Nevin Manimala

Biomaterials. 2021 Oct 22;279:121223. doi: 10.1016/j.biomaterials.2021.121223. Online ahead of print.

ABSTRACT

Harnessing developmental processes for tissue engineering represents a promising yet challenging approach to regenerative medicine. Tooth avulsion is among the most serious traumatic dental injuries, whereas functional tooth regeneration remains uncertain. Here, we established a strategy using decellularized tooth matrix (DTM) combined with human dental pulp stem cell (hDPSC) aggregates to simulate an odontogenesis-related developmental microenvironment. The bioengineered teeth reconstructed by this strategy regenerated three-dimensional pulp and periodontal tissues equipped with vasculature and innervation in a preclinical pig model after implantation into the alveolar bone. These results prompted us to enroll 15 patients with avulsed teeth after traumatic dental injuries in a pilot clinical trial. At 12 months after implantation, bioengineered teeth led to the regeneration of functional teeth, which supported continued root development, in humans. Mechanistically, exosomes derived from hDPSC aggregates mediated the tooth regeneration process by upregulating the odontogenic and angiogenic ability of hDPSCs. Our findings suggest that odontogenic microenvironment engineering by DTM and stem cell aggregates initiates functional tooth regeneration and serves as an effective treatment for tooth avulsion.

PMID:34736149 | DOI:10.1016/j.biomaterials.2021.121223

Leg Med (Tokyo). 2021 Oct 23;54:101983. doi: 10.1016/j.legalmed.2021.101983. Online ahead of print.

ABSTRACT

In the current scenario, DNA typing is the need of forensic science field due to its ability to provide results in much shorter time. In view of advancement of forensic DNA typing and incensement in the number of STRs markers, Promega offered a new VersaPlex™ 27PY system with 27 loci (23 autosomal STR loci, Amelogenin, DYS391 and two rapidly mutating Y-STR loci (DYS570 and DYS576)). In this study, the efficacy of “23 autosomal STR loci” for paternity testing and personal identification was demonstrated in Indian population. For this, 217 central Indians were tested and all the statistical parameters of forensic and population genetic interest were calculated. In addition, sensitivity of the kit was also tested for forensic casework. During investigation with VersaPlex™ 27PY system, allele 11 at locus TPOX was observed to be most frequent with the highest allelic frequency 0.432. Studied 23 loci showed valuable together with highest value of combined power of discrimination (CPD = 1), combined power of exclusion (CPI = 0.9999999989) and lowest value of combined matching probability (CPM = 7.92×10^-28).

PMID:34736143 | DOI:10.1016/j.legalmed.2021.101983

J Surg Res. 2021 Nov 1;270:369-375. doi: 10.1016/j.jss.2021.09.017. Online ahead of print.

ABSTRACT

BACKGROUND: Damage control resuscitation has become the standard of care in military and civilian trauma. Early identification of blood product requirements may aid in optimizing the clinical decision-making process while improving trauma related outcomes. This study aimed to assess and compare multiple machine learning models for predicting patients at highest risk for massive transfusion on the battlefield.

METHODS: Supervised machine learning approaches using logistic regression, support vector machine, neural network, and random forest techniques were used to create predictive models for massive transfusion using standard prehospital and arrival data points from the Department of Defense Trauma Registry, 2008-2016. Seventy percent of the population was used for model development and performance was validated using the remaining 30%. Models were tested for accuracy and compared by standard performance statistics.

RESULTS: A total of 22,158 patients (97% male, 58% penetrating injury, median age 25-29 y/o, average Injury Severity Score 9, with an overall mortality of 3%) were included in the analysis. Massive transfusion was required by 7.4% of patients. Overall accuracy was found to be above 90% in all models tested. Following cross validation and model training, the random forest model outperformed the alternatively tested models for precision, recall, and area under the curve.

CONCLUSION: Machine learning techniques may allow for more optimal and rapid identification of combat trauma patients at highest risk for massive transfusion. These powerful approaches may uncover novel correlations and help improve triage, activation of massive transfusion resources, and trauma-related outcomes. Further research seeking to optimize and apply these algorithms to trauma-centered research should be pursued.

PMID:34736129 | DOI:10.1016/j.jss.2021.09.017

Comput Biol Chem. 2021 Oct 16;95:107593. doi: 10.1016/j.compbiolchem.2021.107593. Online ahead of print.

ABSTRACT

With the growth of high throughput sequencing techniques, the generation of protein sequences has become fast and cheap, leading to a huge increase in the number of known proteins. However, it is challenging to identify the functions being performed by these newly discovered proteins. Machine learning techniques have improved traditional methods’ efficiency by suggesting relevant functions but fails to perform well when the number of functions to be predicted becomes large. In this work, we propose a machine learning-based approach to predict huge set of protein functions that use the inter-relationships between functions to improve the model’s predictability. These inter-relationships of functions is used to reduce the redundancy caused by highly correlated functions. The proposed model is trained on the reduced set of non-redundant functions hindering the ambiguity caused due to inter-related functions. Here, we use two statistical approaches 1) Pearson’s correlation coefficient 2) Jaccard similarity coefficient, as a measure of correlation to remove redundant functions. To have a fair evaluation of the proposed model, we recreate our original function set by inverse transforming the reduced set using the two proposed approaches: Direct mapping and Ensemble approach. The model is tested using different feature sets and function sets of biological processes and molecular functions to get promising results on DeepGO and CAFA3 dataset. The proposed model is able to predict specific functions for the test data which were unpredictable by other compared methods. The experimental models, code and other relevant data are available at https://github.com/richadhanuka/PFP-using-Functional-interrelationship.

PMID:34736126 | DOI:10.1016/j.compbiolchem.2021.107593

J Neurol Sci. 2021 Oct 28;431:120041. doi: 10.1016/j.jns.2021.120041. Online ahead of print.

ABSTRACT

BACKGROUND: To estimate King’s college clinical stage progression rate (ΔKC) at first clinical evaluation in order to define its predictive and prognostic role on survival in a large cohort of Amyotrophic Lateral Sclerosis (ALS) patients.

METHODS: The ΔKC was calculated with the following formula: 0 – KC clinical stage at first clinical evaluation/disease duration from onset to first evaluation, and each result was reported as absolute value. All the evaluations were performed in two cohorts: one from our tertiary centre for motor neuron disease and the other one from a pooled resource open-access ALS clinical trials (PRO-ACT) database. C-statistic was used to evaluate the model discrimination of survival at different time points (1-3 years). Cox proportional hazard model was used to identify factors associated with survival.

RESULTS: ΔKC predicted survival at three years in our centre and in the PRO-ACT cohort (C-statistic 0.83, 95% CI 0.8-0.86, p < 0.0001; 0.7, 95% CI 0.68-0.73, p < 0.0001, respectively). At multivariate analysis, ΔKC was independently associated with survival both in our cohort (HR 3.62 95% CI 2.71-4.83 p = 0.001) and in the PRO-ACT cohort (HR 2.75 95% CI 2.1-3.6 p = 0.001).

CONCLUSIONS: Based on our results, ΔKC could be used as a novel measure of disease progression, hence as an accurate predictor of survival in ALS patients. Indeed, greater values of ΔKC were associated with a 3.5-fold higher risk to experience the event, confirming its robust prognostic value.

PMID:34736124 | DOI:10.1016/j.jns.2021.120041

J Neurol Sci. 2021 Oct 28;431:120045. doi: 10.1016/j.jns.2021.120045. Online ahead of print.

ABSTRACT

The majority of neurological disorders exist in low- and middle-income countries, but these nations have the fewest neurologists and neurological training opportunities worldwide. The objective of this study was to assess the effectiveness, relevance, and feasibility of a five-day neurocritical care course delivered online to African healthcare workers and to understand participants’ prior neurological and neurocritical care training experiences. We offered the Neurocritical Care Society’s Emergency Neurological Life Support (ENLS) course covering 14 neurocritical conditions via Zoom to 403 African healthcare workers over a 4-day period. An additional day was devoted to management of neurological emergencies in resource-limited settings. Participants completed pre- and post-course surveys to assess the effectiveness, relevance, and feasibility of the overall course to their settings. 318 participants (46% female; 56% residents; 24% neurologists; 9.0 ± 6.7 years practicing medicine) from 11 African countries completed the pre-course self-assessment, and 232 completed the post-course self-assessment. 97% reported prior experience caring for patients with neurological emergencies but only 35% reported prior neurology training and 9% prior neurocritical care training. Pre-course and post-course comfort levels showed statistically significant improvements (p < 0.001) across all fourteen neurocritical topics. 95% of participants found the course relevant to their current practice setting, 94% agreed the Zoom online platform was easy to use, and 93% reported it facilitated their learning. Suggestions for course improvement included addition of non-critical neurological conditions and inclusion of locally available diagnostics and treatment modalities. Study results suggest virtual platforms may offer a way to improve neurology training in areas with reduced neurological workforce.

PMID:34736123 | DOI:10.1016/j.jns.2021.120045

Appl Radiat Isot. 2021 Oct 29;179:109949. doi: 10.1016/j.apradiso.2021.109949. Online ahead of print.

ABSTRACT

Considering the difficulties of the low signal-to-noise ratio in weak radioactive source detections, this study proposes an abandon Gaussian tails method based on the analysis of the characteristic information denoted by the full-energy peak of the gamma spectrum of a gamma-emitting radioactive source. Based on the study of the signal-to-background ratio and the statistical fluctuations in the signal of the weak radioactive source, a factor ζ, incorporating the statistical fluctuations of signal and background and the signal-to-background ratio, is suggested to characterize the sensitivity of a radioactive source detection. When ζ reaches its maximum value, the optimal counting window around the centroid of the full-energy peak can be obtained. To evaluate the effectiveness of the proposed approach, comparisons between the proposed abandon Gaussian tails, the conventional full-energy counting, and other experiential methods were performed. The results show that the sensitivity can be significantly improved. Further, experiments with different intensity of radiation sources and duplicated experiments were conducted to examine the stability of the proposed method.

PMID:34736109 | DOI:10.1016/j.apradiso.2021.109949

J Biomech. 2021 Oct 17;129:110818. doi: 10.1016/j.jbiomech.2021.110818. Online ahead of print.

ABSTRACT

In this study, using vertical ground reaction force (VGRF) data and focusing on the stance phase of the gait cycle, the effect of Parkinson’s disease (PD) on gait was investigated. The used dataset consisted of 93 PD and 72 healthy individuals. Multiple comparisons correction ANOVA test and student t-test were used for statistical analyses. Results showed that a longer stance duration with a larger VGRF peak value (p < 0.05) was observed for PD patients during the stance phase. In addition, the VGRF peak value was delayed and blunted in PD cases compared with healthy individuals. These results indicated more time and effort for PD patients for posture stabilization during the stance phase. The time delay for different locations of the foot sole to contact the ground during the stance phase indicated that PD patients might use a different strategy for maintaining their body stability compared with healthy individuals. Although the VGRF time-domain pattern during the stance phase in PD was similar to healthy conditions, its local characteristics like duration and peak value differed significantly. The classification analysis based on the VGRF time-domain extracted features during the stance phase obtained PD recognition with accuracy, sensitivity and specificity of 90.82%, 88.63% and 82.56%, respectively.

PMID:34736084 | DOI:10.1016/j.jbiomech.2021.110818

J Biomech. 2021 Oct 23;129:110810. doi: 10.1016/j.jbiomech.2021.110810. Online ahead of print.

ABSTRACT

The symmetrical center of rotation estimation (SCoRE) is probably one of the most used functional method for estimating the hip join center (HJC). However, it requires of complex multi-plane movements to find accurate estimations of HJC. Thus, using SCoRE for people with limited hip range of motion will lead to poor HJC estimation. In this work, we propose an anisotropic regularized version of the SCoRE formulation (RSCoRE), which is able to estimate the HJC location by using only standard gait trials, avoiding the need of recording complex multi-plane movements. RSCoRE is evaluated in both accuracy and repeatability of the estimation as compared to functional and predictive methods on a self-recorded cohort of fifteen young healthy adults with no hip joint pathologies or other disorders that could affect their gait. Given that, no medical images were available for this study, to quantify the global error of HJC the SCoRE residual was used. RSCoRE presents a global error of about 12 mm, similarly to the best performance of SCoRE. The comparison of the coordinate’s errors at each coordinate indicates that HJC estimations from SCoRE with complex multi-plane movements and RSCoRE are not statistical significantly different. Finally, we show that the repeatability of RSCoRE is similar to the rest of the tested methods, yielding to repeatability values between 0.72 and 0.79. In conclusion, not only the RSCoRE yields similar estimation performance than SCoRE, but it also avoids the need of complex multi-plane movements to be performed by the subject of analysis. For this reason, RSCoRE has the potential to be a valuable approach for estimating the HJC location in people with limited hip ROM.

PMID:34736083 | DOI:10.1016/j.jbiomech.2021.110810

J Healthc Qual. 2021 Nov-Dec 01;43(6):365-373. doi: 10.1097/JHQ.0000000000000291.

ABSTRACT

BACKGROUND: Effective communication among healthcare teams is essential for ensuring handoff-related safety and quality care outcomes.

PURPOSE: The aim of this project was to improve patient safety through the reduction of communication-related errors on an acute hemodialysis unit (AHU) in an academic medical center. A target was set to reduce by 50 percent the communication-related errors using strategies to improve teamwork and communication.

METHODS: Acute hemodialysis unit team members attended Clinical Team Training (CTT) informational sessions on teamwork and communication. A structured handoff tool was implemented in the AHU to improve nurse communication and reduce communication-related patient safety events. Descriptive statistics and comparison of means were conducted to assess the differences between preimplementation and postimplementation audit and safety event data.

RESULTS: There was a statistically significant difference between the preintervention and postintervention groups of handoff tool usage and completion as well as a consistent decrease in handoff-related safety events after implementation.

CONCLUSIONS/IMPLICATIONS: Findings suggest that CTT and a structured handoff tool used to guide nurse-to-nurse care transitions lead to a reduction in communication-related safety events during handoffs in an AHU.

PMID:34734920 | DOI:10.1097/JHQ.0000000000000291