Search Results (1,225)

Seasonal hypoxia in bottom waters of the Bohai Sea poses an escalating threat to marine ecosystems, yet monitoring it via satellite remote sensing continues to be challenging due to the inaccessibility of bottom layers. However, surface bio-optical signals do not instantaneously reflect variation in bottom-water dissolved oxygen (DO); instead, a distinct temporal lag exists between surface biological activity and its influence on bottom DO. Leveraging this insight, an inversion framework was established, integrating multi-source remote sensing data with decision tree-based machine learning models to estimate bottom-water DO concentration. We evaluated multiple lag intervals for satellite-derived bio-optical variables and adopted a 14-day lag as representative of the delayed impact of surface processes on bottom DO. An optimized feature set selected via a genetic algorithm (GA) was used to train the XGBoost model, which achieved high predictive performance (R² = 0.86, RMSE = 0.79 mg/L, MAPE = 8.89%). Interpretability analysis identified the sea surface temperature as the dominant driver of bottom-water DO variation in the Bohai Sea. The framework successfully reproduced the spatiotemporal variability in bottom DO from 2022 to 2024 in the Bohai Sea and captured the locations of summer hypoxic zones. Further analysis demonstrated that incorporating physically based bottom-layer variables substantially enhances model accuracy (R² = 0.89, RMSE = 0.68 mg/L, MAPE = 7.85%), underscoring their critical role in regulating bottom-water DO concentrations. Building on the established inversion framework and integrating extended in situ and satellite observations, we reconstruct the long-term temporal distribution of bottom DO in the Bohai Sea from 2014 to 2025, revealing the considerable potential of satellite data for monitoring bottom-water DO conditions in coastal seas. Full article

Production profiling is essential for optimizing production strategies in oil and gas wells. Conventional production logging tools provide only discrete, time-limited measurements and face operational challenges in long or complex horizontal wells. Distributed fiber-optic sensing (DTS/DAS) enables continuous, full-wellbore monitoring but each sensing modality has limitations when used alone: DTS interpretation is influenced by wellbore disturbances and thermal hysteresis, while DAS acoustic energy does not always correspond to actual inflow zones. This study proposes a joint interpretation method integrating DTS-based temperature inversion with DAS frequency-band energy and apparent velocity analysis. DTS data are processed using a coupled wellbore–formation heat-transfer model to obtain segmental flow rates, while DAS data are analyzed using short-time Fourier transform, cross-correlation, and Hough transform to extract positive and negative apparent velocities indicating fluid migration directions. Field results show that high-production intervals at 4126–4486 m correlate with positive apparent velocities, whereas medium-/low-production and shut-in stages exhibit persistent negative velocities linked to backflow and reinjection. The combined interpretation effectively distinguishes reservoir inflow from wellbore flow by jointly constraining thermal response and flow direction, thereby reducing uncertainties associated with single-method analysis. Full article

Precise spatiotemporal monitoring of soil moisture is fundamental to the efficient regulation and sustainable utilization of agricultural water resources in arid and semi-arid irrigation districts. This study focuses on the Yichang Irrigation District within the Hetao Irrigation Area to elucidate the spatiotemporal dynamics of surface soil moisture during the crop growing season. Multi-year Landsat 8/9 remote sensing imagery (2022–2024) was integrated with the Temperature Vegetation Dryness Index (TVDI) framework to construct two feature spaces, namely Normalized Difference Vegetation Index–Land Surface Temperature (NDVI–LST) and Enhanced Vegetation Index–Land Surface Temperature (EVI–LST). A dual-index complementary inversion strategy was applied for soil moisture estimation, and the outputs were validated against Soil Moisture Active Passive (SMAP) soil moisture products and MOD16 evapotranspiration products. Results indicated that the dry edges of the feature spaces derived from both vegetation indices exhibited double-inflection-point characteristics, with optimal fitting intervals located between the inflection points. The inflection point positions shifted dynamically with variations in crop coverage. During bare-soil and low-vegetation-coverage periods (May, June, and September), the minimum thresholds for low NDVI and EVI values were 0.07 and 0.06, respectively, whereas during high-vegetation-coverage periods in July and August, the minimum thresholds for both indices increased to 0.15. NDVI demonstrated superior performance during May, June, and September, whereas EVI exhibited greater advantages during active crop growth periods in July–August. The optimized model achieved robust inversion accuracy, with a validation R² of 0.81 for the measured soil moisture in the 0–20 cm layer on 12 May 2024. The inversion results exhibited strong correlations with the SMAP soil moisture products (R² = 0.663 during low crop coverage; R² = 0.625 during high crop coverage) and MOD16 evapotranspiration data (R = 0.751). The spatiotemporal patterns of soil moisture were distinctly discerned. Following spring irrigation in May, abundant moisture in certain areas resulted in bimodal distribution patterns in the inversion results. June exhibited the lowest soil moisture content across the study area, with arid zones making up 36.67% of the total area. From July to August, concentrated precipitation coupled with summer irrigation reduced the proportion of extremely arid zones to below 0.98%. Full article

Background/Objectives: Quasi-static inverse dynamics is widely used in biomechanical analyses due to its computational simplicity; however, neglecting inertial effects may introduce joint-specific torque estimation errors during dynamic movements. The purpose of this study was to quantify torque estimation errors introduced by quasi-static assumptions during bodyweight squats performed at different movement frequencies. Methods: A planar MATLAB-based (version R2022a) musculoskeletal model incorporating standard anthropometric parameters was developed to simulate squat motions at 1.00, 0.75, 0.50, and 0.25 Hz. Joint torques calculated using quasi-static inverse dynamics were compared with fully dynamic inverse dynamics at the ankle, knee, and hip. Model agreement was evaluated using Root Mean Square Error (RMSE), normalized percentage error relative to peak dynamic torque, and bootstrapped 95% confidence intervals (CI). Results: Quasi-static modeling produced negligible torque estimation errors at the ankle and knee across all movement frequencies, with percentage errors consistently below 0.1% and narrow confidence intervals. In contrast, the hip joint demonstrated a clear frequency-dependent underestimation of torque when inertial effects were neglected. At 1.00 Hz, the hip RMSE reached 14.4 Nm, corresponding to 14.01% of peak dynamic torque (95% CI: 13.97–14.06%). Error magnitude increased systematically with movement speed. Conclusions: The validity of quasi-static inverse dynamics strongly depends on joint location and movement frequency. While quasi-static models are appropriate for ankle and knee torque estimation during moderate-speed squats, accurate hip torque assessment during faster squats requires full dynamic modeling. These findings provide quantitative benchmarks to inform model selection in biomechanical research, rehabilitation engineering, and assistive device design. Full article

Background: Acne in adult women is increasingly recognized as a condition with systemic endocrine–metabolic correlates. Evidence linking acne to thyroid-related abnormalities and cardiometabolic risk markers remains mixed, and integrated real-world evaluations combining thyroid biochemistry, ultrasound metrics, inflammatory indices, and lipid profile are limited. Methods: We performed a cross-sectional observational analysis of 80 women with acne who underwent routine laboratory testing and thyroid ultrasound assessment. Thyroid status was defined using TSH (reference 0.4–4.5 mIU/L) and free T4 (0.8–1.8 ng/dL), with an additional TSH-only sensitivity definition (high TSH >4.5 mIU/L). Low HDL-cholesterol (HDL-C) was defined as <50 mg/dL. Group comparisons used Mann–Whitney U tests with Hodges–Lehmann shifts; associations were summarized using odds ratios (ORs) with Fisher’s exact tests; correlations used Spearman’s ρ (TSH log-transformed for correlation analyses) with confidence intervals. Multiple testing was controlled within panels using Benjamini–Hochberg FDR. Analyses were complete-case per comparison. Results: Thyroid dysfunction and metabolic–inflammatory abnormalities were common in this cohort. Low HDL-C was more frequent in thyroid dysfunction, and in the TSH-only sensitivity analysis, high TSH (>4.5 mIU/L) was strongly associated with low HDL-C (OR 13.13, 95% CI 1.48–116.04; p = 0.020). In a minimal adjusted model including NLR, high TSH remained associated with low HDL-C (adjusted OR 12.93, 95% CI 1.44–115.70; p = 0.022). HDL-C showed an inverse association with NLR (ρ = −0.28; p = 0.023). Endocrine profiling suggested a positive association between ACTH and log(TSH) (ρ = 0.62; p = 0.004), although this did not remain significant after FDR correction. Thyroid ultrasound metrics showed limited correspondence with thyroid biochemistry. Conclusions: In women with acne, elevated TSH is associated with substantially higher odds of low HDL-C, independent of inflammatory burden as proxied by NLR, while thyroid ultrasound morphology contributes limited functional information. These findings support integrated thyroid–metabolic assessment in adult female acne and motivate prospective studies incorporating acne severity measures and standardized testing to clarify clinical implications. Full article

Background/Objectives: High-quality diets are increasingly recognized as a promising strategy for alleviating mental health problems. This study aimed to evaluate the association between adherence to the EAT-Lancet diet (ELD) and depression and anxiety using a systematic review and meta-analysis of observational studies. Methods: A comprehensive literature search of PubMed, Web of Science, and Embase was conducted. Two independent reviewers screened titles, abstracts, and full texts and extracted data, with any discrepancies resolved by consensus. Results: Ultimately, eight unique studies (10 comparisons) met the predefined inclusion criteria. Adjusted effect estimates (odds ratios (ORs) or hazard ratios (HRs)) and corresponding 95% confidence (95%CI) intervals were extracted and pooled using random-effects meta-analysis. Between-study heterogeneity was quantified with the I² statistic. Compared with the lowest ELD adherence, the highest adherence was associated with a lower risk of depression (OR = 0.78, 95% CI 0.73–0.85; I² = 85.0%); a similar inverse association was observed for anxiety (OR = 0.83, 95% CI 0.79–0.86; I² = 0%) and the co-occurrence of depression and anxiety (HR = 0.76, 95% CI 0.68–0.85; I² = 0%). Conclusions: Our findings indicate that, compared with low adherence, high adherence to the ELD is associated with beneficial effects on mental health and may inform dietary recommendations for the early prevention and intervention of depression, anxiety, and their co-occurrence. Full article

Addressing the challenge of tracing hidden and transient cross-connections in urban drainage networks, this study develops a SWMM–Bayesian coupled model based on the Py SWMM interface using the Daming Lake area in Jinan as a case study. By employing a Markov Chain Monte Carlo (MCMC) algorithm to drive the interaction between dynamic simulation and statistical inference, the model achieves multidimensional joint posterior estimation of pollution source location (J_x), discharge intensity (M), and discharge timing (T). The results indicate: (1) Model accuracy: The coupled model demonstrates strong source tracing capability, with mean absolute errors below 0.6% in single-parameter inversion. Under multi-parameter joint inversion, the true values of all parameters consistently fall within the 95% confidence intervals. (2) Parameter sensitivity: The influence of MCMC step size on the uncertainty of pollution tracing results is systematically clarified. Discrete source location estimates (J_x) exhibit high robustness to step size variation due to spatial heterogeneity in hydraulic responses, whereas continuous physical parameters (M and T) show strong dependence on the selected step size scale. (3) Practical application: The impact of spatial monitoring network configuration on pollution tracing performance is examined. By deploying a complementary monitoring system integrating trunk and branch pipelines, the inversion accuracy for mass (M) and time (T) parameters is significantly improved by 84.2% and 88.5%, respectively. Overall, the proposed pollution source tracing method for urban drainage networks effectively overcomes the multi-solution challenge in complex network inversion, providing critical technical support for refined urban water environment management. Full article

Paleogeomorphology exerts first-order control on the distribution of structural hydrocarbon reservoirs across regional unconformities, whereas variations in pore-throat architecture and flow capacity among different geomorphic units further govern hydrocarbon migration pathways and accumulation sites. Therefore, high-resolution reconstruction of regional paleogeomorphology is essential for effective exploration. This study investigates the Yanwu area of the Ordos Basin, where pre-Jurassic paleogeomorphology was reconstructed based on detailed stratigraphic analyses of the Yan’an Formation and the Yan-10 oil-bearing interval, and its influence on reservoir formation was systematically evaluated. Paleogeomorphology was delineated using well-log-based compensated impression methods integrated with localized 3D seismic inversion. Reservoir samples from distinct geomorphic units were analyzed through thin-section petrography, FESEM imaging, high-pressure mercury intrusion, and visualized micro-scale hydrocarbon charging experiments to characterize pore-throat systems and flow behavior. Four geomorphic units—paleohighs, slope zones, terraces, and valleys—were identified. Seismic inversion across the Yanwu tributary valley and the Honghe paleovalley confirms the reliability of the reconstructed geomorphology. Reservoirs within slope zones and terraces exhibit superior pore-throat structures, dominated by intergranular and dissolution pores, and display grid-like displacement patterns with higher ultimate recovery in micro-charging tests. Portions of the paleohighs show comparable reservoir quality and flow capacity. Results indicate that slope zones and terraces represent the most favorable hydrocarbon accumulation domains. Where overlying strata provide effective sealing, hydrocarbons preferentially accumulate on structural highs within these geomorphic units; in contrast, insufficient sealing transforms them into efficient migration conduits. Certain paleohighs may also host structural-high accumulations when capped by effective traps. The clarified accumulation patterns across geomorphic units offer a robust framework for guiding hydrocarbon exploration and reserve growth in regions with similar tectono-sedimentary settings. Full article

Background: Dementia and cognitive impairment are increasing worldwide, particularly in older adults, imposing substantial health and societal burdens. Obesity and cardiovascular diseases (CVDs) are recognized risk factors; however, existing evidence is inconsistent, and their combined effects remain poorly understood. This study aimed to assess the independent and joint associations of obesity and CVDs with the risk of cognitive impairment and dementia through a systematic review and meta-analysis. Methods: A comprehensive literature search was conducted across three major electronic databases (PubMed, Web of Science, and Scopus) to identify relevant studies published from January 2015 through 30 June, 2025. A random-effects meta-analysis was performed to estimate the independent effects of obesity and CVDs on the outcome of interest, reporting the pooled hazard ratios (HRs) and 95% confidence intervals (CIs). The risk of bias was assessed using the Newcastle–Ottawa Scale (NOS), and the certainty of the evidence was evaluated using the GRADE approach. Results: A total of 25 studies comprising 8,276,914 participants were included. Body mass index (BMI)-defined obesity was associated with a lower risk of cognitive impairment (pooled HR = 0.85; 95% CI: 0.74–0.98; I² = 40.5%) but showed no association with incident dementia (pooled HR = 1.00; 95% CI: 0.86–1.15; I² = 96%). In contrast, central obesity, assessed by waist circumference (WC), was associated with a 14% increased risk of dementia (pooled HR = 1.14; 95% CI: 1.03–1.27; I² = 96%). Coronary heart disease, stroke, and atrial fibrillation were each significantly associated with dementia risk, with pooled HRs of 1.41 (95% CI: 1.29 –1.54; I² = 0%), 1.53 (95% CI: 1.35–1.74; I² = 37%), and 1.30 (95% CI: 1.12–1.50; I² = 68.8%), respectively. Evidence on the joint effects of obesity and CVD was limited to a single study, which reported that men with both conditions had a 58% higher risk of all-cause dementia compared with those of normal weight without CVD (HR = 1.58; 95% CI: 1.37–1.81). Conclusion: BMI-defined obesity was inversely associated with cognitive impairment, whereas central obesity was associated with an increased risk of dementia. Among CVDs, stroke showed the strongest association with incident dementia. However, the overall certainty of evidence across these findings was low, and these findings should be interpreted with caution. Full article

This paper presents a stochastic framework for the inverse identification of structural material degradation (SMD) in cantilever beams. The method combines the Karhunen–Loéve (KL) expansion for the efficient parameterisation of spatially varying material decay with experimental Frequency Response Function (FRF) data within a Bayesian inference scheme. This approach employs a low-dimensional spectral parameterisation via the KL expansion, which mitigates the curse of dimensionality inherent in element-wise model updating, and provides a full-field probabilistic description of SMD. A two-phase constraint strategy was developed to address the fundamental tension between physical plausibility and algorithmic stability of the inverse identification algorithm: (1) physical regularisation during identification stabilises the ill-posed inverse problem, and (2) post-convergence selective regularisation eliminates physically impossible stiffness enhancements (exceeding 1.1 × baseline) that arise from measurement and modelling uncertainties. This phased approach prevents the algorithm distortion that occurs when constraints are applied too stringently during iteration, while ensuring final results respect fundamental physical principles. The framework is experimentally validated on a steel cantilever beam with a symmetric open-edge cut. Laser vibrometry measurements under swept-sine excitation demonstrate successful localisation and quantification of SMD, with the 95% credible interval accurately capturing the damaged region after physical constraint application. The adaptive constraint strategy resolves the delicate balance between mathematical stability and physical plausibility in inverse identification. Full article

Background: The benefit of adding hyperthermic intraperitoneal chemotherapy (HIPEC) to cytoreductive surgery (CRS) in ovarian cancer with peritoneal metastasis remains debated outside selected indications. We performed a systematic review and meta-analysis to quantify survival, perioperative morbidity, and completeness of cytoreduction using study-level data. Methods: PubMed/MEDLINE, Embase, and Web of Science were searched. Eligible English-language studies included ovarian cancer patients undergoing CRS plus HIPEC and reported at least one of the following: overall survival (OS), progression-free survival (PFS), Grade III–IV complications, or CC-0 rate. Random-effects meta-analyses were conducted using inverse-variance pooling. For HR outcomes, DerSimonian–Laird τ² with Hartung–Knapp confidence intervals was applied. Proportions were pooled using logit transformation (PLOGIT) with random-effects models. Results: Twelve studies (n = 567) were included. Only two studies provided extractable HRs for OS and PFS (n = 217). CRS plus HIPEC was associated with improved OS (HR 0.68, 95% CI 0.52–0.90, p = 0.0023; I² = 0%; prediction interval 0.14–3.34) and improved PFS (HR 0.70, 95% CI 0.31–1.57, p = 0.0007; I² = 0%; prediction interval 0.18–2.66). Across 12 studies (n = 563), the pooled Grade III–IV complication rate was 0.18 (95% CI 0.14–0.22; I² = 16.3%; prediction interval 0.12–0.26). In 10 studies (n = 385), the pooled CC-0 rate was 0.87 (95% CI 0.79–0.92; I² = 46.7%; prediction interval 0.66–0.96). Conclusions: CRS plus HIPEC shows a favorable signal for OS and PFS in the limited HR-eligible evidence and appears feasible, with a pooled severe complication rate of ~18% and high CC-0 rates. Current data support HIPEC primarily as a targeted intensification strategy in carefully selected patients, while broader adoption requires additional randomized, context-specific evidence. Full article

Coal mining is a major source of methane emissions globally, and monitoring these emissions has become a sustained area of interest in both scientific research and policy-making. Coal mine methane emissions typically manifest as discrete point sources, such as individual mines or ventilation shafts, and spatially concentrated area sources, such as mining clusters. In recent years, satellite remote sensing technology has become a key tool for monitoring and assessing methane emissions from coal mines. Notable progress has been made in quantifying emissions through point-source inversion using high-resolution satellite data, such as GF-5B/AHSI, and in estimating regional-scale area-source emissions using wide-swath instruments, such as S5P/TROPOMI. However, there remains a lack of systematic comparison between inversion results derived from these two types of satellite data with differing spatial resolutions. This study comprehensively analyzes the strengths and limitations of the GF-5B/AHSI and S5P/TROPOMI sensors for quantifying methane emissions. It conducts a spatiotemporal comparative analysis of point-source and area-source methane emission datasets from the coal-mining regions of Shanxi Province. The research aims to clarify the intrinsic relationship between remote-sensing data at different observational scales and to systematically evaluate how prior information on emission-source locations influences emission quantification results. The comparative analysis between TROPOMI grid-level emissions and GF-5B/AHSI point-source emissions indicates that TROPOMI-gridded emission data, owing to its longer time series, can more effectively characterize the annual-average methane emission levels in mining areas. Meanwhile, high-resolution observations from GF-5B/AHSI show distinct advantages in detecting small-scale plumes and attributing emissions to specific facilities. Although the regional-average emissions derived from TROPOMI are significantly higher than point-source emission rate estimates, their data ranges overlap within their uncertainty intervals, demonstrating substantial consistency between the monitoring results of the two methods. Furthermore, the study reveals that when key emission facilities, such as ventilation shafts, are located far from the core operational areas of mines, relying solely on point-source observations may not fully capture the spatial distribution pattern of methane emissions at the mine scale. Full article

Backgrounds/Objectives: Advances in prevention and medical care in the field of cardiology have led to an increase in the number of older patients with heart failure. In this population, assessment of nutritional status is particularly important. However, the prognostic impact of severity-based nutritional assessment at admission remains unclear. We conducted a study to elucidate the impact of malnutrition severity at admission on the prognosis of older patients hospitalized for acute heart failure (AHF). Methods: This study investigated the relationship between the Geriatric Nutritional Risk Index (GNRI) at admission and prognosis in 214 older patients aged ≥65 years who were hospitalized for AHF (mean age, 85 ± 8 years; male, 49%) between 2019 and 2023. GNRI was assessed by dividing patients into four groups: GNRI > 98 as normal (n = 64), 92 ≤ GNRI < 98 as mild risk (n = 54), 82 ≤ GNRI < 92 as moderate risk (n = 66), and GNRI < 82 as severe risk (n = 30). The discriminative performance of GNRI for 1-year all-cause mortality was compared with that of the Controlling Nutritional Status (CONUT) score and the Prognostic Nutritional Index (PNI). Results: During a median follow-up of 356 days, 76 deaths were observed. Worse GNRI categories were associated with older age, underweight, frailty, and anemia. Multivariable Cox proportional hazards models revealed that moderate GNRI risk (hazard ratio (HR), 2.69; 95% confidence interval (CI), 1.34–5.40) and severe GNRI risk (HR, 9.75; 95% CI, 4.30–22.10) were associated with higher all-cause mortality when compared with normal GNRI, along with age (HR per 1-year increase, 1.07; 95% CI, 1.03–1.11). Sensitivity analysis using GNRI as a continuous variable demonstrated similar results; GNRI was inversely associated with all-cause mortality (HR per 1 GNRI increase, 0.92; 95% CI, 0.90–0.95). In a subgroup analysis of age ≥85 years, the inverse association between GNRI and all-cause mortality was consistent. For 1-year all-cause mortality, GNRI showed moderate discrimination (area under the curve (AUC), 0.71; 95% CI, 0.63–0.80). Although the AUC of GNRI was not significantly different from that of the CONUT score or the PNI, GNRI demonstrated significantly better risk reclassification (net reclassification improvement, 0.47 vs. CONUT, p = 0.05; 0.43 vs. PNI, p = 0.02). Conclusions: In older patients with AHF including the oldest-old, nutritional status assessed by the GNRI at admission was predictive of prognosis. The importance of evaluating nutritional status at admission in clinical settings is reaffirmed. Full article

Metabolic syndrome (MetS) is common in later life and shaped by modifiable lifestyle and clinical factors, yet data specific to rural older adults are limited. This cross-sectional study analyzed rural Koreans aged ≥65 years (unweighted n = 467) from the 2023 Korea National Health and Nutrition Examination Survey, incorporating the complex survey design (strata, clusters, and weights). MetS was defined using National Cholesterol Education Program Adult Treatment Panel III criteria with Asian-specific waist cutoffs (≥3 of 5 components). Sociodemographic, behavioral, and clinical characteristics were compared by MetS status using design-based tests, and complex-sample logistic regression estimated adjusted odds ratios (aORs) with 95% confidence intervals (CIs). The survey-weighted prevalence of MetS was 42.8%. Compared with those without MetS, participants with MetS had higher body mass index (BMI) and waist circumference, more hypertension and diabetes, higher triglycerides, and lower high-density lipoprotein cholesterol; low-density lipoprotein cholesterol did not differ meaningfully. In multivariable models, BMI ≥25 kg/m² (aOR 9.08; 95% CI 6.01–13.71, p ≤ 0.001), hemoglobin A1c ≥ 7.0% (aOR 4.42; 95% CI 1.75–11.16, p = 0.003), and vitamin D deficiency <20 ng/mL (aOR 2.32; 95% CI 1.23–4.35, p = 0.012) were independently associated with higher odds of MetS, whereas meeting the World Health Organization physical activity guideline was inversely associated (aOR 0.50; 95% CI 0.26–0.96, p = 0.039). These findings highlight adiposity, suboptimal glycemic control, and vitamin D deficiency as key, potentially modifiable correlates of MetS in rural older adults and support promotion of guideline-level physical activity as part of integrated cardiometabolic risk management in rural settings. Full article

Objective: To assess the relationship between adherence to various plant-based diets, as measured by overall, healthy, and unhealthy plant-based diet indices (PDI, hPDI, uPDI), and ovarian cancer risk. Methods: We obtained data on 1031 cases of ovarian cancer and 2411 controls from a case-control study conducted in Italy. PDI, hPDI, and uPDI were calculated using data from a validated food frequency questionnaire. We used logistic regression to calculate the odds ratios (ORs) and their corresponding 95% confidence intervals (CIs) of ovarian cancer for PDI, hPDI, and uPDI, adjusting for several possible confounders. Results: PDI and hPDI were inversely related to ovarian cancer risk (OR = 0.70 for the fourth compared to the first quartile, 95% CI: 0.55–0.89, and OR = 0.67, 95% CI: 0.53–0.84, respectively). On the other hand, a higher uPDI was related to a higher risk of ovarian cancer (OR = 1.78, 95% CI: 1.40–2.28). The estimates for a 5-point increment in the indices were 0.88 (95% CI: 0.81–0.95) for PDI, 0.90 (95% CI: 0.83–0.96) for hPDI, and 1.15 (95% CI: 1.07–1.23) for uPDI. Consistent associations for the three indices were observed across strata of age, family history of breast/ovarian cancer, educational level, parity, oral contraceptives use, and menopausal status. Conclusions: Plant-based diets favorably influence ovarian cancer risk; plant-based diets characterized by a high intake of unhealthy plant foods are linked to an increased risk. Promoting diets rich in healthy plant foods could support the reduction of ovarian cancer risk. Full article