokines, ischemia and hypoxia, and drugs. In addition, underlying diseases, especially chronic liver disease, can aggravate COVID-19 liver injury. In the treatment of COVID-19 combined with liver injury, the primary and basic treatment is to treat the etiology and pathogenesis, followed by support, liver protection, and symptomatic treatment according to the clinical classification and severity of liver injury. This article evaluates the incidence, pathogenesis and prevention and treatment of COVID-19 combined with liver injury, and aims to provide countermeasures for the prevention and treatment of COVID-19 combined with liver injury.