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Perilipins (PLINs) are a family of proteins that coat the surface of lipid droplets (LDs), the cell's main storage sites for fats, to control their formation, growth, and breakdown. These proteins share a common structure: an N-terminal PAT domain for initial targeting, a central region of repeating helices that insert into the LD surface, and a C-terminal 4-helix bundle for stable anchoring. While the PAT domain binds diacylglycerol to promote LD formation at the endoplasmic reticulum, the conserved 4-helix bundle's lipid-sensing role has remained elusive. Here, we show that this bundle contains a hydrophobic cleft that specifically binds phosphatidylethanolamine (PE), a cone-shaped lipid promoting membrane bending during LD budding, as predicted by AlphaFold3 (alphafoldserver.com) models and confirmed by docking simulations. Binding assays reveal that the isolated bundle strongly attaches to LD-like particles enriched in PE, but mutations closing the cleft block this interaction. In yeast cells, limiting PE reduces PLIN3 localization to LDs, an effect aggravated by cleft mutations but independent of LD size or number. This PE-binding ability is shared by PLIN2, PLIN4, and PLIN5 but missing in PLIN1, matching their structural differences. Overall, our work reveals how the 4-helix bundle lets PLINs detect and adapt to LD surface lipid makeup, explaining their varied cellular roles and opening paths for treatments in fat-storage diseases such as liver steatosis.
DNA fix needs BRCA1 and BRCA2. These genes stop bad cell growth. They sit on links 17 and 13. Bad changes in some genes raise the risk of womb cancer. This is true for a type called serous. These faults also boost the chance for womb and breast bad growth. BRCA1 faults lead to more severe womb tumors than BRCA2 faults. Drugs like tamoxifen increase this risk more. More breast trouble adds risk, too. The risk of womb cancer in a lifetime is still small - not quite 3% for BRCA1 folk and just 1 or 2% for BRCA2 folk. Yet, BRCA1 faults might bring worse sorts of womb growth. These have bad cell traits and weak fix tools. Now, rules do not say all BRCA folk must have their wombs removed or get womb checks. They say to talk hard about risks. Think of drug use, weight, and past breast issues. New tools look at many cell parts at once. This helps grasp how BRCA links to bad growth. It lets us pick just the right cures, like PARP drugs. But past studies differ. We need wide, long studies with many groups of folks. This will show the real womb cancer risk. So, BRCA faults do play a small but key part in a bad womb growth type. But they mainly boost breast and egg trouble.
Withholding and withdrawing life-sustaining therapy (LST) is common in European ICUs but significant variations exist. Behaviour artificial intelligence technology (BAIT) may help standardize the ethical dilemma to continue or withdraw LST for patients already admitted to the ICU. Several sessions with intensivists of an academic medical centre and a large urban teaching hospital were held to determine the criteria influencing the process. A discrete choice experiment was conducted during which 25 hypothetical cases were presented to the participants. For each case the participants had to decide whether they would continue, continue with a time limited trial of one week, or withdraw LST. The results of the experiment were used to develop a multinomial logistic regression model that was incorporated in a web-based decision-support system. Thirty-six participants (intensivists and fellows in intensive care medicine) completed the experiment. The estimated model consisted of twelve covariates and showed good model fit (McFadden's ρ2 0.25). The most important covariates were age, patient values, expected cardiovascular and pulmonary impairment after ICU discharge and frailty at admission. The BAIT system lets intensivists view expected decisions based on documented criteria and uses color-coding to show the magnitude of the effect and its direction (i.e. to continue or withdraw LST). We developed a BAIT system that may support clinicians facing the dilemma of continuing or withdrawing LST by elucidating the key criteria involved in assessing medical futility.
Defect detection in rubber-metal bushings is difficult because many faults appear as small surface-height changes rather than intensity changes. Here, we present a height-information inspection method that converts 16-bit laser profile measurements into three-channel height-color images for detector input. Paired grayscale images are used only as a baseline for modality comparison. The model replaces the standard attention block in a real-time one-stage detector with a height-aware attention module that combines feature-group channel attention, multi-scale spatial attention, and adaptive feature fusion. This design lets the network emphasize local height transitions while it retains contextual surface information. On 11,000 bushing images, height-color input alone increased recall by 45.3% over grayscale input. With the proposed attention module, the detector reached 92.9% precision, 93.5% recall, and 96.7% mean average precision at 0.5 intersection over union. Compared with representative transformer, graph-based, and multimodal detectors, the method improved mean average precision by 4.9-18.8 percentage points and reduced computation by 40-82%. These results show that joint design of image representation and network architecture improves industrial bushing inspection.
Rebound pain (RP) is recognized after locoregional analgesia, yet its incidence and determinants remain insufficiently characterized in major abdominal oncologic surgery patients managed with patient-controlled epidural analgesia (PCEA). We conducted a retrospective single-center study of patients undergoing major abdominal oncologic surgery with PCEA (n = 199). Maximal Daily Pain Scores (MDPS) were recorded during and after PCEA withdrawal. RP was defined as an increase of ≥3 points on the 11-point Numeric Rating Scale within 8 hours of discontinuation. Data on timing, modality, and prescriptions of systemic relay analgesics were collected. Descriptive, univariate, and multivariate logistic regression analyses evaluated risk factors for RP. Of 199 patients, 92(46%) developed RP within 8 hours after PCEA withdrawal. Median PCEA duration was shorter in RP patients (93.3 h) compared with non-RP patients (112.5 h); p = 0.001, despite most receiving anticipatory relay prescriptions. Longer PCEA duration remained independently associated with a lower risk of RP in multivariate analysis (OR 0.81 per 12-hour increase; 95%CI,0.70-0.92; p = 0.002). The choice of relay analgesia was significantly associated with pain intensity during the two days before PCEA discontinuation, suggesting possible confounding by indication. RP affected nearly half of patients after PCEA withdrawal. Extended PCEA duration significantly reduced its incidence. What was studied: We studied rebound pain: a sudden increase in pain after stopping epidural pain relief following major abdominal cancer surgery. An epidural is a tube placed near the spine to deliver pain medicine. Patient-controlled epidural analgesia (PCEA) lets patients press a button to receive safe, preset doses of pain medicine through this epidural.How did we do it:We reviewed the records of 199 patients who had major abdominal cancer surgery between June 2018 and August 2019. All patients received PCEA after surgery. Pain scores, from 0 to 10, were recorded before and during the 8 hours after PCEA was stopped. Rebound pain was defined as an increase of 3 points or more. We also studied the replacement pain medicines and patient or treatment factors linked to rebound pain.What did we find:Almost half of the patients developed rebound pain within 8 hours. Patients with rebound pain had used PCEA for a shorter time: 93 hours compared with 112 hours in those without rebound pain. Higher pain scores before stopping PCEA were linked to stronger replacement treatments, such as intravenous opioid pain pumps, suggesting that doctors adjusted pain treatment to each patient. However, this did not fully prevent rebound pain. After accounting for other factors, longer PCEA duration was the strongest protective factor.Why it matters:Better planning before stopping epidural pain relief may help reduce sudden severe pain after surgery.
We demonstrate a new charge weighted quality factor, noted Q*, based on the practical application of particle linear energy transfer (LET) and charge (Z)-dependent quality factors for LET-based radiation dosimetry in space. Z contribution factors are defined according to the energy spectra of galactic cosmic rays (GCRs). The Z-weighted quality factor depending solely on the LET (Q*) under solar minimum conditions in free space differs by only a few percent from values obtained with spacecraft shielding or during the solar maximum spectrum. Q* values based on various biological endpoints exhibit peaks at higher LETs than those defined in ICRP-60. The reduced contribution of GCR heavy components under Q*, compared to QICRP of ICRP-60, suggests that current dose assessments may be overestimated. The proposed Q* approach is applicable to different LET- and Z-dependent quality factors, and provides practical dose evaluation using LET measurements.
Existing pangenome file formats are designed for batch processing. Graphs must be loaded into memory, and alignment files must be read sequentially. Indexed file formats that can be used directly from disk would be more appropriate for interactive applications. We propose GBZ-base and GAF-base - SQLite-backed file formats comparable to GBZ and GAF. GBZ-base supports efficient extraction of local subgraphs, and GAF-base lets us extract all alignments to the subgraph. Additionally, GAF-base is smaller than any other file format for sequence-to-graph alignments. From https://github.com/jltsiren/gbz-base and https://crates.io/crates/gbz-base under the MIT license.
Modern pacemakers incorporate arrhythmia-response algorithms, ventricular pacing minimization protocols, and safety mechanisms that generate ECG patterns indistinguishable from pathological AV block, sensing malfunction, or device-mediated tachycardia. Failure to recognize these algorithm-driven signatures leads to unnecessary interventions, misdiagnosis, and inappropriate device reprogramming. This manuscript is the second in a two-part series on pacemaker ECG interpretation. We conducted a narrative review of peer-reviewed literature and device-specific documentation on algorithm-driven ECG behavior, synthesizing evidence across arrhythmia recognition, upper rate physiology, ventricular pacing minimization, mode switching, safety mechanisms, and hysteresis algorithms. Pacemaker-mediated tachycardia produces regular paced wide-complex tachycardia locked at the upper tracking rate, initiated by any event with retrograde VA conduction. Ventricular tachycardia is identified by QRS morphology diverging from the known paced pattern, absent pacing spikes, and AV dissociation. Upper rate Wenckebach behavior mimics Mobitz type I AV block; 2:1 upper rate response mimics second-degree AV block. Ventricular pacing minimization algorithms produce isolated nonconducted P waves and prolonged AV intervals that simulate pathological conduction disease. Mode switching causes abrupt rate drops misidentified as output failure. Ventricular safety pacing generates a conspicuously short, fixed AV interval. Three discrete pacing artifacts reflect AV-sequential cardiac resynchronization therapy (CRT), ventricular safety pacing in CRT, or His-bundle pacing with backup RV output. Rate and AV hysteresis produce pauses and wandering AV intervals mimicking oversensing or Wenckebach periodicity. Recognizing algorithm-driven ECG patterns requires knowledge of device timing intervals and refractory periods, which lets clinicians distinguish programmed behavior from true malfunction or cardiac arrhythmia.
Molecular fingerprints and physicochemical descriptors encode complementary structural information, yet most Tox21 benchmarks evaluate only one representation at a time. This study examined whether integrating both closes the reported gap between classical and graph-based deep learning. Six algorithms (Random Forest, XGBoost, LightGBM, SVM, MLP, Logistic Regression) were trained on a 3,131-dimensional feature vector combining 59 RDKit descriptors with ECFP4, ECFP6, and RDKit topological fingerprints (1,024 bits each), across 8,014 Tox21 compounds and 12 endpoints, evaluated under both stratified 5-fold cross-validation and a Bemis-Murcko scaffold split. Bootstrap resampling (n = 1,000) gave 95% confidence intervals. Random Forest achieved the highest mean AUC-ROC of 0.846 (SD = 0.053) under stratified 5-fold cross-validation, with 10 of 12 endpoints exceeding 0.80. Under a Bemis-Murcko scaffold split matching the deep learning benchmark protocol, RF mean AUC was 0.839, at or slightly above the published aggregate means of AttentiveFP (0.829) and GROVER (0.831); because these benchmarks report only a single aggregate mean, comparison is aggregate-level only. Adding fingerprints to the descriptor-only baseline improved RF on 10 of 12 endpoints (maximum gain +0.028 on SR-ATAD5). A linear-kernel SVM (0.752) outperformed the originally reported RBF-kernel SVM (0.710), a kernel-calibration artifact; SVM remained the weakest baseline. An integrated fingerprint-descriptor representation lets classical machine learning match published graph neural networks on Tox21 under matched scaffold-split evaluation, without specialized hardware, offering a reproducible, interpretable alternative for computational toxicology.
Electroporation has advanced significantly in biomedical applications, particularly in tissue ablation. While the influence of individual cellular features, such as cell size and nucleus-to-cytoplasm ratio, on ablation is recognized, this study highlights the key role of the cell grouping factor, namely cell density. Experiments using 2D cell monolayers demonstrated a density-dependent ablation effect: higher cell density significantly elevates resistance to electroporation, evident from reduced ablation areas and ~30% higher lethal electric thresholds (LETs). To explain this, a cell-cell proximity electroporation model was established representing cell density by intercellular distance and degree of containment. It indicated significant inhibition when cells were closely spaced (0-2 µm), with lower pore density and reduced pore area ratios (PARs). This inhibitory effect decays logarithmically as spacing increases, persisting over several cell diameters. Furthermore, fully surrounded cells exhibit a 33% lower PAR than isolated cells, consistent with the observed LET gap between high- and low-density populations. As such, the shielding effect from neighboring cells leads to the density-dependent electroporation at cellular scale and may therefore account for the density-dependent ablation observed at tissue scale. In light of this, considering tissue-specific cell density are critical for better mimicking in vivo conditions, and improving precision in electroporation-based tissue therapies.
Alternative splicing of precursor mRNA lets a single gene encode multiple isoforms by joining exons in different combinations. Long-read sequencing resolves this isoform diversity across tissues, cohorts, and conditions. However, the resulting pan-transcriptomes are structurally complex, and their analysis requires repeatedly searching the full catalogue, which is impractical without a queryable index. As splicing patterns differ across conditions, a structure is needed that captures the connectivity between exons, not just their coordinates, so isoforms can be compared by structure across cohorts. We present atroplex, a framework that indexes pan-transcriptome annotations and transcript isoforms in a combined spatial index and graph overlay, capturing both exon coordinates and splice connectivity. atroplex classifies query transcripts against the index, tracks per-sample isoform presence, and enables crosscohort isoform comparison. We indexed 21,005 samples spanning multiple reference resources into a single queryable structure, yielding a comprehensive map of isoform complexity that supports improved transcript discovery and structural comparison across cohorts. atroplex is licensed under GPLv3 and available at https://github.com/ylab-hi/atroplex.
An eco-friendly method lets the thermophilic Bacillus sonorensis SS1 synthesize superparamagnetic nano-sized iron sulfide particles (FeSNPs) as bio-factories. These clean and non-toxic FeSNPs have led to increased interest in using inorganic nanoparticles (NPs) in health materials and industrial products. The biosynthesized FeSNPs exhibited an average particle size of approximately 4.5 nm and a negative surface charge (- 16.8 ± 0.6 mV), indicating good colloidal stability. In vitro cytotoxicity evaluation using the MTT assay demonstrated concentration-dependent growth inhibition against five cancer cell lines (A549, HeLa, MCF-7, HepG2, and HCT-116), with IC₅₀ values ranging from 18 to > 100 µg/mL. The highest cytotoxic activity was observed against A549 (IC₅₀ = 18 µg/mL) and HeLa (IC₅₀ = 27 µg/mL) cells, and MCF-7 (IC₅₀ = 75 µg/mL) showed moderate sensitivity, while comparatively lower sensitivity was detected in HepG2 and HCT-116 cells (IC₅₀ value > 100 µg/mL). In contrast, normal peripheral blood mononuclear cells (PBMCs) exhibited minimal cytotoxicity, with an IC₅₀ value > 100 µg/mL, indicating selective anticancer activity of the FeSNPs. The molecular docking studies demonstrated that the FeSNPs exhibit a strong binding affinity to the active sites of Caspase-3, VEGFR, and Aurora-A. These interactions suggest that FeSNPs can effectively inhibit critical pathways involved in tumor growth, angiogenesis, and radiotherapy resistance. The biochemical validation confirmed these interactions, showing a 14.6-fold induction of active Caspase-3 (516.8 ± 20.1 pg/mL) in A549 cells. Additionally, FeSNPs exhibited potent inhibitory activity against VEGFR2 (IC₅₀ = 0.447 ± 0.019 µg/mL) and Aurora-A (IC₅₀ = 0.268 ± 0.012 µg/mL). These findings demonstrate the potential of FeSNPs as multifunctional agents capable of simultaneously triggering apoptosis and disrupting angiogenic and proliferative pathways. The findings focus on the potential of FeSNPs as versatile therapeutic agents in cancer therapy.
The optimal power flow (OPF) problem is essentially about finding the cheapest and safest way to operate a power system without breaking any of the operational limits that govern it. In this paper, we introduce a new Modified Newton-Raphson-Based Optimizer (MNRBO) specifically designed to tackle real-world OPF problems, integrating renewable photovoltaic sources. The NRBO integrates gradient-inspired search using the NR search rule and the trap avoidance strategy. Our MNRBO extends this framework by adding two adaptive components. An Adaptive Crossover Mechanism (ACM) is added that lets solutions dynamically exchange useful information with each other, keeping the population diverse and preventing everyone from getting stuck in the same mediocre spot too soon. Also, a Sigmoid decay mode that smoothly and gradually shifts the algorithm from broad exploration (looking around the whole search space) in the early stages to careful fine-tuning (exploitation) toward the end. This gives much steadier and more predictable convergence than the original abrupt or polynomial decay. The resulting MNRBO algorithm forms a self-evolving optimization framework that automatically adjusts its learning strategy as the search progresses. We thoroughly tested MNRBO on the standard IEEE 30-bus system across a wide range of realistic scenarios: minimizing fuel costs (with smooth quadratic models, valve-point ripples, and multi-fuel options), handling generators with prohibited operating zones, and minimizing transmission losses under normal, peak, and light-load conditions. In every single case, MNRBO delivered better solutions, faster and more consistent convergence, and dramatically lower variation across multiple runs compared to the original NRBO and several other state-of-the-art algorithms. The results clearly show that MNRBO is not only more accurate but also far more robust and dependable, exactly what operators need when solving OPF in real power systems where reliability really matters. To further validate the applicability of the proposed approach under renewable energy uncertainty, a probabilistic OPF framework incorporating photovoltaic renewable generation is developed. In this case study, the integration of renewable solar photovoltaic energy in conditions of variable irradiance is examined using the Point Estimate Method (PEM) with lognormal irradiance modeling. In addition, an ablation study is conducted to quantify the individual contributions of the ACM and sigmoid decay strategy in the presence of renewable photovoltaic sources, demonstrating their significant impact on convergence stability, robustness, and optimization accuracy.
Gliomas remain among the most treatment-resistant malignancies of the central nervous system. Glioblastoma (GBM), the most aggressive adult-type diffuse glioma, is associated with persistently poor survival despite maximal safe resection followed by chemoradiation. Gliomas do not grow in isolation. Work over the past twenty years has dismantled the older tumor-centric view of glioma biology, replacing it with a model in which malignant cells operate within a tumor microenvironment (TME) composed of immune, vascular, stromal, and neural elements that together govern disease behavior. What makes the glioma TME so difficult to treat is not just its composition of immune cells, vasculature, stroma, and neurons, but the fact that these elements are arranged unevenly across the tumor. Different regions harbor different cellular mixtures and signaling environments, and, as a result, different vulnerabilities to therapy. Cytoreduction has not lost its importance, far from it. However, the same surgical window now also serves a different purpose; it lets the surgeon see which tissue is biologically dangerous rather than just visually abnormal, locate the true edge of infiltration, and get therapeutics past a blood-brain barrier (BBB) that has historically locked them out of the brain. This review examines two technology domains, including: (1) optical theranostics (5-aminolevulinic acid fluorescence-guided surgery, fluorescein-guided visualization, Raman spectroscopy, and stimulated Raman histology); and (2) blood-brain barrier disrupting technologies. The direction they collectively point toward is a version of glioma surgery that is guided less by anatomy and more by the biology of the tumor itself.
Prior work often finds racial/ethnic differences in mortality following acute myocardial infarction (AMI), but is subject to selection bias. Similar selection issues arise for differences associated with socioeconomic status (SES). Re-examine the association between patient race/ethnicity, area-level socioeconomic status (area-SES), and post-AMI mortality using a data source that limits patient selection of hospital or cardiologist, or vice-versa, and which lets us distinguish between-hospital from within-hospital sources of differences. We compare mortality disparities with no controls, with controls typical of the prior literature ("typical controls"), and more extensive controls, including hospital, cardiologist, and calendar quarter fixed effects (FEs). The study uses a 100% sample of 681,000 Medicare Fee-for-Service patients aged 68+ hospitalized for incident (first) AMI over 2008-2019. Post-AMI mortality in-hospital, within 30 days after discharge and over periods up to 3 years post-discharge. We study patients with ST-segment elevation MI (STEMI) and non-ST-segment elevation MI (nSTEMI) separately. With no or typical controls, Blacks, Hispanics, and Asians have higher in-hospital mortality than Whites; lower SES also predicts higher mortality. However, higher mortality is substantially explained by between-hospital differences in mortality rates. Longer-term mortality is higher for Black patients, consistent with the importance of post-discharge pathways. Post-AMI disparities in outcomes can be strongly affected by selection effects, especially the tendency for poor and minority persons to be treated at lower-quality hospitals. Disparities measurement and policy discussions should distinguish access-related hospital sorting from within-hospital processes and pay greater attention to post-discharge pathways.
Vehicular Ad Hoc Networks (VANETs) require secure, efficient, and scalable authentication mechanisms to ensure trust among vehicles and roadside units (RSUs). Traditional one-to-one authentication approaches often lead to high communication and computational overheads, making them unsuitable for large-scale vehicular environments. This leads to a security traffic jam, rendering the process inefficient and unsuitable for real-time safety applications. To address this, study proposes a lightweight authentication protocol that supports both batch and transfer authentication, enabling multiple vehicles to be authenticated simultaneously and allowing authentication validity to be transferred between RSUs without re-executing the full protocol. In batch authentication, a roadside unit verifies a group of vehicles simultaneously instead of one at a time, much like a guard approving an entire busload of pre-verified passengers, which greatly reduces delays at intersections or toll booths. In transfer authentication, when a vehicle moves from one roadside unit's area to another, the verification data are securely transferred, allowing seamless continuity without restarting the process, similar to a concert wristband that lets attendees move between stages without repeated checks. This scheme leverages the mathematical properties of Chebyshev polynomials to provide strong security with reduced overhead. Performance analysis shows that the proposed method achieves a communication cost of only 42n bytes and a significantly lower computational complexity than existing schemes. This approach enhances scalability, reduces message exchange, and maintains robust resistance against common attacks, making it well-suited for real-time vehicular communications. Overall, this new protocol enhances the practicality of VANETs by reducing data overhead, accelerating authentication, and efficiently managing high traffic volumes, making it a secure and scalable solution for the future of intelligent transportation systems.