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Journal of Rehabilitation Medicine Jun 2024To explore how people with stroke, discharged to skilled nursing facilities before returning home, experience the chain of care and rehabilitation.
OBJECTIVE
To explore how people with stroke, discharged to skilled nursing facilities before returning home, experience the chain of care and rehabilitation.
DESIGN
Qualitative, semi-structured interview design.
METHODS
Thirteen stroke survivors discharged from a stroke unit to a skilled nursing facility before returning to independent living participated. Semi-structured telephone interviews were conducted 2-5 months after stroke and analysed with content analysis.
RESULTS
The analysis resulted in three categories, Organizational processes, critical and complex, Rehabilitation, the right support at the right time and Adaptation to the changed situation, with a total of 9 subcategories. The informants perceived low participation in planning and goalsetting and limited information. Support from the healthcare services was important to proceed with improvements although the amount of supported training varied. Factors hindering and facilitating managing everyday life were described, as well as lingering uncertainty of what the future would be like.
CONCLUSION
Support and rehabilitation as well as individuals' needs varied, throughout the chain of care. To enable participation in the rehabilitation, assistance in setting goals and repeated information is warranted. Tailored care and rehabilitation throughout the chain of care should be provided, followed up at home, and coordinated for smooth transitions between organizations.
Topics: Humans; Stroke Rehabilitation; Skilled Nursing Facilities; Female; Male; Patient Discharge; Aged; Middle Aged; Qualitative Research; Aged, 80 and over; Stroke; Continuity of Patient Care
PubMed: 38899476
DOI: 10.2340/jrm.v56.35240 -
Frontiers in Psychology 2024Although Cognitive Behavioral Therapy (CBT) is the most often used intervention in forensic treatment, its effectivity is not consistently supported. Interventions...
INTRODUCTION
Although Cognitive Behavioral Therapy (CBT) is the most often used intervention in forensic treatment, its effectivity is not consistently supported. Interventions incorporating knowledge from neuroscience could provide for more successful intervention methods.
METHODS
The current pilot study set out to assess the feasibility and usability of the study protocol of a 4-week neuromeditation training in adult forensic outpatients with impulse control problems. The neuromeditation training, which prompts awareness and control over brain states of restlessness with EEG neurofeedback, was offered in addition to treatment as usual (predominantly CBT).
RESULTS
Eight patients completed the neuromeditation training under guidance of their therapists. Despite some emerging obstacles, overall, the training was rated sufficiently usable and feasible by patients and their therapists.
DISCUSSION
The provided suggestions for improvement can be used to implement the intervention in treatment and set up future trials to study the effectiveness of neuromeditation in offender treatment.
PubMed: 38899124
DOI: 10.3389/fpsyg.2024.1354997 -
PCN Reports : Psychiatry and Clinical... Jun 2024
PubMed: 38899052
DOI: 10.1002/pcn5.219 -
BMC Medical Genomics Jun 2024Immunoregulatory drugs regulate the ubiquitin-proteasome system, which is the main treatment for multiple myeloma (MM) at present. In this study, bioinformatics analysis...
BACKGROUND
Immunoregulatory drugs regulate the ubiquitin-proteasome system, which is the main treatment for multiple myeloma (MM) at present. In this study, bioinformatics analysis was used to construct the risk model and evaluate the prognostic value of ubiquitination-related genes in MM.
METHODS AND RESULTS
The data on ubiquitination-related genes and MM samples were downloaded from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. The consistent cluster analysis and ESTIMATE algorithm were used to create distinct clusters. The MM prognostic risk model was constructed through single-factor and multiple-factor analysis. The ROC curve was plotted to compare the survival difference between high- and low-risk groups. The nomogram was used to validate the predictive capability of the risk model. A total of 87 ubiquitination-related genes were obtained, with 47 genes showing high expression in the MM group. According to the consistent cluster analysis, 4 clusters were determined. The immune infiltration, survival, and prognosis differed significantly among the 4 clusters. The tumor purity was higher in clusters 1 and 3 than in clusters 2 and 4, while the immune score and stromal score were lower in clusters 1 and 3. The proportion of B cells memory, plasma cells, and T cells CD4 naïve was the lowest in cluster 4. The model genes KLHL24, HERC6, USP3, TNIP1, and CISH were highly expressed in the high-risk group. AICAr and BMS.754,807 exhibited higher drug sensitivity in the low-risk group, whereas Bleomycin showed higher drug sensitivity in the high-risk group. The nomogram of the risk model demonstrated good efficacy in predicting the survival of MM patients using TCGA and GEO datasets.
CONCLUSIONS
The risk model constructed by ubiquitination-related genes can be effectively used to predict the prognosis of MM patients. KLHL24, HERC6, USP3, TNIP1, and CISH genes in MM warrant further investigation as therapeutic targets and to combat drug resistance.
Topics: Humans; Multiple Myeloma; Computational Biology; Prognosis; Ubiquitination; Gene Expression Regulation, Neoplastic; Biomarkers, Tumor; Nomograms; Cluster Analysis
PubMed: 38898455
DOI: 10.1186/s12920-024-01937-0 -
Archives of Osteoporosis Jun 2024Interviews and focus groups with patients, FLS clinicians, and GPs identified challenges relating to clinical and shared decision-making about bone health and...
UNLABELLED
Interviews and focus groups with patients, FLS clinicians, and GPs identified challenges relating to clinical and shared decision-making about bone health and osteoporosis medicines. Findings will inform the development of the multicomponent iFraP intervention to address identified training needs and barriers to implementation to facilitate SDM about osteoporosis medicines.
PURPOSE
The iFraP (improving uptake of Fracture Prevention treatments) study aimed to develop a multicomponent intervention, including an osteoporosis decision support tool (DST), to support shared decision-making (SDM) about osteoporosis medicines. To inform iFraP intervention development, this qualitative study explored current practice in relation to communication about bone health and osteoporosis medicines, anticipated barriers to, and facilitators of, an osteoporosis DST, and perceived training needs.
METHODS
Patients attending an FLS consultation (n = 8), FLS clinicians (n = 9), and general practitioners (GPs; n = 7) were purposively sampled to participate in a focus group and/or telephone interview. Data were transcribed, inductively coded, and then mapped to the Theoretical Domains Framework (TDF) as a deductive framework to systematically identify possible barriers to, and facilitators of, implementing a DST.
RESULTS
Inductive codes were deductively mapped to 12 TDF domains. FLS clinicians were perceived to have specialist expertise (knowledge). However, clinicians described aspects of clinical decision-making and risk communication as difficult (cognitive skills). Patients reflected on decisional uncertainty about medicines (decision processes). Discussions about current practice and the proposed DST indicated opportunities to facilitate SDM, if identified training needs are met. Potential individual and system-level barriers to implementation were identified, such as differences in FLS configuration and a move to remote consulting (environmental context and resources).
CONCLUSIONS
Understanding of current practice revealed unmet training needs, indicating that using a DST in isolation would be unlikely to produce a sustained shift to SDM. Findings will shape iFraP intervention development to address unmet needs.
Topics: Humans; Qualitative Research; Osteoporosis; Female; Male; Focus Groups; Bone Density Conservation Agents; Decision Making, Shared; Middle Aged; Aged; Osteoporotic Fractures
PubMed: 38898212
DOI: 10.1007/s11657-024-01410-6 -
Scientific Reports Jun 2024According to the literature, seizure prediction models should be developed following a patient-specific approach. However, seizures are usually very rare events, meaning...
According to the literature, seizure prediction models should be developed following a patient-specific approach. However, seizures are usually very rare events, meaning the number of events that may be used to optimise seizure prediction approaches is limited. To overcome such constraint, we analysed the possibility of using data from patients from an external database to improve patient-specific seizure prediction models. We present seizure prediction models trained using a transfer learning procedure. We trained a deep convolutional autoencoder using electroencephalogram data from 41 patients collected from the EPILEPSIAE database. Then, a bidirectional long short-term memory and a classifier layers were added on the top of the encoder part and were optimised for 24 patients from the Universitätsklinikum Freiburg individually. The encoder was used as a feature extraction module. Therefore, its weights were not changed during the patient-specific training. Experimental results showed that seizure prediction models optimised using pretrained weights present about four times fewer false alarms while maintaining the same ability to predict seizures and achieved more 13% validated patients. Therefore, results evidenced that the optimisation using transfer learning was more stable and faster, saving computational resources. In summary, adopting transfer learning for seizure prediction models represents a significant advancement. It addresses the data limitation seen in the seizure prediction field and offers more efficient and stable training, conserving computational resources. Additionally, despite the compact size, transfer learning allows to easily share data knowledge due to fewer ethical restrictions and lower storage requirements. The convolutional autoencoder developed in this study will be shared with the scientific community, promoting further research.
Topics: Humans; Seizures; Electroencephalography; Databases, Factual; Machine Learning; Female; Male; Neural Networks, Computer; Adult
PubMed: 38898066
DOI: 10.1038/s41598-024-64802-1 -
Scientific Reports Jun 2024One of the focal points in the field of intelligent transportation is the intelligent control of traffic signals (TS), aimed at enhancing the efficiency of urban road...
One of the focal points in the field of intelligent transportation is the intelligent control of traffic signals (TS), aimed at enhancing the efficiency of urban road networks through specific algorithms. Deep Reinforcement Learning (DRL) algorithms have become mainstream, yet they suffer from inefficient training sample selection, leading to slow convergence. Additionally, enhancing model robustness is crucial for adapting to diverse traffic conditions. Hence, this paper proposes an enhanced method for traffic signal control (TSC) based on DRL. This approach utilizes dueling network and double q-learning to alleviate the overestimation issue of DRL. Additionally, it introduces a priority sampling mechanism to enhance the utilization efficiency of samples in memory. Moreover, noise parameters are integrated into the neural network model during training to bolster its robustness. By representing high-dimensional real-time traffic information as matrices, and employing a phase-cycled action space to guide the decision-making of intelligent agents. Additionally, utilizing a reward function that closely mirrors real-world scenarios to guide model training. Experimental results demonstrate faster convergence and optimal performance in metrics such as queue length and waiting time. Testing experiments further validate the method's robustness across different traffic flow scenarios.
PubMed: 38898047
DOI: 10.1038/s41598-024-64885-w -
Scientific Reports Jun 2024Maintaining driving independence is important for older adults. However, cognitive decline, a common issue in older populations, can impair older adults' driving...
Maintaining driving independence is important for older adults. However, cognitive decline, a common issue in older populations, can impair older adults' driving abilities and overall safety on the roads. This study explores how cognitive impairment influences driving patterns and driving choices among older adults. We analyzed real-world driving patterns of 246 older adults using GPS dataloggers. Our sample included 230 cognitively normal older adults (CN; Clinical Dementia Rating [CDR] = 0) and 16 older adults with incident cognitive impairment (ICI; CDR = 0.5). The CN group had an average age of 68.2 years, with 46% females and an average of 16.5 years of education, while the ICI group's average age was 69.2 years, with 36% females and an average of 16.0 years of education. We employed spatial clustering and hashing algorithms to evaluate driving behaviours. Significant differences emerged: The ICI group used fewer distinct routes to their most common destination. These differences can be leveraged to develop driving as a digital biomarker for the early detection and continuous monitoring of cognitive impairment.
Topics: Humans; Automobile Driving; Female; Aged; Male; Cognitive Dysfunction; Aged, 80 and over; Middle Aged; Choice Behavior
PubMed: 38898026
DOI: 10.1038/s41598-024-63663-y -
Frontiers in Neurology 2024This study aims to evaluate the effectiveness of non-pharmacological interventions in improving cognitive function in patients with ischemic stroke through network...
OBJECTIVE
This study aims to evaluate the effectiveness of non-pharmacological interventions in improving cognitive function in patients with ischemic stroke through network meta-analysis.
METHODS
We searched databases including the Cochrane Library, PubMed, EmBase, and Web of Science for randomized controlled trials (RCTs) on non-pharmacological treatments to improve cognitive impairment following ischemic stroke. The publication date was up to 15 March 2023. Due to the insufficiency of included studies, supplementary searches for high-quality Chinese literature were performed in databases such as CNKI, WanFang Data, and VIP Chinese Science Journals Database. Two reviewers independently went through the literature, extracted data, and assessed the risk of bias in the included studies using the risk of bias assessment tool recommended by the Cochrane Handbook for Systematic Reviews of Interventions 5.1.0. By utilizing R 4.2.3 RStudio software and the GeMTC package, a Bayesian network meta-analysis was conducted to assess the improvement in Mini-Mental State Examination (MMSE) and Montreal Cognitive Assessment (MoCA) scores under a variety of non-pharmacological interventions.
RESULTS
A total of 22 RCTs involving 2,111 patients and 14 different non-pharmacological treatments were included. These interventions were transcranial direct current stimulation (tDCS), reminiscence therapy (RT), remote ischemic conditioning (RIC), physical fitness training (PFT), intensive patient care program (IPCP), moderate-intensity continuous training + high-intensity interval training (MICT + HIIT), medium intensity continuous training (MICT), grip training (GT), acupuncture, cognitive behavioral therapy (CBT), cognitive rehabilitation training (CRT), high pressure oxygen (HPO), moxibustion, and repetitive transcranial magnetic stimulation (rTMS). The results of the network meta-analysis indicated that rTMS had the highest likelihood of being the most effective intervention for improving MMSE and MoCA scores.
CONCLUSION
The evidence from this study suggests that rTMS holds promise for improving MMSE and MoCA scores in patients with cognitive impairment following ischemic stroke. However, further high-quality research is needed to confirm and validate this finding.
PubMed: 38895695
DOI: 10.3389/fneur.2024.1327065 -
Health Science Reports Jun 2024Mild cognitive impairment (MCI) is a widespread condition in older individuals, posing significant risk of dementia. However, limited research has been conducted to...
BACKGROUND AND AIMS
Mild cognitive impairment (MCI) is a widespread condition in older individuals, posing significant risk of dementia. However, limited research has been conducted to explore effective interventions and clarify their impact at the neural level. Therefore, this study aimed to investigate the effects of computerized cognitive training (CCT) and explore the associated neural mechanisms in preventing dementia in older individuals with MCI, with a view to inform future intervention efforts.
METHODS
We reviewed the effects of CCT on biomarker outcomes in older adults with MCI. The search was conducted for studies published between 2010 and May 10, 2023, using three search engines: PubMed, Scopus, and Cumulative Index to Nursing and Allied Health Literature. The inclusion criteria were as follows: studies that involved participants diagnosed with MCI, included CCT, included quantitative assessment of biomarker results, and conducted randomized controlled trials.
RESULTS
Sixteen studies that used biomarkers, including magnetic resonance imaging, electroencephalography (EEG), functional near-infrared spectroscopy (fNIRS), and blood or salivary biomarkers, were extracted. The results showed that CCT caused changes in structure and function within the main brain network, including the default mode network, and decreased both theta rhythm activity on EEG and prefrontal activity on fNIRS, with improvement in cognitive function. Furthermore, CCT combined with physical exercise showed more significant structural and functional changes in extensive brain regions compared with CCT alone. Virtual reality-based cognitive training improved not only executive function but also instrumental activities of daily living.
CONCLUSION
CCT causes functional and structural changes in extensive brain regions and improves cognitive function in older adults with MCI. Our findings highlight the potential of individualized intervention methods and biomarker assessment according to the specific causes of MCI. Future research should aim to optimize these personalized therapeutic strategies to maximize the benefits of CCT in older adults with MCI.
PubMed: 38895550
DOI: 10.1002/hsr2.2175