Tag: pubmed

Stem Cell Rev Rep. 2022 Jul 8. doi: 10.1007/s12015-022-10404-1. Online ahead of print.

ABSTRACT

BACKGROUND: Hair follicle stem cells (HFSCs) are derived from the bulge region and are important autologous stem cell sources. Bibliometric is a statistical method that quantitatively analyses the research papers concerned about one special topic. This study aims to estimate the research status and trends of HFSCs worldwide by bibliometric analyses.

METHODS: Data were obtained from the Web of Science by searching keywords related to HFSCs. Publication distributions stratified by countries/regions, institutions, journals, and authors were systematically assessed. The frequency of keywords was assessed, and bibliometric mapping was employed to describe the development of HFSC research.

RESULTS: A total of 458 publications that met our screening criteria were included in this study, consisting of 423 (92.4%) articles and 35 (7.6%) reviews. The United States of America (USA) ranked first in the number of publications at 146 (31.9%), followed by China at 130 (28.4%), which is consistent with the rank of the H-index. Author keywords were classified into three clusters, namely, basic study, applied study, and biomarker; average publication time of keywords in applied study cluster is later than basic study cluster. The keywords “bulge”, “nestin”, and “skin” are the top three most frequent keywords in basic studies; “differentiation”, “proliferation”, and “alopecia” are the top three most frequent keywords in applied studies. With respect to the latest research hotspots, “apoptosis” and “tissue engineering” are relatively new keywords.

CONCLUSIONS: The USA and China were the most productive countries for research on HFSCs. The focus of keywords gradually shifted from basic study to applied study. Research on the differentiation/proliferation of HFSCs and the role of HFSCs in alopecia have been recent research focuses. Apoptosis and tissue engineering are recommended as promising research hotspots. Our study provides profound insights into the research history, current status, and future trend of HFSCs.

PMID:35802225 | DOI:10.1007/s12015-022-10404-1

Int J Comput Assist Radiol Surg. 2022 Jul 8. doi: 10.1007/s11548-022-02712-1. Online ahead of print.

ABSTRACT

PURPOSE: Advanced developments in the medical field have gradually increased the public demand for surgical skill evaluation. However, this assessment always depends on the direct observation of experienced surgeons, which is time-consuming and variable. The introduction of robot-assisted surgery provides a new possibility for this evaluation paradigm. This paper aims at evaluating surgeon performance automatically with novel evaluation metrics based on different surgical data.

METHODS: Urologists ([Formula: see text]) from a hospital were requested to perform a simplified neobladder reconstruction on an ex vivo setup twice with different camera modalities ([Formula: see text]) randomly. They were divided into novices and experts ([Formula: see text], respectively) according to their experience in robot-assisted surgeries. Different performance metrics ([Formula: see text]) are proposed to achieve the surgical skill evaluation, considering both instruments and endoscope. Also, nonparametric tests are adopted to check if there are significant differences when evaluating surgeons performance.

RESULTS: When grouping according to four stages of neobladder reconstruction, statistically significant differences can be appreciated in phase 1 ([Formula: see text]) and phase 2 ([Formula: see text]) with normalized time-related metrics and camera movement-related metrics, respectively. On the other hand, considering experience grouping shows that both metrics are able to highlight statistically significant differences between novice and expert performances in the control protocol. It also shows that the camera-related performance of experts is significantly different ([Formula: see text]) when handling the endoscope manually and when it is automatic.

CONCLUSION: Surgical skill evaluation, using the approach in this paper, can effectively measure surgical procedures of surgeons with different experience. Preliminary results demonstrate that different surgical data can be fully utilized to improve the reliability of surgical evaluation. It also demonstrates its versatility and potential in the quantitative assessment of various surgical operations.

PMID:35802223 | DOI:10.1007/s11548-022-02712-1

Bone Jt Open. 2022 Jul;3(7):543-548. doi: 10.1302/2633-1462.37.BJO-2022-0026.R1.

ABSTRACT

AIMS: Although readmission has historically been of primary interest, emergency department (ED) visits are increasingly a point of focus and can serve as a potentially unnecessary gateway to readmission. This study aims to analyze the difference between primary and revision total joint arthroplasty (TJA) cases in terms of the rate and reasons associated with 90-day ED visits.

METHODS: We retrospectively reviewed all patients who underwent TJA from 2011 to 2021 at a single, large, tertiary urban institution. Patients were separated into two cohorts based on whether they underwent primary or revision TJA (rTJA). Outcomes of interest included ED visit within 90-days of surgery, as well as reasons for ED visit and readmission rate. Multivariable logistic regressions were performed to compare the two groups while accounting for all statistically significant demographic variables.

RESULTS: Overall, 28,033 patients were included, of whom 24,930 (89%) underwent primary and 3,103 (11%) underwent rTJA. The overall rate of 90-day ED visits was significantly lower for patients who underwent primary TJA in comparison to those who underwent rTJA (3.9% vs 7.0%; p < 0.001). Among those who presented to the ED, the readmission rate was statistically lower for patients who underwent primary TJA compared to rTJA (23.5% vs 32.1%; p < 0.001).

CONCLUSION: ED visits present a significant burden to the healthcare system. Patients who undergo rTJA are more likely to present to the ED within 90 days following surgery compared to primary TJA patients. However, among patients in both cohorts who visited the ED, three-quarters did not require readmission. Future efforts should aim to develop cost-effective and patient-centred interventions that can aid in reducing preventable ED visits following TJA. Cite this article: Bone Jt Open 2022;3(7):543-548.

PMID:35801582 | DOI:10.1302/2633-1462.37.BJO-2022-0026.R1

Workplace Health Saf. 2022 Jul 8:21650799221102299. doi: 10.1177/21650799221102299. Online ahead of print.

ABSTRACT

BACKGROUND: We sought the opinions of health care workers (HCWs) at a designated COVID-19 facility receiving the first cases to identify workplace modifications and inform effective changes to maximize health and safety at the onset of a crisis.

METHODS: A cross-sectional study utilized open- and close-ended questions gathered demographic and work details, experiences and perspectives on infection control, communication, support, and the workplace. Qualitative data were analyzed thematically and quantitative were analyzed using descriptive statistics.

FINDINGS: Of 340 HCWs, most approved of the organization’s response to minimizing risk (81.0%), infection control training (81.1%), and supplies (74.3%). Key actions included up-to-date guidelines (93.6%) and specialized infectious diseases clinics (94.9%). Conclusions: HCWs rated the organization’s adaptive changes highly, noting areas for improvement such as transparency and timeliness of communication. Incorporating input from HCWs when responding to health crises was beneficial to maximize staff health and safety and consequently that of patients.

PMID:35801569 | DOI:10.1177/21650799221102299

Math Biosci Eng. 2022 Jun 10;19(8):8554-8579. doi: 10.3934/mbe.2022397.

ABSTRACT

Measles is one of the highly contagious human viral diseases. Despite the availability of vaccines, measles outbreak frequently occurs in many places, including Nepal, partly due to the lack of compliance with vaccination. In this study, we develop a novel transmission dynamics model to evaluate the effects of monitored vaccination programs to control and eliminate measles. We use our model, parameterized with the data from the measles outbreak in Nepal, to calculate the vaccinated reproduction number, $ R_v $, of measles in Nepal. We perform model analyses to establish the global asymptotic stability of the disease-free equilibrium point for $ R_v < 1 $ and the uniform persistence of the disease for $ R_v > 1 $. Moreover, we perform model simulations to identify monitored vaccination strategies for the successful control of measles in Nepal. Our model predicts that the monitored vaccination programs can help control the potential resurgence of the disease.

PMID:35801477 | DOI:10.3934/mbe.2022397

Math Biosci Eng. 2022 Jun 9;19(8):8426-8451. doi: 10.3934/mbe.2022392.

ABSTRACT

Transcription involves gene activation, nuclear RNA export (NRE) and RNA nuclear retention (RNR). All these processes are multistep and biochemical. A multistep reaction process can create memories between reaction events, leading to non-Markovian kinetics. This raises an unsolved issue: how does molecular memory affect stochastic transcription in the case that NRE and RNR are simultaneously considered? To address this issue, we analyze a non-Markov model, which considers multistep activation, multistep NRE and multistep RNR can interpret many experimental phenomena. In order to solve this model, we introduce an effective transition rate for each reaction. These effective transition rates, which explicitly decode the effect of molecular memory, can transform the original non-Markov issue into an equivalent Markov one. Based on this technique, we derive analytical results, showing that molecular memory can significantly affect the nuclear and cytoplasmic mRNA mean and noise. In addition to the results providing insights into the role of molecular memory in gene expression, our modeling and analysis provide a paradigm for studying more complex stochastic transcription processes.

PMID:35801472 | DOI:10.3934/mbe.2022392

Math Biosci Eng. 2022 May 30;19(8):7978-8002. doi: 10.3934/mbe.2022373.

ABSTRACT

Cancer is a manifestation of disorders caused by the changes in the body’s cells that go far beyond healthy development as well as stabilization. Breast cancer is a common disease. According to the stats given by the World Health Organization (WHO), 7.8 million women are diagnosed with breast cancer. Breast cancer is the name of the malignant tumor which is normally developed by the cells in the breast. Machine learning (ML) approaches, on the other hand, provide a variety of probabilistic and statistical ways for intelligent systems to learn from prior experiences to recognize patterns in a dataset that can be used, in the future, for decision making. This endeavor aims to build a deep learning-based model for the prediction of breast cancer with a better accuracy. A novel deep extreme gradient descent optimization (DEGDO) has been developed for the breast cancer detection. The proposed model consists of two stages of training and validation. The training phase, in turn, consists of three major layers data acquisition layer, preprocessing layer, and application layer. The data acquisition layer takes the data and passes it to preprocessing layer. In the preprocessing layer, noise and missing values are converted to the normalized which is then fed to the application layer. In application layer, the model is trained with a deep extreme gradient descent optimization technique. The trained model is stored on the server. In the validation phase, it is imported to process the actual data to diagnose. This study has used Wisconsin Breast Cancer Diagnostic dataset to train and test the model. The results obtained by the proposed model outperform many other approaches by attaining 98.73 % accuracy, 99.60% specificity, 99.43% sensitivity, and 99.48% precision.

PMID:35801453 | DOI:10.3934/mbe.2022373

Math Biosci Eng. 2022 May 23;19(8):7570-7585. doi: 10.3934/mbe.2022356.

ABSTRACT

Hepatitis B is a disease that damages the liver, and its control has become a public health problem that needs to be solved urgently. In this paper, we investigate analytically and numerically the dynamics of a new stochastic HBV infection model with antiviral therapies and immune response represented by CTL cells. Through using the theory of stochastic differential equations, constructing appropriate Lyapunov functions and applying Itô’s formula, we prove that the disease-free equilibrium of the stochastic HBV model is stochastically asymptotically stable in the large, which reveals that the HBV infection will be eradicated with probability one. Moreover, the asymptotic behavior of globally positive solution of the stochastic model near the endemic equilibrium of the corresponding deterministic HBV model is studied. By using the Milstein’s method, we provide the numerical simulations to support the analysis results, which shows that sufficiently small noise will not change the dynamic behavior, while large noise can induce the disappearance of the infection. In addition, the effect of inhibiting virus production is more significant than that of blocking new infection to some extent, and the combination of two treatment methods may be the better way to reduce HBV infection and the concentration of free virus.

PMID:35801436 | DOI:10.3934/mbe.2022356

Math Biosci Eng. 2022 May 19;19(8):7425-7480. doi: 10.3934/mbe.2022351.

ABSTRACT

As uncertainty and sensitivity analysis of complex models grows ever more important, the difficulty of their timely realizations highlights a need for more efficient numerical operations. Non-intrusive Polynomial Chaos methods are highly efficient and accurate methods of mapping input-output relationships to investigate complex models. There is substantial potential to increase the efficacy of the method regarding the selected sampling scheme. We examine state-of-the-art sampling schemes categorized in space-filling-optimal designs such as Latin Hypercube sampling and L1-optimal sampling and compare their empirical performance against standard random sampling. The analysis was performed in the context of L1 minimization using the least-angle regression algorithm to fit the GPCE regression models. Due to the random nature of the sampling schemes, we compared different sampling approaches using statistical stability measures and evaluated the success rates to construct a surrogate model with relative errors of <0.1%, <1%, and <10%, respectively. The sampling schemes are thoroughly investigated by evaluating the y of surrogate models constructed for various distinct test cases, which represent different problem classes covering low, medium and high dimensional problems. Finally, the sampling schemes are tested on an application example to estimate the sensitivity of the self-impedance of a probe that is used to measure the impedance of biological tissues at different frequencies. We observed strong differences in the convergence properties of the methods between the analyzed test functions.

PMID:35801431 | DOI:10.3934/mbe.2022351

Math Biosci Eng. 2022 May 20;19(8):7521-7542. doi: 10.3934/mbe.2022354.

ABSTRACT

With the development of the field of survival analysis, statistical inference of right-censored data is of great importance for the study of medical diagnosis. In this study, a right-censored data survival prediction model based on an improved composite quantile regression neural network framework, called rcICQRNN, is proposed. It incorporates composite quantile regression with the loss function of a multi-hidden layer feedforward neural network, combined with an inverse probability weighting method for survival prediction. Meanwhile, the hyperparameters involved in the neural network are adjusted using the WOA algorithm, integer encoding and One-Hot encoding are implemented to encode the classification features, and the BWOA variable selection method for high-dimensional data is proposed. The rcICQRNN algorithm was tested on a simulated dataset and two real breast cancer datasets, and the performance of the model was evaluated by three evaluation metrics. The results show that the rcICQRNN-5 model is more suitable for analyzing simulated datasets. The One-Hot encoding of the WOA-rcICQRNN-30 model is more applicable to the NKI70 data. The model results are optimal for k=15 after feature selection for the METABRIC dataset. Finally, we implemented the method for cross-dataset validation. On the whole, the Cindex results using One-Hot encoding data are more stable, making the proposed rcICQRNN prediction model flexible enough to assist in medical decision making. It has practical applications in areas such as biomedicine, insurance actuarial and financial economics.

PMID:35801434 | DOI:10.3934/mbe.2022354