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Journal of Hand Surgery Global Online Mar 2024Function and cosmesis may be improved by replantation following digital amputation in pediatric patients. However, accurate failure and complication rate estimates may...
PURPOSE
Function and cosmesis may be improved by replantation following digital amputation in pediatric patients. However, accurate failure and complication rate estimates may be limited as most pertinent studies reflect single center/surgeon experience and therefore are limited by small sample sizes. The primary aim of this study was to assess the rate of failure (amputation) following pediatric digital replantation. Secondary aims include evaluating the rate of complications and associated resource utilization (intensive care unit stays, readmission rate, and hospital length of stay).
METHODS
Digital replantation patients were identified from 47 pediatric hospitals using the 2004 to 2020 Pediatric Health Information System nationwide database. Using applicable International Classification of Disease 9/10 and Current Procedural Terminology codes, we identified complications after replantation, including revision amputation, infection, surgical complications, medical complications, admission to intensive care unit (ICU), and length of stay.
RESULTS
Of the 348 patients who underwent replantation the mean age was 8.3 ± 5.1 years, and 27% were female. Mean hospital length of stay was 5.8 ± 4.7 (range, 1-28) days. Of the 53% of patients who required ICU admission, the mean ICU length of stay was 2.4 ± 3.3 days. Failure/amputation after replantation occurred in 71 (20.4%) patients, at a mean of 9.7 ± 27.2 days postoperatively. Surgical complications occurred in 58 (17%) patients, 30-day hospital readmissions occurred in 5.7% of patients, and 90-day readmissions occurred in 6.3% patients.
CONCLUSION
The estimated rate of failure following pediatric digit replantation was 20%. Our data on failure and complication rates and associated resource utilization may be useful in counseling pediatric replantation patients and their families and provide an update on prior literature.
LEVEL OF EVIDENCE
IV, Prognosis.
PubMed: 38903833
DOI: 10.1016/j.jhsg.2023.12.004 -
Frontiers in Psychology 2024We investigate the development of visuospatial and oculomotor reading skills in a cohort of elementary school children. Employing a longitudinal methodology, the study...
We investigate the development of visuospatial and oculomotor reading skills in a cohort of elementary school children. Employing a longitudinal methodology, the study applies the Topological serial digit Rapid Automated Naming (Top-RAN) battery, which evaluates visuospatial reading skills leveraging metrics addressing crowding, distractors, and voluntary attention orientation. The participant pool comprises 142 students (66 males, 76 females), including 46 non-native speakers (21 males, 25 females), representing a diverse range of ethnic backgrounds. The Top-RAN dataset encompasses performance, error, and self-correction metrics for each subtest and student, underscoring the significance of these factors in the process of reading acquisition. Analytical methods include dimensionality reduction, clustering, and classification algorithms, consolidated into a Python package to facilitate reproducible results. Our results indicate that visuospatial reading abilities vary according to the task and demonstrate a marked evolution over time, as seen in the progressive decrease in execution times, errors, and self-corrections. This pattern supports the hypothesis that the growth of oculomotor, attentional, and executive skills is primarily fostered by educational experiences and maturation. This investigation provides valuable insights into the dynamic nature of these skills during pivotal educational stages.
PubMed: 38903458
DOI: 10.3389/fpsyg.2024.1383969 -
Frontiers in Sports and Active Living 2024The present study aimed to evaluate the effect of acute aerobic exercise on certain cognitive functions known to be affected by Alzheimer's disease (AD), with a...
INTRODUCTION
The present study aimed to evaluate the effect of acute aerobic exercise on certain cognitive functions known to be affected by Alzheimer's disease (AD), with a particular emphasis on sex differences.
METHODS
A total of 53 patients, with a mean age of 70.54 ± 0.88 years and moderate AD, voluntarily participated in the study. Participants were randomly assigned to two groups: the experimental group (EG), which participated in a 20-min moderate-intensity cycling session (60% of the individual maximum target heart rate recorded at the end of the 6-min walk test); and the control group (CG), which participated in a 20-min reading activity. Cognitive abilities were assessed before and after the physical exercise or reading session using the Stroop test for selective attention, the forward and backward digit span test for working memory, and the Tower of Hanoi task for problem-solving abilities.
RESULTS
At baseline, both groups had comparable cognitive performance ( > 0.05 in all tests). Regardless of sex, aerobic acute exercise improved attention in the Stroop test ( < 0.001), enhanced memory performance in both forward ( < 0.001) and backward ( < 0.001) conditions, and reduced the time required to solve the problem in the Tower of Hanoi task ( < 0.001). No significant differences were observed in the number of movements. In contrast, the CG did not significantly improve after the reading session for any of the cognitive tasks ( > 0.05). Consequently, the EG recorded greater performance improvements than the CG in most cognitive tasks tested ( < 0.0001) after the intervention session.
DISCUSSION
These findings demonstrate that, irrespective to sex, a single aerobic exercise session on an ergocycle can improve cognitive function in patients with moderate AD. The results suggest that acute aerobic exercise enhances cognitive function similarly in both female and male patients, indicating promising directions for inclusive therapeutic strategies.
PubMed: 38903391
DOI: 10.3389/fspor.2024.1383119 -
Cureus May 2024Introduction With technology advancing across all fields, the utility of digital screens is increasing among all age groups for various purposes. Research indicates...
Introduction With technology advancing across all fields, the utility of digital screens is increasing among all age groups for various purposes. Research indicates that while digital technology presents clear advantages, prolonged exposure can have detrimental effects on various aspects of health, behavior, emotions, and cognitive functions like attention and working memory. A crucial cognitive process for learning and information processing which is working memory, can be affected by factors including screen time. Studies have found that the impact of screen time on working memory can be negative, positive, or show no discernible relationship. However, earlier investigations are limited to smartphone use as screen time exposure and further to only active screen time. As there is a dearth of studies in the Indian context and young adults are more exposed to screen time, it is important to investigate along these lines. Hence, the present study aimed to investigate the impact of active and passive screen time exposure on modality-specific working memory in young adults. Methods Seventy-seven neurotypical individuals aged between 18 and 22 years were recruited. The study utilized auditory and visual reverse digit span tasks and the Corsi-backward task to measure working memory span. Screen time data of the participants were collected through a self-administered 18-item questionnaire covering active and background screen time domains. Results and discussion The present study concluded that only active screen time has a significant effect on visual reverse digit span and supports the notion of the visual superiority effect against an auditory superior effect as suggested by earlier findings. The preliminary findings of correlation observed exclusively within the visual domain in this study could be attributed to the potential impact of screen time exposure (active screen time and textual content). Screen usage demands effective switching between various visual stimuli and ongoing updates of information in memory. Nonetheless, interpreting this explanation and generalization requires caution, given the low ecological validity of the task employed in the study. Future investigations should aim to collect screen time exposure data more objectively, perhaps through online tracking techniques. Furthermore, it would be prudent to expand the correlation analysis to include other cognitive processes and populations.
PubMed: 38903378
DOI: 10.7759/cureus.60626 -
NPJ Digital Medicine Jun 2024The current prostate cancer (PCa) screen test, prostate-specific antigen (PSA), has a high sensitivity for PCa but low specificity for high-risk, clinically significant...
The current prostate cancer (PCa) screen test, prostate-specific antigen (PSA), has a high sensitivity for PCa but low specificity for high-risk, clinically significant PCa (csPCa), resulting in overdiagnosis and overtreatment of non-csPCa. Early identification of csPCa while avoiding unnecessary biopsies in men with non-csPCa is challenging. We built an optimized machine learning platform (ClarityDX) and showed its utility in generating models predicting csPCa. Integrating the ClarityDX platform with blood-based biomarkers for clinically significant PCa and clinical biomarker data from a 3448-patient cohort, we developed a test to stratify patients' risk of csPCa; called ClarityDX Prostate. When predicting high risk cancer in the validation cohort, ClarityDX Prostate showed 95% sensitivity, 35% specificity, 54% positive predictive value, and 91% negative predictive value, at a ≥ 25% threshold. Using ClarityDX Prostate at this threshold could avoid up to 35% of unnecessary prostate biopsies. ClarityDX Prostate showed higher accuracy for predicting the risk of csPCa than PSA alone and the tested model-based risk calculators. Using this test as a reflex test in men with elevated PSA levels may help patients and their healthcare providers decide if a prostate biopsy is necessary.
PubMed: 38902526
DOI: 10.1038/s41746-024-01167-9 -
NPJ Digital Medicine Jun 2024Middle-ear conditions are common causes of primary care visits, hearing impairment, and inappropriate antibiotic use. Deep learning (DL) may assist clinicians in...
Middle-ear conditions are common causes of primary care visits, hearing impairment, and inappropriate antibiotic use. Deep learning (DL) may assist clinicians in interpreting otoscopic images. This study included patients over 5 years old from an ambulatory ENT practice in Strasbourg, France, between 2013 and 2020. Digital otoscopic images were obtained using a smartphone-attached otoscope (Smart Scope, Karl Storz, Germany) and labeled by a senior ENT specialist across 11 diagnostic classes (reference standard). An Inception-v2 DL model was trained using 41,664 otoscopic images, and its diagnostic accuracy was evaluated by calculating class-specific estimates of sensitivity and specificity. The model was then incorporated into a smartphone app called i-Nside. The DL model was evaluated on a validation set of 3,962 images and a held-out test set comprising 326 images. On the validation set, all class-specific estimates of sensitivity and specificity exceeded 98%. On the test set, the DL model achieved a sensitivity of 99.0% (95% confidence interval: 94.5-100) and a specificity of 95.2% (91.5-97.6) for the binary classification of normal vs. abnormal images; wax plugs were detected with a sensitivity of 100% (94.6-100) and specificity of 97.7% (95.0-99.1); other class-specific estimates of sensitivity and specificity ranged from 33.3% to 92.3% and 96.0% to 100%, respectively. We present an end-to-end DL-enabled system able to achieve expert-level diagnostic accuracy for identifying normal tympanic aspects and wax plugs within digital otoscopic images. However, the system's performance varied for other middle-ear conditions. Further prospective validation is necessary before wider clinical deployment.
PubMed: 38902477
DOI: 10.1038/s41746-024-01159-9 -
NPJ Digital Medicine Jun 2024Sleep monitoring has become widespread with the rise of affordable wearable devices. However, converting sleep data into actionable change remains challenging as diverse...
Sleep monitoring has become widespread with the rise of affordable wearable devices. However, converting sleep data into actionable change remains challenging as diverse factors can cause combinations of sleep parameters to differ both between people and within people over time. Researchers have attempted to combine sleep parameters to improve detecting similarities between nights of sleep. The cluster of similar combinations of sleep parameters from a night of sleep defines that night's sleep phenotype. To date, quantitative models of sleep phenotype made from data collected from large populations have used cross-sectional data, which preclude longitudinal analyses that could better quantify differences within individuals over time. In analyses reported here, we used five million nights of wearable sleep data to test (a) whether an individual's sleep phenotype changes over time and (b) whether these changes elucidate new information about acute periods of illness (e.g., flu, fever, COVID-19). We found evidence for 13 sleep phenotypes associated with sleep quality and that individuals transition between these phenotypes over time. Patterns of transitions significantly differ (i) between individuals (with vs. without a chronic health condition; chi-square test; p-value < 1e-100) and (ii) within individuals over time (before vs. during an acute condition; Chi-Square test; p-value < 1e-100). Finally, we found that the patterns of transitions carried more information about chronic and acute health conditions than did phenotype membership alone (longitudinal analyses yielded 2-10× as much information as cross-sectional analyses). These results support the use of temporal dynamics in the future development of longitudinal sleep analyses.
PubMed: 38902390
DOI: 10.1038/s41746-024-01125-5 -
NPJ Digital Medicine Jun 2024The discovery of patterns associated with diagnosis, prognosis, and therapy response in digital pathology images often requires intractable labeling of large quantities...
The discovery of patterns associated with diagnosis, prognosis, and therapy response in digital pathology images often requires intractable labeling of large quantities of histological objects. Here we release an open-source labeling tool, PatchSorter, which integrates deep learning with an intuitive web interface. Using >100,000 objects, we demonstrate a >7x improvement in labels per second over unaided labeling, with minimal impact on labeling accuracy, thus enabling high-throughput labeling of large datasets.
PubMed: 38902336
DOI: 10.1038/s41746-024-01150-4 -
PloS One 2024Based on the CRISP theory (Content Representation, Intrinsic Sequences, and Pattern completion), we present a computational model of the hippocampus that allows for...
Based on the CRISP theory (Content Representation, Intrinsic Sequences, and Pattern completion), we present a computational model of the hippocampus that allows for online one-shot storage of pattern sequences without the need for a consolidation process. In our model, CA3 provides a pre-trained sequence that is hetero-associated with the input sequence, rather than storing a sequence in CA3. That is, plasticity on a short timescale only occurs in the incoming and outgoing connections of CA3, not in its recurrent connections. We use a single learning rule named Hebbian descent to train all plastic synapses in the network. A forgetting mechanism in the learning rule allows the network to continuously store new patterns while forgetting those stored earlier. We find that a single cue pattern can reliably trigger the retrieval of sequences, even when cues are noisy or missing information. Furthermore, pattern separation in subregion DG is necessary when sequences contain correlated patterns. Besides artificially generated input sequences, the model works with sequences of handwritten digits and natural images. Notably, our model is capable of improving itself without external input, in a process that can be referred to as 'replay' or 'offline-learning', which helps in improving the associations and consolidating the learned patterns.
Topics: Neural Networks, Computer; Models, Neurological; Humans; Neuronal Plasticity; Learning; Hippocampus; Synapses
PubMed: 38900733
DOI: 10.1371/journal.pone.0304076 -
JMIR Formative Research Jun 2024Mobile health (mHealth) apps have proven useful for people with multiple sclerosis (MS). Thus, easy-to-use digital solutions are now strongly required to assess and...
BACKGROUND
Mobile health (mHealth) apps have proven useful for people with multiple sclerosis (MS). Thus, easy-to-use digital solutions are now strongly required to assess and monitor cognitive impairment, one of the most disturbing symptoms in MS that is experienced by almost 43% to 70% of people with MS. Therefore, we developed DIGICOG-MS (Digital assessment of Cognitive Impairment in Multiple Sclerosis), a smartphone- and tablet-based mHealth app to self-assess cognitive impairment in MS.
OBJECTIVE
This study aimed to test the validity and usability of the novel mHealth app with a sample of people with MS.
METHODS
DIGICOG-MS includes 4 digital tests assumed to evaluate the most affected cognitive domains in MS (visuospatial memory [VSM], verbal memory [VM], semantic fluency [SF], and information processing speed [IPS]) and inspired by traditional paper-based tests that assess the same cognitive functions (10/36 Spatial Recall Test, Rey Auditory Verbal Learning Test, Word List Generation, Symbol Digit Modalities Test). Participants were asked to complete both digital and traditional assessments in 2 separate sessions. Convergent validity was analyzed using the Pearson correlation coefficient to determine the strength of the associations between digital and traditional tests. To test the app's reliability, the agreement between 2 repeated measurements was assessed using intraclass correlation coefficients (ICCs). Usability of DIGICOG-MS was evaluated using the System Usability Scale (SUS) and mHealth App Usability Questionnaire (MAUQ) administered at the conclusion of the digital session.
RESULTS
The final sample consisted of 92 people with MS (60 women) followed as outpatients at the Italian Multiple Sclerosis Society (AISM) Rehabilitation Service of Genoa (Italy). They had a mean age of 51.38 (SD 11.36) years, education duration of 13.07 (SD 2.74) years, disease duration of 12.91 (SD 9.51) years, and a disability level (Expanded Disability Status Scale) of 3.58 (SD 1.75). Relapsing-remitting MS was most common (68/92, 74%), followed by secondary progressive (15/92, 16%) and primary progressive (9/92, 10%) courses. Pearson correlation analyses indicated significantly strong correlations for VSM, VM, SF, and IPS (all P<.001), with r values ranging from 0.58 to 0.78 for all cognitive domains. Test-retest reliability of the mHealth app was excellent (ICCs>0.90) for VM and IPS and good for VSM and SF (ICCs>0.80). Moreover, the SUS score averaged 84.5 (SD 13.34), and the mean total MAUQ score was 104.02 (SD 17.69), suggesting that DIGICOG-MS was highly usable and well appreciated.
CONCLUSIONS
The DIGICOG-MS tests were strongly correlated with traditional paper-based evaluations. Furthermore, people with MS positively evaluated DIGICOG-MS, finding it highly usable. Since cognitive impairment poses major limitations for people with MS, these findings open new paths to deploy digital cognitive tests for MS and further support the use of a novel mHealth app for cognitive self-assessment by people with MS in clinical practice.
PubMed: 38900535
DOI: 10.2196/56074