Multifunctional Actions of Ninjinyoeito, a Japanese Kampo Medicine: Accumulated Scientific Evidence Based on Experiments With Cells and Animal Models, and Clinical Studies

Herbal medicines are currently employed for the treatment of several types of diseases, and also employed for the improvement of Quality of Life (QOL) of patients over the world, in particular, in Asian countries. In Japan, a Japanese herbal medicine namely kampo medicine has been prescribed for the improvement of QOL of patients. Ninjinyoeito (NYT), composed of 12 herbal plants, is one of kampo medicines and used for helping recovery of diseases and improving several symptoms that suffer patients such as anemia, anorexia and fatigue. Recent scientific research approaches to kampo medicines with cells and animal models enable to prove that NYT has multiple functions for improvement of symptoms. Also, clinical studies using NYT support such actions to be widely used for the improvement of symptoms that reduce the QOL of patients

The clinical use of Kampo medicines (traditional Japanese herbal treatments) for controlling cancer patients’ symptoms in Japan: a national cross-sectional survey

BACKGROUND: Kampo medicines are traditional Japanese medicines produced from medicinal plants and herbs. Even though the efficacy of Kampo medicines for controlling cancer-related symptoms is being reported, their actual nationwide clinical use has not been comprehensively investigated. We aimed to investigate physicians’ recognition of Kampo medicines and their clinical use for cancer patients in the field of palliative care. METHODS: A cross-sectional self-administered anonymous questionnaire was distributed to 549 physicians working in palliative care teams at 388 core cancer treatment hospitals and 161 certified medical institutions that have palliative care units (PCUs). RESULTS: Valid responses were obtained from 311 physicians (response rate, 56.7%) who were evenly distributed throughout the country without significant geographical biases. Kampo medicines were prescribed for controlling cancer-related symptoms by 64.3% of the physicians. The symptoms treated with Kampo medicines were numbness/hypoesthesia (n = 99, 49.5%), constipation (n = 76, 38.0%), anorexia/weight loss (n = 72, 36%), muscle cramps (n = 71, 35.5%) and languor/fatigue (n = 64, 32.0%). Regarding open issues about prescription, 60.7% (n = 173) of the physicians raised the issue that the dosage forms need to be better devised. CONCLUSIONS: To increase the clinical use of Kampo medicines, more evidence from clinical studies is necessary. In addition, their mechanisms of action should be clarified through laboratory studies

Onset Temperatures for Superconducting Fluctuations in Te-annealed FeTe1−x_{1-x}Sex_x Single Crystals: Evidence for the BCS-BEC Crossover

Recently, the superconductors' community has witnessed an unsettled debate regarding whether iron-based superconductors, in particular FeSe and FeSe$_{1-x}$S$_x$, are in the Bardeen-Cooper-Shrieffer (BCS) - Bose-Einstein condensation (BEC) crossover regime. Nonetheless, one particular system, FeTe$_{1-x}$Se$_x$, has been less investigated in this regard owing to the screening of its intrinsic superconducting properties by the inevitable iron excess. Herein, the onset temperatures for superconducting fluctuations ($T_{scf}$) are investigated by measuring the magnetoresistance (MR) of Te-annealed, high-quality FeTe$_{1-x}$Se$_x$ ($x$ = 0.1, 0.2, 0.3, and 0.4) single crystals. The results reveal very high $T_{scf}$ values for these crystals. Particularly for $x$ = 0.4, $T_{scf}$ reaches approximately 40 K, which is 2.7 times larger than $T_c$. This indicates that the superconductivity of the FeTe$_{1-x}$Se$_x$ system is well within the BCS-BEC crossover regime.Comment: 6 pages, 3 figures, and 1 table. to be published in JPS Conference Proceeding

Prediction of Opioid-Induced Respiratory Depression on Inpatient Wards Using Continuous Capnography and Oximetry: An International Prospective, Observational Trial

Background: Opioid-related adverse events are a serious problem in hospitalized patients. Little is known about patients who are likely to experience opioid-induced respiratory depression events on the general care floor and may benefit from improved monitoring and early intervention. The trial objective was to derive and validate a risk prediction tool for respiratory depression in patients receiving opioids, as detected by continuous pulse oximetry and capnography monitoring. Methods: PRediction of Opioid-induced respiratory Depression In patients monitored by capnoGraphY (PRODIGY) was a prospective, observational trial of blinded continuous capnography and oximetry conducted at 16 sites in the United States, Europe, and Asia. Vital signs were intermittently monitored per standard of care. A total of 1335 patients receiving parenteral opioids and continuously monitored on the general care floor were included in the analysis. A respiratory depression episode was defined as respiratory rate ≤5 breaths/min (bpm), oxygen saturation ≤85%, or end-tidal carbon dioxide ≤15 or ≥60 mm Hg for ≥3 minutes; apnea episode lasting >30 seconds; or any respiratory opioid-related adverse event. A risk prediction tool was derived using a multivariable logistic regression model of 46 a priori defined risk factors with stepwise selection and was internally validated by bootstrapping. Results: One or more respiratory depression episodes were detected in 614 (46%) of 1335 general care floor patients (43% male; mean age, 58 ± 14 years) continuously monitored for a median of 24 hours (interquartile range [IQR], 17-26). A multivariable respiratory depression prediction model with area under the curve of 0.740 was developed using 5 independent variables: age ≥60 (in decades), sex, opioid naivety, sleep disorders, and chronic heart failure. The PRODIGY risk prediction tool showed significant separation between patients with and without respiratory depression (P < .001) and an odds ratio of 6.07 (95% confidence interval [CI], 4.44-8.30; P < .001) between the high- and low-risk groups. Compared to patients without respiratory depression episodes, mean hospital length of stay was 3 days longer in patients with ≥1 respiratory depression episode (10.5 ± 10.8 vs 7.7 ± 7.8 days; P < .0001) identified using continuous oximetry and capnography monitoring. Conclusions: A PRODIGY risk prediction model, derived from continuous oximetry and capnography, accurately predicts respiratory depression episodes in patients receiving opioids on the general care floor. Implementation of the PRODIGY score to determine the need for continuous monitoring may be a first step to reduce the incidence and consequences of respiratory compromise in patients receiving opioids on the general care floor

Data-driven categorization of postoperative delirium symptoms using unsupervised machine learning

BackgroundPhenotyping analysis that includes time course is useful for understanding the mechanisms and clinical management of postoperative delirium. However, postoperative delirium has not been fully phenotyped. Hypothesis-free categorization of heterogeneous symptoms may be useful for understanding the mechanisms underlying delirium, although evidence is currently lacking. Therefore, we aimed to explore the phenotypes of postoperative delirium following invasive cancer surgery using a data-driven approach with minimal prior knowledge.MethodsWe recruited patients who underwent elective invasive cancer resection. After surgery, participants completed 5 consecutive days of delirium assessments using the Delirium Rating Scale-Revised-98 (DRS-R-98) severity scale. We categorized 65 (13 questionnaire items/day × 5 days) dimensional DRS-R-98 scores using unsupervised machine learning (K-means clustering) to derive a small set of grouped features representing distinct symptoms across all participants. We then reapplied K-means clustering to this set of grouped features to delineate multiple clusters of delirium symptoms.ResultsParticipants were 286 patients, of whom 91 developed delirium defined according to Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition, criteria. Following the first K-means clustering, we derived four grouped symptom features: (1) mixed motor, (2) cognitive and higher-order thinking domain with perceptual disturbance and thought content abnormalities, (3) acute and temporal response, and (4) sleep–wake cycle disturbance. Subsequent K-means clustering permitted classification of participants into seven subgroups: (i) cognitive and higher-order thinking domain dominant delirium, (ii) prolonged delirium, (iii) acute and brief delirium, (iv) subsyndromal delirium-enriched, (v) subsyndromal delirium-enriched with insomnia, (vi) insomnia, and (vii) fit.ConclusionWe found that patients who have undergone invasive cancer resection can be delineated using unsupervised machine learning into three delirium clusters, two subsyndromal delirium clusters, and an insomnia cluster. Validation of clusters and research into the pathophysiology underlying each cluster will help to elucidate the mechanisms of postoperative delirium after invasive cancer surgery