Category: Nevin Manimala

BMC Surg. 2024 Aug 7;24(1):225. doi: 10.1186/s12893-024-02520-1.

ABSTRACT

BACKGROUND: Pediatric patients undergoing liver transplantation are particularly susceptible to complications arising from intraoperative fluid management strategies. Conventional liberal fluid administration has been challenged due to its association with increased perioperative morbidity. This study aimed to assess the impact of intraoperative high-volume fluid therapy on pediatric patients who are undergoing living donor liver transplantation (LDLT).

METHODS: Conducted at the Children’s Hospital of Chongqing Medical University from March 2018 to April 2021, this retrospective study involved 90 pediatric patients divided into high-volume and non-high-volume fluid administration groups based on the 80th percentile of fluid administered. We collected the perioperative parameters and postoperative information of two groups. Multivariable logistic regression was utilized to assess the association between estimated blood loss (EBL) and high-volume FA. Kaplan-Meier survival analysis was used to compare patient survival after pediatric LDLT.

RESULTS: Patients in the high-volume FA group received a higher EBL and longer length of stay than that in the non-high-volume FA group. Multivariate logistic regression analysis indicated that hours of maintenance fluids and fresh frozen plasma were significantly associated risk factors for the occurrence of EBL during pediatric LDLT. In addition, survival analysis showed no significant differences in one-year mortality between the groups.

CONCLUSIONS: High-volume fluid administration during LDLT is linked with poorer intraoperative and postoperative outcomes among pediatric patients. These findings underscore the need for more conservative fluid management strategies in pediatric liver transplantations to enhance recovery and reduce complications.

PMID:39113003 | DOI:10.1186/s12893-024-02520-1

Respir Res. 2024 Aug 7;25(1):303. doi: 10.1186/s12931-024-02908-w.

ABSTRACT

BACKGROUND: Acute lung injury (ALI) following pneumonia involves uncontrolled inflammation and tissue injury, leading to high mortality. We previously confirmed the significantly increased cargo content and extracellular vesicle (EV) production in thrombin-preconditioned human mesenchymal stromal cells (thMSCs) compared to those in naïve and other preconditioning methods. This study aimed to investigate the therapeutic efficacy of EVs derived from thMSCs in protecting against inflammation and tissue injury in an Escherichia coli (E. coli)-induced ALI mouse model.

METHODS: In vitro, RAW 264.7 cells were stimulated with 0.1 µg/mL liposaccharides (LPS) for 1 h, then were treated with either PBS (LPS Ctrl) or 5 × 10⁷ particles of thMSC-EVs (LPS + thMSC-EVs) for 24 h. Cells and media were harvested for flow cytometry and ELISA. In vivo, ICR mice were anesthetized, intubated, administered 2 × 10⁷ CFU/100 µl of E. coli. 50 min after, mice were then either administered 50 µL saline (ECS) or 1 × 10⁹ particles/50 µL of thMSC-EVs (EME). Three days later, the therapeutic efficacy of thMSC-EVs was assessed using extracted lung tissue, bronchoalveolar lavage fluid (BALF), and in vivo computed tomography scans. One-way analysis of variance with post-hoc TUKEY test was used to compare the experimental groups statistically.

RESULTS: In vitro, IL-1β, CCL-2, and MMP-9 levels were significantly lower in the LPS + thMSC-EVs group than in the LPS Ctrl group. The percentages of M1 macrophages in the normal control, LPS Ctrl, and LPS + thMSC-EV groups were 12.5, 98.4, and 65.9%, respectively. In vivo, the EME group exhibited significantly lower histological scores for alveolar congestion, hemorrhage, wall thickening, and leukocyte infiltration than the ECS group. The wet-dry ratio for the lungs was significantly lower in the EME group than in the ECS group. The BALF levels of CCL2, TNF-a, and IL-6 were significantly lower in the EME group than in the ECS group. In vivo CT analysis revealed a significantly lower percentage of damaged lungs in the EME group than in the ECS group.

CONCLUSION: Intratracheal thMSC-EVs administration significantly reduced E. coli-induced inflammation and lung tissue damage. Overall, these results suggest therapeutically enhanced thMSC-EVs as a novel promising therapeutic option for ARDS/ALI.

PMID:39112999 | DOI:10.1186/s12931-024-02908-w

BMC Oral Health. 2024 Aug 7;24(1):905. doi: 10.1186/s12903-024-04694-9.

ABSTRACT

BACKGROUND: Warfarin patients who need dental extraction face the problem of bleeding and no sufficient hemostasis results in dry socket and postoperative pain. This study aimed to evaluate and compare the efficacy of the topical application of tranexamic acid-soaked absorbable Gelfoam (TXA-Gel) and saline-soaked absorbable Gelfoam (saline-Gel) in relieving postoperative pain following bilateral simple extraction of permanent mandibular molars in warfarin patients.

METHODS: This was a randomized, triple-blinded, split-mouth, active-controlled clinical trial. It was performed at the Department of Oral and Maxillofacial Surgery, Faculty of Dentistry, Damascus University, between November 2021 and October 2023. 60 bilateral permanent mandibular molars, which were indicated for simple extraction in 30 warfarin patients randomly assigned into two groups according to the topical hemostatic agents after extraction used: Group 1: control group, saline-Gel (n = 30). Group 2: TXA-Gel (n = 30). A simple randomization method was performed by flipping a coin. The primary outcome measure was the visual analogue scale (VAS). The intensity of pain was evaluated at the baseline (t₀), and on the 1st (t₁), 2nd (t₂), 3rd (t₃), 4th (t₄), 5th (t₅), 6th (t₆), and 7th (t₇) days following extraction. The Kolmogorov-Smirnov test and the Mann-Whitney U test were performed. The level of significance was set at 0.05 (p < 0.05).

RESULTS: The mean vas scores was 4.17 ± 1.76 at t1 and decreased to 0.73 ± 0.78 at t7 in the TXA-Gel group. However, in the Gelfoam group, the mean vas scores was 4.83 ± 2.18 at t1 and decreased to 1.80 ± 1.00 at t7. The results of the Mann-Whitney U test showed that there was no statistically significant difference between the two groups at t1 (p = 0.236) and t2 (p = 0.155). However, there was a statistically significance difference at the rest time points (p < 0.05).

CONCLUSIONS: TXA-Gel played a prominent role in alleviating post-extraction pain in warfarin patients.

TRIAL REGISTRATION: The trail was retrospectively registered at the ISRCTN registry (ISRCTN71901901).

PMID:39112998 | DOI:10.1186/s12903-024-04694-9

J Environ Manage. 2024 Aug 6;367:122018. doi: 10.1016/j.jenvman.2024.122018. Online ahead of print.

ABSTRACT

Climate change has a significant impact on dissolved oxygen (DO) concentrations, particularly in coastal inlets where numerous human activities occur. Due to the various water quality (WQ), hydrological, and climatic parameters that influence this phenomenon, predicting and modeling DO variation is a challenging process. Accordingly, this study introduces an innovative Deep Learning Neural Network (DLNN) methodology to model and predict DO concentrations for the Egyptian Rashid coastal inlet, leveraging field-recorded WQ and hydroclimatic datasets. Initially, statistical and exploratory data analyses are performed to provide a thorough understanding of the relationship between DO fluctuations and associated WQ and hydroclimatic stressors. As an initial step towards developing an effective DO predictive model, conventional Machine Learning (ML) approaches such as Gaussian Process Regression (GPR), Support Vector Regression (SVR), and Decision Tree Regressor (DTR) are employed. Subsequently, a DLNN approach is utilized to validate the prediction capabilities of the investigated conventional ML approaches. Finally, a sensitivity analysis is conducted to evaluate the impact of WQ and hydroclimatic parameters on predicted DO. The outcomes demonstrate that DLNN significantly improves DO prediction accuracy by 4% compared to the best-performing ML approach, achieving a Correlation Coefficient of 0.95 with a root mean square error (RMSE) of 0.42 mg/l. Solar radiation (SR), pH, water levels (WL), and atmospheric pressure (P) emerge as the most significant hydroclimatic parameters influencing DO fluctuations. Ultimately, the developed models could serve as effective indicators for coastal authorities to monitor DO changes resulting from accelerated climate change along the Egyptian coast.

PMID:39111007 | DOI:10.1016/j.jenvman.2024.122018

J Environ Manage. 2024 Aug 6;367:122080. doi: 10.1016/j.jenvman.2024.122080. Online ahead of print.

ABSTRACT

The rapid advancement of artificial intelligence (AI) in the 21st century is driving profound societal changes and playing a crucial role in optimizing energy systems to achieve carbon neutrality. Most G20 nations have developed national AI strategies and are advancing AI applications in energy, manufacturing, and agriculture sectors to meet this goal. However, disparities exist among these nations, creating an “AI divide” that needs to be addressed for regulatory consistency and fair distribution of AI benefits. Here, we look at the linear effects of AI and the Paris Agreement (AI), as well as their potential interaction on carbon neutrality. We also investigate whether geopolitical risk (GPR) can hinder or enhance efforts to attain carbon neutrality through energy transition (ET). To measure carbon neutrality of G20 countries, we employed a robust parametric Malmquist index combined with the fixed-effect panel stochastic frontier model to account for heterogeneity. Results indicate that from 1990 to 2022, carbon neutrality has improved primarily due to technological advancements. Developed G20 countries led in technological progress, while developing countries showed modest gains in carbon efficiency. Using the Driscoll-Kraay robust standard error method, we found that AI has a positive but insignificant linear effect on carbon neutrality. However, the interaction between PA and AI was positive and statistically significant, suggesting that PA augments AI’s potential in accelerating carbon neutrality. Energy transition accelerates carbon neutrality in both developed and developing G20 countries. However, the role of energy transition in achieving carbon neutrality becomes negative when the interaction term between energy transition and geopolitical risk (ET × GRP) is incorporated. Regarding control variables, green innovation positively impacts carbon neutrality, whereas financial development has an insignificant effect. Industrial structure and foreign direct investment both negatively affect carbon neutrality, thereby supporting the pollution haven hypothesis. It is recommended that strategies to bridge the “AI divide” and uphold geopolitical stability are crucial to achieve carbon neutrality.

PMID:39111003 | DOI:10.1016/j.jenvman.2024.122080

Appl Physiol Nutr Metab. 2024 Aug 7. doi: 10.1139/apnm-2023-0619. Online ahead of print.

ABSTRACT

A competitive sport season represents a multidimensional stressor where physical and psychological stress may render an athlete susceptible to energy deficiency (ED). Downstream effects of ED can include a reduction in measured-to-predicted resting metabolic rate (RMRratio), indicating metabolic compensation. A pathway linking stress, eating attitudes, metabolic compensation has not been explored. To test if sport-specific stress is associated with eating attitudes and metabolism in endurance athletes (18-22yr) at different phases of a competitive season, we assessed two groups of athletes; 26 swimmers (15 female, 11 male) during peak season (PEAK), and 26 runners (female); across pre- (PRE) and off-season (OFF). Stress (RESTQ-52), eating attitudes (cognitive restraint (CR), drive for muscularity (DM), and body dissatisfaction (BD)), and metabolism (RMRratio) were assessed. In PRE, sport-specific stress and CR were negatively correlated with RMRratio (R=-.58; p<.05, R=-.55; p<.05, respectively). In PEAK, sport-specific stress and DM were negatively correlated with RMRratio (R=-.64; p<.05; R=-.40; p<.05, respectively). DM was positively related to sport-specific stress (R=.55; p<.05). During OFF, there was no relation between RMRratio and sport-specific stress. In runners, there was a change in stress from PRE-to-OFF with highest reported stress during PRE (p<.05) versus OFF. Regression analyses revealed that sport-specific stress and CR were significant predictors of RMRratio during PRE and PEAK (p<.05), but not OFF (p>.05). Associations between stress, eating attitudes, and metabolic compensation in endurance athletes during PRE and PEAK season suggest that during heavier training, metabolic compensation may be linked to upstream eating attitudes associated with sport-stressors.

PMID:39110996 | DOI:10.1139/apnm-2023-0619

JMIR Form Res. 2024 Aug 7;8:e46823. doi: 10.2196/46823.

ABSTRACT

BACKGROUND: According to the Organisation for Economic Co-operation and Development, its member states experienced worsening mental health during the COVID-19 pandemic, leading to an increase of 60% to 1000% in digital counseling access. Hong Kong, too, witnessed a surge in demand for crisis intervention services during the pandemic, attracting both nonrepeat and repeat service users during the process. As a result of the continuing demand, platforms offering short-term emotional support are facing an efficiency challenge in managing caller responses.

OBJECTIVE: This aim of this paper was to assess the queuing performance of a 24-hour text-based web-based crisis counseling platform using a Python-based discrete-event simulation (DES) model. The model evaluates the staff combinations needed to meet demand and informs service priority decisions. It is able to account for unbalanced and overlapping shifts, unequal simultaneous serving capacities among custom worker types, time-dependent user arrivals, and the influence of user type (nonrepeat users vs repeat users) and suicide risk on service durations.

METHODS: Use and queue statistics by user type and staffing conditions were tabulated from past counseling platform database records. After calculating the data distributions, key parameters were incorporated into the DES model to determine the supply-demand equilibrium and identify potential service bottlenecks. An unobserved-components time-series model was fitted to make 30-day forecasts of the arrival rate, with the results piped back to the DES model to estimate the number of workers needed to staff each work shift, as well as the number of repeat service users encountered during a service operation.

RESULTS: The results showed a marked increase (from 3401/9202, 36.96% to 5042/9199, 54.81%) in the overall conversion rate after the strategic deployment of human resources according to the values set in the simulations, with an 85% chance of queuing users receiving counseling service within 10 minutes and releasing an extra 39.57% (3631/9175) capacity to serve nonrepeat users at potential risk.

CONCLUSIONS: By exploiting scientifically informed data models with DES, nonprofit web-based counseling platforms, even those with limited resources, can optimize service capacity strategically to manage service bottlenecks and increase service uptake.

PMID:39110974 | DOI:10.2196/46823

JMIR Hum Factors. 2024 Aug 7;11:e51872. doi: 10.2196/51872.

ABSTRACT

BACKGROUND: Helsinki University Hospital has developed a digital care pathway (DCP) for people with multiple sclerosis (MS) to improve the care quality. DCP was designed for especially newly diagnosed patients to support adaptation to a chronic disease.

OBJECTIVE: This study investigated the MS DCP user behavior and its impact on patient education-mediated changes in health care use, patient-perceived impact of MS on psychological and physical functional health, and patient satisfaction.

METHODS: We collected data from the service launch in March 2020 until the end of 2022 (observation period). The number of users, user logins, and their timing and messages sent were collected. The association of the DCP on health care use was studied in a case-control setting in which patients were allowed to freely select whether they wanted to use the service (DCP group n=63) or not (control group n=112). The number of physical and remote appointments either to a doctor, nurse, or other services were considered in addition to emergency department visits and inpatient days. The follow-up time was 1 year (study period). Furthermore, a subgroup of 36 patients was recruited to fill out surveys on net promoter score (NPS) at 3, 6, and 12 months, and their physical and psychological functional health (Multiple Sclerosis Impact Scale) at 0, 3, 6, and 12 months.

RESULTS: During the observation period, a total of 225 patients had the option to use the service, out of whom 79.1% (178/225) logged into the service. On average, a user of the DCP sent 6.8 messages and logged on 7.4 times, with 72.29% (1182/1635) of logins taking place within 1 year of initiating the service. In case-control cohorts, no statistically significant differences between the groups were found for physical doctors’ appointments, remote doctors’ contacts, physical nurse appointments, remote nurse contacts, emergency department visits, or inpatient days. However, the MS DCP was associated with a 2.05 (SD 0.48) visit increase in other services, within 1 year from diagnosis. In the prospective DCP-cohort, no clinically significant change was observed in the physical functional health between the 0 and 12-month marks, but psychological functional health was improved between 3 and 6 months. Patient satisfaction improved from the NPS index of 21 (favorable) at the 3-month mark to the NPS index of 63 (excellent) at the 12-month mark.

CONCLUSIONS: The MS DCP has been used by a majority of the people with MS as a complementary service to regular operations, and we find high satisfaction with the service. Psychological health was enhanced during the use of MS DCP. Our results indicate that DCPs hold great promise for managing chronic conditions such as MS. Future studies should explore the potential of DCPs in different health care settings and patient subgroups.

PMID:39110966 | DOI:10.2196/51872

JMIR Hum Factors. 2024 Aug 7;11:e57082. doi: 10.2196/57082.

ABSTRACT

BACKGROUND: Digital Mental Health (DMH) tools are an effective, readily accessible, and affordable form of mental health support. However, sustained engagement with DMH is suboptimal, with limited research on DMH engagement. The Health Action Process Approach (HAPA) is an empirically supported theory of health behavior adoption and maintenance. Whether this model also explains DMH tool engagement remains unknown.

OBJECTIVE: This study examined whether an adapted HAPA model predicted engagement with DMH via a self-guided website.

METHODS: Visitors to the Mental Health America (MHA) website were invited to complete a brief survey measuring HAPA constructs. This cross-sectional study tested the adapted HAPA model with data collected using voluntary response sampling from 16,078 sessions (15,619 unique IP addresses from United States residents) on the MHA website from October 2021 through February 2022. Model fit was examined via structural equation modeling in predicting two engagement outcomes: (1) choice to engage with DMH (ie, spending 3 or more seconds on an MHA page, excluding screening pages) and (2) level of engagement (ie, time spent on MHA pages and number of pages visited, both excluding screening pages).

RESULTS: Participants chose to engage with the MHA website in 94.3% (15,161/16,078) of the sessions. Perceived need (β=.66; P<.001), outcome expectancies (β=.49; P<.001), self-efficacy (β=.44; P<.001), and perceived risk (β=.17-.18; P<.001) significantly predicted intention, and intention (β=.77; P<.001) significantly predicted planning. Planning was not significantly associated with choice to engage (β=.03; P=.18). Within participants who chose to engage, the association between planning with level of engagement was statistically significant (β=.12; P<.001). Model fit indices for both engagement outcomes were poor, with the adapted HAPA model accounting for only 0.1% and 1.4% of the variance in choice to engage and level of engagement, respectively.

CONCLUSIONS: Our data suggest that the HAPA model did not predict engagement with DMH via a self-guided website. More research is needed to identify appropriate theoretical frameworks and practical strategies (eg, digital design) to optimize DMH tool engagement.

PMID:39110965 | DOI:10.2196/57082

Int J Prosthodont. 2024 Aug 2:1-24. doi: 10.11607/ijp.9147. Online ahead of print.

ABSTRACT

Purpose: To investigate the available evidence on the accuracy of conventional and digital workflows for complete arch implant supported frameworks. Materials and methods: This scoping review was conducted according to the 5-stage framework of Arksey and O’Malley. A systematic literature search was performed adhering to the PRISMA guidelines to identify studies with a direct comparison of conventional and digital methods for the fabrication of complete arch implant supported frameworks. 58 in-vitro studies with the focus on edentulous arches with at least four implants published between 2000 and 2024 were included. The reported outcomes were examined to determine the value of a statistical analysis for adding up the individual errors to a cumulative error of the workflow. Results: Evidence on the accuracy assessment of digital and conventional workflows for complete arch implant supported frameworks is available. However, also studies with the same assessment methods and outcome units appear to be too heterogeneous to perform a statistical analysis of error accumulation. While there is no consensus in the impression and cast fabrication stage, digital techniques show a superior accuracy for the fabrication of complete arch implant supported frameworks compared to conventional casting. Conclusion: In-vitro studies assessing the accuracy of entire workflows and classifying their outcomes regarding the clinical relevance are lacking.

PMID:39110949 | DOI:10.11607/ijp.9147