ObjectiveThe widespread adoption of portable fundus cameras for primary care and community screening is hindered by limitations in current autofocus(AF) technologies. Image-based methods relying on sharpness evaluation require iterative searches, resulting in slow convergence, while projection-based techniques are susceptible to optical artifacts and calibration errors. To address these challenges, this study introduces a novel AF system based on direct wavefront sensing, designed to deliver simultaneous high speed, high precision, and operational robustness within the compact form factor essential for portable ophthalmic devices. MethodsOur approach fundamentally reimagines the AF process by directly measuring the ocular wavefront aberration. We developed a custom portable fundus camera integrating a miniaturized Shack-Hartmann wavefront sensor (SHWS) into the optical path. An 850 nm laser diode projects a point source onto the retina via oblique illumination to minimize corneal reflections. Light scattered from this spot carries the eye’s refractive error through the imaging optics and is directed to the SHWS, positioned at a plane optically conjugate to the primary color CMOS imaging sensor. A microlens array within the SHWS samples the incident wavefront, generating a pattern of focal spots on a CCD. Real-time centroid analysis of these spots provides a map of local wavefront slopes. These measurements are processed through a singular value decomposition (SVD) algorithm to fit a Zernike polynomial basis set, enabling real-time reconstruction of the wavefront phase. The defocus component (S) is extracted from the second-order Zernike coefficients, providing a direct, quantitative measure of the refractive error in diopters. This value serves as a precise error signal in a closed-loop control system, which commands a voice-coil actuated focusing lens to its null position in a single, deterministic step, eliminating the need for iterative search algorithms. ResultsComprehensive evaluation demonstrated the system’s high performance. Testing on a calibrated model eye (OEMI-7) established a highly linear relationship between the computed defocus S and the focusing lens position across a ±20 Diopter (D) compensation range, achievable within a 5 mm mechanical travel. The system achieved a focusing precision of 0.08 D, corresponding to an 18-fold improvement over a conventional projection spot-size method tested under identical conditions. The total focus acquisition time, encompassing wavefront measurement, computation, and lens actuation, averaged under 0.5 s. Clinical validation with 25 human volunteers (50 eyes, refractive range -15 D to +10 D) confirmed practical efficacy. The wavefront-sensing AF succeeded in 92% of attempts with a mean time of 0.5 s, substantially outperforming a projection-based benchmark which achieved only a 32% success rate with an average time of 4.25 s. The system provided instantaneous directional guidance and maintained stability during minor ocular movements. Objective assessment of image quality, via amplitude contrast of retinal vasculature, showed consistent and significant enhancement following AF correction across the entire tested diopter range. ConclusionThis work successfully implements and validates a direct wavefront-sensing autofocus paradigm for portable fundus cameras. By directly quantifying and compensating for the optical defocus aberration, this method bypasses the fundamental limitations of image-processing and projection-based techniques, enabling rapid, precise, and deterministic diopter compensation. The developed system delivers an exceptional combination of a wide operational range (±20 D), high accuracy (0.08 D), fast convergence (0.5 s), and a compact physical footprint. This technology provides a practical and high-performance focusing solution capable of enhancing the reliability, throughput, and diagnostic utility of portable retinal imaging in large-scale screening applications. Future efforts will be directed towards system cost optimization and performance adaptation for diverse ocular conditions.

ObjectiveThe controllability changes of structural brain network were explored based on the control and brain network theory in young smokers, this may reveal that the controllability indicators can serve as a powerful factor to predict the sleep status in young smokers. MethodsFifty young smokers and 51 healthy controls from Inner Mongolia University of Science and Technology were enrolled. Diffusion tensor imaging (DTI) was used to construct structural brain network based on fractional anisotropy (FA) weight matrix. According to the control and brain network theory, the average controllability and the modal controllability were calculated. Two-sample t-test was used to compare the differences between the groups and Pearson correlation analysis to examine the correlation between significant average controllability and modal controllability with Fagerström Test of Nicotine Dependence (FTND) in young smokers. The nodes with the controllability score in the top 10% were selected as the super-controllers. Finally, we used BP neural network to predict the Pittsburgh Sleep Quality Index (PSQI) in young smokers. ResultsThe average controllability of dorsolateral superior frontal gyrus, supplementary motor area, lenticular nucleus putamen, and lenticular nucleus pallidum, and the modal controllability of orbital inferior frontal gyrus, supplementary motor area, gyrus rectus, and posterior cingulate gyrus in the young smokers’ group, were all significantly different from those of the healthy controls group (P<0.05). The average controllability of the right supplementary motor area (SMA.R) in the young smokers group was positively correlated with FTND (r=0.393 0, P=0.004 8), while modal controllability was negatively correlated with FTND (r=-0.330 1, P=0.019 2). ConclusionThe controllability of structural brain network in young smokers is abnormal. which may serve as an indicator to predict sleep condition. It may provide the imaging evidence for evaluating the cognitive function impairment in young smokers.

ObjectiveTo investigate the application value of a multimodal model integrating radiomics features， deep learning features， and clinical structured data in predicting severe acute pancreatitis （SAP）， and to provide more accurate tools for the early identification of SAP in clinical practice. MethodsThe patients with acute pancreatitis （AP） who attended The First Affiliated Hospital of Soochow University， Jintan Hospital Affiliated to Jiangsu University， and Suzhou Yongding Hospital from January 1， 2017 to December 31， 2023 were included. Related data were collected， including demographic information， previous medical history， etiology， laboratory test data， and systemic inflammatory response syndrome （SIRS） within 24 hours after admission， as well as imaging data within 72 hours after admission， while related scores were calculated， including Ranson score， modified CT severity index （MCTSI）， bedside index for severity in acute pancreatitis （BISAP）， and systemic inflammatory response syndrome， albumin， blood urea nitrogen and pleural effusion （SABP） score. The model was constructed in the following process： （1） three-dimensional CT images were used to extract and identify radiomics features， and a radiomics classification model was established based on the extreme gradient Boost （XGBoost） algorithm； （2） U-Net is used to perform semantic segmentation of three-dimensional CT images， and then the results of segmentation were imported into 3D ResNet50 to construct a deep learning classification model； （3） the predicted values of the above two models were integrated with clinical structured data to establish a multimodal model based on the XGBoost algorithm. The variable importance plot and local interpretability plot were used to perform visual interpretation of the model. The independent samples t-test was used for comparison of normally distributed continuous data between groups， and the Mann-Whitney U test was used for comparison of non-normally distributed continuous data between groups； the chi-square test or Fisher’s exact test was used for comparison of categorical data between groups. The receiver operating characteristic （ROC） curve was plotted for each model and existing scoring systems， and the area under the ROC curve （AUC） was calculated to assess their performance； the Delong test was used for comparison of AUC. ResultsA total of 609 patients who met the criteria were included， among whom 114 （18.7%） developed SAP. In this study， the data of 426 patients from The First Affiliated Hospital of Soochow University was used as the training set， and the data of 183 patients from Jintan Hospital Affiliated to Jiangsu University and Suzhou Yongding Hospital were used as the independent test set. The multimodal model had an AUC of 0.914 in the test set， which was significantly higher than the AUC of traditional scoring systems such as MCTSI （AUC=0.827）， Ranson score （AUC=0.675）， BISAP （AUC=0.791）， and SABP score （AUC=0.648）； in addition， the multimodal model showed a significant improvement in performance compared with the radiomics classification model （AUC=0.739） and the deep learning classification model （AUC=0.685） （the Delong test： Z=-3.23， -4.83， -3.48， -4.92， -4.31， and -4.59， all P <0.01）. The top 10 variables in terms of importance in the multimodal model were pleural effusion， predicted value of the deep learning model， predicted value of the radiomics model， triglycerides， calcium ions， SIRS， white blood cell count， age， platelets， and C-reactive protein， suggesting that the above variables had significant contributions to the performance of the model in predicting SAP. ConclusionBased on structured data， radiomic features， and deep learning features， this study constructs a multicenter prediction model for SAP based on the XGBoost algorithm， which has a better predictive performance than existing traditional scoring systems and unimodal models.

As a common neurological disease in China, stroke has an extremely high rate of death and disability, of which 80% is ischemic stroke (IS), causing a serious burden to individuals and society. Neuronal death is an important factor in the pathogenesis of stroke. Studies have shown that mitochondrial dynamics, as a key mechanism regulating intracellular energy metabolism and cell death, plays an important role in the pathogenesis of IS. In recent years, targeting mitochondrial dynamics has become an emerging therapeutic tool to improve neurological impairment after stroke. This paper reviews the research advance in recent years in IS mitochondrial dynamics, summarizing and discussing the overview of mitochondrial dynamics, the role of mitochondrial dynamics in IS, and the studies on mitochondrial dynamics-based treatment of IS. This paper helps to explore the mechanism of the role of mitochondrial dynamics in IS and effective interventions, and provides a theoretical strategy for targeting mitochondrial dynamics to treat IS in the clinic.
Humans ; Mitochondrial Dynamics/physiology* ; Ischemic Stroke/metabolism* ; Mitochondria/physiology* ; Animals ; Brain Ischemia/physiopathology* ; Energy Metabolism

Objective To evaluate the diagnostic performance of the minimum-cost-path-based CT angiography-derived fractional flow reserve(MCP-FFR)and the deep learning-driven CT angiography-derived fractional flow reserve(DeepCL-FFR),and to particularly explore the potential value of the DeepCL algorithm in improving diagnostic accuracy within the"gray zone."Methods A retrospective analysis was conducted on 151 coronary vessels from 109 patients with coronary artery disease,who were hospitalized at the General Hospital of the People's Liberation Army between January 2020 and June 2021.Pearson correlation and Bland-Altman plots were employed to assess the correlation and agreement of the two CT-FFR methods with invasive FFR.A CT-FFR range of 0.70-0.80 was defined as the diagnostic"gray zone."The accuracy,sensitivity,specificity,positive predictive value,and negative predictive value for detecting hemodynamic abnormalities were calculated and analyzed.The DeLong test was used to compare the areas under the receiver operating characteristic curves(AUC)between the two CT-FFR calculation methods.Results Both CT-FFR methods exhibited a positive correlation with invasive FFR(MCP-FFR:r=0.75,P＜0.001;DeepCL-FFR:r=0.86,P＜0.001)and showed good agreement(MCP-FFR:mean difference=0.010,P=0.351;DeepCL-FFR:mean difference=-0.003,P=0.772).Both DeepCL-FFR(AUC 0.97,95％CI 0.94-0.99)and MCP-FFR(AUC 0.92,95％CI 0.88-0.97)demonstrated favorable diagnostic performance for detecting hemodynamic abnormalities(P=0.122).In the"gray zone"for hemodynamic abnormality,the diagnostic accuracy of MCP-FFR was 68.8％,whereas DeepCL-FFR increased it to 89.7％.DeepCL-FFR also exhibited superior diagnostic performance(AUC 0.89,95％CI 0.73-0.99)within the"gray zone,"which was significantly higher than that of MCP-FFR(AUC 0.71,95％CI 0.54-0.87)(P＜0.001).Conclusions The deep learning-driven coronary centerline extraction algorithm,DeepCL,demonstrates superior diagnostic performance in CT-FFR for detecting hemodynamic abnormalities,particularly by significantly improving diagnostic accuracy in the"gray zone."

Objective To investigate the dynamic functional connectivity differences between insomnia patients and healthy controls in triple brain networks[the significant network(SN),the default mode network(DMN),and the executive control network(ECN)]using functional magnetic resonance imaging,and uncover their associations with cognitive ability.Methods Dynamic functional connectivity analysis was performed on functional magnetic resonance imaging data from 40 insomnia patients and 40 healthy controls.The changes in dynamic functional connectivity values were studied for SN,DMN,ECN[including the left executive control network(LECN)and the right executive control network(RECN)];the similarities and differences in time characteristic indicators such as time score,average dwell time,and conversion rate were explored;and their associations with clinical information were analyzed.Results The SN-LECN and DMN-RECN dynamic functional connectivity was significantly higher in insomnia patients than in healthy controls(P=0.013,0.047),while the RECN-LECN and RECN internal functional connectivity strength was lower in insomnia patients than in healthy controls(P<0.001).Additionally,the fractional time in state 2 in insomnia group was significantly higher than that in healthy controls(P<0.001),and it was positively correlated with the Pittsburgh sleep quality index(r=0.524,P=0.001).Conclusion Insomnia patients exhibit significant abnormalities in triple brain network dynamic functional connectivity,which may be related to abnormalities in cognitive control and sensory processing in insomnia patients.These findings provide a new perspective for further research on the neural mechanisms and potential intervention strategies for insomnia.

Objective To investigate the dynamic functional connectivity differences between insomnia patients and healthy controls in triple brain networks[the significant network(SN),the default mode network(DMN),and the executive control network(ECN)]using functional magnetic resonance imaging,and uncover their associations with cognitive ability.Methods Dynamic functional connectivity analysis was performed on functional magnetic resonance imaging data from 40 insomnia patients and 40 healthy controls.The changes in dynamic functional connectivity values were studied for SN,DMN,ECN[including the left executive control network(LECN)and the right executive control network(RECN)];the similarities and differences in time characteristic indicators such as time score,average dwell time,and conversion rate were explored;and their associations with clinical information were analyzed.Results The SN-LECN and DMN-RECN dynamic functional connectivity was significantly higher in insomnia patients than in healthy controls(P=0.013,0.047),while the RECN-LECN and RECN internal functional connectivity strength was lower in insomnia patients than in healthy controls(P<0.001).Additionally,the fractional time in state 2 in insomnia group was significantly higher than that in healthy controls(P<0.001),and it was positively correlated with the Pittsburgh sleep quality index(r=0.524,P=0.001).Conclusion Insomnia patients exhibit significant abnormalities in triple brain network dynamic functional connectivity,which may be related to abnormalities in cognitive control and sensory processing in insomnia patients.These findings provide a new perspective for further research on the neural mechanisms and potential intervention strategies for insomnia.

Objective To evaluate the diagnostic performance of the minimum-cost-path-based CT angiography-derived fractional flow reserve(MCP-FFR)and the deep learning-driven CT angiography-derived fractional flow reserve(DeepCL-FFR),and to particularly explore the potential value of the DeepCL algorithm in improving diagnostic accuracy within the"gray zone."Methods A retrospective analysis was conducted on 151 coronary vessels from 109 patients with coronary artery disease,who were hospitalized at the General Hospital of the People's Liberation Army between January 2020 and June 2021.Pearson correlation and Bland-Altman plots were employed to assess the correlation and agreement of the two CT-FFR methods with invasive FFR.A CT-FFR range of 0.70-0.80 was defined as the diagnostic"gray zone."The accuracy,sensitivity,specificity,positive predictive value,and negative predictive value for detecting hemodynamic abnormalities were calculated and analyzed.The DeLong test was used to compare the areas under the receiver operating characteristic curves(AUC)between the two CT-FFR calculation methods.Results Both CT-FFR methods exhibited a positive correlation with invasive FFR(MCP-FFR:r=0.75,P＜0.001;DeepCL-FFR:r=0.86,P＜0.001)and showed good agreement(MCP-FFR:mean difference=0.010,P=0.351;DeepCL-FFR:mean difference=-0.003,P=0.772).Both DeepCL-FFR(AUC 0.97,95％CI 0.94-0.99)and MCP-FFR(AUC 0.92,95％CI 0.88-0.97)demonstrated favorable diagnostic performance for detecting hemodynamic abnormalities(P=0.122).In the"gray zone"for hemodynamic abnormality,the diagnostic accuracy of MCP-FFR was 68.8％,whereas DeepCL-FFR increased it to 89.7％.DeepCL-FFR also exhibited superior diagnostic performance(AUC 0.89,95％CI 0.73-0.99)within the"gray zone,"which was significantly higher than that of MCP-FFR(AUC 0.71,95％CI 0.54-0.87)(P＜0.001).Conclusions The deep learning-driven coronary centerline extraction algorithm,DeepCL,demonstrates superior diagnostic performance in CT-FFR for detecting hemodynamic abnormalities,particularly by significantly improving diagnostic accuracy in the"gray zone."

Objective:To analyze the impact of transcervical resection of adhesion (TCRA) on obstetric complications in patients after frozen-thawed embryo transfer (FET) and its associated factors.Methods:A retrospective cohort study was conducted by collecting clinical data from patients who underwent autologous oocyte FET treatment and gave birth to at least one live newborn at the Reproductive Medicine Center of Nanjing Women and Children's Healthcare Hospital from April 2015 to May 2022. Based on the uterine condition, patients were divided into three groups: control group with normal uterine morphology (712 cases); the intrauterine adhesion (IUA) group consisting of IUA patients who did not undergo TCRA surgery (45 cases); the TCRA group, which included IUA patients who received TCRA treatment (51 cases). The relationship between uterine conditions and obstetric complications among the three groups was investigated using propensity score matching (PSM). Multivariate logistic regression analysis was applied to identify risk factors associated with obstetric complications related to TCRA. The performance of the constructed multivariate logistic regression model was evaluated using calibration curves and receiver operating characteristic (ROC) curves.Results:1) Before PSM, statistically significant differences were observed among the three groups regarding endometrial thickness, the presence of a scarred uterus, numbers of pregnancies, deliveries, miscarriages, induced abortions, and transferred embryos (all P<0.05). After PSM, baseline characteristics were balanced across the groups. The rates of placenta accreta spectrum disorders (PAS) in the TCRA group [48.8% (20/41)] and the IUA group [45.2% (19/42)] were significantly higher than those in control group [24.7% (18/73), P=0.016; 22.8% (18/79), P=0.019]. 2) Multivariable logistic regression analysis revealed that endometrial thickness ( OR=0.79, 95% CI: 0.69-0.90, P<0.001], number of pregnancies (2 times, OR=2.25, 95% CI: 1.33-3.82, P=0.003), endometrial preparation protocol (gonadotropin-releasing hormone agonist plus hormone replacement therapy, OR=2.29, 95% CI: 1.16-4.52, P=0.017), the presence of a scarred uterus ( OR=2.19, 95% CI: 1.39-3.45, P<0.001), and uterine cavity conditions (IUA and TCRA, OR=2.11, 95% CI: 1.07-4.17, P=0.031; OR=2.70, 95% CI: 1.37-5.31, P=0.004) were independent predictors of PAS occurrence. 3) The area under the ROC curve for this model was 0.732 (95% CI: 0.686-0.778). Calibration curve results, after internal validation, showed good consistency between predicted risks and actual outcomes, demonstrating good discriminative ability and calibration ( P=0.540). Conclusion:The incidence of obstetric complications such as placenta previa, postpartum hemorrhage, and premature rupture of membranes in patients who underwent TCRA surgery was comparable to that of patients with a normal uterine morphology. However, TCRA significantly increased the risk of PAS in patients with IUA undergoing FET assisted reproductive treatment.

Objective:To analyze the impact of transcervical resection of adhesion (TCRA) on obstetric complications in patients after frozen-thawed embryo transfer (FET) and its associated factors.Methods:A retrospective cohort study was conducted by collecting clinical data from patients who underwent autologous oocyte FET treatment and gave birth to at least one live newborn at the Reproductive Medicine Center of Nanjing Women and Children's Healthcare Hospital from April 2015 to May 2022. Based on the uterine condition, patients were divided into three groups: control group with normal uterine morphology (712 cases); the intrauterine adhesion (IUA) group consisting of IUA patients who did not undergo TCRA surgery (45 cases); the TCRA group, which included IUA patients who received TCRA treatment (51 cases). The relationship between uterine conditions and obstetric complications among the three groups was investigated using propensity score matching (PSM). Multivariate logistic regression analysis was applied to identify risk factors associated with obstetric complications related to TCRA. The performance of the constructed multivariate logistic regression model was evaluated using calibration curves and receiver operating characteristic (ROC) curves.Results:1) Before PSM, statistically significant differences were observed among the three groups regarding endometrial thickness, the presence of a scarred uterus, numbers of pregnancies, deliveries, miscarriages, induced abortions, and transferred embryos (all P<0.05). After PSM, baseline characteristics were balanced across the groups. The rates of placenta accreta spectrum disorders (PAS) in the TCRA group [48.8% (20/41)] and the IUA group [45.2% (19/42)] were significantly higher than those in control group [24.7% (18/73), P=0.016; 22.8% (18/79), P=0.019]. 2) Multivariable logistic regression analysis revealed that endometrial thickness ( OR=0.79, 95% CI: 0.69-0.90, P<0.001], number of pregnancies (2 times, OR=2.25, 95% CI: 1.33-3.82, P=0.003), endometrial preparation protocol (gonadotropin-releasing hormone agonist plus hormone replacement therapy, OR=2.29, 95% CI: 1.16-4.52, P=0.017), the presence of a scarred uterus ( OR=2.19, 95% CI: 1.39-3.45, P<0.001), and uterine cavity conditions (IUA and TCRA, OR=2.11, 95% CI: 1.07-4.17, P=0.031; OR=2.70, 95% CI: 1.37-5.31, P=0.004) were independent predictors of PAS occurrence. 3) The area under the ROC curve for this model was 0.732 (95% CI: 0.686-0.778). Calibration curve results, after internal validation, showed good consistency between predicted risks and actual outcomes, demonstrating good discriminative ability and calibration ( P=0.540). Conclusion:The incidence of obstetric complications such as placenta previa, postpartum hemorrhage, and premature rupture of membranes in patients who underwent TCRA surgery was comparable to that of patients with a normal uterine morphology. However, TCRA significantly increased the risk of PAS in patients with IUA undergoing FET assisted reproductive treatment.