Category: Nevin Manimala

Commun Chem. 2026 Jan 3. doi: 10.1038/s42004-025-01866-8. Online ahead of print.

ABSTRACT

Machine learning (ML) and artificial intelligence (AI) techniques are transforming the way chemical reactions are studied today. Datasets from high-throughput experimentation (HTE) are generated to better understand the reaction conditions crucial for outcomes such as yields and selectivities. However, it is often overlooked that datasets from such designed experiments possess a specific structure, which can be captured by a statistical model. Ignoring these data structures when applying ML/AI algorithms can result in misleading conclusions. In contrast, leveraging knowledge about the data-generating process yields reliable, interpretable, and comprehensive insights into reaction mechanisms. A particularly complex dataset is available for the Buchwald-Hartwig amination. Using this dataset, a statistical model for such HTE-generated chemical data is introduced, and a parameter estimation algorithm is developed. Based on the estimated model, new insights into the Buchwald-Hartwig amination are discussed. Our approach is applicable to a wide range of HTE-generated data for chemical reactions and beyond.

PMID:41484279 | DOI:10.1038/s42004-025-01866-8

Eye (Lond). 2026 Jan 4. doi: 10.1038/s41433-025-04229-8. Online ahead of print.

NO ABSTRACT

PMID:41484256 | DOI:10.1038/s41433-025-04229-8

Eur Radiol. 2026 Jan 4. doi: 10.1007/s00330-025-12272-y. Online ahead of print.

ABSTRACT

OBJECTIVES: To evaluate the effect of prostate volume on the risk of clinically significant prostate cancer (csPCa) across a range of PSA-density (PSAd) values, and to explore the relationship between PI-RADS category and PSAd in predicting csPCa.

MATERIALS AND METHODS: We retrospectively analyzed 2190 patients undergoing mpMRI for suspected PCa. Patients were classified as csPCa and clinically insignificant (negative and insignificant PCa). Logistic regression was performed to assess the csPCa risk at different PSAd cut-offs across different prostate volume subgroups (< 40, 40-60, 60-80, > 80 mL) and PI-RADS categories. The effect of prostate volume on PSAd performance was evaluated using ROC analysis. To assess robustness, we performed an 80:20 split-sample internal validation.

RESULTS: 747/2190 (34.1%) patients had PCa, including 571 (26.1%) with csPCa. Regardless of PSAd, csPCa risk exceeded 30% for PI-RADS 4 and 50% for PI-RADS 5. At a 10% csPCa risk threshold, the optimal PSAd cut-offs were 0.20 ng/mL² for PI-RADS 1-2 and 0.12 ng/mL² for PI-RADS 3. Logistic regression showed a significant inverse correlation between prostate volume and csPCa probability. Notably, 79% of csPCa patients with prostate volume ≤ 40 mL had a PSAd ≥ 0.15 ng/mL², compared to only 22.4% with volumes ≥ 60 mL. PSAd performed significantly worse for larger glands (≥ 60 mL), with AUCs of 0.70 versus 0.84 (≤ 40 mL) and 0.82 (40-60 mL), both p < 0.001.

CONCLUSION: The optimal PSAd cut-offs for guiding biopsy decisions were 0.20 ng/mL² for PI-RADS 1-2 and 0.12 ng/mL² for PI-RADS 3. When using PSAd to guide biopsy decision for PI-RADS 1-3 patients with large prostates (> 60 mL), caution is warranted, as PSAd becomes significantly less accurate.

KEY POINTS: Question The optimal PSA-density thresholds for biopsy decisions in PI-RADS 1-3 patients remain uncertain, and data on the impact of prostate volume on its performance are limited. Findings Optimal PSA-density thresholds were 0.20 ng/mL² for PI-RADS 1-2 and 0.12 ng/mL² for PI-RADS 3. Diagnostic performance of PSA-density decreased significantly in men with larger glands (≥ 60 mL). Clinical relevance PSA-density cut-offs of 0.20 (PI-RADS 1-2) and 0.12 ng/mL² (PI-RADS 3) can guide biopsy decisions. In PI-RADS 1-3 patients with large prostate (≥ 60 mL), PSA-density becomes significantly less predictive, and low values may not reliably exclude csPCa.

PMID:41484253 | DOI:10.1007/s00330-025-12272-y

Commun Eng. 2026 Jan 3. doi: 10.1038/s44172-025-00576-2. Online ahead of print.

ABSTRACT

Large-volume 3D dense prediction is essential in industrial applications like energy exploration and medical image segmentation. However, existing deep learning models struggle to process full-size volumetric inputs at inference due to memory constraints and inefficient operator execution. Conventional solutions-such as tiling or compression-often introduce artifacts, compromise spatial consistency, or require retraining. Here we present a retraining-free inference optimization framework that enables accurate, efficient, whole-volume prediction without performance degradation. Our approach integrates operator spatial tiling, operator fusion, normalization statistic aggregation, and on-demand feature recomputation to reduce memory usage and accelerate runtime. Validated across multiple seismic exploration models, our framework supports full size inference on volumes exceeding 1024³ voxels. On FaultSeg3D, for instance, it completes inference on a 1024³ volume in 7.5 seconds using just 27.6 GB of memory-compared to conventional inference, which can handle only 448³ inputs under the same budget, marking a 13 × increase in volume size without loss in performance. Unlike traditional patch-wise inference, our method preserves global structural coherence, making it particularly suited for tasks inherently incompatible with chunked processing, such as implicit geological structure estimation. This work offers a generalizable, engineering-friendly solution for deploying 3D models at scale across industrial domains.

PMID:41484251 | DOI:10.1038/s44172-025-00576-2

Sci Rep. 2026 Jan 3. doi: 10.1038/s41598-025-33777-y. Online ahead of print.

ABSTRACT

Volatile Organic Compounds (VOCs) from nail products pose potential health risks to technicians and clients. The present study investigates the exposure of nail technicians to VOC and assesses the effectiveness of dilution ventilation in mitigating these exposures. A test chamber was configured to simulate a nail salon environment, where three common activities were performed: applying nail polish, removing nail extensions, and nail extension application. A MultiRae Lite gas meter was used to monitor VOC concentrations in the technicians’ breathing zones and the ambient environment under both non-ventilated and dilution-ventilated conditions. The average VOC concentrations in the technicians’ breathing zones were measured at 8.3 (± 8.576) ppm, 116.2 (± 120.04) ppm, and 29.73 (± 7.876) ppm during nail polish application, nail extension removal, and nail extension application, respectively (n = 30 measured over 15 min). For the same activities, the average ambient VOC concentrations in the non-ventilated chamber were 25.03 (± 13.006) ppm, 192.17 (± 114.900) ppm, and 40.90 (± 30.891) ppm. These concentrations were significantly lowered by dilution ventilation to 0.93 (± 0.179) ppm, 12.6 (± 5.21) ppm, and 2.63 (± 0.812) ppm. For all three activities, a t-test verified a statistically significant drop in VOC concentration with dilution ventilation (p < 0.001). Additionally, the study discovered that VOC emissions could be reduced by over 65% at an air change rate (ACH) of 10 per hour. The results indicate that dilution ventilation can significantly decrease VOC exposure; nonetheless, the study concludes that it is insufficient as the sole control strategy in nail salons because concentrations failed to reach the limit levels, even at the highest tested ACH (100 ACH). This research provides a new scientific perspective on ventilation as a means of controlling occupational exposure to VOC in nail salons.

PMID:41484233 | DOI:10.1038/s41598-025-33777-y

Sci Rep. 2026 Jan 2. doi: 10.1038/s41598-025-34330-7. Online ahead of print.

ABSTRACT

Phytophthora rot, caused by the pathogen Phytophthora capsici, is one of the most destructive diseases affecting a wide range of plants, from vegetables to trees. This disease can attack the roots, aerial organs, and even the fruits of the affected plants. One of the latest control methods involves utilizing DNA interference (DNAi) technology, which employs short DNA oligonucleotides to disrupt the expression of pathogenic genes. In this research study, a 26-nucleotide double-stranded OligoDNA fragment, synthesized from the region of the cellulose synthase gene (CesA3), was used to regulate the expression of CesA3. The oligonucleotide sequence was encapsulated using gelatin and whey protein concentrate (WPC), separately, through electrospraying in an electrospinning machine. The physicochemical properties of the produced microcapsules were then evaluated. Pathogenicity tests were conducted using a completely randomized design, which involved baiting hemp seeds infected with P. capsici and treating the roots of bell pepper plants with the encapsulated OligoDNA. The development of the disease was monitored daily for two months, and the disease severity index was assessed. Statistical analysis, using the paired t-test, was performed on the collected data. The pathogen was cultured in a Corn meal agar (CMA) medium to investigate further the impact of the encapsulated OligoDNA on the growth of P. capsici. The results showed that OligoDNA encapsulated with the WPC polymer significantly reduced the disease index (DI) of P. capsici to approximately 16%, compared to other treatments, including the control, WPC, G, and G-Oligo, which exhibited DIs ranging from 35 to 40%. In the colony growth assay, both treatments, WPC and WPC-Oligo DNA, result in reduced growth compared to the control, with WPC-Oligo DNA having the most suppressive effect.

PMID:41484212 | DOI:10.1038/s41598-025-34330-7

Sci Rep. 2026 Jan 2. doi: 10.1038/s41598-025-34282-y. Online ahead of print.

ABSTRACT

Curcumin’s clinical utility is limited by poor bioavailability and dose-dependent toxicity. Although nano-encapsulation can address these shortcomings, rationally optimizing nanocarrier biosafety remains challenging due to the highly multidimensional design space. Here, we develop an interpretable Physics-Informed Machine Learning (PIML) framework that integrates experimental data from 75 curcumin nanocarriers with DLVO stability theory and drug-release kinetics to predict and optimize cytotoxicity. Among the evaluated models, XGBoost attained the highest statistical performance (R² = 0.89), although the PIML model provided physically coherent predictions with similar accuracy (R² = 0.86). SHAP research indicated a moderate negative zeta potential (-30 to -40 mV), chitosan-based coatings, and particle sizes of 150-250 nm as the principal factors contributing to decreased cytotoxicity. Multi-objective Bayesian optimization delineated a Pareto-optimal design space, facilitating approximately 82% toxicity reduction compared to free curcumin, while preserving around 70% loading efficiency. The study develops a proven, generalizable computational methodology that converts intricate nanocarrier design interactions into practical guidelines for the fabrication of safer curcumin-based nanotherapeutics.

PMID:41484189 | DOI:10.1038/s41598-025-34282-y

Sci Rep. 2026 Jan 3. doi: 10.1038/s41598-025-27634-1. Online ahead of print.

ABSTRACT

Reliable cause-specific mortality statistics are crucial for defining health priorities, public health programs, allocating resources, designing and implementing policies to improve healthcare quality and accessibility. India accounts for almost 18 percent of the world’s population. The 2020 report from the Office of the Registrar General of India indicates that the Medical Certification of Cause of Death (MCCD) rate is only 22.5%, with a minimal improvement of just 2.5% over the past decade. This study is the first to provide a comprehensive evaluation of MCCD-patterns across India over the past 15 years, addressing a critical-gap in the literature by identifying regional patterns, disparities, and healthcare variables that have previously been underexplored. Based on MCCD-trends over this period, the states and Union-Territories of India can be categorized into three clusters. Cluster1 includes 23 states with the lowest-average MCCD-rate of 18%, attributed to a low 0.14 doctors per 1000 people, with only 27.4% of hospitals actively reporting-MCCD. In contrast, Clusters2 and 3 have higher-average MCCD-rates of 63% and 60%, respectively, supported by higher 0.27 and 0.33 doctors per 1000 people, with over 80% of hospitals actively reporting-MCCD. Although, the findings indicate that active MCCD-reporting is a major factor associated with MCCD rates, other factors including healthcare infrastructure, state-specific healthcare policies, socioeconomic factors, and administrative management also influence MCCD-rates.

PMID:41484187 | DOI:10.1038/s41598-025-27634-1

Sci Rep. 2026 Jan 2. doi: 10.1038/s41598-025-34323-6. Online ahead of print.

ABSTRACT

LASER Beam Machining (LBM) has emerged as a highly precise and non-contact thermal machining process, widely adopted for cutting complex geometries in advanced engineering materials. Its ability to machine difficult-to-cut alloys with minimal mechanical stress makes it particularly suitable for aerospace and defense components. This paper presents an experimental investigation and multi-objective optimization of LASER Beam Machining (LBM) for XG3 steel, a high-performance alloy used in aerospace and defense applications. The study evaluates the impact of four process parameters i.e. cutting speed (8, 10, 12 m/min), gas pressure (0.5, 0.7, 0.9 Bar), focus point (2, 4, 6 mm), and depth of cut (3, 6, 9 mm) on four output responses: surface roughness, machining time, surface hardness, and burr thickness. Experiments were conducted using a Taguchi L₂₇ orthogonal array on three distinct hole geometries: circular, triangular, and square. Analysis of Variance (ANOVA) revealed that cutting speed was the most dominant factor, contributing over 82% to the variation in surface roughness, 74% for machining time, 81% for surface hardness, and 84% for burr thickness. The interaction between cutting speed and depth of cut was also found to be statistically significant. For single-objective optimization, the ideal parameters to minimize surface roughness were a cutting speed of 12 m/min, gas pressure of 0.5 bar, focus point of 2 mm, and depth of cut of 3 mm. Multi-objective optimization using a Genetic Algorithm (MOGA) generated Pareto fronts to identify balanced trade-off solutions; for a circular profile, this resulted in surface roughness values of 1.10-1.16 μm and machining times of 2.44-2.52 s. Furthermore, two predictive models, Response Surface Methodology (RSM) and a Back-Propagation Artificial Neural Network (BPANN), were developed. Comparative analysis showed the BPANN model was significantly more accurate, with regression coefficients (R) exceeding 0.999 and Mean Absolute Percentage Error (MAPE) values of 1.48% for surface roughness and 0.72% for surface hardness, confirming its superior predictive capability.

PMID:41484160 | DOI:10.1038/s41598-025-34323-6

Sci Rep. 2026 Jan 2. doi: 10.1038/s41598-025-34114-z. Online ahead of print.

ABSTRACT

To investigate temporal changes in the relationship between central venous pressure (CVP) and B-line scores through dynamic monitoring of the two parameters in patients undergoing transurethral resection of the prostate (TURP). A total of 101 patients who underwent TURP were enrolled in the study, with the procedure being performed under general anesthesia. B-line scores (quantified via transthoracic lung ultrasound), CVP, PaO₂/FiO₂ ratio, hemoglobin (Hb), peak airway pressure (Ppeak), arterial sodium (Na⁺), and potassium (K⁺) concentrations were measured at the following time points: baseline at the start of surgery (T0) and upon irrigation fluid volumes of 5000 mL (T1), 10,000 mL (T2), 15,000 mL (T3), and 20,000 mL (T4). Generalized additive mixed models (GAMMs) were used to analyze the longitudinal data. In the longitudinal study of 101 patients undergoing TURP, 90 were included in the final analysis. Poisson GAMM analyses revealed a significant time effect on B-line scores (F = 62.029-69.486, P < 0.001 across models), showing progressive increases from T0 to T4. CVP demonstrated a modest main effect (F = 3.156-4.026, P = 0.045-0.076) and a non-significant interaction with time (F = 1.441-1.967, P = 0.161-0.231), persisting after adjustments for age, weight, height, effective tidal volume, and intraoperative use of furosemide (adjusted pseudo-R² = 0.553). Quantitative and percentile analyses showed only small and uncertain associations between CVP and B-line scores, with confidence intervals consistently crossing the null. Across multiple sensitivity analyses, the estimated CVP effect remained minimal and statistically non-significant regardless of modeling approach. B-line scores increased progressively during TURP, whereas the association between CVP and B-line scores remained small and statistically non-significant across all analyses. These findings suggest that intraoperative B-line dynamics primarily reflect temporal changes rather than CVP fluctuations.Trial registrationThe trial registration number: ChiCTR2200065753, 14/11/2022. Title: “Application of transthoracic lung ultrasound in patients undergoing prostatectomy”. Website: https://www.chictr.ogr.cn.

PMID:41484158 | DOI:10.1038/s41598-025-34114-z