Myeloid sarcomas: a histologic, immunohistochemical, and cytogenetic study

This is an Open Access article distributed under the terms of the Creative Commons Attribution Licens

Analysis of resistance and virulence genes of foodborne Staphylococcus aureus in northwestern Hubei Province

Objective To understand the prevalence, drug resistance, and virulence gene distribution of foodborne Staphylococcus aureus in northwestern Hubei Province. Methods A total of 303 food samples were collected from Xiangyang City, Shiyan City, and Suizhou City of Hubei Province for Staphylococcus aureus screening. Toxic genes and methicillin-resistant Staphylococcus aureus (MRSA) were detected with PCR method. The drug resistance of Staphylococcus aureus was determined with K-B paper diffusion method. Results Staphylococcus aureus strain was determined from 41 samples with the positive rate of 13.53%. Among them, the highest detection rate was from raw meat products (23.91%, 22/92). Among the enterotoxigenic strains, the strain carrying sea was the most common, accounting for 87.80% (36/41). The strains carrying eta and tst accounted for 97.56% (40/41) and 7.32% (3/41), respectively. Strains carrying three or more enterotoxin genes accounted for 17.07% (7/41). 2.44% (1/41) of strains carry eta and tst simultaneously. The drug susceptibility result showed that the penicillin, tetracycline, erythromycin, and doxycycline accounted for 78.05% (32/41), 43.90% (18/41), 31.71% (13/41) and 21.95% (9/41) respectively. The mecA gene test showed that 19.51% (8/41) of the strains were MRSA strains. Conclusion Foodborne Staphylococcus aureus in northwestern Hubei Province had a higher detection rate, toxic gene carring rate, and multiple drug resistance. Related departments need to strengthen food safety monitoring to control the spread of the strain

Flexible Analytical Methods for Adding a Treatment Arm Mid-Study to an Ongoing Clinical Trial

It is not uncommon to have experimental drugs under different stages of development for a given disease area. Methods are proposed for use when another treatment arm is to be added mid-study to an ongoing clinical trial. Monte Carlo simulation was used to compare potential analytical approaches for pairwise comparisons through a difference in means in independent normal populations including 1.) a linear model adjusting for the design change (stage effect), 2.) pooling data across the stages, or 3.) the use of an adaptive combination test. In the presence of intra-stage correlation (or a non-ignorable fixed stage effect), simply pooling the data will result in a loss of power and will inflate the type I error rate. The linear model approach is more powerful, but the adaptive methods allow for flexibility (re-estimating sample size). The flexibility to add a treatment arm to an ongoing trial may result in cost savings as treatments that become ready for testing can be added to ongoing studies

Prediction model of obstructive sleep apnea–related hypertension: Machine learning–based development and interpretation study

BackgroundObstructive sleep apnea (OSA) is a globally prevalent disease closely associated with hypertension. To date, no predictive model for OSA-related hypertension has been established. We aimed to use machine learning (ML) to construct a model to analyze risk factors and predict OSA-related hypertension.Materials and methodsWe retrospectively collected the clinical data of OSA patients diagnosed by polysomnography from October 2019 to December 2021 and randomly divided them into training and validation sets. A total of 1,493 OSA patients with 27 variables were included. Independent risk factors for the risk of OSA-related hypertension were screened by the multifactorial logistic regression models. Six ML algorithms, including the logistic regression (LR), the gradient boosting machine (GBM), the extreme gradient boosting (XGBoost), adaptive boosting (AdaBoost), bootstrapped aggregating (Bagging), and the multilayer perceptron (MLP), were used to develop the model on the training set. The validation set was used to tune the model hyperparameters to determine the final prediction model. We compared the accuracy and discrimination of the models to identify the best machine learning algorithm for predicting OSA-related hypertension. In addition, a web-based tool was developed to promote its clinical application. We used permutation importance and Shapley additive explanations (SHAP) to determine the importance of the selected features and interpret the ML models.ResultsA total of 18 variables were selected for the models. The GBM model achieved the most extraordinary discriminatory ability (area under the receiver operating characteristic curve = 0.873, accuracy = 0.885, sensitivity = 0.713), and on the basis of this model, an online tool was built to help clinicians optimize OSA-related hypertension patient diagnosis. Finally, age, family history of hypertension, minimum arterial oxygen saturation, body mass index, and percentage of time of SaO2 < 90% were revealed by the SHAP method as the top five critical variables contributing to the diagnosis of OSA-related hypertension.ConclusionWe established a risk prediction model for OSA-related hypertension patients using the ML method and demonstrated that among the six ML models, the gradient boosting machine model performs best. This prediction model could help to identify high-risk OSA-related hypertension patients, provide early and individualized diagnoses and treatment plans, protect patients from the serious consequences of OSA-related hypertension, and minimize the burden on society

Genomic analysis of oesophageal squamous-cell carcinoma identifies alcohol drinking-related mutation signature and genomic alterations

Approximately half of the world's 500,000 new oesophageal squamous-cell carcinoma (ESCC) cases each year occur in China. Here, we show whole-genome sequencing of DNA and RNA in 94 Chinese individuals with ESCC. We identify six mutational signatures (E1–E6), and Signature E4 is unique in ESCC linked to alcohol intake and genetic variants in alcohol-metabolizing enzymes. We discover significantly recurrent mutations in 20 protein-coding genes, 4 long non-coding RNAs and 10 untranslational regions. Functional analyses show six genes that have recurrent copy-number variants in three squamous-cell carcinomas (oesophageal, head and neck and lung) significantly promote cancer cell proliferation, migration and invasion. The most frequently affected genes by structural variation are LRP1B and TTC28. The aberrant cell cycle and PI3K-AKT pathways seem critical in ESCC. These results establish a comprehensive genomic landscape of ESCC and provide potential targets for precision treatment and prevention of the cancer

Generalization of Wei's urn design to unequal allocations in sequential clinical trials

Wei's urn design was proposed in 1987 for subject randomization in trials comparing m≥2 treatments with equal allocation. In this manuscript, two modified versions of Wei's urn design are presented to accommodate unequal allocations. First one uses a provisional allocation of r12:r22 to achieve the target allocationr1:r2, and the second one uses equal allocation for r1+r2 arms to achieve an unequal allocationr1:r2 based on the concept Kaiser presented in his recent paper. The properties of these two designs are evaluated based on treatment imbalance and allocation predictability under different sample sizes and unequal allocation ratios. Simulations are performed to compare the two designs to other designs used for unequal allocations, include the complete randomization, permuted block randomization, block urn design, maximal procedure, and the mass weighted urn design

Back-EMF Decay Transient Based Identification Method of Rotor Time Constant of Inverter-Fed Induction Motors for Energy Conservation

Variable frequency speed regulation technology is widely used in energy-saving control systems for fan and pump loads. Replacing mechanical adjustment devices such as valves with variable frequency speed regulation can effectively reduce energy consumption. The energy-saving and speed regulation performance of induction motor variable frequency drive system based on the indirect rotor flux-oriented control (I-RFOC) heavily depends on the accuracy of rotor time constant. Traditional rotor time constant measurement methods often require the motor to be in a static state, which cannot reflect the different load conditions of the motor. In the process of stator back electromotive force decay of induction motor, the rotor side information is reflected in the change law of stator side voltage and current through flux linkage coupling. Aiming at the transient experimental phenomenon of stator winding back electromotive force decay after power is disconnected, this paper proposes a rotor time constant identification method based on curve fitting and differential evolution algorithm. The identification can be completed by measuring stator voltage and current. The influence of load, zero-sequence component and variable frequency supply conditions on the identification process is also analyzed. Finally, with a 55-kW induction motor, experimental validation is also performed to verify the proposed identification method

Mandarin Chinese Tone Recognition with an Artificial Neural Network

AbstractMandarin Chinese tone patterns vary in one of the four ways, i.e, (1) high level; (2) rising; (3) low falling and rising; and (4) high falling. The present study is to examine the efficacy of an artificial neural network in recognizing these tone patterns. Speech data were recorded from 12 children (3-6 years of age) and 15 adults. All subjects were native Mandarin Chinese speakers. The fundamental frequencies (F0) of each monosyllabic word of the speech data were extracted with an autocorrelation method. The pitch data(i.e., the F0 contours) were the inputs to a feed-forward backpropagation artificial neural network. The number of inputs to the neural network varied from 1 to 16 and the hidden layer of the network contained neurons that varied from 1 to 16 in number. The output of the network consisted of four neurons representing the four tone patterns of Mandarin Chinese. After being trained with the Levenberg-Marquardt optimization, the neural network was able to successfully classify the tone patterns with an accuracy of about 90% correct for speech samples from both adults and children. The artificial neural network may provide an objective and effective way of assessing tone production in prelingually-deafened children who have received cochlear implants