Pioneering Data Processing for Convolutional Neural Networks to Enhance the Diagnostic Accuracy of Traditional Chinese Medicine Pulse Diagnosis for Diabetes.

Traditional Chinese medicine (TCM) has relied on pulse diagnosis as a cornerstone of healthcare assessment for thousands of years. Despite its long history and widespread use, TCM pulse diagnosis has faced challenges in terms of diagnostic accuracy and consistency due to its dependence on subjective interpretation and theoretical analysis. This study introduces an approach to enhance the accuracy of TCM pulse diagnosis for diabetes by leveraging the power of deep learning algorithms, specifically LeNet and ResNet models, for pulse waveform analysis. LeNet and ResNet models were applied to analyze TCM pulse waveforms using a diverse dataset comprising both healthy individuals and patients with diabetes. The integration of these advanced algorithms with modern TCM pulse measurement instruments shows great promise in reducing practitioner-dependent variability and improving the reliability of diagnoses. This research bridges the gap between ancient wisdom and cutting-edge technology in healthcare. LeNet-F, incorporating special feature extraction of a pulse based on TMC, showed improved training and test accuracies (73% and 67%, respectively, compared with LeNet's 70% and 65%). Moreover, ResNet models consistently outperformed LeNet, with ResNet18-F achieving the highest accuracy (82%) in training and 74% in testing. The advanced preprocessing techniques and additional features contribute significantly to ResNet18-F's superior performance, indicating the importance of feature engineering strategies. Furthermore, the study identifies potential avenues for future research, including optimizing preprocessing techniques to handle pulse waveform variations and noise levels, integrating additional time-frequency domain features, developing domain-specific feature selection algorithms, and expanding the scope to other diseases. These advancements aim to refine traditional Chinese medicine pulse diagnosis, enhancing its accuracy and reliability while integrating it into modern technology for more effective healthcare approaches.

A Frequency Estimation Scheme Based on Gaussian Average Filtering Decomposition and Hilbert Transform: With Estimation of Respiratory Rate as an Example.

Frequency estimation plays a critical role in vital sign monitoring. Methods based on Fourier transform and eigen-analysis are commonly adopted techniques for frequency estimation. Because of the nonstationary and time-varying characteristics of physiological processes, time-frequency analysis (TFA) is a feasible way to perform biomedical signal analysis. Among miscellaneous approaches, Hilbert-Huang transform (HHT) has been demonstrated to be a potential tool in biomedical applications. However, the problems of mode mixing, unnecessary redundant decomposition and boundary effect are the common deficits that occur during the procedure of empirical mode decomposition (EMD) or ensemble empirical mode decomposition (EEMD). The Gaussian average filtering decomposition (GAFD) technique has been shown to be appropriate in several biomedical scenarios and can be an alternative to EMD and EEMD. This research proposes the combination of GAFD and Hilbert transform that is termed the Hilbert-Gauss transform (HGT) to overcome the conventional drawbacks of HHT in TFA and frequency estimation. This new method is verified to be effective for the estimation of respiratory rate (RR) in finger photoplethysmography (PPG), wrist PPG and seismocardiogram (SCG). Compared with the ground truth values, the estimated RRs are evaluated to be of excellent reliability by intraclass correlation coefficient (ICC) and to be of high agreement by Bland-Altman analysis.

Specific Forms of Graphene Quantum Dots Induce Apoptosis and Cell Cycle Arrest in Breast Cancer Cells.

Graphene quantum dots (GQDs), nanomaterials derived from graphene and carbon dots, are highly stable, soluble, and have exceptional optical properties. Further, they have low toxicity and are excellent vehicles for carrying drugs or fluorescein dyes. Specific forms of GQDs can induce apoptosis and could be used to treat cancers. In this study, three forms of GQDs (GQD (nitrogen:carbon = 1:3), ortho-GQD, and meta-GQD) were screened and tested for their potential to inhibit breast cancer cell (MCF-7, BT-474, MDA-MB-231, and T-47D) growth. All three GQDs decreased cell viability after 72 h of treatment and specifically affected breast cancer cell proliferation. An assay for the expression of apoptotic proteins revealed that p21 and p27 were up-regulated (1.41-fold and 4.75-fold) after treatment. In particular, ortho-GQD-treated cells showed G2/M phase arrest. The GQDs specifically induced apoptosis in estrogen receptor-positive breast cancer cell lines. These results indicate that these GQDs induce apoptosis and G2/M cell cycle arrest in specific breast cancer subtypes and could potentially be used for treating breast cancers.

A Novel Method for Baroreflex Sensitivity Estimation Using Modulated Gaussian Filter.

The evaluation of baroreflex sensitivity (BRS) has proven to be critical for medical applications. The use of α indices by spectral methods has been the most popular approach to BRS estimation. Recently, an algorithm termed Gaussian average filtering decomposition (GAFD) has been proposed to serve the same purpose. GAFD adopts a three-layer tree structure similar to wavelet decomposition but is only constructed by Gaussian windows in different cutoff frequency. Its computation is more efficient than that of conventional spectral methods, and there is no need to specify any parameter. This research presents a novel approach, referred to as modulated Gaussian filter (modGauss) for BRS estimation. It has a more simplified structure than GAFD using only two bandpass filters of dedicated passbands, so that the three-level structure in GAFD is avoided. This strategy makes modGauss more efficient than GAFD in computation, while the advantages of GAFD are preserved. Both GAFD and modGauss are conducted extensively in the time domain, yet can achieve similar results to conventional spectral methods. In computational simulations, the EuroBavar dataset was used to assess the performance of the novel algorithm. The BRS values were calculated by four other methods (three spectral approaches and GAFD) for performance comparison. From a comparison using the Wilcoxon rank sum test, it was found that there was no statistically significant dissimilarity; instead, very good agreement using the intraclass correlation coefficient (ICC) was observed. The modGauss algorithm was also found to be the fastest in computation time and suitable for the long-term estimation of BRS. The novel algorithm, as described in this report, can be applied in medical equipment for real-time estimation of BRS in clinical settings.

Relevance of Copy Number Variation at Chromosome X in Male Fetuses Inherited from the Mother May Be Ascertained by Including Male Relatives from the Maternal Lineage in Addition to Trio Analyses.

Chromosome microarray analysis has been used for prenatal detection of copy number variations (CNVs) and genetic counseling of CNVs has been greatly improved after the accumulation of knowledge from postnatal outcomes in terms of the genotype-phenotype correlation. However, a significant number of CNVs are still regarded as variants of unknown significance (VUS). CNVs at the chromosome X (X-CNVs) represent a unique group of genetic changes in genetic counseling; X-CNVs are similar to X-linked recessive monogenic disorders in that the prognosis in males is expected to be poor. Trio analysis is typically advised to patients with X-CNVs but such an approach may be inadequate in prenatal settings since the clinical relevance is sometimes uninformative, particularly for the maternally inherited X-CNVs in male fetuses. Here, we reported four healthy women whose male fetuses were found to have X-CNVs inherited from the mothers. The X-CNVs were initially recognized as VUS or likely pathogenic in males according to the publicly available information. After extending genetic analyses to male relatives of the maternal lineages, however, the relevance of the X-CNVs was reconsidered to be likely benign. The results highlight that an extended analysis to include more relatives, in addition to the parents, provides further information for genetic counseling when X-CNVs are encountered in prenatal settings.

Prediction of Patient-Controlled Analgesic Consumption: A Multimodel Regression Tree Approach.

Several factors contribute to individual variability in postoperative pain, therefore, individuals consume postoperative analgesics at different rates. Although many statistical studies have analyzed postoperative pain and analgesic consumption, most have identified only the correlation and have not subjected the statistical model to further tests in order to evaluate its predictive accuracy. In this study involving 3052 patients, a multistrategy computational approach was developed for analgesic consumption prediction. This approach uses data on patient-controlled analgesia demand behavior over time and combines clustering, classification, and regression to mitigate the limitations of current statistical models. Cross-validation results indicated that the proposed approach significantly outperforms various existing regression methods. Moreover, a comparison between the predictions by anesthesiologists and medical specialists and those of the computational approach for an independent test data set of 60 patients further evidenced the superiority of the computational approach in predicting analgesic consumption because it produced markedly lower root mean squared errors.

Decision tree-based learning to predict patient controlled analgesia consumption and readjustment.

BACKGROUND: Appropriate postoperative pain management contributes to earlier mobilization, shorter hospitalization, and reduced cost. The under treatment of pain may impede short-term recovery and have a detrimental long-term effect on health. This study focuses on Patient Controlled Analgesia (PCA), which is a delivery system for pain medication. This study proposes and demonstrates how to use machine learning and data mining techniques to predict analgesic requirements and PCA readjustment. METHODS: The sample in this study included 1099 patients. Every patient was described by 280 attributes, including the class attribute. In addition to commonly studied demographic and physiological factors, this study emphasizes attributes related to PCA. We used decision tree-based learning algorithms to predict analgesic consumption and PCA control readjustment based on the first few hours of PCA medications. We also developed a nearest neighbor-based data cleaning method to alleviate the class-imbalance problem in PCA setting readjustment prediction. RESULTS: The prediction accuracies of total analgesic consumption (continuous dose and PCA dose) and PCA analgesic requirement (PCA dose only) by an ensemble of decision trees were 80.9% and 73.1%, respectively. Decision tree-based learning outperformed Artificial Neural Network, Support Vector Machine, Random Forest, Rotation Forest, and Naïve Bayesian classifiers in analgesic consumption prediction. The proposed data cleaning method improved the performance of every learning method in this study of PCA setting readjustment prediction. Comparative analysis identified the informative attributes from the data mining models and compared them with the correlates of analgesic requirement reported in previous works. CONCLUSION: This study presents a real-world application of data mining to anesthesiology. Unlike previous research, this study considers a wider variety of predictive factors, including PCA demands over time. We analyzed PCA patient data and conducted several experiments to evaluate the potential of applying machine-learning algorithms to assist anesthesiologists in PCA administration. Results demonstrate the feasibility of the proposed ensemble approach to postoperative pain management.

Pattern discovery from patient controlled analgesia demand behavior.

Unlike previous research on patient controlled analgesia, this study explores patient demand behavior over time. We apply clustering methods to disclose demand patterns among patients over the first 24h of analgesic medication after surgery. We consider demographic, biomedical, and surgery-related data in statistical analyses to determine predictors for patient demand behavior, and use stepwise regression and Bayes risk analysis to evaluate the influence of demand pattern on analgesic requirements. We identify three demand patterns from 1655 patient controlled analgesia request log files. Statistical tests show correlations of gender (p=.0022), diastolic blood pressure (p=.025), surgery type (p=.0028), and surgical duration (p<.0095) with demand patterns. Stepwise regression and Bayes risk analysis show demand pattern plays the most important role in analgesic consumption prediction (p=0.E+0). This study suggests analgesia request patterns over time exist among patients, and clustering can disclose demand behavioral patterns.

Whole-genome sequencing and identification of Morganella morganii KT pathogenicity-related genes.

BACKGROUND: The opportunistic enterobacterium, Morganella morganii, which can cause bacteraemia, is the ninth most prevalent cause of clinical infections in patients at Changhua Christian Hospital, Taiwan. The KT strain of M. morganii was isolated during postoperative care of a cancer patient with a gallbladder stone who developed sepsis caused by bacteraemia. M. morganii is sometimes encountered in nosocomial settings and has been causally linked to catheter-associated bacteriuria, complex infections of the urinary and/or hepatobiliary tracts, wound infection, and septicaemia. M. morganii infection is associated with a high mortality rate, although most patients respond well to appropriate antibiotic therapy. To obtain insights into the genome biology of M. morganii and the mechanisms underlying its pathogenicity, we used Illumina technology to sequence the genome of the KT strain and compared its sequence with the genome sequences of related bacteria. RESULTS: The 3,826,919-bp sequence contained in 58 contigs has a GC content of 51.15% and includes 3,565 protein-coding sequences, 72 tRNA genes, and 10 rRNA genes. The pathogenicity-related genes encode determinants of drug resistance, fimbrial adhesins, an IgA protease, haemolysins, ureases, and insecticidal and apoptotic toxins as well as proteins found in flagellae, the iron acquisition system, a type-3 secretion system (T3SS), and several two-component systems. Comparison with 14 genome sequences from other members of Enterobacteriaceae revealed different degrees of similarity to several systems found in M. morganii. The most striking similarities were found in the IS4 family of transposases, insecticidal toxins, T3SS components, and proteins required for ethanolamine use (eut operon) and cobalamin (vitamin B12) biosynthesis. The eut operon and the gene cluster for cobalamin biosynthesis are not present in the other Proteeae genomes analysed. Moreover, organisation of the 19 genes of the eut operon differs from that found in the other non-Proteeae enterobacterial genomes. CONCLUSIONS: This is the first genome sequence of M. morganii, which is a clinically relevant pathogen. Comparative genome analysis revealed several pathogenicity-related genes and novel genes not found in the genomes of other members of Proteeae. Thus, the genome sequence of M. morganii provides important information concerning virulence and determinants of fitness in this pathogen.

Simultaneous use of traditional Chinese medicine (si-ni-tang) to treat septic shock patients: study protocol for a randomized controlled trial.

BACKGROUND: Even though there are continually upgraded recommendations for managing sepsis, such as "Surviving Sepsis Campaign: international guidelines for management of severe sepsis and septic shock", mortality is still high. Si-ni-tang, a remedy documented in Shanghan Lun, a medical collection from ancient China, is used for treating patients with sepsis and septic shock. Using a well-designed clinical trial, we are eager to survey the effectiveness of the concurrent use of this remedy in restoring these patients' hemodynamic status, or "Yang Qi". METHODS/DESIGN: Patients admitted to our medical intensive care units with the diagnosis of septic shock, defined as persistent hypotension induced by sepsis despite adequate fluid resuscitation, are eligible for participation. The inclusion criteria include: age from 20 to 85 years, conditions meeting the definition of septic shock, use of vasopressors within 24 hours of entering the study, and use of a nasogastric tube for feeding. The enrolled patients are randomly allocated either to the si-ni-tang group or the placebo group. The prescription of the trial drugs (si-ni-tang/placebo) is 2.25 grams 4 times a day for 7 days or till shock reversal (if shock reversal occurs in less than 7 days). Data, including duration of vasopressor infusion, gender, age, co-morbidities, APACHE II score, predicted mortality, ICU mortality, ICU length of stay, hospital mortality, hospital length of stay, source of sepsis, and culture results, are collected for the following analysis. DISCUSSION: Si-ni-tang is composed of processed Zingiber officinale, Glycyrrhiza uralensis, and Aconitum carmichaeli. Zingiber officinale and Glycyrrhiza uralensis are found to have the ability to reduce pro-inflammatory cytokine production, to inhibit lipopolisaccharide-induced macrophage activation and function, and to lessen the bacterial load and suppress acute and chronic inflammation. Aconitum carmichaeli is known to have vasopressor activity, and positive chronotropic and inotropic effects. As this remedy has a potential benefit in treating septic shock patients, we designed a double-blind, prospective, randomized controlled trial and would like to publish the results and conclusions later. TRIAL REGISTRATION: ClinicalTrials.gov: NCT01223430.

Effect of honokiol on activity of GAD(65) and GAD(67) in the cortex and hippocampus of mice.

Honokiol, an active agent extracted from magnolia bark, has been reported that induces anxiolytic action in a mouse elevated plus-maze test. However, the mechanism of anxiolytic action induced by honokiol remains unclear. This study was to investigate the change in two forms of glutamic acid decarboxylase (GABA synthesized enzymes) GAD(65) and GAD(67) in the cortex and hippocampus areas while the anxiolytic actions induced by chronic administration of honokiol in mice. Mice treated with 7 daily injection of honokiol (1mg/kg, p.o.) caused anxiolytic action which was similar to that was induced by 7 daily injection of diazepam (2mg/kg, p.o.) in the elevated plus-maze test. In addition, the activity of hippocampal GAD(65) of honokiol treated mice was significantly increased than that of the vehicle or diazepam treated groups. These data suggest that honokiol causes diazepam-like anxiolytic action, which may be mediated by altering the synthesis of GABA in the brain of mice.

Genetic copy number variants in myocardial infarction patients with hyperlipidemia.

BACKGROUND: Cardiovascular disease is the chief cause of death in Taiwan and many countries, of which myocardial infarction (MI) is the most serious condition. Hyperlipidemia appears to be a significant cause of myocardial infarction, because it causes atherosclerosis directly. In recent years, copy number variation (CNV) has been analyzed in genomewide association studies of complex diseases. In this study, CNV was analyzed in blood samples and SNP arrays from 31 myocardial infarction patients with hyperlipidemia. RESULTS: We identified seven CNV regions that were associated significantly with hyperlipidemia and myocardial infarction in our patients through multistage analysis (P<0.001), at 1p21.3, 1q31.2 (CDC73), 1q42.2 (DISC1), 3p21.31 (CDCP1), 10q11.21 (RET) 12p12.3 (PIK3C2G) and 16q23.3 (CDH13), respectively. In particular, the CNV region at 10q11.21 was examined by quantitative real-time PCR, the results of which were consistent with microarray findings. CONCLUSIONS: Our preliminary results constitute an alternative method of evaluating the relationship between CNV regions and cardiovascular disease. These susceptibility CNV regions may be used as biomarkers for early-stage diagnosis of hyperlipidemia and myocardial infarction, rendering them valuable for further research and discussion.

Combined use of MS-222 (tricaine) and isoflurane extends anesthesia time and minimizes cardiac rhythm side effects in adult zebrafish.

As an important vertebrate model organism, zebrafish are typically studied at the embryonic stage to take advantage of their properties of transparency and rapid development. However, more and more studies require assays to be done on adults. Consequently, a good anesthetic is needed to sedate and immobilize the adult zebrafish during experimental manipulation. To date, MS-222 (tricaine methanesulfonate) is the only Food and Drug Administration approved anesthetic for aquaculture and is widely used by the zebrafish research community. Nevertheless, in adult zebrafish, MS-222 reduces heart rate and causes high mortality under long-term sedation. Consequently, adult zebrafish have limited research applications. In this study, we present a new anesthetic formula for the adult zebrafish that results in minimal side effects on its physiology under prolonged sedation. The combined use of MS-222 with isoflurane effectively extended the time of anesthesia, and the zebrafish recovered faster than when anesthetized with the traditional MS-222. Moreover, MS-222 + isoflurane did not cause reduction of heart rates, which enabled long-term electrocardiogram recording and microscopic observation on the adult zebrafish. Taken together, the new MS-222 + isoflurane formula will facilitate general applications of adult zebrafish in time-consuming experiments with minimal side effects on the model organism's overall physiology.

Predicting melting temperature directly from protein sequences.

Proteins of both hyperthermophilic and mesophilic microorganisms generally constitute from the same 20 amino acids; however, the extent of thermal tolerance of any given protein is an inherent property of its amino acid sequence. The present study is the first to report a rapid method for predicting Tm (melting temperature), the temperature at which 50% of the protein is unfolded, directly from protein sequences (the Tm Index program is available at http://tm.life.nthu.edu.tw/). We examined 75 complete microbial genomes using the Tm Index, and the analysis clearly differentiated hyperthermophilic from mesophilic microorganisms on this global genomic basis. These results are consistent with the previous hypothesis that hyperthermophiles express a greater number of high Tm proteins compared with mesophiles. The Tm Index will be valuable for modifying existing proteins (enzymes, protein drugs and vaccines) or designing novel proteins having a desired melting temperature.

The use of portable computer for information acquirement during anesthesiologist's ward round in acute pain service.

BACKGROUND: The Acute Pain Service Information Management System (APSIMS), as we coined, is the utilization of a portable computer to register the data of the patients who need acute pain management during anesthesiologist's ward round. Initially, the data of the daily acute pain assessment at the ward are recorded on a sheet of paper by the rounding anesthesiologist, which are subsequently entered into the hospital main frame computer by an anesthetic nurse. In order to save manpower in data entry, we planned to introduce the personal digital assistant (PDA) into acute pain assessment. The anesthesiologist can record a patient's data directly into the PDA device at the bedside. After acute pain assessment is finished, we can directly up load the data from the PDA to the hospital mainframe computer without the need of further manpower for doing data input. This study was to evaluate the use of PDA for acute pain assessment and compare the PDA-based method with that of the current paper-transcription method in work efficiency. METHODS: Two computer applications were developed: the APS Mobile Assistant and the Data Transformation Wizard (DTW). The APS Mobile Assistant is a PDA application running on a portable computer with Windows Mobile 2003 operation system. The anesthesiologist can use this application to perform APS assessment at the bedside. The Data Transformation Wizard is a PC application which can transfer data from the PDA device to the hospital mainframe computer, by which the data in the PDA system can be integrated into the hospital information system. The evaluation included the reckoning of the timings of two periods i.e. the time spent by the physician to perform acute pain assessment at the bedside and the time required for data management by the nurse. To compare the paper-transcription method with the PDA-based technique, the Student's t test was performed to assess the data of time of each category collected. A P value less than 0.05 was considered to be significant. RESULTS: When the time required for assessment of acute pain was determined, no statistically significant difference was observed between the use of the paper-transcription-based system and the PDA system (P = 0.258). In comparison the PDA system was clearly shown to facilitate faster management of data (Paper-transcription method: 1.57 +/- 0.08 min per patient compared with PDA-based method: 0.24 +/- 0.01 min per patient, P < 0.0001). CONCLUSIONS: Implementation of PDA device during APS assessment can provide the anesthesiologists with more time to acquire information during APS visits. Using the PDA technology in clinical settings can increase work efficiency. We can save manpower and are convinced that data collection is more complete with the use of a PDA system.

Mining colon cancer specific alternative splicing in EST database.

Among 75218 splicing sites, 137 colon cancer specific alternative splicing isoforms were found by mining EST database. Alternative splicing database were first constructed by aligning EST to genomic sequence. Numbers of ESTs from normal or cancer colon tissue supporting splicing isoform at each splicing site were then queried and analyzed with Fisher exact test. There were 53 3' splicing, 42 5' splicing, 40 exon skipping and 2 mutual exclusive cancer specific splicing isoforms.

Acute pain service management system. The successful experience of the hospital computerization and utilization of personal digital assistant.

Personal digital assistants (PDA) are increasingly being used in many medical fields. The object of this study is to introduce how the hospital computerization and the utilization of PDA can help to reduce the time of data processing for Acute Pain Service. After this study completed, we found that the use of PDA can dramatically reduce the time of data management and improve the quality of medical records.