Prediction of gait recovery using machine learning algorithms in patients with spinal cord injury.

With advances in artificial intelligence, machine learning (ML) has been widely applied to predict functional outcomes in clinical medicine. However, there has been no attempt to predict walking ability after spinal cord injury (SCI) based on ML. In this situation, the main purpose of this study was to predict gait recovery after SCI at discharge from an acute rehabilitation facility using various ML algorithms. In addition, we explored important variables that were related to the prognosis. Finally, we attempted to suggest an ML-based decision support system (DSS) for predicting gait recovery after SCI. Data were collected retrospectively from patients with SCI admitted to an acute rehabilitation facility between June 2008 to December 2021. Linear regression analysis and ML algorithms (random forest [RF], decision tree [DT], and support vector machine) were used to predict the functional ambulation category at the time of discharge (FAC_DC) in patients with traumatic or non-traumatic SCI (n = 353). The independent variables were age, sex, duration of acute care and rehabilitation, comorbidities, neurological information entered into the International Standards for Neurological Classification of SCI worksheet, and somatosensory-evoked potentials at the time of admission to the acute rehabilitation facility. In addition, the importance of variables and DT-based DSS for FAC_DC was analyzed. As a result, RF and DT accurately predicted the FAC_DC measured by the root mean squared error. The root mean squared error of RF and the DT were 1.09 and 1.24 for all participants, 1.20 and 1.06 for those with trauma, and 1.12 and 1.03 for those with non-trauma, respectively. In the analysis of important variables, the initial FAC was found to be the most influential factor in all groups. In addition, we could provide a simple DSS based on strong predictors such as the initial FAC, American Spinal Injury Association Impairment Scale grades, and neurological level of injury. In conclusion, we provide that ML can accurately predict gait recovery after SCI for the first time. By focusing on important variables and DSS, we can guide early prognosis and establish personalized rehabilitation strategies in acute rehabilitation hospitals.

Deep Learning-Based Prediction Model for Gait Recovery after a Spinal Cord Injury.

Predicting gait recovery after a spinal cord injury (SCI) during an acute rehabilitation phase is important for planning rehabilitation strategies. However, few studies have been conducted on this topic to date. In this study, we developed a deep learning-based prediction model for gait recovery after SCI upon discharge from an acute rehabilitation facility. Data were collected from 405 patients with acute SCI admitted to the acute rehabilitation facility of Korea University Anam Hospital between June 2008 and December 2022. The dependent variable was Functional Ambulation Category at the time of discharge (FAC-DC). Seventy-one independent variables were selected from the existing literature: basic information, International Standards for Neurological Classification of SCI scores, neurogenic bladders, initial FAC, and somatosensory-evoked potentials of the lower extremity. Recurrent neural network (RNN), linear regression (LR), Ridge, and Lasso methods were compared for FAC-DC prediction in terms of the root-mean-squared error (RMSE). RNN variable importance, which is the RMSE gap between a complete RNN model and an RNN model excluding a certain variable, was used to evaluate the contribution of this variable. Based on the results of this study, the performance of the RNN was far better than that of LR, Ridge, and Lasso. The respective RMSEs were 0.3738, 2.2831, 1.3161, and 1.0246 for all the participants; 0.3727, 1.7176, 1.3914, and 1.3524 for those with trauma; and 0.3728, 1.7516, 1.1012, and 0.8889 for those without trauma. In terms of RNN variable importance, lower-extremity motor strength (right and left ankle dorsiflexors, right knee extensors, and left long toe extensors) and the neurological level of injury were ranked among the top five across the boards. Therefore, initial FAC was the seventh, third, and ninth most important predictor for all participants, those with trauma, and those without trauma, respectively. In conclusion, this study developed a deep learning-based prediction model with excellent performance for gait recovery after SCI at the time of discharge from an acute rehabilitation facility. This study also demonstrated the strength of deep learning as an explainable artificial intelligence method for identifying the most important predictors.

Automated non-contact measurement of the spine curvature at the sagittal plane using a deep neural network.

BACKGROUND: Non-radiographical techniques have been suggested to measure the spine curvature at the sagittal plane. However, a neural network has not been used to measure the curvature. METHODS: A single video camera captured images of a standing posture at the sagittal plane from twenty healthy males. Six marker positions along the spine's contour in each image were identified for measuring inclination, thoracic kyphosis, and lumbar lordosis angles. We estimated three inflection points around the neck, hip, and between the neck and hip, followed by identifying two adjacent marker positions per inflection point to compute its tangent. The angular deviation of each tangent line from the horizontal was computed to measure inclination angles. Thoracic kyphosis and lumbar lordosis angles were computed by the angular difference between the two adjacent tangents. A deep neural network was trained with 500,000 iterations using the labeled images from 18 participants (388 and 44 images for training and test set) and then evaluated using the unseen images (2 participants, 48 images; evaluation set). FINDINGS: The mean total training and test errors were <2 pixels (∼ 0.6 cm). The total error in the evaluation set was qualitatively comparable (∼ 3 pixels = âˆ¼ 0.9 cm), suggesting the model performance was maintained in the unseen data. The angle values between labeled and network-predicted marker positions were similar in the evaluation set. INTERPRETATION: The network training with a relatively small number of images was successful based on the small error values observed in the evaluation set. The model may be an affordable, automated, and non-contact measurement tool for the human spine curvature.

Clinical efficacy of overground powered exoskeleton for gait training in patients with subacute stroke: A randomized controlled pilot trial.

BACKGROUND: To investigate the efficacy and usefulness of 12 sessions of overground robot-assisted gait training (RAGT) in subacute stroke patients. METHODS: In this pilot study, 17 subacute stroke survivors were randomly assigned to the intervention (n = 9) and control (n = 8) groups. In addition to the conventional stroke neurorehabilitation program, the intervention group received 30 minutes of overground exoskeletal RAGT, while the control group received 30 minutes of conventional gait training by a physiotherapist. All interventions were performed in 12 sessions (3 times/week for 4 weeks). The primary aim was to assess ambulation ability using the functional ambulation category (FAC). The 10-m walk test, Berg Balance Scale, timed-up-and-go Timed-up-and-go, Fugl-Meyer assessment of lower extremity, pulmonary function test, the Korean version of the modified Barthel index, and Euro quality of life-5 dimensions (EQ-5D) were assessed. All outcomes were evaluated both before and after the intervention. RESULTS: The Berg Balance Scale, Korean version of the modified Barthel index, and EQ-5D scores (P < .05) improved significantly in both groups. Only those in the RAGT group improved significantly in the FAC, timed-up-and-go, and 10-m walk test (P < .05). In the FAC and EQ-5D, the intervention group showed greater improvement than the control group (P < .05). CONCLUSION: We found that 4 weeks of overground RAGT combined with conventional training may improve walking independence and quality of life in patients with subacute stroke.

Effects of electrical muscle stimulation on core muscle activation and physical performance in non-athletic adults: A randomized controlled trial.

BACKGROUND: Electrical muscle stimulation (EMS) activates muscles through electrical currents, resulting in involuntary muscle contractions. This study aimed to evaluate the immediate clinical effects of superimposing EMS on strength training compared with conventional exercise in healthy non-athletic adults. METHODS: This study was a randomised, controlled, parallel-group trial conducted at a single centre. Forty-one healthy young volunteers were recruited and randomised into two groups: strengthening with superimposed EMS (S+E) and strengthening (S) groups. All participants underwent the 30 minutes of strength training program, three times a week for 8 weeks, consisting of core muscle exercises. Additionally, the S+E group received EMS during training, which stimulated the bilateral abdominal, gluteus, and hip adductor muscles. As the primary outcome measure, we evaluated the changes in muscle thickness, including the abdominal, gluteal, and hip adductor muscles, using ultrasound. Muscle thickness was measured in both resting and contracted states. For secondary outcomes, physical performance (Functional Movement System score, McGill's core stability test, and hip muscle power) and body composition analysis were evaluated. All assessments were performed at the beginning and end of the intervention. RESULTS: 39 participants (S+E group = 20, S group = 19) completed the study. The clinical characteristics and baseline functional status of each group did not differ significantly between the groups. After completion of the training, the S+E group showed more efficient contraction in most of the evaluated muscles. The resting muscle thickness did not differ significantly between the groups; however, the contracted muscle thickness in the S+E group was higher than that in the S group (p < 0.05). Physical performance and body composition were not significantly different between the two groups. No intervention-related complications were reported during the study. CONCLUSION: EMS seems to be a safe and reasonable modality for improving physical fitness in healthy individuals.

Vowel speech recognition from rat electroencephalography using long short-term memory neural network.

Over the years, considerable research has been conducted to investigate the mechanisms of speech perception and recognition. Electroencephalography (EEG) is a powerful tool for identifying brain activity; therefore, it has been widely used to determine the neural basis of speech recognition. In particular, for the classification of speech recognition, deep learning-based approaches are in the spotlight because they can automatically learn and extract representative features through end-to-end learning. This study aimed to identify particular components that are potentially related to phoneme representation in the rat brain and to discriminate brain activity for each vowel stimulus on a single-trial basis using a bidirectional long short-term memory (BiLSTM) network and classical machine learning methods. Nineteen male Sprague-Dawley rats subjected to microelectrode implantation surgery to record EEG signals from the bilateral anterior auditory fields were used. Five different vowel speech stimuli were chosen, /a/, /e/, /i/, /o/, and /u/, which have highly different formant frequencies. EEG recorded under randomly given vowel stimuli was minimally preprocessed and normalized by a z-score transformation to be used as input for the classification of speech recognition. The BiLSTM network showed the best performance among the classifiers by achieving an overall accuracy, f1-score, and Cohen's κ values of 75.18%, 0.75, and 0.68, respectively, using a 10-fold cross-validation approach. These results indicate that LSTM layers can effectively model sequential data, such as EEG; hence, informative features can be derived through BiLSTM trained with end-to-end learning without any additional hand-crafted feature extraction methods.

Quantification of stroke lesion volume using epidural EEG in a cerebral ischaemic rat model.

Precise monitoring of the brain after a stroke is essential for clinical decision making. Due to the non-invasive nature and high temporal resolution of electroencephalography (EEG), it is widely used to evaluate real-time cortical activity. In this study, we investigated the stroke-related EEG biomarkers and developed a predictive model for quantifying the structural brain damage in a focal cerebral ischaemic rat model. We enrolled 31 male Sprague-Dawley rats and randomly assigned them to mild stroke, moderate stroke, severe stroke, and control groups. We induced photothrombotic stroke targeting the right auditory cortex. We then acquired EEG signal responses to sound stimuli (frequency linearly increasing from 8 to 12 kHz with 750 ms duration). Power spectral analysis revealed a significant correlation of the relative powers of alpha, theta, delta, delta/alpha ratio, and (delta + theta)/(alpha + beta) ratio with the stroke lesion volume. The auditory evoked potential analysis revealed a significant association of amplitude and latency with stroke lesion volume. Finally, we developed a multiple regression model combining EEG predictors for quantifying the ischaemic lesion (R2 = 0.938, p value < 0.001). These findings demonstrate the potential application of EEG as a valid modality for monitoring the brain after a stroke.

Transient lower extremity lymphedema following COVID-19 vaccination: A case report.

RATIONALE: Complications from COVID-19 vaccines have yet to be sufficiently analyzed because they are rapidly approved without long-term data. In particular, there are no case reports of lymphedema in a healthy patient following vaccination. Herein, we report a patient who underwent transient lymphedema after vaccination with BNT16b2. PATIENT CONCERNS: A 79-year-old woman with pitting edema in both lower legs after administration of a second dose of Pfizer vaccine was referred to our clinic. In the absence of clinical evidence of swelling during the laboratory evaluation, we suspected deep vein thrombosis. However, ultrasonographic findings revealed no evidence of venous thrombosis or varicose veins. DIAGNOSIS: On the basis of lymphoscintigraphy, the patient was diagnosed with transient lymphedema with decreased lymphatic transport in both lower extremities. INTERVENTION: The patient received intensive physiotherapy, including complex decongestive physiotherapy and pneumatic pump compression, to improve the lymphatic circulation. Furthermore, the patient was trained to apply a multilayer compressive bandage to the lower extremities. OUTCOMES: At 2 months follow-up after rehabilitative treatment, the patient's symptoms improved without recurring lymphedema. LESSONS: In the absence of clinical evidence of swelling during laboratory evaluation or ultrasonographic investigations suggesting deep vein thrombosis, we should consider the possibility of lymphatic disorders.

Myoelectric Signal Classification of Targeted Muscles Using Dictionary Learning.

Surface electromyography (sEMG) signals comprise electrophysiological information related to muscle activity. As this signal is easy to record, it is utilized to control several myoelectric prostheses devices. Several studies have been conducted to process sEMG signals more efficiently. However, research on optimal algorithms and electrode placements for the processing of sEMG signals is still inconclusive. In addition, very few studies have focused on minimizing the number of electrodes. In this study, we investigated the most effective method for myoelectric signal classification with a small number of electrodes. A total of 23 subjects participated in the study, and the sEMG data of 14 different hand movements of the subjects were acquired from targeted muscles and untargeted muscles. Furthermore, the study compared the classification accuracy of the sEMG data using discriminative feature-oriented dictionary learning (DFDL) and other conventional classifiers. DFDL demonstrated the highest classification accuracy among the classifiers, and its higher quality performance became more apparent as the number of channels decreased. The targeted method was superior to the untargeted method, particularly when classifying sEMG signals with DFDL. Therefore, it was concluded that the combination of the targeted method and the DFDL algorithm could classify myoelectric signals more effectively with a minimal number of channels.

Development of 3D-printed myoelectric hand orthosis for patients with spinal cord injury.

BACKGROUND: Spinal cord injury (SCI) is a severe medical condition affecting the hand and locomotor function. New medical technologies, including various wearable devices, as well as rehabilitation treatments are being developed to enhance hand function in patients with SCI. As three-dimensional (3D) printing has the advantage of being able to produce low-cost personalized devices, there is a growing appeal to apply this technology to rehabilitation equipment in conjunction with scientific advances. In this study, we proposed a novel 3D-printed hand orthosis that is controlled by electromyography (EMG) signals. The orthosis was designed to aid the grasping function for patients with cervical SCI. We applied this hand exoskeleton system to individuals with tetraplegia due to SCI and validated its effectiveness. METHODS: The 3D architecture of the device was designed using computer-aided design software and printed with a polylactic acid filament. The dynamic hand orthosis enhanced the tenodesis grip to provide sufficient grasping function. The root mean square of the EMG signal was used as the input for controlling the device. Ten subjects with hand weakness due to chronic cervical SCI were enrolled in this study, and their hand function was assessed before and after wearing the orthosis. The Toronto Rehabilitation Institute Hand Function Test (TRI-HFT) was used as the primary outcome measure. Furthermore, improvements in functional independence in daily living and device usability were evaluated. RESULTS: The newly developed orthosis improved hand function of subjects, as determined using the TRI-HFT (p < 0.05). Furthermore, participants obtained immediate functionality on eating after wearing the orthosis. Moreover, most participants were satisfied with the device as determined by the usability test. There were no side effects associated with the experiment. CONCLUSIONS: The 3D-printed myoelectric hand orthosis was intuitive, easy to use, and showed positive effects in its ability to handle objects encountered in daily life. This study proved that combining simple EMG-based control strategies and 3D printing techniques was feasible and promising in rehabilitation engineering. TRIAL REGISTRATION: Clinical Research Information Service (CRiS), Republic of Korea. KCT0003995. Registered 2 May 2019 - Retrospectively registered.

Gesture Classification from Compressed EMG Based on Compressive Covariance Sensing.

Electromyogram (EMG) based human computer interface (HCI) is an attractive technique to monitor a patient, control an artificial arm, or play a game. Since EMG processing requires high sampling and transmission rates, a compression technique is important to implement an ultra-low power wireless EMG system. Previous study has a limitation due to the complexity of algorithm and the non-sparsity nature of EMG. In this study, we proposed a new EMG compression scheme based on a compressive covariance sensing (CCS). The covariance recovered from compressed EMG was used to classify user's gestures. The proposed method was verified with NinaPro open source data, which contains 49 gestures with 6 times repetition. As a result, the proposed CCS based EMG compression technique showed good covariance recovery performance and high classification rate as well as superior compression rate.

Efficacy of Bedside Respiratory Muscle Training in Patients With Stroke: A Randomized Controlled Trial.

OBJECTIVE: Inspiratory and expiratory muscles are important for effective respiratory function. This study aimed to investigate the efficacy of bedside respiratory muscle training on pulmonary function and stroke-related disabilities in stroke rehabilitation. DESIGN: Patients with stroke (N = 40) in a rehabilitation unit were randomly assigned to either the intervention group (n1 = 20) or the control group (n2 = 20). Both groups participated in a conventional stroke rehabilitation program. During the study period, the intervention group received bedside respiratory muscle training twice a day for 3 wks. The respiratory muscle training consisted of (1) a breath stacking exercise, (2) inspiratory muscle training, and (3) expiratory muscle training. The primary outcomes were measures of pulmonary function: forced vital capacity, forced expiratory volume in 1 sec, and peak flow. Secondary outcomes were stroke-related disabilities assessed using the National Institutes of Health Stroke Scale, Modified Barthel Index, Berg Balance Scale, Fugl-Meyer Assessment, the Korean Mini-Mental State Examination, and pneumonia incidence. RESULTS: Pulmonary function was significantly improved in the intervention group after 3 wks of respiratory muscle training (P < 0.05). This improvement in pulmonary function was independent of the improvement in stroke-related disabilities. CONCLUSION: Three weeks of respiratory muscle training had significant effects on pulmonary function in stroke survivors. TO CLAIM CME CREDITS: Complete the self-assessment activity and evaluation online at http://www.physiatry.org/JournalCME CME OBJECTIVES: Upon completion of this article, the reader should be able to: (1) Appreciate the respiratory function changes that occur in patients following a stroke; (2) Describe appropriate inspiratory and expiratory muscle training techniques to improve pulmonary function in patients following a stroke; (3) Enhance ability to implement inpatient; and (4) Determine appropriate respiratory training programs for patients following stroke. LEVEL: Advanced ACCREDITATION: The Association of Academic Physiatrists is accredited by the Accreditation Council for Continuing Medical Education to provide continuing medical education for physicians.The Association of Academic Physiatrists designates this Journal-based CME activity for a maximum of 1.0 AMA PRA Category 1 Credit(s)™. Physicians should only claim credit commensurate with the extent of their participation in the activity.

Clinical Efficacy of Selective Focal Ablation by Navigable Percutaneous Disc Decompression Device in Patients With Cervical Herniated Nucleus Pulposus.

OBJECTIVE: To evaluate the clinical efficacy and safety following percutaneous disc decompression, using navigable disc decompression device for cervical herniated nucleus pulposus (HNP). METHODS: Twenty subjects diagnosed with cervical HNP and refractory to conservative management were enrolled for the study. The herniated discs were decompressed under fluoroscopic guidance, using radiofrequency ablation device with navigable wand. The sagittal and axial plain magnetic resonance images of the clinically significant herniated disc, decided the space between the herniated base and outline as the target area for ablation. Clinical outcome was determined by Numeric Rating Scale (NRS), Neck Disability Index (NDI), and Bodily Pain scale of Short Form-36 (SF-36 BP), assessed after 48 weeks. After the procedure, we structurally matched the magnetic resonance imaging (MRI) and C-arm images through bony markers. The wand position was defined as being 'correct' if the tip was placed within the target area of both AP and lateral views; if not, the position was stated as 'incorrect'. RESULTS: The average NRS fell from 7 to 1 at 48 weeks post procedure (p<0.05). In addition, statistically significant improvement was noted in the NDI and SF-36BP (p<0.05). The location of the wand tip resulted in 16 correct and 4 incorrect placements. Post-48 weeks, 3 of the incorrect tip cases and 1 correct tip case showed unsuccessful outcomes. CONCLUSION: The study demonstrated the promising results and safety of the procedure. Thus, focal plasma ablation of cervical HNP with navigable wand can be another effective treatment option.

Feasibility of a Smartphone-Based Exercise Program for Office Workers With Neck Pain: An Individualized Approach Using a Self-Classification Algorithm.

OBJECTIVE: To explore the feasibility of a newly developed smartphone-based exercise program with an embedded self-classification algorithm for office workers with neck pain, by examining its effect on the pain intensity, functional disability, quality of life, fear avoidance, and cervical range of motion (ROM). DESIGN: Single-group, repeated-measures design. SETTING: The laboratory and participants' home and work environments. PARTICIPANTS: Offices workers with neck pain (N=23; mean age ± SD, 28.13±2.97y; 13 men). INTERVENTION: Participants were classified as having 1 of 4 types of neck pain through a self-classification algorithm implemented as a smartphone application, and conducted corresponding exercise programs for 10 to 12min/d, 3d/wk, for 8 weeks. MAIN OUTCOME MEASURES: The visual analog scale (VAS), Neck Disability Index (NDI), Medical Outcomes Study 36-Item Short-Form Health Survey (SF-36), Fear-Avoidance Beliefs Questionnaire (FABQ), and cervical ROM were measured at baseline and postintervention. RESULTS: The VAS (P<.001) and NDI score (P<.001) indicated significant improvements in pain intensity and functional disability. Quality of life showed significant improvements in the physical functioning (P=.007), bodily pain (P=.018), general health (P=.022), vitality (P=.046), and physical component scores (P=.002) of the SF-36. The FABQ, cervical ROM, and mental component score of the SF-36 showed no significant improvements. CONCLUSIONS: The smartphone-based exercise program with an embedded self-classification algorithm improves the pain intensity and perceived physical health of office workers with neck pain, although not enough to affect their mental and emotional states.

Amusia After Right Temporoparietal Lobe Infarction: A Case Report.

Which brain regions participate in musical processing remains controversial. During singing and listening a familiar song, it is necessary to retrieve information from the long-term memory. However, the precise mechanism involved in musical processing is unclear. Amusia is impaired perception, understanding, or production of music not attributable to disease of the peripheral auditory pathways or motor system. We report a case of a 36-year-old right-handed man who lost the ability to discriminate or reproduce rhythms after a right temporoparietal lobe infarction. We diagnosed him as an amusic patient using the online version of Montreal Battery of Evaluation of Amusia (MBEA). This case report suggests that amusia could appear after right temporoparietal lobe infarction. Further research is needed to elucidate the dynamic musical processing mechanism and its associated neural structures.

The Factors Associated With the Successful Outcomes of Percutaneous Disc Decompression in Patients With Lumbar Herniated Nucleus Pulposus.

OBJECTIVE: To determine clinical and radiological factors that predict the successful outcome of percutaneous disc decompression (PDD) in patients with lumbar herniated nucleus pulposus (HNP). METHODS: We retrospectively reviewed the clinical and radiological features of patients who underwent lumbar PDD from April 2009 to March 2013. Sixty-nine patients with lumbar HNP were studied. Clinical outcome was assessed by the visual analogue scale (VAS) and the Oswestry Disability Index (ODI). Multivariate logistic regression analysis was performed to assess relationship among clinical and radiological factors and the successful outcome of the PDD. RESULTS: The VAS and the ODI decreased significantly at 1 year follow-up (p<0.01). One year after PDD, the reduction of the VAS (ΔVAS) was significantly greater in the patients with pain for <6 months (p=0.03) and subarticular HNP (p=0.015). The reduction of the ODI (ΔODI) was significantly greater in the patients with high intensity zone (p=0.04). Multivariate logistic regression analysis revealed the following 5 factors that were associated with the successful outcome after PDD: pain duration for <6 months (odds ratio [OR]=14.036; p=0.006), positive straight leg raising test (OR=8.425, p=0.014), the extruded HNP (OR=0.106, p=0.04), the sequestrated HNP (OR=0.037, p=0.026), and the subarticular HNP (OR=10.876, p=0.012). CONCLUSION: PDD provided significant improvement of pain and disability of patients. The results of the analysis indicated that the duration of pain <6 months, positive straight leg raising test, the subarticular HNP, and the protruded HNP were predicting factors associated with the successful response of PDD in patients with lumbar HNP.

Design of Multifunctional Liposomal Nanocarriers for Folate Receptor-Specific Intracellular Drug Delivery.

As a novel carrier for folate receptor (FR)-targeted intracellular delivery, we designed two types of targetable liposomal systems using Pep-1 peptide (Pep1) and folic acid as a cell-penetrating peptide (CPP) and target molecule, respectively. Folate-linked Pep1 (Fol-Pep1) was synthesized by solid phase peptide synthesis (SPPS) and verified using (1)H NMR and far-ultraviolet (UV) circular dichroism (CD). The chimeric ligand (Fol-Pep1)-modified liposome (cF-P-L) was prepared by coupling Fol-Pep1 to maleimide-derivatized liposomes at various ratios. The dual ligand (folate and Pep1)-modified liposome (dF/P-L) was prepared by separately attaching both ligands to the liposomal surface via a short (PEG2000) or long (PEG3400) linker. The physical and conformational characteristics including vesicle size, zeta potential, and the number of conjugated ligands were determined. Intracellular uptake specificities of various fluorescent probe-containing cF-P-L and dF/P-L systems were assessed using FR-positive HeLa and FR-negative HaCaT cells. Cellular uptake behavior was visualized by confocal laser scanning microscopy (CLSM). Internalization was time-dependent. Fol-Pep1 and Pep-1 cytotoxicities were negligible up to 25 µM in FR-positive and FR-negative cells. Empty cF-P-L and dF/P-L were nontoxic at the concentration used. The optimized dF3/P2(450/90) system carrying 450 PEG3400-linked folate and 90 PEG2000-linked Pep1 molecules could be a good candidate for FR-specific intracellular drug delivery.

RIPL peptide (IPLVVPLRRRRRRRRC)-conjugated liposomes for enhanced intracellular drug delivery to hepsin-expressing cancer cells.

BACKGROUND: To facilitate selective drug delivery to hepsin (Hpn)-expressing cancer cells, the RIPL peptide (IPLVVPLRRRRRRRRC; 16mer; 2.1 kDa) was synthesized as a novel cell penetrating/homing peptide (CPHP) and conjugated to a liposomal carrier. METHODS: RIPL peptide-conjugated liposomes (RIPL-Lipo) were prepared by conjugating RIPL peptides to maleimide-derivatized liposomal vesicles via the thiol-maleimide reaction. Vesicle size and zeta potential were examined using a Zetasizer. Intracellular uptake specificity of the RIPL peptide, or RIPL-Lipo, was assessed by measuring mean fluorescence intensity (MFI) after treatment with a fluorescent marker in various cell lines: SK-OV-3, MCF-7, and LNCaP for Hpn(+); DU145, PC3, and HaCaT for Hpn(-). FITC-dextran was used as a model compound. Selective translocational behavior of RIPL-Lipo to LNCaP cells was visualized by fluorescence microscopy and confocal laser scanning microscopy. Cytotoxicities of the RIPL peptide and RIPL-Lipo were evaluated by WST-1 assay. RESULTS: RIPL peptides exhibited significant Hpn-selectivity. RIPL-Lipo systems were of positively charged nanodispersion (165 nm in average; 6-24 mV depending on RIPL conjugation ratio). RIPL-Lipo with the conjugation of 2300 peptide molecules revealed the greatest MFI in all cell lines tested. Cellular uptake of RIPL-Lipo increased by 20- to 70-fold in Hpn(+) cells, and 5- to 7-fold in Hpn(-) cells, compared to the uptake of FITC-dextran. Cytosolic internalization of RIPL-Lipo was time-dependent: bound instantly; internalized within 30 min; distributed throughout the cytoplasm after 1 h. Cytotoxicities of RIPL peptide (up to 50 µM) and RIPL-Lipo (up to 10%) were minor (cell viability >90%) in LNCaP and HaCaT cells. CONCLUSION: By employing a novel CPHP, the RIPL-Lipo system was successfully developed for Hpn-specific drug delivery.

In situ intestinal permeability and in vivo oral bioavailability of celecoxib in supersaturating self-emulsifying drug delivery system.

In order to characterize the in situ intestinal permeability and in vivo oral bioavailability of celecoxib (CXB), a poorly water-soluble cyclooxygenase (COX)-2 inhibitor, various formulations including the self-emulsifying drug delivery system (SEDDS) and supersaturating SEDDS (S-SEDDS) were compared. The S-SEDDS formulation was obtained by adding Soluplus as a precipitation inhibitor to SEDDS, composed of Capryol 90 as oil, Tween 20 as surfactant, and Tetraglycol as cosurfactant (1:4.5:4.5 in volume ratio). An in situ single pass intestinal perfusion study in rats was performed with CXB-dissolved solutions at a concentration of 40 µg/mL. The effective permeability (Peff) of CXB in the control solution (2.5 v/v% Tween 20-containing PBS) was 6.39 × 10(-5) cm/s. The Peff value was significantly increased (P < 0.05) by the lipid-based formulation, yielding 1.5- and 2.9-fold increases for the SEDDS and S-SEDDS solutions, respectively, compared to the control solution. After oral administration of various formulations to rats at the equivalent dose of 100 mg/kg of CXB, the plasma drug level was measured by LC-MS/MS. The relative bioavailabilities of SEDDS and S-SEDDS were 263 and 355 %, respectively, compared to the CXB suspension as a reference. In particular, S-SEDDS revealed the highest Cmax and the smallest Tmax, indicating rapid and enhanced absorption with this formulation. This study illustrates the potential use of the S-SEDDS formulation in the oral delivery of poorly water-soluble compounds.