Using the Natural Language Processing System Medical Named Entity Recognition-Japanese to Analyze Pharmaceutical Care Records: Natural Language Processing Analysis.

BACKGROUND: Large language models have propelled recent advances in artificial intelligence technology, facilitating the extraction of medical information from unstructured data such as medical records. Although named entity recognition (NER) is used to extract data from physicians' records, it has yet to be widely applied to pharmaceutical care records. OBJECTIVE: In this study, we aimed to investigate the feasibility of automatic extraction of the information regarding patients' diseases and symptoms from pharmaceutical care records. The verification was performed using Medical Named Entity Recognition-Japanese (MedNER-J), a Japanese disease-extraction system designed for physicians' records. METHODS: MedNER-J was applied to subjective, objective, assessment, and plan data from the care records of 49 patients who received cefazolin sodium injection at Keio University Hospital between April 2018 and March 2019. The performance of MedNER-J was evaluated in terms of precision, recall, and F1-score. RESULTS: The F1-scores of NER for subjective, objective, assessment, and plan data were 0.46, 0.70, 0.76, and 0.35, respectively. In NER and positive-negative classification, the F1-scores were 0.28, 0.39, 0.64, and 0.077, respectively. The F1-scores of NER for objective (0.70) and assessment data (0.76) were higher than those for subjective and plan data, which supported the superiority of NER performance for objective and assessment data. This might be because objective and assessment data contained many technical terms, similar to the training data for MedNER-J. Meanwhile, the F1-score of NER and positive-negative classification was high for assessment data alone (F1-score=0.64), which was attributed to the similarity of its description format and contents to those of the training data. CONCLUSIONS: MedNER-J successfully read pharmaceutical care records and showed the best performance for assessment data. However, challenges remain in analyzing records other than assessment data. Therefore, it will be necessary to reinforce the training data for subjective data in order to apply the system to pharmaceutical care records.

Exact solution to quantum dynamical activity.

The quantum dynamical activity constitutes a thermodynamic cost in trade-off relations such as the quantum speed limit and the quantum thermodynamic uncertainty relation. However, calculating the quantum dynamical activity has been a challenge. In this paper, we present the exact solution for the quantum dynamical activity by deploying the continuous matrix product state method. Moreover, using the derived exact solution, we determine the upper bound of the dynamical activity, which comprises the standard deviation of the system Hamiltonian and jump operators. We confirm the exact solution and the upper bound by performing numerical simulations.

Upper bound for entropy production in Markov processes.

The second law of thermodynamics states that entropy production cannot be negative. Recent developments concerning uncertainty relations in stochastic thermodynamics, such as thermodynamic uncertainty relations and speed limits, have yielded refined second laws that provide lower bounds of entropy production by incorporating information from current statistics or distributions. In contrast, in this study we bound the entropy production from above by terms comprising the dynamical activity and maximum transition-rate ratio. We derive two upper bounds: One applies to steady-state conditions, whereas the other applies to arbitrary time-dependent conditions. We verify these bounds through numerical simulation and identify several potential applications.

Transferability Based on Drug Structure Similarity in the Automatic Classification of Noncompliant Drug Use on Social Media: Natural Language Processing Approach.

BACKGROUND: Medication noncompliance is a critical issue because of the increased number of drugs sold on the web. Web-based drug distribution is difficult to control, causing problems such as drug noncompliance and abuse. The existing medication compliance surveys lack completeness because it is impossible to cover patients who do not go to the hospital or provide accurate information to their doctors, so a social media-based approach is being explored to collect information about drug use. Social media data, which includes information on drug usage by users, can be used to detect drug abuse and medication compliance in patients. OBJECTIVE: This study aimed to assess how the structural similarity of drugs affects the efficiency of machine learning models for text classification of drug noncompliance. METHODS: This study analyzed 22,022 tweets about 20 different drugs. The tweets were labeled as either noncompliant use or mention, noncompliant sales, general use, or general mention. The study compares 2 methods for training machine learning models for text classification: single-sub-corpus transfer learning, in which a model is trained on tweets about a single drug and then tested on tweets about other drugs, and multi-sub-corpus incremental learning, in which models are trained on tweets about drugs in order of their structural similarity. The performance of a machine learning model trained on a single subcorpus (a data set of tweets about a specific category of drugs) was compared to the performance of a model trained on multiple subcorpora (data sets of tweets about multiple categories of drugs). RESULTS: The results showed that the performance of the model trained on a single subcorpus varied depending on the specific drug used for training. The Tanimoto similarity (a measure of the structural similarity between compounds) was weakly correlated with the classification results. The model trained by transfer learning a corpus of drugs with close structural similarity performed better than the model trained by randomly adding a subcorpus when the number of subcorpora was small. CONCLUSIONS: The results suggest that structural similarity improves the classification performance of messages about unknown drugs if the drugs in the training corpus are few. On the other hand, this indicates that there is little need to consider the influence of the Tanimoto structural similarity if a sufficient variety of drugs are ensured.

Identification of reproducible radiomic features from on-board volumetric images: A multi-institutional phantom study.

BACKGROUND: Radiomics analysis using on-board volumetric images has attracted research attention as a method for predicting prognosis during treatment; however, the lack of standardization is still one of the main concerns. PURPOSE: This study investigated the factors that influence the reproducibility of radiomic features extracted from on-board volumetric images using an anthropomorphic radiomics phantom. Furthermore, a phantom experiment was conducted with different treatment machines from multiple institutions as external validation to identify reproducible radiomic features. METHODS: The phantom was designed to be 35 × 20 × 20 cm with eight types of heterogeneous spheres (⌀ = 1, 2, and 3 cm). On-board volumetric images were acquired using 15 treatment machines from eight institutions. Of these, kilovoltage cone-beam computed tomography (kV-CBCT) image data acquired from four treatment machines at one institution were used as an internal evaluation dataset to explore the reproducibility of radiomic features. The remaining image data, including kV-CBCT, megavoltage-CBCT (MV-CBCT), and megavoltage computed tomography (MV-CT) provided by seven different institutions (11 treatment machines), were used as an external validation dataset. A total of 1,302 radiomic features, including 18 first-order, 75 texture, 465 (i.e., 93 × 5) Laplacian of Gaussian (LoG) filter-based, and 744 (i.e., 93 × 8) wavelet filter-based features, were extracted within the spheres. The intraclass correlation coefficient (ICC) was calculated to explore feature repeatability and reproducibility using an internal evaluation dataset. Subsequently, the coefficient of variation (COV) was calculated to validate the feature variability of external institutions. An absolute ICC exceeding 0.85 or COV under 5% was considered indicative of a highly reproducible feature. RESULTS: For internal evaluation, ICC analysis showed that the median percentage of radiomic features with high repeatability was 95.2%. The ICC analysis indicated that the median percentages of highly reproducible features for inter-tube current, reconstruction algorithm, and treatment machine were decreased by 20.8%, 29.2%, and 33.3%, respectively. For external validation, the COV analysis showed that the median percentage of reproducible features was 31.5%. A total of 16 features, including nine LoG filter-based and seven wavelet filter-based features, were indicated as highly reproducible features. The gray-level run-length matrix (GLRLM) was classified as containing the most frequent features (N = 8), followed by the gray-level dependence matrix (N = 7) and gray-level co-occurrence matrix (N = 1) features. CONCLUSIONS: We developed the standard phantom for radiomics analysis of kV-CBCT, MV-CBCT, and MV-CT images. With this phantom, we revealed that the differences in the treatment machine and image reconstruction algorithm reduce the reproducibility of radiomic features from on-board volumetric images. Specifically, the most reproducible features for external validation were LoG or wavelet filter-based GLRLM features. However, the acceptability of the identified features should be examined in advance at each institution before applying the findings to prognosis prediction.

On Relations Between the Relative Entropy and χ2-Divergence, Generalizations and Applications.

The relative entropy and the chi-squared divergence are fundamental divergence measures in information theory and statistics. This paper is focused on a study of integral relations between the two divergences, the implications of these relations, their information-theoretic applications, and some generalizations pertaining to the rich class of f-divergences. Applications that are studied in this paper refer to lossless compression, the method of types and large deviations, strong data-processing inequalities, bounds on contraction coefficients and maximal correlation, and the convergence rate to stationarity of a type of discrete-time Markov chains.

Structural and functional characterization of a glycoside hydrolase family 3 ß-N-acetylglucosaminidase from Paenibacillus sp. str. FPU-7.

Chitin, a ß-1,4-linked homopolysaccharide of N-acetyl-d-glucosamine (GlcNAc), is one of the most abundant biopolymers on Earth. Paenibacillus sp. str. FPU-7 produces several different chitinases and converts chitin into N,N'-diacetylchitobiose ((GlcNAc)2) in the culture medium. However, the mechanism by which the Paenibacillus species imports (GlcNAc)2 into the cytoplasm and divides it into the monomer GlcNAc remains unclear. The gene encoding Paenibacillus ß-N-acetyl-d-glucosaminidase (PsNagA) was identified in the Paenibacillus sp. str. FPU-7 genome using an expression cloning system. The deduced amino acid sequence of PsNagA suggests that the enzyme is a part of the glycoside hydrolase family 3 (GH3). Recombinant PsNagA was successfully overexpressed in Escherichia coli and purified to homogeneity. As assessed by gel permeation chromatography, the enzyme exists as a 57-kDa monomer. PsNagA specifically hydrolyses chitin oligosaccharides, (GlcNAc)2-4, 4-nitrophenyl N-acetyl ß-d-glucosamine (pNP-GlcNAc) and pNP-(GlcNAc)2-6, but has no detectable activity against 4-nitrophenyl ß-d-glucose, 4-nitrophenyl ß-d-galactosamine and colloidal chitin. In this study, we present a 1.9 Å crystal structure of PsNagA bound to GlcNAc. The crystal structure reveals structural features related to substrate recognition and the catalytic mechanism of PsNagA. This is the first study on the structural and functional characterization of a GH3 ß-N-acetyl-d-glucosaminidase from Paenibacillus sp.

Verification of the dose attenuation of a newly developed vacuum cushion for intensity-modulated radiation therapy of prostate cancer.

This study measured the dose attenuation of a newly developed vacuum cushion for intensity-modulated radiation therapy (IMRT) of prostate cancer, and verified the effect of dose-correction accuracy in a radiation treatment planning system (RTPS). The new cushion was filled with polystyrene foams inflated 15-fold (Sφ â‰’ 1 mm) to reduce contraction caused by air suction and was compared to normal polystyrene foam inflated to 50-fold (Sφ â‰’ 2 mm). The dose attenuation at several thicknesses of compression bag filled with normal and low-inflation materials was measured using an ionization chamber; and then the calculated RTPS dose was compared to ionization chamber measurements, while the new cushion was virtually included as region of interest in the calculation area. The dose attenuation rate of the normal cushion was 0.010 %/mm (R (2) = 0.9958), compared to 0.031 %/mm (R (2) = 0.9960) in the new cushion. Although the dose attenuation rate of the new cushion was three times that of the normal cushion, the high agreement between calculated dose by RTPS and ionization chamber measurements was within approximately 0.005 %/mm. Thus, the results of the current study indicate that the new cushion may be effective in clinical use for dose calculation accuracy in RTPS.

Highly efficient aerobic oxidation of alcohols by using less-hindered nitroxyl-radical/copper catalysis: optimum catalyst combinations and their substrate scope.

The oxidation of alcohols into their corresponding carbonyl compounds is one of the most fundamental transformations in organic chemistry. In our recent report, 2-azaadamantane N-oxyl (AZADO)/copper catalysis promoted the highly chemoselective aerobic oxidation of unprotected amino alcohols into amino carbonyl compounds. Herein, we investigated the extension of the promising AZADO/copper-catalyzed aerobic oxidation of alcohols to other types of alcohol. During close optimization of the reaction conditions by using various alcohols, we found that the optimum combination of nitroxyl radical, copper salt, and solution concentration was dependent on the type of substrate. Various alcohols, including highly hindered and heteroatom-rich ones, were efficiently oxidized into their corresponding carbonyl compounds under mild conditions with lower amounts of the catalysts.

Effects of oral health care on salivary flow rate in patients with type 2 diabetes: preliminary study.

The purpose of this study is to evaluate the effects of the authors' oral health care program on the stimulated whole salivary flow rate and buffer capacity before and after a 6-month intervention. The authors conducted the intervention study among 25 participants with diabetes. The salivary flow rate and buffer capacity were evaluated before and after this intervention. Overall, the results showed a significant increase in salivary flow rate and no significant change in buffer capacity. Also, it was likely that salivary flow rate significantly increased among patients with more than 20 remaining teeth and patients with well-controlled diabetes. The findings suggest that this program for type 2 diabetes led to an increase in the stimulated whole salivary flow rate.

[Functional improvement program of musculoskeletal system of the elderly in an isolated island of Kagoshima prefecture].

Under the coming amendment of long term care insurance (LTCI), the preventive activities will be more strengthened in order to ameliorate the quality of life (QOL) levels of the slightly frail elderly and then to rationalize the LTCI expenditures. The functional training of the musculoskeletal system is regarded as especially important. In order to validate its effectiveness and availability in a rural setting, we organized a physical fitness program for the slightly frail elderly in an isolated island of Kagoshima prefecture. According to the results of our preliminary study, statistically significant improvement was observed for some items of the physical fitness level, but not for life style, activities of daily living (ADL), instrumental activities of daily living (IADL) levels or Frenchay Activities Index. In addition to the relatively small number of participants, it is thought that physical fitness training alone cannot increase the elderly activity volume. Further studies are necessary to clarify the usefulness of physical training on the ADL/IADL level of the elderly.

[Effect of oral health care program on the oral function of the elderly].

In order to prepare a new prevention program under the long term care insurance from the fiscal year 2006, we have organized an oral health care program for the elderly in a town of Kagoshima prefecture. We conducted a series of oral health education, i.e., instruction on brushing and flossing, and oral function exercises. Before and after the three months program, we evaluated the unstimulated and stimulated salivary flow rate, the counts of S. mutans, the counts of repetitive saliva swallowing test (RSST), bilateral bite force and the number of permanent teeth and artificial teeth. According to the results, a statistically significant improvement was observed in RSST and the unstimulated and stimulated salivary flow rate, but not in the S. mutans level and bite force. The present research suggests the effectiveness of the oral health care program for the aged. A larger sized and longer period intervention will be necessary in order to validate our findings.