Predictive Test Research Articles

RETRACTED ARTICLE: Research on the Prediction of the Inauguration Development Direction of College Students’ Entrepreneurship Education Based on Educational Data Mining

In many related studies, educational data mining technology has been proven to play an important role in predicting the development direction of entrepreneurship education for college students. To further improve the accuracy of the prediction, we chose the grey prediction model as the basic prediction model and automatically optimized the weighting method to improve the model. To solve the problem of predicting the development direction of students’ employment in the guidance of entrepreneurship and employment in colleges and universities, the study selects the grey prediction model as the basic prediction model and chooses the automatic optimization and weighting method to improve the model. Meanwhile, the study establishes a variable system containing six dimensions: academic achievement; physical and mental development; cultural, physical, and artistic quantified status; ideological and political quantified status; scientific and technological innovation quantified status; social work quantified status. The final study used the actual prediction test to analyze the prediction effect. We have selected a variable system consisting of six dimensions, which are the results of extensive research. These dimensions include academic achievement, physical and mental development, cultural/sports/art quantitative status, ideological and political quantitative status, technological innovation quantitative status, and social work quantitative status. Each dimension provides us with important predictions about student entrepreneurship and employment. The results show that the model designed by the survey has only two cases of error in the prediction of 20 actual samples. At the same time, there is no prediction error in the two prediction directions of entrepreneurship and social employment. This shows that the model designed by the study is stable and accurate, and the prediction results are more reliable in the prediction directions of entrepreneurship and social employment. Compared with other relevant research results, our model performs well in predicting accuracy, especially in predicting entrepreneurial and social employment directions, without any prediction errors, indicating that our model has superior performance in predicting stability and accuracy compared to other studies.

Efficacy of Eustachian Tube Function Tests in Predicting Myringoplasty Success Rate

Abstract Background Tympanoplasty is a commonly performed surgical procedure to close perforations of the tympanic membrane. The results of tympanic membrane based on multiple factors as the eustachian tube dysfunction. Aim of the Work to assess the efficacy of the eustachian tube function test in predicting myringoplasty success rate and compare the efficacy of different tests in prediction of myringoplasty outcome. Material and Methods An electronic search via PubMed, Cochrane library, google scholar and web of science (WOS) until janurary2021.We included articles published in English language and conducts adult cases only. We excluded studies based on expert opinion and laboratory studies. Outcomes of interest were success rate of tympanoplasty according to eustachian tube function test and the efficacy of the different tests of eustachian tube function. Analysis was conducted using Comprehensive Meta Analysis© version 3 (Biostat© Englewood, NJ) and the jamovi project (2021); jamovi (Version 1.6.23.0) [Computer Software] for the efficacy of the eustachian tube function tests in predicting tympanoplasty success rate and using Review Manager (RevMan) [Computer program]. Version 5.4. The Cochrane Collaboration, 2020, and MetaDTA: Diagnostic Test Accuracy Meta-Analysis v2.01, 2021 for detecting and comparing the accuracy of different eustachian tube function tests. Results Regarding tympanoplasty success rate meta-analysis of the revelent 14 studies shows that preoperative normal Eustachian tube function is associated with significantly higher probability of successful graft taking (Log odds ratio = 1.78, 95% CI = 1.10 to 2.46).Regarding to the different accuracy of eustachian tube function test meta-analysis of the revelent 14 studies shows that accuracy of the tests are ranked as follows: methylene blue (most accurate), saccharine, impedance audiometry, tubomanometry and sonotubometry (least accurate). Further studies are needed to validate our findings. Conclusions Our pooled estimate suggest a good benefits of preoperative eustachian tube function test and tympanoplasty success rate. According to efficacy of different test of tympanoplasty our pooled estimate that methylene blue test is the most accurate test while the sonotubometry test is the least accurate test.However, our evidence have several limitations and there is controversy among these studies. We suggest further studies are needed to validate our findings to detect the most accurate test of eustachian tube function.

Опыт обращения к превентивным биотехнологиям в рамках заботы о себе и близких (по результатам эмпирического исследования)

New preventive genetic and cellular technologies are empowering people within the framework of self-preserving behavior and caring for family members. The purpose of this article is to describe the experience of using preventive biotechnologies, the motives and factors for doing so, and the fears associated with these technologies. The empirical is a series of free interviews with people having taken a predictive genetic test for themselves or their children, or who have turned to cord blood preservation technology. The respondents ranged in age from 25 to 56. It was shown that the main motivations for turning to new technologies are: early concern for one’s own health or the health of a child/grandchild or granddaughter; active longevity; family planning; and curiosity. He common factors in the decision of turning to the indicated technologies include: trust in medical knowledge; concern for the future; and cost of the service. Separate attention in the study was paid to the willingness to apply the results of genetic testing in practice. It was found that the majority of the respondents are interested in purely cognitive prevention. The study showed that users of new biotechnologies are characterized by an active, responsible approach to preventing and predicting health risks for themselves and their beloved ones. The data obtained in the article can be useful for modern biomedicine and society as a whole for expanding the awareness of new medical options.

Summarization of Remaining Life Prediction Methods for Special Power Plants

With continuous improvements in integration, totalization and automation, remaining useful life predictions of mechanical equipment have become a key feature of technology and core element of equipment prediction and health management. The traditional method based on degradation mechanisms is not fully capable of predicting remaining useful life, especially for special power plants that use industrial transmissions, barrel launchers, etc. The expected service requirements are higher for condition monitoring and remaining service life prediction. The effective prediction of the remaining useful life of such special power plants is a major challenge and technical bottleneck in the industrial field and national defense equipment construction. This paper analyzes and expands on the research on the remaining life prediction methods for special power plants and analyzes the remaining life prediction methods of existing dynamic models, as well as data-driven and data–model fusion drives, and specific ideas for future research and development in four aspects, including remaining useful life prediction tests supplemented with soft measurements. Additionally, future research directions for the remaining life prediction of special power plants are provided.

Patient-Derived Ex Vivo Cultures and Endpoint Assays with Surrogate Biomarkers in Functional Testing for Prediction of Therapeutic Response.

Prediction of therapeutic outcomes is important for cancer patients in order to reduce side effects and improve the efficacy of anti-cancer drugs. Currently, the most widely accepted method for predicting the efficacy of anti-cancer drugs is gene panel testing based on next-generation sequencing. However, gene panel testing has several limitations. For example, only 10% of cancer patients are estimated to have druggable mutations, even if whole-exome sequencing is applied. Additionally, even if optimal drugs are selected, a significant proportion of patients derive no benefit from the indicated drug treatment. Furthermore, most of the anti-cancer drugs selected by gene panel testing are molecularly targeted drugs, and the efficacies of cytotoxic drugs remain difficult to predict. Apart from gene panel testing, attempts to predict chemotherapeutic efficacy using ex vivo cultures from cancer patients have been increasing. Several groups have retrospectively demonstrated correlations between ex vivo drug sensitivity and clinical outcome. For ex vivo culture, surgically resected tumor tissue is the most abundant source. However, patients with recurrent or metastatic tumors do not usually undergo surgery, and chemotherapy may be the only option for those with inoperable tumors. Therefore, predictive methods using small amounts of cancer tissue from diagnostic materials such as endoscopic, fine-needle aspirates, needle cores and liquid biopsies are needed. To achieve this, various types of ex vivo culture and endpoint assays using effective surrogate biomarkers of drug sensitivity have recently been developed. Here, we review the variety of ex vivo cultures and endpoint assays currently available.

Markers of systemic endotheliosis in early-onset preeclampsia relapse

Aim: to assess the blood level of biological endothelial damage markers in women with previous early-onset preeclampsia (еPE) during both the preconception (PC) stage and the first trimester of pregnancy.Materials and Methods. A prospective single-center cohort study was conducted. The control group comprised 40 women with uncomplicated pregnancies. The observation group consisted of 97 patients with a history of ePE, stratified based on pregnancy outcome into the comparison group (n = 59) characterized by a favorable gestational course, and the main group (n = 38) with relapsed еPE (30 patients were excluded from the study). Markers of systemic endotheliosis were determined in women at the PC stage and at 11–13 weeks of gestation: endothelin-1 (ET-1) and endothelial extracellular vesicles (EVs; CD-144).Results. At the PC stage, women in the control group and patients from the comparison group had comparable median (Me) ET-1 levels – 0.39 and 0.40 pmol/ml (p = 0.5935), respectively; at the same time, patients with relapsed еPE vs. control group had it significantly elevated (Me = 0.55 pmol/ml; p = 0.0382). At gestational age of 11–13 weeks, ET-1 level was significantly higher in the group with relapsed еPE (Me = 0.93 pmol/ml) than in control group (Me = 0.29 pmol/ml; p < 0.0001) and comparison group (Me = 0.42 pmol/ml; p = 0.0003). No significant changes in E-EVs (CD-144) level at various PС stages were observed, whereas in the study groups such parameters remained differed.Conclusion. Biological markers evidencing about systemic endothelial dysfunction/destruction – ET-1 and E-EVs (CD-144) in patients with previous ePE can be considered as predictive tests of disease relapse with an accuracy of 63.5 % and 83.0 % at the PС stage and 85.7 % and 94.2 % at gestational age of 11–13 weeks, respectively.

The Active Leg Raise Test

Background and Purpose: There are important reasons to provide hospitalized patients with opportunities to move while hospitalized. Yet, it is apparent that, other than actually attempting the task of standing up, there is no predictive test available that would help a hospital professional decide whether the patient has adequate ability to safely attempt standing. The purpose of this research report is to describe a novel test that clinicians can use to help predict whether a patient is anticipated to be capable of standing up with little or no assistance. Approach: Hospitalized patients were requested to lift each lower extremity, one at a time, for 5 seconds, above a minimum height. To determine the predictive ability of the test to identify the ability to stand, results were compared against the “gold standard” of whether the patients were able to stand successfully as part of a physical therapy evaluation performed after the leg raise test. Outcomes: The sensitivity of the test was 0.91 (95% CI = 0.84-0.96) and the specificity was 0.71 (95% CI = 0.48-0.89). The positive predictive value was 0.94 (95% CI = 0.88-0.98) and the negative predictive value was 0.60 (95% CI = 0.39-0.79). Discussion and Conclusion: This test is simple, short, and well suited to the rapid pace of clinical decision-making that health care professionals face on a daily basis. This test is useful in helping acute care professionals answer a clinical question they encounter often, “Is it safe for this patient to try to stand up?”

Controlled mixing during colloidal quantum dot synthesis: A proxy-concept based on equivalent parameters

Mixing is a key process parameter during liquid phase particle synthesis that requires special attention, in particular for scalable process and reactor development. This issue is especially pressing for advanced materials where on the one hand multifunctional properties require products of utmost quality, while on the other hand only few strategies for process design are yet developed. One material class in this context is quantum dot (QD) nanocrystals. By making use of automation and high throughout (HT) experimentation, we could recently demonstrate qualitatively the role of mixing on focusing and defocusing during QD synthesis by hot injection. In this work, we demonstrate the application of HT methodologies to investigate the effect of mixing as an important process parameter during process optimization and scale up. We performed reaction optimization by screening a multi-parameter design space using design of experiments (DoE) and machine learning (ML). We developed a so-called cold model as proxy that is based on equivalent mixing times (EMTs) derived from a competing parallel reaction system. Experimentally, we realized a fully automated approach using a HT platform for particle synthesis. The final dataset (128 experiments for model development including repeats and 29 experiments for prediction testing) was combined with DoE and complimented with machine learning. By doing so, with DoE, we could reduce the number of experiments required to develop a predictive, statistical knowledge base for EMTs at ambient conditions. Finally, we demonstrated the applicability of EMTs for two defined stirrer geometries to fine tune the widths of the particle size distribution and photoluminescence of QDs at increasing reaction volumes from 20 mL to 80 mL. This opens the door towards scalable processes for QDs at increased reaction volumes while maintaining product quality.

The role of fetal heart rate in first trimester sonograms in prediction of fetal sex: a systematic review and meta-analysis

BackgroundEarly fetal sex determination is worthy of providing alertness about possible x-linked disorders, as well as predicting sex-related pregnancy complications and outcomes. Satisfying the curiosity of parents is another advantage. In this way, several studies have been performed which have shown conflicting results.AimWe planned a systematic review for identifying any plausible role of Fetal Heart Rate (FHR) for early predicting fetal sex during the first trimester of non-complicated pregnancies.MethodsThis is a meta-analysis in which PubMed and Scopus databases were searched using different related keywords to find similar articles up to December 2022. Then the articles were screened to find eligible articles and finally, the articles entered in the meta-analysis were analyzed using Stata software (Stata Corp, College Station, TX). Standardized mean difference (SMD) and their 95% confidence interval (CI) were estimated.ResultsA total of 223 articles were evaluated and five articles were included in the meta-analysis. The results showed that there is a significant heterogeneity between the articles (p = 0.012, I-squared = 69.0%). The results of meta-analysis with a random model showed that there is no significant difference between male and female genders in terms of mean FHR (SMD = 0.04, 95%CI = -0.09–0.16, Z = 0.59, p = 0.553).ConclusionThis systematic review and meta-analysis showed that even though male fetuses show faster FHR but such sex-related difference is minimal. Therefore, first-trimester FHR is not a reliable predictive test for fetal sex determination. Further studies are recommended to achieve a more precise conclusion.Trial registrationPROSPERO: CRD42023418291.

Predicting Nutritional Quality of Dual-Purpose Cowpea Using NIRS and the Impacts of Crop Management

Cowpea fodder has been one of the favored livestock forages for centuries in sub-Saharan Africa, particularly in Senegal. However, little research has been conducted on quantifying the nutritional quality of cowpea fodder because of the costly wet chemistry analysis. The main objective of this study was to develop predictive equations for a sustainable quantification of the nutritional quality of dual-purpose cowpea fodder using near infrared spectroscopy (NIRS) and to investigate the influence of cropping system, fertilizer, genotype, and their interaction on biomass yield and cowpea forage nutritional value. In this study, 120 samples from a dual-purpose cowpea variety trial were used to develop NIRS equations to estimate forage quality parameters including concentrations of crude protein (CP), acid detergent fiber (ADF), neutral detergent fiber (NDF), calcium (Ca), phosphorus (P), potassium (K), and iron (Fe). Partial least squares (PLS) regression generated prediction equations using NIRS wavelength measurements, and reference wet chemistry analysis from calibration samples were developed. The PLS prediction equations for the different forage quality parameters had an R2 of calibration 0.94, 0.93, 0.88, 0.63, 0.69, 0.87, and 0.94 for CP, ADF, NDF, Ca, P, K, and Fe, respectively. Using these prediction equations, correlation of the predicted values of the calibration subset and the prediction test subset resulted in significant positive relationships, with R2 of 0.83, 0.74, 0.70, 0.63, 0.59, 0.75, and 0.83 for CP, ADF, NDF, Ca, P, K, and Fe, respectively. The corresponding RMSE of these relationships was 0.91, 2.68, 3.45, 0.23, 0.06, 0.11, and 100 for CP, ADF, NDF, Ca, P, K, and Fe, respectively. The range and mean concentrations of the calibration subset overlapped with that of the prediction subset for all parameters evaluated. Cross-validation procedures indicated good correlations between wet chemistry analysis and NIRS forage quality estimates. Results of the second experiment showed that the cropping system had no significant effect on cowpea forage yield and nutritive value. However, cowpea variety and fertilizer, both individually and their interaction, had a significant effect on fodder yield and cowpea forage quality. We conclude that the NIRS calibration equations developed can be used to accurately predict the cowpea forage quality parameters evaluated in this study.

VUS: Variant of uncertain significance or very unclear situation?

The advancements in population screening, including newborn screening, enables the identification of disease-causing variants and timely initiation of treatment. However, screening may also identify mild variants, non-disease variants, and variants of uncertain significance (VUS). The identification of a VUS poses a challenge in terms of diagnostic uncertainty and confusion. X-linked adrenoleukodystrophy (ALD) serves as an illustrative example of this complex issue. ALD is a monogenic neurometabolic disease with a complex clinical presentation and a lack of predictive tests for clinical severity. Despite the success of ALD newborn screening, a significant proportion (62%) of missense variants identified through newborn screening exhibit uncertainty regarding their pathogenicity. Resolving this issue requires ongoing efforts to accurately classify variants and refine screening protocols. While it is undisputable that ALD newborn screening greatly benefits boys with the disease, the identification of VUS underscores the need for continuous research and collaboration in improving screening practices.

Artificial intelligence-based cancer progression prediction of oral premalignant lesions via self-supervised deep learning on histopathology.

90 Background: Premalignant lesions within the oral cavity demonstrate varied degrees of dysplasia. Oral stratified squamous epithelium is sensitive to carcinogenic insults and lesions progress to cancer at variable rates. The current diagnostic standard involves visual and tactile inspection followed by a biopsy. However, thus far, there is clinical ambiguity about which lesions may progress to carcinoma and there are insufficient prognostic tools available to guide clinical decision-making for follow-up. Deep learning is a form of artificial intelligence which can be applied to images. Self-supervised learning (SSL) and attention-based multiple instance learning (aMIL), are deep learning extensions which eliminate the need for pathologist annotations of histology and are more generalizable as the model learns general representations of unlabeled data, compared to traditional tile-based methods. We developed a novel artificial intelligence pipeline to predict progression to oral carcinoma using histological images from oral premalignant lesions. Methods: Digital histopathology images and clinical data cohorts were obtained from the US National Cancer Institute (n=561) and the University of Iowa (n=193) for training, and from the University of Chicago (n=214) for external validation. Patients were defined as progressing if initially non-malignant lesions had documented progression to carcinoma within 12 months of follow-up. Model training and evaluation was done in Python using our publicly available open-source Slideflow platform. We developed a deep learning pipeline utilizing SimCLR as an SSL method for feature extraction paired with an aMIL head for classification. The SimCLR feature generator was trained on only the training cohort, then used to generate features for both training and validation data. The aMIL model was trained on SimCLR-generated features. Trained model performance was evaluated on the external University of Chicago cohort and measured via areas under the receiver operating characteristic curve and precision recall curve (AUROC and AUPRC, respectively). During training and validation, model reliability was interpreted using Class Activation Mapping (Grad-CAM) and generative adversarial network (GAN) methods. Results: On training data, the model achieved average 3-fold cross validation AUROC of 0.86 with AUPRC of 0.83. In the external validation data from UC, the model achieved an AUROC of 0.80 with an AUPRC of 0.79. Deployed as a “rule-in” test for early progression prediction, the model achieved a validation data set specificity of 91% with a sensitivity of 40%. Conclusions: We developed a deep learning model to automatically predict cancer progression from premalignant lesions histopathology using a multi-institutional cohort, with promising external validation performance.

Role of aspartate aminotransferase to platelet ratio in the prediction and prognosis of intrahepatic cholestasis of pregnancy: A case-control study from a tertiary center.

To evaluate the aspartate aminotransferase to platelet ratio (APRI) score as a predictive and prognostic test in intrahepatic cholestasis of pregnancy (ICP). This study was conducted in 198 patients diagnosed with ICP and 204 healthy pregnant women who presented to a tertiary center between 2019 and 2022. APRI scores; laboratory findings in the first, second, and third trimesters; and perinatal outcomes were compared between the two groups. The ICP group was evaluated for correlation between APRI scores and composite adverse outcomes. Two different receiver operating characteristic analyses were performed to determine optimal cutoff values of predictive APRI score of ICP and composite adverse outcomes in patients with ICP. Aspartate aminotransferase values and APRI scores were significantly higher in the ICP group in all trimesters (P < 0.001). The optimal cutoff values of APRI scores to predict ICP for the first, second, and third trimesters were 0.101 (79.7% sensitivity, 79.6% specificity), 0.103 (78.4% sensitivity, 76.3% specificity), and 0.098 (72.5% sensitivity, 72% specificity), respectively. APRI scores were statistically higher in patients with ICP with composite adverse outcomes in all trimesters (P values of 0.03, 0.04, and 0.01, respectively). APRI score was found to be a valuable predictor of ICP and its adverse outcomes during the entire pregnancy.

Cryptocurrencies and Lucky Factors: The value of technical and fundamental analysis

AbstractThis study explores the effectiveness of technical and fundamental analysis in predicting and trading the returns of 12 cryptocurrencies, namely Bitcoin, Ethereum, Ripple, Dash, Cardano, Avalanche, Binance Coin, Dogecoin, Polkadot, Litecoin, Terra and Solana. A universe of 7846 technical rules, five log moving average‐based ratios and 59 fundamental factors are used to test predictability and profitability through the Lucky Factors methodology and Superior Predictive Ability test. We observe predictability for a small set of technical and fundamental rules, while only the short‐term log moving average‐based ratio and Hashrate Index demonstrate genuine in‐sample and out‐of‐sample profitability. Our findings question the value of both technical and fundamental analysis on cryptocurrencies.

Perfusion index as a predictor of hypotension after induction of general anesthesia in elderly patients – a prospective observational study

ABSTRACT Introduction The elderly patients are at a greater risk for hypotension after induction of anesthesia. Perfusion index (PI) predicted patients vascular responses in many situations. This study hypothesized that baseline value of PI would predict hypotension in geriatric patients after anesthetic induction as population at risk for postinduction hypotension. Methods This observational, prospective study involved 30 patients aged >65 years who were scheduled for elective procedures under general anesthesia. From baseline to 15 min after propofol induction of anesthesia, the PI, heart rate, and blood pressure (BP) were recorded every minute. Hypotension was defined as a decrease greater than 30% in systolic BP from baseline. Results Early hypotension within 3 min after induction was experienced in 53.3%, while 36.7% had late hypotension within 15 min. Receiver operating characteristic (ROC) curves for PI as a predictive test of hypotension at 3 min showed good predictability of the baseline PI for early postinduction hypotension with area under receiver operating characteristic curve (AUC) 0.97, sensitivity 88%, specificity 93%, positive predictive value (PPV) 93%, negative predictive value (NPV) 87%, and a cutoff value of ≤1.3. After intubation, the predictive value decreased, with PI > 0.91 to predict late hypotension with sensitivity 91%, specificity 58%, PPV 56%, NPV 92%, and AUC 0.66. Conclusions PI could predict early hypotension following propofol induction in the geriatric population. So, it can be considered a valuable tool in monitoring this vulnerable population after induction of anesthesia.

DEEP JOINT RP-NET-BASED SEGMENTATION ALGORITHM AND OPTIMIZED DEEP LEARNING FOR SEVERITY PREDICTION OF BRAIN TUMOR

The uncontrolled growth of cells in a particular area is referred to as a tumor. The premature and precise identification of the tumor and its level have a straight impression on the patient’s survival, treatment process, and tumor progression computation. However, in the medical field, picture segmentation and classification are more important and difficult processes. Typically, the Magnetic Resonance Imaging (MRI) modality can detect malignancy. Segmenting tumor images with respect to Cerebrospinal Fluid (CSF), Grey Matter (GM), and White Matter is the most important task in MRI identification or classification (WM). The introduction of medical image analysis based on radiology pictures is a result of the significant contributions of engineering, data sciences, and medicine. The precise and automatic segmentation of tumors affords excessive support to doctors in the medicinal area, speed detection in the treatment process, computer-aided operation, radiation treatment and so on. Thus, Remora Aquila optimization (RAO)-enabled deep learning is devised for tumor classification and its severity classification. The deep learning approach is utilized for categorizing tumors as normal or abnormal as well as their severity grades. The RAO-aided deep learning system achieved improved performance with prediction error, specificity, sensitivity and testing accuracy of 0.072, 0.905, 0.925, and 0.917, respectively.

Considerations of an efficiency-intelligent geo-localised mobile application for personalised SME market predictions

The few applications used by Small and Medium Scale Enterprises (SMEs’) businesses lack efficiency and the appropriate intelligence to save them from price instability, inventory carrying costs, excess inventory, wrong decision making, inaccurate monitoring of stock levels, etc. through predictive analytics. The study explored various Artificial Intelligence Machine Learning (AI/ML) models and data structure array types that could be used with the day-to-day local weather conditions of low and high temperatures to predict market parameters and aid SMEs with predictive data to use for combating wrong decision-making, inaccurate business monitoring and excess inventory, etc. Among the ML models explored included sequential minimal optimisation, iterative reweighted least-squares, Fan-Chen-Lin support vector regression, linear regression newton method and multivariate linear regression Ordinary least squares for a multivariate linear regression and logistic regression. The models were compiled using visual C# and Accord.Net libraries. Multivariate linear regression Ordinary least squares models recorded the least predictive accuracy loss, for the test quantity prediction test samples, and varying acceptable square loss values, for usage in geo-localised mobile intelligent systems for SME predictions due to their favourable scores. The jagged array overall performed better than the multi-dimensional array on some time and space complexity tests. This work is contributing to the body of knowledge by evaluatively suggesting better data structures and ML models for building intelligent systems in Xamarin forms using C# and small data for the model training for applications in mobile phone systems that will aid SMEs’ in adjusting spending and sales targets.

A Long-Term Prediction Method of Computer Parameter Degradation Based on Curriculum Learning and Transfer Learning

The long-term prediction of the degradation of key computer parameters improves maintenance performance. Traditional prediction methods may suffer from cumulative errors in iterative prediction, which affect the model’s long-term prediction accuracy. Our network adopts curriculum learning and transfer learning methods, which can effectively solve this problem. The training network uses a dual-branch Siamese network. One branch intermixes the predicted and annotated data as input and uses curriculum learning to train. The other branch uses the original annotated data for training. To further align the hidden distributions of the two branches, the transfer learning method calculates the covariance matrices of the time series of the two branches by correlation alignment loss. A single branch is used in the test for prediction without increasing the inference computation. Compared with the current mainstream networks, our method can effectively improve the accuracy of long-term prediction with the improvements above.

Significance of concurrent HPV testing with unsatisfactory Papanicolaou test for prediction of follow-up HPV, Papanicolaou test, and biopsy results.

Approximately 1% to 2% of routine cytologic specimens collected for Papanicolaou testing are unsatisfactory for evaluation. The American Society for Colposcopy and Cervical Pathology 2019 guidelines recommend repeat testing within 2 to 4 months of an unsatisfactory Papanicolaou test (UPT) result. We evaluated the utility of follow-up Papanicolaou testing, human papillomavirus (HPV) testing, and biopsy in 258 cases of UPTs. High-risk HPV testing was positive in 17.4% (n = 45) and negative in 82.6% (n = 213) of cases at the time of initial UPT; 8.1% (n = 21) of cases had discordant HPV test results. Similarly, 3.8% (n = 8) of initially HPV-negative cases were reported to be HPV-positive on follow-up; 28.9% (n = 13) of initially HPV-positive cases were reported to be HPV negative on follow-up. In total, 27.1% (n = 70) of cases underwent biopsy. Biopsies with significant findings were present in 40% (n = 12) of HPV-positive cases and 7.5% (n = 3) of HPV-negative cases. Low-grade squamous intraepithelial lesion (LSIL) (low-grade cervical intraepithelial neoplasia [CIN-1]) was the most significant finding in all 3 HPV-negative biopsies; 58.3% (n = 7) of HPV-positive biopsies showed LSIL (CIN-1), 13.3% (n = 4) showed HSIL (high-grade CIN), and 3.3% (n = 1) showed invasive carcinoma. The sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of concurrent HPV testing at the time of UPT for predicting follow-up HPV test result within 1 year of initial UPT are 80.0%, 94.0%, 71.1%, and 96.2%, respectively. The sensitivity, specificity, PPV, and NPV of initial HPV test results for predicting follow-up Papanicolaou test results are 67.7%, 89.7%, 48.8%, and 95.0%, respectively. Concurrent HPV testing in the setting of UPT can be a sensitive tool for predicting follow-up HPV status and significant findings of squamous intraepithelial lesions on follow-up Papanicolaou tests and biopsy.

Time to extubation for lung transplant recipients represents a pragmatic end-point to guide the development of prognostic tests

The field of transplantation would benefit from the integration of advanced precision medicine techniques. Although predictive tests for lung transplantation require a well-defined clinical end-point, there exists no consensus regarding which outcomes are optimal end-points for these purposes. While many possible candidate end-points exist, we propose that time-to-extubation is an optimal end-point for prognostic tests because of its: clinical relevance; objectiveness; stability over time; and association with healthcare expenditure. Herein, we describe the rationale for this selection and present the limitations of alternative outcomes for this purpose. Using a 72-hour cut-off, time to extubation correlated well with Primary Graft Dysfunction Grade 3, intensive care unit and hospital length of stay, and a greater than 2-fold increase in healthcare cost ratios. Given that time-to-extubation is an objective measure that is readily measured by all lung transplant centers, this metric represents a preferred primary end-point for prognostic tests developed for lung transplantation.