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Raman-based technologies have enabled many ground-breaking scientific discoveries related to surface science, single-molecule chemistry and biology. For example, researchers have identified surface-bound molecules by their Raman vibrational modes and demonstrated polarization-dependent Raman gain. However, a surface-constrained Raman laser has yet to be demonstrated because of the challenges associated with achieving a sufficiently high photon population located at a surface to transition from spontaneous to stimulated Raman scattering. Here, advances in surface chemistry and in integrated photonics are combined to demonstrate lasing based on surface stimulated Raman scattering (SSRS). By creating an oriented, constrained Si–O–Si monolayer on the surface of integrated silica optical microresonators, the requisite conditions for SSRS are achieved with low threshold powers (200 μW). The expected polarization dependence of SSRS due to the orientation of the Si–O–Si bond is observed. Owing to the ordered monolayer, the Raman lasing efficiency is improved from ~5% to over 40%.Surface stimulated Raman scattering-based lasing is achieved using just a monolayer of molecules on silica optical microresonators.

Although a variety of nanoparticles (NPs) have been used for drug delivery applications, their surfaces are immediately covered by plasma protein corona upon systemic administration. As a result, the adsorbed proteins create a unique biological identity of the NPs that lead to unpredictable performance. The protein corona on NPs could also impede active targeting, induce off-target effects, trigger particle clearance and even provoke toxicity. This article reviews the fundamentals of NP-plasma protein interaction, the consequences of the interactions, and provides insights into the correlations of protein corona with biodistribution and cellular delivery. We hope that this review will trigger additional questions and possible solutions that lead to more favorable developments in NP-based targeted delivery systems.

Image dehazing is a crucial task in computer vision, aimed at restoring the clarity of images impacted by atmospheric conditions like fog, haze, or smog, which degrade image quality by reducing contrast, color fidelity, and detail. Recent advancements in deep learning, particularly convolutional neural networks (CNNs), have shown significant improvements by directly learning features from hazy images to produce clear outputs. However, color distortion remains an issue, as many methods focus on contrast and clarity without adequately addressing color restoration. To overcome this, we propose a Color-Correction Network (CCD-Net) based on dual-branch fusion of different color spaces for image dehazing, that simultaneously handles image dehazing and color correction. The dehazing branch utilizes an encoder–decoder structure aimed at restoring haze-affected images. Unlike conventional methods that primarily focus on haze removal, our approach explicitly incorporates a dedicated color-correction branch in the Lab color space, ensuring both clarity enhancement and accurate color restoration. Additionally, we integrate attention mechanisms to enhance feature extraction and introduce a novel fusion loss function that combines loss in both RGB and Lab spaces, achieving a balance between structural preservation and color fidelity. The experimental results demonstrate that CCD-Net outperforms existing methods in both dehazing performance and color accuracy, with CIEDE reduced by 40.81% on RESIDE-indoor and 45.57% on RESIDE-6K compared to the second-best-performing model, showcasing its superior color-restoration capability.

Purpose Measurement of central venous pressure (CVP) can be a useful clinical tool. However, the formal utility of CVP measurement in preventing mortality in septic patients has never been proven. Methods The Medical Information Mart for Intensive Care III (MIMIC-III) database was searched to identify septic patients with and without CVP measurements. The primary outcome was 28-day mortality. Multivariate regression was used to elucidate the relationship between CVP measurement and 28-day mortality, and propensity score matching (PSM) and an inverse probability of treatment weighing (IPTW) were employed to validate our findings. Results A total of 10,275 patients were included in our study, of which 4516 patients (44%) underwent CVP measurement within 24 h of intensive care unit (ICU) admission. The risk of 28-day mortality was reduced in the CVP group (OR 0.60 (95% CI 0.51–0.70; p  < 0.001)). Patients in the CVP group received more fluid on day 1 and had a shorter duration of mechanical ventilation and vasopressor use, and the reduction in serum lactate was greater than that in the no CVP group. The mediating effect of serum lactate reduction was significant for the whole cohort ( p  = 0.04 for the average causal mediation effect (ACME)) and patients in the CVP group with an initial CVP level below 8 mmHg ( p  = 0.04 for the ACME). Conclusion CVP measurement was associated with decreased risk-adjusted 28-day mortality among patients with sepsis and was proportionally mediated through serum lactate reduction.

Background Sex differentiation is a crucial process that determines the sex ratio of a population, impacting reproductive success and ecological dynamics. In parasitoid wasps and other insects, the sex ratios significantly influence ecological adaptability and biological control potential. Particularly in parasitoid wasps, the regulation of sex ratio is closely linked to the shift between gamogenesis and parthenogenesis. However, the molecular mechanisms regulating sex ratios remain largely unresolved. Results We found that when the female wasp Copidosomopsis nacoleiae (Eady) reproduces through parthenogenesis, the offspring are all male. However, when the wasp reproduces sexually, females accounted for 24.65 ± 3.64%, and males accounted for 75.35 ± 3.64%. Additionally, we identified 123,982 isoforms and predicted 5,675 alternative splicing events through transcriptomics. Interestingly, we found that the squid gene exhibited different splicing patterns in male and female offspring under different reproductive modes, suggesting that it may play a role in sex differentiation in C. nacoleiae . Conclusions Overall, the study provides important insights into the reproductive biology of C. nacoleiae and sheds light on the molecular mechanisms underlying sex differentiation in this species.

Oxygen isotope ( δ 18 O) of seawater is an excellent proxy for tracing the origins of water masses and their mixing processes. Combining with hydrographic observation, hybrid coordinate ocean model (HYCOM) analysis data, and seawater oxygen isotope, we investigated the source of the South China Sea Warm Current (SCSWC) in the southwestern Taiwan Strait and its underlying mechanism. Results show that the Kuroshio subsurface water (KSSW) can intrude the continental slope in the southwestern Taiwan Strait, and thereby climb up the continental slope coupled with upwelling. The δ 18 O-salinity relationship further indicates that in spring, the SCSWC in the southwestern Taiwan Strait originates from the upslope deflection of the slope current formed by the KSSW intrusion into the South China Sea, rather than from the west segment of the SCSWC formed to the east of Hainan Island. In addition, the southward flowing Zhe-Min Coastal Current (ZMCC) can reach as far as the Taiwan Bank (TB) and deflects offshore over the western TB at approximately 23.5°N, to some extent affecting the SCSWC. Moreover, this study reveals that seawater δ 18 O is exquisitely sensitive to the determination of the origin and transport of water masses as compared with traditional potential temperature-salinity plot ( θ -S) and HYCOM analysis data. In addition, their coupling can more reliably interpret the mixing processes of shelf water masses.

Background Although the mean arterial pressure (MAP) target of 65 mmHg was achieved, diastolic blood pressure (DBP) was still low in some septic shock patients. The effects of DBP on the prognosis and optimal target for patients with septic shock are unclear. We sought to investigate the relationship between DBP and 28-day mortality in septic shock patients. Methods In this retrospective observational study, we obtained data from the Chinese Database in Intensive Care (CDIC). We included patients with an admission diagnosis of septic shock and shock was controlled. DBP was measured every 1 h, and the mean DBP during the first 24 h (mDBP 24h ) was recorded. The primary outcome was 28-day mortality. Multivariable logistic regression determined the relationship between mDBP 24h and 28-day mortality. Results In total, 1251 patients were finally included. The 28-day mortality of included septic shock patients was 28.3%. The mDBP 24h , not mSBP 24h , was higher among 28-day survivors compared with non-survivors. 28-day mortality was inversely associated with mDBP 24h (unadjusted OR 0.814 per 10 mmHg higher mDBP 24h , P  = 0.003), with a stepwise increase in 28-day mortality at lower mDBP 24h . The 28-day mortality of patients with mDBP 24h  < 59 mmHg had an absolute risk reduction of 9.4% ( P  = 0.001). And mDBP 24h  < 59 mmHg was the remaining high risk factor inversely associated with 28-day mortality after multivariable adjustment (adjusted OR 1.915, 95% CI 1.037–3.536, P  = 0.038), while mMAP 24h and mSBP 24h were not. Conclusion In patients with septic shock after initial resuscitation, we observed an inverse association between mDBP 24h and 28-day mortality. The poor outcomes in patients with mDBP 24h  < 59 mmHg provide indirect evidence supporting a further DBP goal of 59 mmHg for patients with septic shock after MAP of 65 mmHg was achieved.

In the control of agricultural and forestry pests, excessive reliance on chemical pesticides has led to increasingly severe issues, such as toxic residues and heightened pest resistance. The effective use of biological control has become a major focus in pest management. Parasitoid wasps, as a critical natural enemy of pests, are widely distributed, diverse in species, and play an essential role in natural pest control. Copidosomopsis nacoleiae, a recently discovered polyembryonic endoparasitoid wasp, parasitizes Diaphania angustalis; yet, its biological characteristics remain insufficiently studied. The artificial rearing and population propagation of this wasp have not yet been achieved, and its reproductive behavior and rhythm are not fully understood. To better utilize natural enemy resources and maximize their pest control benefits, we conducted laboratory rearing, behavioral observation, and population surveys to investigate the morphological characteristics, life history, and behavioral patterns of C. nacoleiae. Under laboratory conditions, C. nacoleiae has a generational cycle of 48.71 ± 0.48 days, with an egg–larval period of 32.17 ± 0.20 days and a pupal period of 14.36 ± 0.27 days. Adult wasps require nutritional supplementation and have a maximum lifespan of 2.18 ± 0.09 days when fed 10% honey water. The pre-mating period for adults is 4.72 ± 0.24 h, with an average mating frequency of 5.17 ± 1.65 times per lifetime. Females have a pre-oviposition period of 2.80 ± 0.31 h and an oviposition period of 4.52 ± 0.12 h, laying between 2 and 95 eggs, with an average of 12.75 ± 9.99 eggs, totaling 107.55 ± 28.38 eggs over their lifespan. Offspring production increases with the body length of the host’s mature larvae. Through the successful establishment of a laboratory population of D. angustalis, the biological characteristics, reproductive behavior, and rhythms of C. nacoleiae were systematically examined in this study, and its occurrence dynamics in the field were investigated. These results provide a theoretical foundation for the large-scale propagation and application of C. nacoleiae to control D. angustalis populations effectively.

Background This study aims to develop and validate predictive models for postoperative complications and early recurrence in hepatocellular carcinoma (HCC) patients. Methods In a 7:3 ratio, 633 post-hepatectomy HCC patients were modeled and validated. Using 1561 radiomic features from two-dimensional shear wave elastography (2D-SWE), predictive models for the Comprehensive Complication Index (CCI) and recurrence were constructed, refined by LASSO regression, and integrated with clinical and pathologic data. Model performance was assessed using calibration plots and AUCs. Results The CCI model included Rad signature ( P  < 0.001), platelet count ( P  = 0.012), neutrophil levels ( P  = 0.032), Emax ( P  = 0.023), and differentiation status ( P  = 0.045), with AUCs of 0.896 for the modeling cohort and 0.832 for validation. The recurrence model included Rad signature ( P  < 0.001), arginase ( P  = 0.038), patient age ( P  = 0.001), adjacent tissue’s Emean ( P  = 0.009), and blood flow ( P  = 0.03), with AUCs of 0.854 and 0.844 for modeling and validation cohorts, respectively. The Rad signature AUC of 0.884 surpassed the nomogram’s AUC of 0.854 in the modeling set. Conclusion Radiomic-based CCI and recurrence models effectively predict complications and short-term recurrence in post-hepatectomy HCC patients, aiding in identifying high-risk individuals for timely intervention.

Background A key challenge in the medical field is managing gallbladder polyps (GBP) > 10 mm, especially when their nature is uncertain. GBP with a diameter exceeding 10 mm are associated with an increased risk of gallbladder cancer, making the key to their management the differentiation between benign and malignant types. The current practice, due to the inability to predict accurately, leads to excessive surgeries and ineffective follow-ups, increasing patient risks and medical burdens. Purpose This study aims to establish an imaging radiomics model using clinical data and contrast-enhanced ultrasound (CEUS) to predict neoplastic GBP exceeding 10 mm in diameter preoperatively. Materials and methods Data from 119 patients with GBP > 10 mm of unknown origin were analyzed. A total of 1197 features were extracted from the GBP area using conventional ultrasound (US) and CEUS. Significant features were identified using the Mann–Whitney U test and further refined with a least absolute shrinkage and selection operator (LASSO) regression model to construct radiomic features. By integrating clinical characteristics, a radiomics nomogram was developed. The diagnostic efficacy of the preoperative logistic regression (LR) model was validated using receiver operating characteristic (ROC) curves, calibration plots, and the Hosmer–Lemeshow test. CEUS is an examination based on conventional ultrasound, and conventional two-dimensional ultrasound still poses significant challenges in differential diagnosis. CEUS has a high accuracy rate in diagnosing the benign or malignant nature of gallbladder space-occupying lesions, which can significantly reduce the preoperative waiting time for related examinations and provide more reliable diagnostic information for clinical practice. Results Feature selection via Lasso led to a final LR model incorporating high-density lipoprotein, smoking status, basal width, and Rad_Signature. This model, derived from machine learning frameworks including Support Vector Machine (SVM), Logistic Regression (LR), Multilayer Perceptron (MLP), k-Nearest Neighbors (KNN), and eXtreme Gradient Boosting (XGBoost) with fivefold cross-validation, showed AUCs of 0.95 (95% CI: 0.90–0.99) and 0.87 (95% CI: 0.72–1.0) in internal validation. The model exhibited excellent calibration, confirmed by calibration graphs and the Hosmer–Lemeshow test (P = 0.551 and 0.544). Conclusion The LR model accurately predicts neoplastic GBP > 10 mm preoperatively. Radiomics with CEUS is a powerful tool for analysis of GBP > 10 mm. The model not only improves diagnostic accuracy and reduces healthcare costs but also optimizes patient management through personalized treatment plans, enhancing clinical outcomes and ensuring resources are more precisely allocated to patients who need surgery. Key results The final radiomic features, post-Lasso selection, were fed into various machine-learning algorithms including SVM, LR, MLP, KNN, and XGBoost, facilitating the formulation of risk models. The subsequent development of a radiomics nomogram, which amalgamates Rad Signature with clinical indicators, exhibited an AUC of 0.95 (95% CI: 0.90–0.99). Furthermore, sensitivity, specificity, positive predictive value, negative predictive value, and overall diagnostic accuracy were established to be 94.4%, 84.6%, 63%, 98.2%, and 63%, respectively. CEUS radiomic signatures had an AUC of 0.89 (95% CI: 0.82–0.97), outperforming US radiomic signatures (AUC: 0.85, 95% CI: 0.75–0.94) in predicting neoplastic GBP > 10 mm, offering valuable clinical insights.