Nevin Manimala

PLoS Negl Trop Dis. 2023 Aug 18;17(8):e0011018. doi: 10.1371/journal.pntd.0011018. Online ahead of print.

ABSTRACT

Zoonotic pathogens spread by wildlife continue to spill into human populations and threaten human lives. A potential way to reduce this threat is by vaccinating wildlife species that harbor pathogens that are infectious to humans. Unfortunately, even in cases where vaccines can be distributed en masse as edible baits, achieving levels of vaccine coverage sufficient for pathogen elimination is rare. Developing vaccines that self-disseminate may help solve this problem by magnifying the impact of limited direct vaccination. Although models exist that quantify how well these self-disseminating vaccines will work when introduced into temporally stable wildlife populations, how well they will perform when introduced into populations with pronounced seasonal population dynamics remains unknown. Here we develop and analyze mathematical models of fluctuating wildlife populations that allow us to study how reservoir ecology, vaccine design, and vaccine delivery interact to influence vaccine coverage and opportunities for pathogen elimination. Our results demonstrate that the timing of vaccine delivery can make or break the success of vaccination programs. As a general rule, the effectiveness of self-disseminating vaccines is optimized by introducing after the peak of seasonal reproduction when the number of susceptible animals is near its maximum.

PMID:37594985 | DOI:10.1371/journal.pntd.0011018

PLoS One. 2023 Aug 18;18(8):e0288917. doi: 10.1371/journal.pone.0288917. eCollection 2023.

ABSTRACT

BACKGROUND: The World Health Organization (WHO) has published estimates revealing that around one out of every three women across the globe has been a victim of either physical and/or sexual violence from an intimate partner or non-partner throughout their lifetime. The available evidence on intimate partner violence in East Africa is limited Consequently, the objective of this study was to evaluate the occurrence and factors linked to intimate partner violence in East Africa.

METHODS: The study utilized the most recent data from the Demographic and Health Surveys (DHS) conducted between 2011 and 2018/19 in 11 countries in Eastern Africa. A total of 59,000 women were included in the study. Descriptive and inferential statistics were used to exmine factors associated with IPV. A mixed effect robust Poisson regression model was fitted to identify factors associated with intimate partner violence. The adjusted prevalence ratio (aPR) and its corresponding 95% confidence interval (CI) were employed to determine the presence of a significant association between intimate partner violence and the independent variables.

RESULTS: In this study, the prevalence of intimate partner violence in East Africa was 43.72% with 95% CI 43.32% to 44.12%. In the mixed effect robust Poisson regression model:-Marital status, working status, parity, sex of household headed, wealth index, community poverty, and residence, were significantly associated with intimate partner violence.

CONCLUSION: The prevalence of intimate partner violence in East Africa is high as compared to the global prevalence 30%, which hinders The Sustainable Development Goals (SDGs), specifically goal 5, aim to attain gender equality and empower women and girls worldwide by the year 2030 Women being previously married and cohabitated, working, having a high number of children, rural residents were positively associated with IPV and household and community wealth index and sex of household headed were negatively related with IPV in East Africa. Therefore, we recommend establishing effective health and legal response using an integrated policy approach and Special attention should be given to women who live rural and poorest to reduce IPV and to achieve Sustainable Development Goals (SDGs) goal 5.

PMID:37594977 | DOI:10.1371/journal.pone.0288917

PLoS One. 2023 Aug 18;18(8):e0290098. doi: 10.1371/journal.pone.0290098. eCollection 2023.

ABSTRACT

The number of health-related incidents caused using illegal and legal psychoactive substances (PAS) has dramatically increased over two decades worldwide. In Colombia, the use of illicit substances has increased up to 10.3%, while the consumption alcohol and tobacco has increased to 84% and 12%, respectively. It is well-known that identifying drug consumption patterns in the general population is essential in reducing overall drug consumption. However, existing approaches do not incorporate Machine Learning and/or Deep Data Mining methods in combination with spatial techniques. To enhance our understanding of mental health issues related to PAS and assist in the development of national policies, here we present a novel Deep Neural Network-based Clustering-oriented Embedding Algorithm that incorporates an autoencoder and spatial techniques. The primary goal of our model is to identify general and spatial patterns of drug consumption and abuse, while also extracting relevant features from the input data and identifying clusters during the learning process. As a test case, we used the largest publicly available database of legal and illegal PAS consumption comprising 49,600 Colombian households. We estimated and geographically represented the prevalence of consumption and/or abuse of both PAS and non-PAS, while achieving statistically significant goodness-of-fit values. Our results indicate that region, sex, housing type, socioeconomic status, age, and variables related to household finances contribute to explaining the patterns of consumption and/or abuse of PAS. Additionally, we identified three distinct patterns of PAS consumption and/or abuse. At the spatial level, these patterns indicate concentrations of drug consumption in specific regions of the country, which are closely related to specific geographic locations and the prevailing social and environmental contexts. These findings can provide valuable insights to facilitate decision-making and develop national policies targeting specific groups given their cultural, geographic, and social conditions.

PMID:37594973 | DOI:10.1371/journal.pone.0290098

JMIR Form Res. 2023 Aug 18;7:e47434. doi: 10.2196/47434.

ABSTRACT

BACKGROUND: The normalization of institution names is of great importance for literature retrieval, statistics of academic achievements, and evaluation of the competitiveness of research institutions. Differences in authors’ writing habits and spelling mistakes lead to various names of institutions, which affects the analysis of publication data. With the development of deep learning models and the increasing maturity of natural language processing methods, training a deep learning-based institution name normalization model can increase the accuracy of institution name normalization at the semantic level.

OBJECTIVE: This study aimed to train a deep learning-based model for institution name normalization based on the feature fusion of affiliation data from multisource literature, which would realize the normalization of institution name variants with the help of authority files and achieve a high specification accuracy after several rounds of training and optimization.

METHODS: In this study, an institution name normalization-oriented model was trained based on bidirectional encoder representations from transformers (BERT) and other deep learning models, including the institution classification model, institutional hierarchical relation extraction model, and institution matching and merging model. The model was then trained to automatically learn institutional features by pretraining and fine-tuning, and institution names were extracted from the affiliation data of 3 databases to complete the normalization process: Dimensions, Web of Science, and Scopus.

RESULTS: It was found that the trained model could achieve at least 3 functions. First, the model could identify the institution name that is consistent with the authority files and associate the name with the files through the unique institution ID. Second, it could identify the nonstandard institution name variants, such as singular forms, plural changes, and abbreviations, and update the authority files. Third, it could identify the unregistered institutions and add them to the authority files, so that when the institution appeared again, the model could identify and regard it as a registered institution. Moreover, the test results showed that the accuracy of the normalization model reached 93.79%, indicating the promising performance of the model for the normalization of institution names.

CONCLUSIONS: The deep learning-based institution name normalization model trained in this study exhibited high accuracy. Therefore, it could be widely applied in the evaluation of the competitiveness of research institutions, analysis of research fields of institutions, and construction of interinstitutional cooperation networks, among others, showing high application value.

PMID:37594844 | DOI:10.2196/47434

Surg Infect (Larchmt). 2023 Aug 18. doi: 10.1089/sur.2023.083. Online ahead of print.

ABSTRACT

Background: Patients with fungal pneumonias sometimes progress to acute respiratory distress syndrome (ARDS). Mortality has been reported as high as 60% to 90% in this group. Venovenous extracorporeal membrane oxygenation (VV-ECMO) can be used to support such patients, however, outcomes are not well understood. Patients and Methods: This was a retrospective study across the four adult ECMO centers in Minnesota for one decade (2012-2022). The outcomes of interest were duration of ECMO, survival rate, and complications. Data were extracted from the electronic medical record and analyzed using descriptive statistics. Results: Fungal pneumonia was the etiology of ARDS in 22 of 422 (5%) adults supported with VV-ECMO during the 10-year study period. Median patient age was 43 years (interquartile range [IQR], 35-56) and 68% were male. By type of fungal infection, 16 (72%) had blastomycosis, five (22%) had pneumocystis, and one (5%) had cryptococcus. Of the 16 patients with blastomycosis two were immunosuppressed whereas all five of the pneumocystis patients were immunosuppressed. The overall survival rate was 73%; most patients with blastomycosis (67%) and pneumocystis (80%) survived to hospital discharge. The duration of ECMO support was greater for the pneumocystis group (median, 30 days; IQR, 21-43) compared with blastomycosis (median, 10 days; IQR, 8-18). Conclusions: Our findings support the use of VV-ECMO for ARDS caused by fungal pneumonias in select immunocompetent and immunocompromised patients. Although survival was high, patients with pneumocystis required longer ECMO runs.

PMID:37594771 | DOI:10.1089/sur.2023.083

JAMA Health Forum. 2023 Aug 4;4(8):e232517. doi: 10.1001/jamahealthforum.2023.2517.

ABSTRACT

IMPORTANCE: Better evidence is needed on whether Medicare Advantage (MA) plans can control the use of postacute care services while achieving excellent outcomes.

OBJECTIVE: To compare self-reported use of postacute care services and outcomes among traditional Medicare (TM) beneficiaries and MA enrollees.

DESIGN, SETTING, AND PARTICIPANTS: This cohort study used data from the National Health and Aging Trends Study (NHATS) with linked Medicare enrollment data from 2015 to 2017. Participants were community-dwelling MA or TM beneficiaries 70 years and older; those with dual Medicare and Medicaid eligibility were also identified. Analyses were conducted from May 2022 to February 2023 and were weighted to account for the complex survey design.

EXPOSURES: Enrollment in MA and dual eligibility for Medicare and Medicaid.

MAIN OUTCOMES AND MEASURES: Postacute care service use including site of use, duration, primary indication, and whether participants met their goals or experienced improved functional status during or after services.

RESULTS: Included in the analysis were 2357 Medicare beneficiaries who used postacute care. Of these beneficiaries, 815 (32.6%; 62.0% were females [weighted percentages]) had MA and 1542 (67.4%; 59.5% were females [weighted percentages]) had TM. Enrollees in MA reported using postacute care services across all NHATS survey rounds: between 16.2% (95% CI, 14.3%-18.4%) and 17.7% (95% CI, 15.4%-20.4%) of MA enrollees reported using postacute care services each round, vs 22.4% (95% CI, 20.9%-24.1%) to 24.1% (95% CI, 21.8%-26.6%) of TM beneficiaries (P for all rounds <.002). Enrollees in MA reported less functional improvement during postacute care use (63.1% [95% CI, 59.2%-66.8%] vs 71.7% [95% CI, 68.9%-74.3%], P < .001). Among beneficiaries who ended postacute service use, fewer MA enrollees than TM enrollees reported that they met their goals (70.5% [95% CI, 65.1%-75.3%] vs 76.2% [95% CI, 73.1%-79.1%]; P = .053) or had improved functional status (43.9% [95% CI, 38.9%-49.1%] vs 46.0% [95% CI, 42.5%-49.5%]; P = .42), but differences were not statistically significant. Differences in postacute care use and functional improvement were not statistically significant between MA and TM enrollees with dual eligibility.

CONCLUSIONS AND RELEVANCE: In this cohort study of Medicare beneficiaries, we found that MA enrollees overall used less postacute care services than their TM counterparts. Among users of postacute care services, MA enrollees reported less favorable outcomes compared with TM enrollees. These findings highlight the importance of assessing patient-reported outcomes, especially as MA and other payment models seek to reduce inefficient use of postacute care services.

PMID:37594745 | DOI:10.1001/jamahealthforum.2023.2517

Environ Sci Pollut Res Int. 2023 Aug 18. doi: 10.1007/s11356-023-29250-w. Online ahead of print.

ABSTRACT

This study uses the difference-in-difference (DID) method to explore the relative effectiveness and mechanism of the “Ten Measures on Air Pollution Prevention and Control” (TMAPPC) policy and the “carbon trading” pilot (CTP) policy on the digital transformation of enterprises. The research results show that the incentive effect of market-incentive environmental regulation on the digital transformation of enterprises is better than that of command-control environmental regulation. In addition, there are differences in the mechanism of action; the level of digital economy development and market competition can strengthen the incentive effect of market-incentive environmental regulation on the digital transformation of enterprises; the government support and media attention can strengthen the incentive effect of command-control environmental regulation on enterprises’ digital transformation. The results of heterogeneity analysis show that, compared with the TMAPPC policy, the CTP policy can better drive the digital transformation of enterprises in the eastern region, enterprises in regions with low to medium digital development levels, and enterprises in regions with low environmental regulation intensity, as well as high-tech enterprises. Moreover, the two environmental regulation policies have more significant driving effects on large enterprises and enterprises with low financing constraints. Based on the research conclusions, this study puts forward relevant policy recommendations for further improving environmental regulation policies and promoting the digital transformation of enterprises.

PMID:37594714 | DOI:10.1007/s11356-023-29250-w

Environ Sci Pollut Res Int. 2023 Aug 18. doi: 10.1007/s11356-023-29049-9. Online ahead of print.

ABSTRACT

Flooding events are determining a significant amount of damages, in terms of economic loss and also casualties in Asia and Pacific areas. Due to complexity and ferocity of severe flooding, predicting flood-prone areas is a difficult task. Thus, creating flood susceptibility maps at local level is though challenging but an inevitable task. In order to implement a flood management plan for the Balrampur district, an agricultural dominant landscape of India, and strengthen its resilience, flood susceptibility modeling and mapping are carried out. In the present study, three hybrid machine learning (ML) models, namely, fuzzy-ANN (artificial neural network), fuzzy-RBF (radial basis function), and fuzzy-SVM (support vector machine) with 12 topographic, hydrological, and other flood influencing factors were used to determine flood-susceptible zones. To ascertain the relationship between the occurrences and flood influencing factors, correlation attribute evaluation (CAE) and multicollinearity diagnostic tests were used. The predictive power of these models was validated and compared using a variety of statistical techniques, including Wilcoxon signed-rank, t-paired tests and receiver operating characteristic (ROC) curves. Results show that fuzzy-RBF model outperformed other hybrid ML models for modeling flood susceptibility, followed by fuzzy-ANN and fuzzy-SVM. Overall, these models have shown promise in identifying flood-prone areas in the basin and other basins around the world. The outcomes of the work would benefit policymakers and government bodies to capture the flood-affected areas for necessary planning, action, and implementation.

PMID:37594709 | DOI:10.1007/s11356-023-29049-9

Int Endod J. 2023 Aug 18. doi: 10.1111/iej.13960. Online ahead of print.

ABSTRACT

AIM: To assess the influence of a flat-side design on the geometry, metallurgy, mechanical performance and shaping ability of a novel nickel-titanium rotary instrument.

METHODOLOGY: Sixty-five new 25-mm flat-side rotary instruments (size 25, taper 0.04) and their nonflat-side prototypes (n = 65) were assessed for major deformations and examined regarding macroscopic and microscopic design, determination of nickel and titanium elements ratio, measurement of phase transformation temperature and evaluation of mechanical performance parameters including time/cycles to fracture, maximum torque, angle of rotation, maximum bending and buckling strengths and cutting ability. Additionally, unprepared canal areas, volume of hard tissue debris and percentage reduction of dentine thickness were calculated for each tested instrument after preparing mesial canals of mandibular molars (n = 12), using micro-CT imaging. Statistical analyses were performed using the U-Mann-Whitney test and independent Student t-test (α = 5%).

RESULTS: The number of spirals (n = 8) and blade direction (clockwise) were similar between both flat and nonflat instruments, whilst the helical angles were equivalent (⁓25°). Flat-instruments showed inconsistencies in the homogeneity of the gold colour on the flat-side surface, blade discontinuity, and incomplete and variable S-shaped cross-sections. The titanium-to-nickel ratios were equivalent, but significant differences in the R-phase finish and austenitic start phase transformation temperatures were observed between the flat and nonflat-side instruments. The flat-side instruments demonstrated superior cutting ability compared to the nonflat instruments, as well as, significantly lower values for time to fracture, rotation to fracture and maximum torque to fracture (p < .001). No statistical difference was observed between tested instruments regarding angle of rotation (p = .437), maximum bending (p = .152) and buckling load (p = .411). Preparation protocols using flat and nonflat instruments did not show any statistically significant differences (p > .05). All flat-side instruments exhibited deformation after shaping procedures.

CONCLUSIONS: The flat-side instrument showcased enhanced cutting ability compared to its nonflat counterpart. However, it exhibited inferior performance in terms of time, rotation and maximum torque to fracture, along with distinct phase transformation temperatures. No differences were observed in the titanium-to-nickel ratios, angle of rotation, maximum bending, buckling load, preparation time, percentage of untouched canal walls, volume of hard tissue debris and percentage reduction of dentine thickness.

PMID:37594701 | DOI:10.1111/iej.13960

Neuropsychol Rev. 2023 Aug 18. doi: 10.1007/s11065-023-09604-4. Online ahead of print.

ABSTRACT

Dr. Leonhard presents a comprehensive and insightful critique of the existing malingering research literature and its implications for neuropsychological practice. Their statistical critique primarily focuses on the crucial issue of diagnostic inference when multiple tests are involved. While Leonhard effectively addresses certain misunderstandings, there are some overlooked misconceptions within the literature and a few new confusions were introduced. In order to provide a balanced commentary, this evaluation considers both Leonhard’s critiques and the malingering research literature. Furthermore, a concise introduction to Bayesian diagnostic inference, utilizing the results of multiple tests, is provided. Misunderstandings regarding Bayesian inference are clarified, and a valid approach to Bayesian inference is elucidated. The assumptions underlying the simple Bayes model are thoroughly discussed, and it is demonstrated that the chained likelihood ratios method is an inappropriate application of this model due to one reason identified by Leonhard and another reason that has not been previously recognized. Leonhard’s conclusions regarding the primary dependence of incremental validity on unconditional correlations and the alleged mathematical incorrectness of the simple Bayes model are refuted. Finally, potential directions for future research and practice in this field are explored and discussed.

PMID:37594692 | DOI:10.1007/s11065-023-09604-4