Drug cytotoxicity screening using human intestinal organoids propagated with extensive cost-reduction strategies.

Organoids are regarded as physiologically relevant cell models and useful for compound screening for drug development; however, their applications are currently limited because of the high cost of their culture. We previously succeeded in reducing the cost of human intestinal organoid culture using conditioned medium (CM) of L cells co-expressing Wnt3a, R-spondin1, and Noggin. Here, we further reduced the cost by replacing recombinant hepatocyte growth factor with CM. Moreover, we showed that embedding organoids in collagen gel, a more inexpensive matrix than Matrigel, maintains organoid proliferation and marker gene expression similarly when using Matrigel. The combination of these replacements also enabled the organoid-oriented monolayer cell culture. Furthermore, screening thousands of compounds using organoids expanded with the refined method identified several compounds with more selective cytotoxicity against organoid-derived cells than Caco-2 cells. The mechanism of action of one of these compounds, YC-1, was further elucidated. We showed that YC-1 induces apoptosis through the mitogen-activated protein kinase/extracellular signal-regulated kinase pathway, the mechanism of which was distinct from cell death caused by other hit compounds. Our cost-cutting methodology enables large-scale intestinal organoid culture and subsequent compound screening, which could expand the application of intestinal organoids in various research fields.

Clinical prediction rule for locomotive syndrome in older adults: A decision tree model.

BACKGROUND: No previous studies have proposed a clinical prediction rule that analyzes the factors related to the severity of locomotive syndrome. This study developed and assessed a clinical prediction rule for the severity of locomotive syndrome in older adults. METHODS: A total of 186 patients were assessed using the locomotive syndrome risk test. Classification and regression tree methodologies were used to develop the clinical prediction rule. This study developed three prediction models based on the severity of the locomotive syndrome, of which Model 3 assessed the most severe condition. The following potential predictive factors were measured and entered into each model; single-leg standing time, grip strength, preferred and maximum walking time, and timed up and go test. RESULTS: The single-leg standing test (≤59.4 or >59.4 s) was the best single discriminator for Model 1. Among those with a single-leg standing time >59.4 s, the next best predictor was grip strength (≤37.8 or >37.8 kg). In Model 2, the single-leg standing test was also the best single discriminator (≤12.6 or >12.6 s). Among those with a single-leg standing time ≤12.6, the next best predictor was TUG (≤7.9 or >7.9 s). Additionally, among those with a single-leg standing time >12.6, the next best predictor was single-leg standing time (≤55.3 or >55.3 s). In Model 3, predictive value in Model 2 was the best single discriminator (0 or 1). Among those with 1, the next best predictor was maximum walking time (≤3.75 or >3.75 s). The area under the receiver operating characteristic curves of Models 1, 2, and 3 were 0.737, 0.763, and 0.704, respectively. CONCLUSIONS: A clinical prediction rule was developed to assess the accuracy of the models. These results can be used to screen older adults for suspected locomotive syndrome.

Developing a Clinical Prediction Rule for Gait Independence at Discharge in Patients with Stroke: A Decision-Tree Algorithm Analysis.

OBJECTIVES: To develop a clinical prediction rule (CPR) for gait independence at discharge in patients with stroke, using the decision-tree algorithm and to investigate the usefulness of CPR at admission to the rehabilitation ward. MATERIALS AND METHODS: We included 181 subjects with stroke during the postacute phase. The Chi-squared automatic interaction detection analysis method with 10-fold cross-validation was used to develop two CPRs; CPR 1 using easily obtainable data available at admission; CPR 2 using easily obtainable data available 1 month after admission, for prediction of gait independence at discharge. RESULTS: The degree of independence of toileting was extracted as a first node in the development of two CPRs to predict gait independence at discharge. CPR 1 included the presence of delirium. CPR 2 included problem-solving abilities. The accuracy and area under the curve of CPR 1 were 84.5% and 0.911, respectively; those of CPR 2 were 89.0% and 0.958, respectively. CONCLUSIONS: Toileting independence is a key factor in predicting the gait independence for the discharge of patients with stroke during the postacute phase. Early intervention, during the acute phase, for delirium and cognitive decline, as well as for toileting, increases the possibility of gait independence at discharge.

Decision Tree Algorithm Identifies Stroke Patients Likely Discharge Home After Rehabilitation Using Functional and Environmental Predictors.

BACKGROUND AND PURPOSE: The importance of environmental factors for stroke patients to achieve home discharge was not scientifically proven. There are limited studies on the application of the decision tree algorithm with various functional and environmental variables to identify stroke patients with a high possibility of home discharge. The present study aimed to identify the factors, including functional and environmental factors, affecting home discharge after stroke inpatient rehabilitation using the machine learning method. METHOD: This was a cohort study on data from the maintained database of all patients with stroke who were admitted to the convalescence rehabilitation ward of our facility. In total, 1125 stroke patients were investigated. We developed three classification and regression tree (CART) models to identify the possibility of home discharge after inpatient rehabilitation. RESULTS: Among three models, CART model incorporating basic information, functional factor, and environmental factor variables achieved the highest accuracy for identification of home discharge. This model identified FIM dressing of the upper body (score of ≤2 or >2) as the first single discriminator for home discharge. Performing house renovation was associated with a high possibility of home discharge even in patients with stroke who had a poor FIM score in the ability to dress the upper body (≤2) at admission into the convalescence rehabilitation ward. Interestingly, many patients who performed house renovation have achieved home discharge regardless of the degree of lower limb paralysis. CONCLUSION: We identified the influential factors for realizing home discharge using the decision tree algorithm, including environmental factors, in patients with convalescent stroke.

A clinical prediction rule for predicting a delay in quality of life recovery at 1 month after total knee arthroplasty: A decision tree model.

BACKGROUND: There is no clinical prediction rule for predicting the prognosis of quality of life after total knee arthroplasty and for assessing its accuracy. The study aimed to develop and assess a clinical prediction rule to predict decline in quality of life at 1 month after total knee arthroplasty. METHODS: This study included 116 patients with total knee arthroplasty in Japan. Potential predictors such as sociodemographic factors, medical information, and motor functions were measured. Quality of life was measured using the Japanese Knee Osteoarthritis Measure at 1 day before surgery and 1 month after total knee arthroplasty. The classification and regression tree methodology was used for developing a clinical prediction rule. RESULTS: The Japanese Knee Osteoarthritis Measure score pre-total knee arthroplasty (≦34.0 or >34.0) was the best single discriminator. Among those with the Japanese Knee Osteoarthritis Measure score pre-total knee arthroplasty ≦34.0, the next best predictor was knee flexor muscle strength on the affected side (≦0.45 or >0.45 N m/kg). Among those with knee flexor muscle strength on the affected side >0.45, the next predictor was knee flexion range of motion on the affected side (≦132.5°or >132.5°). The area under the receiver operating characteristic curves of the model was 0.805 (95% confidence interval, 0.701-0.909). CONCLUSIONS: In this study, 4 variables were selected as the significant predictor. However, the results of knee flexor muscle strength and knee flexion range of motion were paradoxical. This result suggests that it should be careful to perform surgery to the patients with good preoperative knee function. The clinical prediction rule was developed for predicting quality of life decline 1 month after total knee arthroplasty, and the accuracy was moderate. This clinical prediction rule can be used for screening of patients with total knee arthroplasty.

Clinical Features for Identifying the Possibility of Toileting Independence after Convalescent Inpatient Rehabilitation in Severe Stroke Patients: A Decision Tree Analysis Based on a Nationwide Japan Rehabilitation Database.

BACKGROUND AND PURPOSE: In severe stroke patients, considerable concern should be given to toileting activity in rehabilitative support. Recently, the application of artificial intelligence, including machine learning (ML), has expanded into the stroke medical field, which could clarify the factors affecting toileting independence in severe stroke patients. This study aimed to identify the factors affecting toileting independence in severe stroke patients using ML. METHODS: We used the Japan Rehabilitation Database from 2005 to 2015 to investigate data from 2292 severe stroke patients. We performed the chi-squared automatic interaction detection (CHAID) algorithm with various explanatory variables. RESULTS: The CHAID model identified modified Rankin scale (mRS) score as the first discriminator. Among those with an mRS score ≤4, the next discriminator was age (score ≤72, 73-80, or >80). Among those with an mRS score > 4, the next discriminator was also age (score ≤57, 58-72, 73-80, or >80). Interestingly, some patients achieved toileting independence, although this study focused on severe stroke patients. In branches based on age, the percentage of the patients who achieved toileting independence at discharge decreased progressively with age. CONCLUSION: We identified the influential factors, including reference values, for achieving toileting independence in convalescent severe stroke patients.

Minimal clinically important difference for the Fugl-Meyer assessment of the upper extremity in convalescent stroke patients with moderate to severe hemiparesis.

[Purpose] To estimate the minimal clinically important difference for the Fugl-Meyer assessment of the upper extremity by using anchor-based methods in stroke patients with moderate to severe hemiparesis. [Participants and Methods] Fourteen patients who were hospitalized in a convalescent phase rehabilitation ward were included in this study. Fugl-Meyer assessment of the upper extremity was used to assess the impairment prior to intervention and at follow-up (six weeks later). Participants were asked to evaluate the degree of improvement of paresis of the upper extremity using the global rating of change scale at follow-up. The mean change in Fugl-Meyer assessment scores in the group of patients who answered "a little better, meaningful in daily life" in the global rating of change scale was considered as the minimal clinically important difference. [Results] The mean post-onset period of participants for analysis was 49.4 days. The minimal clinically important difference of the Fugl-Meyer assessment scores were 12.4 (upper extremity), 5.6 (upper arm), and 4.9 (wrist/hand). [Conclusion] A score of 12.4 in the Fugl-Meyer assessment of the upper extremity is likely to be perceived as meaningful in stroke patients with moderate to severe hemiparesis.