Category: Nevin Manimala

J Am Acad Orthop Surg. 2023 Apr 20. doi: 10.5435/JAAOS-D-22-01192. Online ahead of print.

ABSTRACT

BACKGROUND: Most outcome studies of total ankle arthroplasty (TAA) do not discriminate by arthritis etiology. The primary purpose of this study was to compare the complications of TAA between posttraumatic fracture osteoarthritis (fracture PTOA) and primary osteoarthritis (POA).

METHODS: Ninety-nine patients who underwent TAA were retrospectively evaluated with a mean follow-up of 3.2 years (range 2 to 7.6 years). 44 patients (44%) had a diagnosis of POA while 55 patients (56%) had a diagnosis of fracture PTOA (40 malleolar fractures [73%], 14 pilon fractures[26%], and 1 talar fracture [1%]). Patient demographics, preoperative coronal plane alignment, postoperative complications, and revision surgery data were collected. Categorical variables were compared with chi square and Fisher exact tests and means with the Student t-test. Survival was assessed with Kaplan-Meier and log-rank analyses.

RESULTS: A higher overall complication rate was associated with fracture PTOA (53%) compared with POA (30%) (P = 0.04). No difference was observed in rates of any specific complication by etiology. Survival, defined as revision surgery with TAA prosthesis retention, was comparable between POA (91%) and fracture PTOA (87%) (P = 0.54). When defined as failure requiring prosthesis explant, POA demonstrated significantly greater survival (100%) as compared with fracture PTOA (89%) (P = 0.03). A higher rate of talar implant subsidence and loosening was noted in TAA with prior pilon (29%) as compared to malleolar fractures (8%) that was not statistically significant (P = 0.07). Fracture PTOA was associated with preoperative valgus deformity (P = 0.04). Compared with varus and normal alignment, preoperative valgus deformity was associated with the need for any revision surgery (P = 0.01) and prosthesis explant (P = 0.02).

CONCLUSIONS: Compared with POA, fracture PTOA was associated with a markedly higher complication rate after TAA and was at higher risk of failure requiring prosthesis explant. Fracture PTOA was markedly associated with preoperative valgus malalignment, an identified risk factor in this series for revision surgery and prosthesis explant. Pilon fractures may represent a group at risk of complications related to talar implant subsidence and loosening compared with malleolar fractures and thus warrants additional investigation.

LEVEL OF EVIDENCE: III.

PMID:37079718 | DOI:10.5435/JAAOS-D-22-01192

PLoS One. 2023 Apr 20;18(4):e0284718. doi: 10.1371/journal.pone.0284718. eCollection 2023.

ABSTRACT

BACKGROUND: Every year, antenatal care (ANC) remains a life-saving health intervention for millions of pregnant women worldwide. Yet, many pregnant women do not receive adequate ANC, particularly in sub-Saharan Africa. The study aimed to determine the factors associated with the receipt of adequate ANC among pregnant women in Rwanda.

METHODS: A cross-sectional study was conducted using the 2019-2020 Rwanda Demographic and Health Survey data. The study included women aged 15-49 years who had a live birth in the previous five years (n = 6,309). Descriptive statistics and multivariable logistic regression analyses were performed.

RESULTS: Overall, 27.6% of participants received adequate ANC. The odds of receiving adequate ANC were higher among those in the middle household wealth index (AOR 1.24; 1.04, 1.48) and rich index (AOR 1.37; 1.16, 1.61) compared to those in the poor wealth index category. Similarly, having health insurance was positively associated with receiving adequate ANC (AOR 1.33; 1.10, 1.60). The odds of receiving adequate ANC were lower among urban dwellers compared to rural (AOR 0.74; 0.61, 0.91); for women who wanted pregnancy later (AOR 0.60; 0.52, 0.69) or never wanted pregnancy (AOR 0.67; 0.55, 0.82) compared to those who wanted pregnancy; for women who perceived distance to a health facility as a big problem (AOR 0.82; 0.70, 0.96) compared to those that did not; and for women whose ANC was provided by nurses and midwives (AOR 0.63; 0.47, 0.8), or auxiliary midwives (AOR 0.19; 0.04, 0.82) compared to those who received ANC from doctors.

CONCLUSION: The prevalence of women who receive adequate ANC remains low in Rwanda. Effective interventions to increase access and utilization of adequate ANC are urgently needed to further improve the country’s maternal and child health outcomes.

PMID:37079648 | DOI:10.1371/journal.pone.0284718

PLoS One. 2023 Apr 20;18(4):e0284390. doi: 10.1371/journal.pone.0284390. eCollection 2023.

ABSTRACT

In 2015 the unprecedented arrival of refugees in Europe posed serious challenges for the EU and its member countries on how to deal with such an influx. A key element in better managing refugee flows is to understand what drives these flows in a certain direction. A refugee who comes to Europe has to make trade-offs in terms of cost and benefits, duration, uncertainty and the multi-staged character of the journey. Real options models are a suitable tool for modelling these kind of decision dynamics. On the basis of a case-study, that compares three routes from Syria to Europe, we demonstrate how well the real options analysis is in line with the development of the refugee flows.

PMID:37079636 | DOI:10.1371/journal.pone.0284390

PLoS One. 2023 Apr 20;18(4):e0284762. doi: 10.1371/journal.pone.0284762. eCollection 2023.

ABSTRACT

BACKGROUND: Human trafficking is considered a hidden global crime with unsubstantiated numbers. Despite the challenges in counting or measuring this crime, reports revealed the presence of around 40.3 million victims worldwide. Human trafficking results in severe detrimental impacts on both mental and physical health. Given the sensitivity and negative consequences of human trafficking on the global system and victims, and considering the scarce research in this area, our current study aimed at describing the (i) Sociodemographic profiles of anonymized victims, (ii) Means of control, and (iii) Purpose of trafficking, utilizing the largest anonymized and publicly available dataset on victims of human trafficking.

METHODS: This is a retrospective secondary analysis of the Counter-Trafficking Data Collaborative (CTDC) data pool in the period from 2010 to 2020. The utilized dataset is called the k-anonymized global victim of trafficking dataset, and it is considered the largest global dataset on victims of human trafficking. Data from the k-anonymized data pool were extracted and exported to Statistical Package for Social Sciences, SPSS® version 27.0 for Windows (IBM Corp. Version 27.0. Armonk, NY) for quality check and analysis using descriptive statistics.

RESULTS: A total of 87003 victims of human trafficking were identified in the period from 2010 to 2020. The most age category encountered among victims was 9-17 years with 10326 victims (11.9%), followed by 30-38 years with 8562 victims (9.8%). Females comprised 70% of the sample with 60938 victims. The United States (n = 51611), Russia (n = 4570), and the Philippines (n = 1988) comprised the most countries of exploitation/trafficking. Additionally, the year 2019 witnessed the greatest number of victims registered for assistance by anti-trafficking agencies with around 21312 victims (24.5%). Concerning means of control, threats, psychological abuse, restriction of the victim’s movement, taking the victim’s earnings, and physical abuse were the most reported means. 42685 victims (49.1%) reported sexual exploitation as the purpose of their trafficking, followed by forced labor with 18176 victims (20.9%).

CONCLUSION: Various means and methods can be used by traffickers to control the victims to be trafficked for many purposes, with sexual exploitation and forced labor being the most common ones. Global anti-trafficking efforts should be brought together in solidarity through utilizing the paradigm of protection of victims, prosecution of traffickers, prevention of trafficking, and inter-sectoral partnerships. Despite being a global concern with various reports that tried to capture the number of trafficked victims worldwide, human trafficking still has many unseen aspects that impose a significant challenge and adds to the global burden in combatting this threat.

PMID:37079616 | DOI:10.1371/journal.pone.0284762

Acta Biochim Biophys Sin (Shanghai). 2023 Apr 25. doi: 10.3724/abbs.2023041. Online ahead of print.

ABSTRACT

Fatty acid oxidation (FAO) has been proven to be an accomplice in tumor progression. Carnitine palmitoyltransferase 1C (CPT1C), a rate-limiting enzyme in FAO, mainly functions to catalyze fatty acid carnitinylation and guarantee subsequent entry into the mitochondria for FAO in colorectal cancer (CRC). Gene expression data and clinical information extracted from The Cancer Genome Atlas (TCGA) database show significantly higher expression of CPT1C in patients with metastatic CRC ( P=0.005). Moreover, overexpression of CPT1C is correlated with worse relapse-free survival in CRC (HR 2.1, P=0.0006), while no statistical significance is indicated for CPT1A and CPT1B. Further experiments demonstrate that downregulation of CPT1C expression leads to a decrease in the FAO rate, suppression of cell proliferation, cell cycle arrest and repression of cell migration in CRC, whereas opposite results are obtained when CPT1C is overexpressed. Furthermore, an FAO inhibitor almost completely reverses the enhanced cell proliferation and migration induced by CPT1C overexpression. In addition, analysis of TCGA data illustrates a positive association between CPT1C expression and HIF1α level, suggesting that CPT1C is a transcriptional target of HIF1α. In conclusion, CPT1C overexpression indicates poor relapse-free survival of patients with CRC, and CPT1C is transcriptionally activated by HIF1α, thereby promoting the proliferation and migration of CRC cells.

PMID:37078750 | DOI:10.3724/abbs.2023041

IEEE/ACM Trans Comput Biol Bioinform. 2023 Apr 20;PP. doi: 10.1109/TCBB.2023.3268661. Online ahead of print.

ABSTRACT

Protein-protein interaction (PPI) plays an important role in almost all life activities. Many protein interaction sites have been confirmed by biological experiments, but these PPI site identification methods are time-consuming and expensive. In this study, a deep learning-based PPI prediction method, named DeepSG2PPI, is developed. Firstly, the protein sequence information is retrieved and the local context information of each amino acid residue is calculated. A two-dimensional convolutional neural network (2D-CNN) model is employed to extract features from a two-channel coding structure, in which an attention mechanism is embedded to assign higher weights to key features. Secondly, the global statistical information of each amino acid residue and the relationship graph between the protein and GO (Gene Ontology) function annotation are built, and the graph embedding vector is constructed to represent the biological features of the protein. Finally, a 2D-CNN model and two 1D-CNN models are combined for PPI prediction. The comparison analysis with existing algorithms shows that the DeepSG2PPI method has better performance. It provides more accurate and effective PPI site prediction, which will be helpful in reducing the cost and failure rate of biological experiments.

PMID:37079417 | DOI:10.1109/TCBB.2023.3268661

IEEE J Biomed Health Inform. 2023 Apr 20;PP. doi: 10.1109/JBHI.2023.3268729. Online ahead of print.

ABSTRACT

This work represents the first attempt to provide an overview of how to face data integration as the result of a dialogue between neuroscientists and computer scientists. Indeed, data integration is fundamental for studying complex multifactorial diseases, such as the neurodegenerative diseases. This work aims at warning the readers of common pitfalls and critical issues in both medical and data science fields. In this context, we define a road map for data scientists when they first approach the issue of data integration in the biomedical domain, highlighting the challenges that inevitably emerge when dealing with heterogeneous, large-scale and noisy data and proposing possible solutions. Here, we discuss data collection and statistical analysis usually seen as parallel and independent processes, as cross-disciplinary activities. Finally, we provide an exemplary application of data integration to address Alzheimer’s Disease (AD), which is the most common multifactorial form of dementia worldwide. We critically discuss the largest and most widely used datasets in AD, and demonstrate how the emergence of machine learning and deep learning methods has had a significant impact on disease’s knowledge particularly in the perspective of an early AD diagnosis.

PMID:37079415 | DOI:10.1109/JBHI.2023.3268729

IEEE Trans Neural Netw Learn Syst. 2023 Apr 20;PP. doi: 10.1109/TNNLS.2023.3264740. Online ahead of print.

ABSTRACT

Federated learning (FL) is a distributed machine learning framework that allows resource-constrained clients to train a global model jointly without compromising data privacy. Although FL is widely adopted, high degrees of systems and statistical heterogeneity are still two main challenges, which leads to potential divergence and nonconvergence. Clustered FL handles the problem of statistical heterogeneity straightly by discovering the geometric structure of clients with various data generation distributions and getting multiple global models. The number of clusters contains prior knowledge about the clustering structure and has a significant impact on the performance of clustered FL methods. Existing clustered FL methods are inadequate for adaptively inferring the optimal number of clusters in environments with high systems’ heterogeneity. To address this issue, we propose an iterative clustered FL (ICFL) framework in which the server dynamically discovers the clustering structure by successively performing incremental clustering and clustering in one iteration. We focus on the average connectivity within each cluster and give incremental clustering and clustering methods that are compatible with ICFL based on mathematical analysis. We evaluate ICFL in experiments on high degrees of systems and statistical heterogeneity, multiple datasets, and convex and nonconvex objectives. Experimental results verify our theoretical analysis and show that ICFL outperforms several clustered FL baseline methods.

PMID:37079405 | DOI:10.1109/TNNLS.2023.3264740

JMIR Form Res. 2023 Apr 20;7:e45305. doi: 10.2196/45305.

ABSTRACT

BACKGROUND: Early treatment dropout among youths and young adults (28%-75%) puts them at risk for poorer outcomes. Family engagement in treatment is linked to lower dropout and better attendance in outpatient, in-person treatment. However, this has not been studied in intensive or telehealth settings.

OBJECTIVE: We aimed to examine whether family members’ participation in telehealth intensive outpatient (IOP) therapy for mental health disorders in youths and young adults is associated with patient’s treatment engagement. A secondary aim was to assess demographic factors associated with family engagement in treatment.

METHODS: Data were collected from intake surveys, discharge outcome surveys, and administrative data for patients who attended a remote IOP for youths and young adults, nationwide. Data included 1487 patients who completed both intake and discharge surveys and either completed or disengaged from treatment between December 2020 and September 2022. Descriptive statistics were used to characterize the sample’s baseline differences in demographics, engagement, and participation in family therapy. Mann-Whitney U and chi-square tests were used to explore differences in engagement and treatment completion between patients with and those without family therapy. Binomial regression was used to explore significant demographic predictors of family therapy participation and treatment completion.

RESULTS: Patients with family therapy had significantly better engagement and treatment completion outcomes than clients with no family therapy. Youths and young adults with ≥1 family therapy session were significantly more likely to stay in treatment an average of 2 weeks longer (median 11 weeks vs 9 weeks) and to attend a higher percentage of IOP sessions (median 84.38% vs 75.00%). Patients with family therapy were more likely to complete treatment than clients with no family therapy (608/731, 83.2% vs 445/752, 59.2%; P<.001). Different demographic variables were associated with an increased likelihood of participating in family therapy, including younger age (odds ratio 1.3) and identifying as heterosexual (odds ratio 1.4). After controlling for demographic factors, family therapy remained a significant predictor of treatment completion, such that each family therapy session attended was associated with a 1.4-fold increase in the odds of completing treatment (95% CI 1.3-1.4).

CONCLUSIONS: Youths and young adults whose families participate in any family therapy have lower dropout, greater length of stay, and higher treatment completion than those whose families do not participate in services in a remote IOP program. The findings of this quality improvement analysis are the first to establish a relationship between participation in family therapy and an increased engagement and retention in remote treatment for youths and young patients in IOP programing. Given the established importance of obtaining an adequate dosage of treatment, bolstering family therapy offerings is another tool that could contribute to the provision of care that better meets the needs of youths, young adults, and their families.

PMID:37079372 | DOI:10.2196/45305

J Med Internet Res. 2023 Apr 20;25:e43664. doi: 10.2196/43664.

ABSTRACT

BACKGROUND: Although evidence supporting the feasibility of large-scale mobile health (mHealth) systems continues to grow, privacy protection remains an important implementation challenge. The potential scale of publicly available mHealth applications and the sensitive nature of the data involved will inevitably attract unwanted attention from adversarial actors seeking to compromise user privacy. Although privacy-preserving technologies such as federated learning (FL) and differential privacy (DP) offer strong theoretical guarantees, it is not clear how such technologies actually perform under real-world conditions.

OBJECTIVE: Using data from the University of Michigan Intern Health Study (IHS), we assessed the privacy protection capabilities of FL and DP against the trade-offs in the associated model’s accuracy and training time. Using a simulated external attack on a target mHealth system, we aimed to measure the effectiveness of such an attack under various levels of privacy protection on the target system and measure the costs to the target system’s performance associated with the chosen levels of privacy protection.

METHODS: A neural network classifier that attempts to predict IHS participant daily mood ecological momentary assessment score from sensor data served as our target system. An external attacker attempted to identify participants whose average mood ecological momentary assessment score is lower than the global average. The attack followed techniques in the literature, given the relevant assumptions about the abilities of the attacker. For measuring attack effectiveness, we collected attack success metrics (area under the curve [AUC], positive predictive value, and sensitivity), and for measuring privacy costs, we calculated the target model training time and measured the model utility metrics. Both sets of metrics are reported under varying degrees of privacy protection on the target.

RESULTS: We found that FL alone does not provide adequate protection against the privacy attack proposed above, where the attacker’s AUC in determining which participants exhibit lower than average mood is over 0.90 in the worst-case scenario. However, under the highest level of DP tested in this study, the attacker’s AUC fell to approximately 0.59 with only a 10% point decrease in the target’s R² and a 43% increase in model training time. Attack positive predictive value and sensitivity followed similar trends. Finally, we showed that participants in the IHS most likely to require strong privacy protection are also most at risk from this particular privacy attack and subsequently stand to benefit the most from these privacy-preserving technologies.

CONCLUSIONS: Our results demonstrated both the necessity of proactive privacy protection research and the feasibility of the current FL and DP methods implemented in a real mHealth scenario. Our simulation methods characterized the privacy-utility trade-off in our mHealth setup using highly interpretable metrics, providing a framework for future research into privacy-preserving technologies in data-driven health and medical applications.

PMID:37079370 | DOI:10.2196/43664