Dendritic morphology of developing hippocampal neurons in Cyp11a1 null mice.

INTRODUCTION: Neurosteroids have a variety of neurological functions, such as neurite growth, neuroprotection, myelination, and neurogenesis. P450scc, encoded by CYP11A1 gene, is the cholesterol side chain cleavage enzyme that catalyzes the first and rate limiting step in the steroidogenesis. In this study, we examine the dendritic morphology in developing hippocampal neurons of Cyp11a1 null mice at P15, a critical period for synapse formation and maturation. METHODS: Knockout mice were maintained until P15 with hormone administration. The Golgi-Cox method stained CA1 and CA3 pyramidal neurons in the hippocampus to reveal dendritic morphology. RESULTS: We demonstrated that Cyp11a1 null mice usually die within 7 days after birth and thus collected brain samples at postnatal day 5 (P5) for examination. There were significant shrinkage of dendrite size and diminishment of dendritic branching in CA1 and CA3 pyramidal neurons in the hippocampus of Cyp11a1 null mice, suggesting a developmental delay. We wonder if this delay may catch up later in life. Since the age of P15 is a critical period for synapse formation and maturation, the Cyp11a1 null mice were rescued by receiving hormone administration until P15 that the dendritic morphology in the developing hippocampal neurons could be examined. The results indicated that the total dendritic length, number of dendritic branches, as well as dendritic arborization in the CA1 and CA3 pyramidal neurons are significantly decreased in P15 knockout mice when compared to the wild type. The spine densities were also significantly decreased. In addition, the western blot analysis revealed decrease PSD-95 expression levels in the knockout mice compared to the wild type at P15. CONCLUSION: These results suggested that Cyp11a1 deficiency impairs the dendritic structures in the developing hippocampal pyramidal neurons.

A SAR Ship Detection Method Based on Adversarial Training.

SAR (synthetic aperture radar) ship detection is a hot topic due to the breadth of its application. However, limited by the volume of the SAR image, the generalization ability of the detector is low, which makes it difficult to adapt to new scenes. Although many data augmentation methods-for example, clipping, pasting, and mixing-are used, the accuracy is improved little. In order to solve this problem, the adversarial training is used for data generation in this paper. Perturbation is added to the SAR image to generate new samples for training, and it can make the detector learn more abundant features and promote the robustness of the detector. By separating batch normalization between clean samples and disturbed images, the performance degradation on clean samples is avoided. By simultaneously perturbing and selecting large losses of classification and location, it can keep the detector adaptable to more confrontational samples. The optimization efficiency and results are improved through K-step average perturbation and one-step gradient descent. The experiments on different detectors show that the proposed method achieves 8%, 10%, and 17% AP (Average Precision) improvement on the SSDD, SAR-Ship-Dataset, and AIR-SARShip, compared to the traditional data augmentation methods.

High-Efficiency Photo-Fenton-like Catalyst of FeOOH/g-C3N4 for the Degradation of PNP: Characterization, Catalytic Performance and Mechanism Exploration.

The composite photocatalyst FeOOH/g-C3N4 was prepared through thermal polycondensation and co-precipitation methods, followed by XRD, SEM and UV-vis characterization. The stability of FeOOH/g-C3N4 was explored by the recycling test. The active species in the reaction system were investigated by the capture experiment. The results indicated that the optimal preparation condition for g-C3N4 involved calcination at 600 °C for 4 h. XRD analysis revealed that g-C3N4 exhibits a high-purity phase, and Fe in FeOOH/g-C3N4 exists in a highly dispersed amorphous state. SEM analysis showed that FeOOH/g-C3N4 has a rough surface with an irregular layered structure. Element composition analysis confirmed that the content of elements in the prepared catalyst is consistent with the theoretical calculation. FeOOH/g-C3N4 possesses the largest specific surface area of 143.2 m2/g and a suitable pore distribution. UV-vis DRS analysis showed that the absorption intensity of FeOOH/g-C3N4 is stronger than that of g-C3N4. When the catalyst dosage was 1.0 g/L, the H2O2 dosage was 4 mmol/L, the PNP initial concentration was 10 mg/L and the initial pH value was 5, the PNP removal could reach 92% in 120 min. Even after 5 cycles, the efficiency of PNP removal by FeOOH/g-C3N4 remains nearly 80%. The capture experiment indicated that both •OH and •O2- play roles in the photocatalytic degradation of PNP, with •OH being more significant. These findings affirm that FeOOH has been successfully incorporated into g-C3N4, resulting in a conspicuous catalytic effect on the degradation of PNP in the visible light-assisted Fenton-like reaction.

A universal AC electrokinetics-based strategy toward surface antifouling of underwater optics.

The practical applications of underwater optical devices, such as cameras or sensors, often suffer from widespread surface biofouling. Current antifouling techniques are primarily hindered by low efficiency, poor compatibility, as well as environmental pollution issues. This paper presents a transparent electrode coating as antifouling system of underwater optics as potential substitute for alternating current electrokinetic (ACEK)-based systems. A strong-coupling model is established to predict the Joule heating induced fluid flows and the negative dielectrophoretic (nDEP) effect for mobilizing organisms or deposited sediments on optic surfaces. The performance of the proposed antifouling system is numerically evaluated through simulations of electrostatic, fluid and temperature fields as well as trajectories of submicron particles, which is then experimentally verified and found to be in good agreement. A parametric study revealed that the degree of electrodes asymmetry is the key factor affecting the flow pattern and therefore the overall performance of the system. This ACEK-based universal strategy is expected to shed light on designing high performance and non-toxic platforms toward energy-efficient surface antifouling applications of underwater optics.

Symmetry-preserving modeling for lithographic imaging.

In computational imaging and lithography, it has been a challenge for a numerical model to faithfully preserve symmetries in the physical imaging system. In this Letter, we present a project-to-symmetry-subspace (PTSS) method to prevent symmetry loss during the iterative generation of optical kernels. Essentially, PTSS is to project iterative vectors onto a predefined symmetric subspace when decomposing the transmission cross coefficient (TCC). Simulation results demonstrate the PTSS-generation of a truncated set of optical kernels that are substantially free of symmetry error, regardless of the order of truncation.

Predicting hepatocellular carcinoma: A new non-invasive model based on shear wave elastography.

BACKGROUND: Integrating conventional ultrasound features with 2D shear wave elastography (2D-SWE) can potentially enhance preoperative hepatocellular carcinoma (HCC) predictions. AIM: To develop a 2D-SWE-based predictive model for preoperative identification of HCC. METHODS: A retrospective analysis of 884 patients who underwent liver resection and pathology evaluation from February 2021 to August 2023 was conducted at the Oriental Hepatobiliary Surgery Hospital. The patients were divided into the modeling group (n = 720) and the control group (n = 164). The study included conventional ultrasound, 2D-SWE, and preoperative laboratory tests. Multiple logistic regression was used to identify independent predictive factors for malignant liver lesions, which were then depicted as nomograms. RESULTS: In the modeling group analysis, maximal elasticity (Emax) of tumors and their peripheries, platelet count, cirrhosis, and blood flow were independent risk indicators for malignancies. These factors yielded an area under the curve of 0.77 (95% confidence interval: 0.73-0.81) with 84% sensitivity and 61% specificity. The model demonstrated good calibration in both the construction and validation cohorts, as shown by the calibration graph and Hosmer-Lemeshow test (P = 0.683 and P = 0.658, respectively). Additionally, the mean elasticity (Emean) of the tumor periphery was identified as a risk factor for microvascular invasion (MVI) in malignant liver tumors (P = 0.003). Patients receiving antiviral treatment differed significantly in platelet count (P = 0.002), Emax of tumors (P = 0.033), Emean of tumors (P = 0.042), Emax at tumor periphery (P < 0.001), and Emean at tumor periphery (P = 0.003). CONCLUSION: 2D-SWE's hardness value serves as a valuable marker for enhancing the preoperative diagnosis of malignant liver lesions, correlating significantly with MVI and antiviral treatment efficacy.

Characteristics of deformation and damage of surrounding rock along the top roadway in the working face of an isolated island and its evolution law.

This study investigates the deformation and damage characteristics of the surrounding rock along the top return mining roadway of an isolated island working face at different stages and reveals its damage mechanism and evolution law. Utilizing a mine in Yangquan City, Shanxi Province, China, as the engineering background, this research employs FLAC 3D numerical simulation and on-site measurements. The findings suggest that the evolution of the plastic zone along the top roadway of the 15,106 island face is largely similar during both the excavation and mining periods. The plastic zones on either side of the roadway are expanding asymmetrically and gradually merging into the plastic zone of the coal pillar. In the destructive stage, the sub-gangs of the roadway are penetrated, indicating the progression into the plastic zone. The investigation points to extensive damage on the larger side of the roadway, the development of fissures, and the significant depth of damage as primary causes of roadway deformation. Moreover, the extent of the plastic zones on both sides of the roadway correlates positively with their relative distance. Continuous monitoring reveals an ongoing increase in roadway displacement, consistent with general observations in coal mining. The results provide valuable insights for optimizing support structures in similar mining environments.

A Review of Design Strategies in SiO/C Composite Anodes for Rechargeable Lithium-Ion Batteries.

Lithium-ion batteries (LIBs) are widely used in electric vehicles, portable electronic devices, clean energy storage, and other fields due to their long service life, high energy density, and low self-discharge rate, which also puts forward higher requirements for the performance of lithium-ion batteries. As an anode for lithium-ion batteries, SiO materials have garnered significant attention from researchers due to its high specific capacity (2400 mAh·g-1), abundance of raw materials, and simple preparation. However, its large volume change (~ 200%) and poor electrical conductivity hinder its large-scale commercial application. Researchers employ various methods to reduce the volume change of SiO during lithium intercalation and improve its structural stability during cycling. This work mainly reviews the chemical structure and lithium storage mechanism of SiO, as well as the latest research progress on the preparation methods of SiO/C anode materials, focusing on summarizing the following preparation strategies: chemical vapor deposition, mechanical ball milling, spray drying, and in-situ reduction/oxidation methods. The obtained SiO-based anode materials' structural characteristics and electrochemical properties are compared and summarized. Finally, this review discusses the advantages and disadvantages of the current preparation methods, the future research directions, and the development prospects of SiO-based anode materials.

Effect of COVID-19 on Thrombosis Incidence and Patient Prognosis in Kidney Transplant Recipients.

BACKGROUND Thrombosis poses a grave threat to patients undergoing kidney transplants, with a heightened risk of mortality. While previous studies have established a link between COVID-19 and thrombosis, the specific association between COVID-19 and thrombosis in this patient population remains unexplored. MATERIAL AND METHODS We conducted a retrospective analysis utilizing data from 394 individuals who underwent kidney transplantation within the period of September 1, 2015, to April 1, 2023. To evaluate overall survival, we employed Kaplan-Meier analysis and utilized a logistic regression model for risk analysis. Furthermore, we developed a prediction model and assessed its accuracy through calibration curves. RESULTS Out of the 394 patients included in our study, a total of 51 individuals experienced thrombosis, resulting in 2 deaths. Our analysis revealed that COVID-19 infection significantly increased the risk of thrombosis (odds ratio [OR] 8.60, 95% confidence interval 3.13-24.74, P<0.01). Additionally, the use of cyclosporine was found to elevate the risk of death (OR 20.86, 95% CI 7.93-59.24, P<0.01) according to multifactorial analysis. Logistic models were employed to screen variables, and predictive models were constructed based on the presence of COVID-19 infection and the usage of cyclosporine. A nomogram was developed, demonstrating promising accuracy in estimating the risk of thrombosis during internal validation, with a corrected C-index of 0.869. CONCLUSIONS Our study suggests that both COVID-19 infection and the use of cyclosporine can serve as reliable predictors of thrombosis risk in patients undergoing renal transplantation. Furthermore, we developed a mortality risk prediction model based on COVID-19 in assessing thrombosis.

Prognostic significance of dynamic changes in liver stiffness measurement in patients with chronic hepatitis B and compensated advanced chronic liver disease.

BACKGROUND AND AIM: Liver stiffness measurements (LSMs) are promising for monitoring disease progression or regression. We assessed the prognostic significance of dynamic changes in LSM over time on liver-related events (LREs) and death in patients with chronic hepatitis B (CHB) and compensated advanced chronic liver disease (cACLD). METHODS: This retrospective study included 1272 patients with CHB and cACLD who underwent at least two measurements, including LSM and fibrosis score based on four factors (FIB-4). ΔLSM was defined as [(follow-up LSM - baseline LSM)/baseline LSM × 100]. We recorded LREs and all-cause mortality during a median follow-up time of 46 months. Hazard ratios (HRs) and confidence intervals (CIs) for outcomes were calculated using Cox regression. RESULTS: Baseline FIB-4, baseline LSM, ΔFIB-4, ΔLSM, and ΔLSM/year were independently and simultaneously associated with LREs (adjusted HR, 1.04, 95% CI, 1.00-1.07; 1.02, 95% CI, 1.01-1.03; 1.06, 95% CI, 1.03-1.09; 1.96, 95% CI, 1.63-2.35, 1.02, 95% CI, 1.01-1.04, respectively). The baseline LSM combined with the ΔLSM achieved the highest Harrell's C (0.751), integrated AUC (0.776), and time-dependent AUC (0.737) for LREs. Using baseline LSM and ΔLSM, we proposed a risk stratification method to improve clinical applications. The risk proposed stratification based on LSM performed well in terms of prognosis: low risk (n = 390; reference), intermediate risk (n = 446; HR = 3.38), high risk (n = 272; HR = 5.64), and extremely high risk (n = 164; HR = 11.11). CONCLUSIONS: Baseline and repeated noninvasive tests measurement allow risk stratification of patients with CHB and cACLD. Combining baseline and dynamic changes in the LSM improves prognostic prediction.

Safety and efficacy of baricitinib in steroid-resistant or relapsed immune thrombocytopenia: An open-label pilot study.

Patients with steroid-resistant or relapsed immune thrombocytopenia (ITP) suffer increased bleeding risk and impaired quality of life. Baricitinib, an oral Janus-associated kinases (JAK) inhibitor, could alleviate both innate and adaptive immune disorders without inducing thrombocytopenia in several autoimmune diseases. Accordingly, an open-label, single-arm, phase 2 trial (NCT05446831) was initiated to explore the safety and efficacy of baricitinib in ITP. Eligible patients were adults with primary ITP who were refractory to corticosteroids and at least one subsequent treatment, and had platelet counts below 30 × 109/L at enrolment. Participants received baricitinib 4 mg daily for 6 months. The primary endpoint was durable response at the 6-month follow-up. A total of 35 patients were enrolled. Durable response was achieved in 20 patients (57.1%, 95% confidence interval, 39.9 to 74.4), and initial response in 23 (65.7%) patients. For patients responding to baricitinib, the median time to response was 12 (IQR 6-20) days, and the median peak platelet count was 94 (IQR 72-128) × 109/L. Among the 27 patients undergoing extend observation, 12 (44.4%) remained responsive for a median duration of approximately 20 weeks after baricitinib discontinuation. Adverse events were reported in 11 (31.4%) patients, including infections in 6 (17.1%) patients during the treatment period. Treatment discontinuation due to an adverse event was reported in 2 (5.7%) patients. Evidence from this pilot study suggested that baricitinib might be a novel candidate for the armamentarium of ITP-modifying agents. Future studies are warranted to validate the safety, efficacy, and optimal dosing of baricitinib in patients with ITP.

Embedding Single Pd Atoms on NiGa Intermetallic Surfaces for Efficient and Selective Alkyne Semi-hydrogenation.

Catalytic removal of alkynes is essential in industry for producing polymer-grade alkenes from steam cracking processes. Non-noble Ni-based catalysts hold promise as effective alternatives to industrial Pd-based catalysts but suffer from low activity. Here we report embedding of single-atom Pd onto the NiGa intermetallic surface with replacing Ga atoms via a well-defined synthesis strategy to design Pd1-NiGa catalyst for alkyne semi-hydrogenation. The fabricated Pd1Ni2Ga1 ensemble sites deliver remarkably higher specific mass activity under superb alkene selectivity of >96% than the state-of-the-art catalysts under industry-relevant conditions. Integrated experimental and computational studies reveal that the single-atom Pd located synergizes with the neighbouring Ni sites to facilitate the σ-adsorption of alkyne and dissociation of hydrogen while suppress the alkene adsorption. Such synergistic effects confer the single-atom Pd on the NiGa intermetallic with a Midas touch for alkyne semi-hydrogenation, providing an effective strategy for stimulating low active Ni-based catalysts for other selective hydrogenations in industry.

Cytology or Multiparameter Flow Cytometry Positivity in the Cerebrospinal Fluid Before Transplantation is Predictive of Poor Outcomes After Allotransplantation in Acute Myeloid Leukemia Patients.

INTRODUCTION: Central nervous system leukemia (CNSL) remains a serious complication in patients with acute myeloid leukemia (AML) and an ambiguous prognostic factor for those receiving allo-geneic hematopoiesis stem cell transplantation (allo-HSCT). It is unknown whether using more sensitive tools, such as multiparameter flow cytometry (MFC), to detect blasts in the cerebrospinal fluid (CSF) would have an impact on outcome. METHODS: We retrospectively analyzed the clinical outcomes of 1472 AML patients with or without cytology or MFC positivity in the CSF before transplantation. Abnormal CSF (CSF+) was detected via conventional cytology and MFC in 44 patients at any time after diagnosis. A control group of 175 CSF-normal (CSF-) patients was generated via propensity score matching (PSM) analyses according to sex, age at transplant, and white blood cell count at diagnosis. RESULTS: Compared to those in the CSF-negative group, the conventional cytology positive and MFC+ groups had comparable 8-year nonrelapse mortality (NRM) (4%, 4%, and 6%, p = 0.82), higher cumulative incidence of relapse (CIR) (14%, 31%, and 32%, p = 0.007), lower leukemia-free survival (LFS) (79%, 63%, and 64%, p = 0.024), and overall survival (OS) (83%, 63%, and 68%, p = 0.021), with no significant differences between the conventional cytology positive and MFC+ groups. Furthermore, multivariate analysis confirmed that CSF involvement was an independent factor affecting OS and LFS. CONCLUSION: Our results indicate that pretransplant CSF abnormalities are adverse factors independently affecting OS and LFS after allotransplantation in AML patients.

Intelligent breast cancer diagnosis with two-stage using mammogram images.

Breast cancer (BC) significantly contributes to cancer-related mortality in women, underscoring the criticality of early detection for optimal patient outcomes. Mammography is a key tool for identifying and diagnosing breast abnormalities; however, accurately distinguishing malignant mass lesions remains challenging. To address this issue, we propose a novel deep learning approach for BC screening utilizing mammography images. Our proposed model comprises three distinct stages: data collection from established benchmark sources, image segmentation employing an Atrous Convolution-based Attentive and Adaptive Trans-Res-UNet (ACA-ATRUNet) architecture, and BC identification via an Atrous Convolution-based Attentive and Adaptive Multi-scale DenseNet (ACA-AMDN) model. The hyperparameters within the ACA-ATRUNet and ACA-AMDN models are optimized using the Modified Mussel Length-based Eurasian Oystercatcher Optimization (MML-EOO) algorithm. The performance is evaluated using a variety of metrics, and a comparative analysis against conventional methods is presented. Our experimental results reveal that the proposed BC detection framework attains superior precision rates in early disease detection, demonstrating its potential to enhance mammography-based screening methodologies.

Measurable residual disease testing by next generation sequencing is more accurate compared with multiparameter flow cytometry in adults with B-cell acute lymphoblastic leukemia.

Results of measurable residual disease (MRD)-testing by next-generation sequencing (NGS) correlate with relapse risk in adults with B-cell acute lymphoblastic leukemia (ALL) receiving chemotherapy or an allotransplant from a human leukocyte antigen (HLA)-identical relative or HLA-matched unrelated donor. We studied cumulative incidence of relapse (CIR) and survival prediction accuracy using a NGS-based MRD-assay targeting immunoglobulin genes after 2 courses of consolidation chemotherapy cycles in 93 adults with B-cell ALL most receiving HLA-haplotype-matched related transplants. Prediction accuracy was compared with MRD-testing using multi-parameter flow cytometry (MPFC). NGS-based MRD-testing detected residual leukemia in 28 of 65 subjects with a negative MPFC-based MRD-test. In Cox regression multi-variable analyses subjects with a positive NGS-based MRD-test had a higher 3-year CIR (Hazard Ratio [HR] = 3.37; 95 % Confidence Interval [CI], 1.34-8.5; P = 0.01) and worse survival (HR = 4.87 [1.53-15.53]; P = 0.007). Some data suggest a lower CIR and better survival in NGS-MRD-test-positive transplant recipients but allocation to transplant was not random. Our data indicate MRD-testing by NGS is more accurate compared with testing by MPFC in adults with B-cell ALL in predicting CIR and survival. (Registered in the Beijing Municipal Health Bureau Registration N 2007-1007 and in the Chinese Clinical Trial Registry [ChiCTR-OCH-10000940 and ChiCTROPC-14005546]).

Effect of plasma-activated water on the quality of wheat starch gel-forming 3D printed samples.

In this study, a new method for preparing gels suitable for 3D printing of food structures using wheat starch and plasma activated water (PAW) is presented. The investigation focused on the effect of PAW on starch pasting and the final 3D printed product. It was found that the use of PAW for 15 min in the preparation of wheat starch gels optimized carrier stability and improved height retention in the printed constructs, showing significant shape retention even after prolonged storage. This durability can be attributed to the hindrance of polymerization between starch molecules and the promotion of intermolecular starch polymerization when reactive groups and ions are integrated into the starch structure. The incorporation of PAW with soluble reactive groups, ions and acidity not only accelerates the breakdown of the starch molecules but also facilitates additional hydrogen bonding within the double helix, which strengthens the structure of the gel. This interaction accelerates the retrogradation of the starch, thereby enhancing its overall stability. This study provides a new green approach to modify the 3D printing properties of starch gels.

Development and validation of a prognostic model for assessing long COVID risk following Omicron wave-a large population-based cohort study.

BACKGROUND: Long coronavirus disease (COVID) after COVID-19 infection is continuously threatening the health of people all over the world. Early prediction of the risk of Long COVID in hospitalized patients will help clinical management of COVID-19, but there is still no reliable and effective prediction model. METHODS: A total of 1905 hospitalized patients with COVID-19 infection were included in this study, and their Long COVID status was followed up 4-8 weeks after discharge. Univariable and multivariable logistic regression analysis were used to determine the risk factors for Long COVID. Patients were randomly divided into a training cohort (70%) and a validation cohort (30%), and factors for constructing the model were screened using Lasso regression in the training cohort. Visualize the Long COVID risk prediction model using nomogram. Evaluate the performance of the model in the training and validation cohort using the area under the curve (AUC), calibration curve, and decision curve analysis (DCA). RESULTS: A total of 657 patients (34.5%) reported that they had symptoms of long COVID. The most common symptoms were fatigue or muscle weakness (16.8%), followed by sleep difficulties (11.1%) and cough (9.5%). The risk prediction nomogram of age, diabetes, chronic kidney disease, vaccination status, procalcitonin, leukocytes, lymphocytes, interleukin-6 and D-dimer were included for early identification of high-risk patients with Long COVID. AUCs of the model in the training cohort and validation cohort are 0.762 and 0.713, respectively, demonstrating relatively high discrimination of the model. The calibration curve further substantiated the proximity of the nomogram's predicted outcomes to the ideal curve, the consistency between the predicted outcomes and the actual outcomes, and the potential benefits for all patients as indicated by DCA. This observation was further validated in the validation cohort. CONCLUSIONS: We established a nomogram model to predict the long COVID risk of hospitalized patients with COVID-19, and proved its relatively good predictive performance. This model is helpful for the clinical management of long COVID.

The effect of cold plasma on starch: Structure and performance.

The inherent side effects of the physico-chemical properties of native starches often severely limit their use in food and non-food industries. Plasma is a non-thermal technology that allows rapid improvement of functional properties. This review provides a comprehensive summary of the sources and mechanisms of action of cold plasma and assesses its effects on starch morphology, crystal structure, molecular chain structure and physicochemical properties. The complex relationship between structure and function of plasma-treated starch is also explored. Potential applications of plasma-modified starch are also discussed in detail. The outcome of the modification process is influenced by factors such as starch type and concentration, plasma source, intensity and duration. The properties of starch can be effectively optimised using plasma technology. Plasma-based technologies therefore have the potential to modify starch to create a range of functionalities to meet the growing market demand for clean label ingredients.

Superhigh gain InGaN/GaN visible-light photodetector using polarization heterointerface barrier and single-carrier superlattices.

Visible-light detection with high sensitivity and strong wavelength selectivity is highly desired in emerging applications. Here, we demonstrate a high-performance visible-light photodetector with an active region composed of a polarization induced barrier and single-carrier superlattices (SCSLs). The barrier at SCSLs/GaN heterointerface brings both a low dark current and a high gain originating from the photoinduced barrier reduction effect. Meanwhile, the designed InGaN/GaN SCSLs allow the photoelectrons in the quantum wells to escape, but photogenerated holes are weakly localized, thus generating the additional photoconductive gain. The resulting devices exhibited a super-high gain of 7.8 × 104, a large detectivity of 1.2 × 1016 jones, and a relatively fast response speed with rise/falling time of 2.5/89.6 ns. Also, a 400/500-nm rejection ratio greater than 3 × 105 was shown at 1 V, indicating excellent wavelength selectivity.