Tag: nevin manimala

Updates Surg. 2025 Jul 20. doi: 10.1007/s13304-025-02293-z. Online ahead of print.

ABSTRACT

BACKGROUND AND OBJECTIVES: Laparoscopic liver resection (LLR) for hepatocellular carcinoma (HCC) has been linked to several advantages compared to open approach, but the actual benefit of robotic liver resection (RLR) over LLR in HCC needs further investigation.

METHODS: We performed a multicentric propensity-score matched (PSM) analysis comparing perioperative and oncologic outcomes of LLR vs. RLR for HCC. The PSM model was estimated using a multivariable logistic regression, with type of surgery as dependent variable and age, BMI, clinically-significant portal hypertension, αFP, size of principal lesion, number of nodules and Kawaguchi difficulty score as covariates. Overall (OS) and recurrence-free (RFS) survivals were estimated using the Kaplan-Meier method.

RESULTS: Six-hundred-forty-seven HCC patients from 12 IGoMILS registry centers treated by LLR (553 patients) or RLR (94 patients) were included. After PSM, RLR resulted in wider surgical margins (median: 10 vs 5 mm; p = 0.002) with higher prevalence of R0 resection (98.9 vs 93.1%; p = 0.037), lower conversion rate (2.1 vs. 8.5%; p = 0.039) and shorter hospital stay (median: 4 vs 5 days; p = 0.025), with no significant difference in postoperative complication rate. We observed similar OS among RLR and LLR cohorts [5-y OS: 68.7 vs 65.0%; univariable HR = 0.95 (95% CI: 0.60-1.49); p = 0.82], with significantly better RFS in RLR cohort [5-y RFS: 46.8 vs 24.0%; univariable HR = 0.71 (95% CI: 0.52-0.98); p = 0.04].

CONCLUSIONS: Perioperative outcomes were significantly better in the RLR cohort, with a lower conversion rate and shorter hospital stay, although the latter may be influenced by the multi-institutional study design. Notably, we observed wider resection margins in the RLR group, which were associated with significantly improved RFS.

PMID:40685493 | DOI:10.1007/s13304-025-02293-z

Pharmacoecon Open. 2025 Jul 20. doi: 10.1007/s41669-025-00593-z. Online ahead of print.

ABSTRACT

BACKGROUND/OBJECTIVES: Alzheimer’s disease (AD) exerts a considerable economic burden on South Korea’s aging population. Lecanemab, an amyloid-targeting therapy, has demonstrated efficacy in mitigating cognitive decline in early-stage AD but remains non-reimbursed in South Korea due to concerns over its economic feasibility. This study aimed to examine the cost-effectiveness of lecanemab using nationwide claims data for cost estimation within the South Korean healthcare system. Considering the substantial societal burden of AD, we also evaluated the cost-effectiveness of lecanemab from a limited societal perspective.

METHODS: A Markov state transition cohort model was developed to compare costs and outcomes of lecanemab combined with standard of care (SoC) versus SoC alone. The model simulated five stages of AD progression: mild cognitive impairment, mild AD, moderate AD, severe AD, and death. Transition probabilities between health states were derived from data provided by the National Alzheimer’s Coordinating Center. Formal medical costs and long-term care costs were obtained from the national claims database, while drug cost and other medical expenses were derived from previous studies. Additional cost components such as opportunity cost of caregiver time, out-of-pocket expenses, and time and travel costs for hospital visits were included in the limited societal perspective. Korean-specific utilities for patients and caregivers differentiated by states of AD progression and care settings were obtained from the published literature. Effectiveness was measured in quality-adjusted life years (QALYs) over a lifetime horizon. Scenario analyses were conducted by varying compositions of the cohort, age of onset, and drug pricing.

RESULTS: The incremental cost-effectiveness ratio (ICER) of lecanemab combined with SoC was 198,171,820 Korean Won (KRW)/QALY from the healthcare payer perspective and 181,185,820 KRW/QALY from the limited societal perspective, which significantly exceeded South Korea’s willingness-to-pay (WTP) threshold of 30 million KRW/QALY. Sensitivity analyses revealed that the ICER was highly influenced by variations in treatment effect and discount rates. The result of scenario analyses suggested that targeting lecanemab to patients with mild AD or implementing price reductions could substantially improve its cost-effectiveness.

CONCLUSIONS: Lecanemab’s high cost poses a challenge to its inclusion in the National Health Insurance formulary under South Korea’s current WTP thresholds. Strategic price adjustments and patient targeting are essential to enhance its economic viability. These findings provide valuable insights for policymakers and stakeholders in balancing treatment outcomes and resource allocation for AD management.

PMID:40685475 | DOI:10.1007/s41669-025-00593-z

Ann Surg Oncol. 2025 Jul 20. doi: 10.1245/s10434-025-17838-0. Online ahead of print.

ABSTRACT

INTRODUCTION: Breast-conserving oncoplastic surgery (BCOS), in association with radiotherapy, is the state of the art in the surgical treatment of breast cancer. In this study, we aimed to systematize and validate a novel, four-level complexity classification system for BCOS and associate it with surgical morbidity.

METHODS: We conducted a retrospective, observational study of consecutive female patients who underwent breast-conserving surgery between August 2022 and January 2024 at our breast center. Descriptive statistics were used to summarize the main sample characteristics. The primary outcome was surgical morbidity associated with the novel four-level complexity classification category of surgery performed, computed through a logistic regression model.

RESULTS: Overall, 304 patients underwent the procedures of interest in this study. Surgery complexity levels 1, 2, 3, and 4 were performed in 28, 121, 114, and 41 patients, respectively. A total of 95 patients had complications, including infection, seroma, hematoma, dehiscence, or other complications. A total of 28 patients required re-interventions after definitive diagnosis. The odds of complications increased according to the surgery complexity level, independently of risk factors for complications and factors linked to the surgery type selection, even when considering only clinically relevant complications.

CONCLUSIONS: We concluded that there is an association between morbidity and the complexity level of the surgery performed, with the most complex techniques being associated with higher rates of overall complications and the need for re-intervention, validating the need for a new stratification system for surgeries to improve patients’ quality of life.

PMID:40685460 | DOI:10.1245/s10434-025-17838-0

Sci Rep. 2025 Jul 20;15(1):26327. doi: 10.1038/s41598-025-12163-8.

ABSTRACT

Snow water equivalent (SWE) is a critical variable for understanding water availability and snowmelt-driven streamflow in mountainous regions. Yet, its spatial and temporal estimation is constrained by scarce in situ measurements and the inherent challenges of deriving SWE directly from satellite observations. Thus, accurate SWE assessment is essential for predicting the spatial distribution of snowpack and its temporal contributions to downstream outflow, particularly in semi-arid snow-fed basins like Morocco’s High Atlas regions. In this study, we simulate the local and spatial distribution of SWE and outflow at 500 m using Snow17 model, ERA5-Land and satellite-derived fractional Snow Cover Area (fSCA) from Moderate Resolution Imaging Spectroradiometer (MODIS) for the period 2000-2022. The reanalysis data was downscaled and bias corrected using machine learning models (e.g. random forest). To validate results, we compared simulated snow cover area (fSCA) (transformed from SWE simulation) with fSCA issued from MODIS. The methodology was tested in the Rheraya sub-basin (Tensift basin) and applied in Ait Ouchene and Tillouguite sub-basins (Oum Er Rbia basin) in Morocco’s High Atlas Mountains. Statistical analysis shows strong model performance, with Nash-Sutcliffe Efficiency (NSE) values exceeding 0.84 for snow depth (SD) simulations. Moreover, spatio-temporal analysis revealed that SWE and snow depth are significantly higher above 2,500 m elevation, with SWE exceeding 300 mm and SD surpassing 60 cm in Tillouguite and Rheraya sub-basins. Findings also demonstrated that snowmelt contributions to outflow varied significantly with elevation, accounting for 40-46% of annual outflow above 2,500 m and playing a dominant role during spring (55-57% of seasonal outflow). Our research provides a framework for enhancing SWE/outflow estimation and understanding snowpack dynamics in semi-arid mountainous regions, highlighting the vital role of high-altitude snowpacks in water resource sustainability and management under climate change.

PMID:40685453 | DOI:10.1038/s41598-025-12163-8

Sci Rep. 2025 Jul 20;15(1):26322. doi: 10.1038/s41598-025-11610-w.

ABSTRACT

Investigation of antibiotic use in the pediatric population is crucial, in particular in the hospital setting. Measures of antibiotic consumption, such as Duration-Of-Therapy (DOT) and Length-Of-Therapy (LOT) are recommended as indicators of use in pediatrics. This study is aimed to estimate DOT and LOT in hospitalized children using data from electronic medical records (EMRs). We included all patients hospitalized in a tertiary care children’s hospital in Italy, from January to December 2022. Measures DOT and LOT were derived from data recorded in patient EMRs. DOT/1000 patient-days was estimated by patient characteristics, antibiotic molecule prescribed and type of inpatient ward. The time trend of antimicrobial consumption in hospitalized children was also estimated. At least one antibiotic was prescribed in 8,518 out of 21,787 children (39.1%). Overall, DOT and LOT/1000 patient-days were 539 and 354, with a DOT/LOT ratio of 1.5. The molecule with the highest DOT/1000 patient-days was piperacillin/tazobactam followed by meropenem (81.3 and 59.6 DOT/1000 patient-days). Oncohematology and Intensive Care Units were the type of wards with highest DOT/1000 patient-days (1112 and 944, respectively) and LOT/1000 patient-days (591 and 547, respectively). EMRs enhances data accessibility to measure antibiotic use in hospitalized children and their integration into pediatric antibiotic stewardship programs.

PMID:40685445 | DOI:10.1038/s41598-025-11610-w

Odontology. 2025 Jul 20. doi: 10.1007/s10266-025-01157-7. Online ahead of print.

ABSTRACT

Non-coding RNAs (ncRNAs) regulate inflammatory responses and bone resorption in periodontitis. This systematic review and meta-analysis aimed to evaluate the diagnostic value of ncRNA in periodontitis. Studies were selected from PubMed, EMBASE, and Web of Science. Heterogeneity was assessed using the Q test and I² statistic. Diagnostic value was determined by pooled sensitivity, specificity, and area under the curve (AUC) of the summary receiver operating characteristic (SROC). Subgroup analysis was performed to explore heterogeneity sources. Thirteen articles (38 tests, 820 controls, 948 cases) were included. The overall sensitivity of ncRNAs for diagnosing periodontitis was 0.79 (95% CI: 0.73-0.84), specificity was 0.83 (95% CI: 0.78-0.86), and AUC was 0.88 (95% CI: 0.85-0.90), indicating good diagnostic performance. The positive likelihood ratio was 4.53 (95% CI: 3.64-5.65), and the negative likelihood ratio was 0.26 (95% CI: 0.20-0.33), indicating limited clinical applicability of the pooled results. The diagnostic score was 2.88 (95% CI: 2.50-3.26), and the DOR was 17.75 (95% CI: 12.12-26.00), suggesting significant diagnostic accuracy for dysregulated ncRNAs in periodontitis. Dysregulated ncRNAs can distinguish periodontitis from healthy controls, highlighting their potential as diagnostic biomarkers.

PMID:40685444 | DOI:10.1007/s10266-025-01157-7

Sci Rep. 2025 Jul 20;15(1):26317. doi: 10.1038/s41598-025-88745-3.

ABSTRACT

Metaheuristics, which are general-purpose algorithms, are commonly used to solve complex optimization problems. These algorithms manipulate multiple potential solutions to converge on the optimum, balancing the exploration and exploitation phases. A recent algorithm, the Parrot Optimizer (PO), is inspired by the behavior of domestic parrots to improve the diversity of solutions. However, while promising, the PO may encounter difficulties such as convergence to sub-optimal solutions or slow convergence speed. This paper proposes an improvement to the PO algorithm by integrating chaotic maps to solve complex optimization problems. The improved algorithm, called Chaotic Parrot Optimizer (CPO), is characterized by a better ability to avoid local minima and reach globally optimal solutions thanks to a dynamic diversification strategy based on chaotic maps. The effectiveness of the CPO algorithm has been rigorously evaluated through in-depth statistical analysis, using 23 benchmark functions as well as IEEE CEC 2019 and CEC 2020 benchmarks, covering a wide range of optimization challenges. The results show that CPO outperforms not only the original PO algorithm, but also six recent metaheuristics in terms of convergence speed and solution quality. In addition, it has been successfully applied to three complex engineering illustrating its ability to solve real-world, multi-constraint problems. Its integration with Kapur entropy also enabled precise segmentation of medical images, underlining its strong potential for critical biomedical applications. The CPO source code will be available on the Github account: [email protected] after acceptance.

PMID:40685431 | DOI:10.1038/s41598-025-88745-3

Sci Rep. 2025 Jul 20;15(1):26320. doi: 10.1038/s41598-025-12086-4.

ABSTRACT

Plant disease management based on pesticide use has numerous detrimental effects on health and the environment. As a result, the adoption of environment-friendly disease management options is the best alternative to pesticide use. Therefore, the identification of locally available bio-agents like Trichoderma species using morpho-cultural and molecular approaches specifically targeting the internal transcribed spacer (ITS) region, translation elongation factor 1-alpha (TEF 1-α) and RNA polymerase B subunit II (RPB2) is necessary. In this study, we characterized 24 Trichoderma strains isolated from the apple rhizosphere. Significant variations were observed in the morpho-cultural characteristics of Trichoderma isolates and categorized them into four groups (I-IV) that were identified as T. harzianum complex, T. koningiopsis, T. viride, and T. hamatum, comprising 4, 4, 6 and 10 isolates, respectively. The concatenated sequence data set derived from the ITS region, TEF 1-α and RPB2 grouped 24 Trichoderma isolates into 03 independent clades. Specifically, the sequencing based on ITS region grouped them into four sub-clades, which were identified as T. harzianum complex, T. viride, T. asperelloides, and T. koningiopsis, comprising 4, 6, 5 and 7 isolates, respectively, and two independent lineages, each represented by a single isolate. In contrast, sequencing of the TEF 1-α and RPB2 genes grouped 24 Trichoderma isolates into two distinct clades and six sub-clades that were identified as T. asperelloides, T. asperellum, T. hamatum, T. viride, T. koningiopsis and T. harzianum complex, comprising 5, 5, 3, 4, 3 and 4 isolates, respectively. Thus, the final identification of 24 Trichoderma strains was achieved through a combined morpho-cultural and molecular approach, resulting in the identification of six species viz., T. koningiopsis, T. viride, T. asperellum, T. asperelloides, T. hamatum and T. harzianum complex comprising 5, 5, 3, 4, 3 and 4 isolates, respectively in accordance with the reference sequences retrieved from NCBI. Notably, to our knowledge, this is the first report of T. koningiopsis, T. viride, T. asperellum, T. asperelloides, and T. hamatum from the apple rhizosphere.

PMID:40685429 | DOI:10.1038/s41598-025-12086-4

Sci Rep. 2025 Jul 20;15(1):26343. doi: 10.1038/s41598-025-10704-9.

ABSTRACT

To address the problems of centralized attribute authority, inefficient encryption and invalid access control strategy in the data sharing scheme based on attribute-based encryption technology, a smart grid data sharing scheme that supports policy update and traceability is proposed. The smart contract of the blockchain is used to generate the user’s key, which does not require a centralized attribute authority. Combined with attribute-based encryption and symmetric encryption technology, the confidentiality of smart grid data is protected and flexible data access control is achieved. In addition, online/offline encryption and outsourced computing technologies complete most of the computing tasks in the offline stage or cloud server, which greatly reduces the computing burden of data owners and data access users. By introducing the access control policy update mechanism, the data owner can flexibly modify the key ciphertext stored in the cloud server. Finally, the analysis results show that this scheme can protect the privacy of smart grid data, verify the integrity of smart grid data, resist collusion attacks and track the identity of malicious users who leak private keys, and its efficiency is better than similar data sharing schemes.

PMID:40685425 | DOI:10.1038/s41598-025-10704-9

Sci Rep. 2025 Jul 20;15(1):26330. doi: 10.1038/s41598-025-11601-x.

ABSTRACT

The construction sector is proactively working to minimize the environmental impact of cement manufacturing by adopting alternative cementitious substances and cutting carbon emissions tied to concrete. This study investigates the viability of using waste industrial materials as a replacement of cement in concrete mixes. The primary goal is to predict the compressive strength of waste-incorporated concrete by evaluating the effects of materials such as cement, fly ash (FA), silica fume (SF), ground granulated blast furnace slag (GGBFS), metakaolin (MK), water usage, aggregate levels, and superplasticizer dosages. A total of 441 data entries were sourced from various publications. Multiple machine learning techniques, such as light gradient boosting (LGB), extreme gradient boosting (XGB), and decision trees (DT), along with hybrid approaches like XGB-LGB and XGB-DT, were utilized to study how these variables influence compressive strength. The dataset was partitioned into training and testing, and statistical tools were employed to assess the correlation between input variables and strength. Model accuracy was gauged using metrics such as mean absolute percentage error (MAPE), root mean square error (RMSE), and the coefficient of determination (R²). Among the models, the XGB and DT approach delivered the highest precision, with an R² of 0.928 in the training stage. Among hybrid models, XGB-DT exhibited a balanced performance having R² value of 0.907 and 0.785 for training and testing phase. Additionally, SHAP (SHapley Additive exPlanations) and partial dependence plots (PDP) were employed to pinpoint the optimal ranges for each variable’s contribution to the improvement of compressive strength. SHAP and PDP analyses identified coarse aggregate, superplasticizers, water and cement content have high influence on model’s output. Additionally, 150-200 kg/m³ of GGBFS as key factors for optimizing compressive strength. The study concludes that the hybrid models along with the single models, can effectively forecast the compressive strength of concrete incorporating industrial byproducts, assisting the construction industry in efficiently evaluating material properties and understanding the influence of various input factors.

PMID:40685423 | DOI:10.1038/s41598-025-11601-x