A novel homozygous nonsense variant of AK7 is associated with multiple morphological abnormalities of the sperm flagella.

RESEARCH QUESTION: Is the novel homozygous nonsense variant of AK7 associated with multiple morphological abnormalities of the sperm flagella (MMAF), a specific type of oligoasthenoteratozoospermia leading to male infertility? DESIGN: Whole-exome sequencing and Sanger sequencing were performed to identify potential gene variants. Immunoblotting and immunofluorescence were applied to confirm the relationship between mutated genes and disease phenotypes. The concentration of reactive oxygen species and the rate of apoptosis were measured to evaluate the mitochondrial function of spermatozoa. Transmission electron microscopy and scanning electron microscopy were employed to observe sperm ultrastructure. RESULTS: A novel homozygous nonsense variant of AK7, c.1153A>T (p. Lys385*), was identified in two infertile siblings with asthenoteratozoospermia through whole-exome sequencing. Both immunoblotting and immunofluorescence assays showed practically complete absence of AK7 in the patient's spermatozoa. Additionally, the individual with the novel AK7 variant exhibited a phenotype characterized by severe oxidative stress and apoptosis caused by mitochondrial metabolic dysfunction of spermatozoa. Notably, remarkable flagellar defects with multiple axonemes in uniflagellate spermatozoa, accompanied by mitochondrial vacuolization, were observed; this has not been reported previously in patients with other AK7 variants. CONCLUSIONS: This study found that a novel identified homozygous nonsense variant of AK7 may be associated with MMAF-related asthenoteratozoospermia. The observed functional associations between mitochondria and sperm flagellar assembly provide evidence for potential mutual regulation between AK7 and flagella-associated proteins during spermatogenesis.

Integrated treatment guided by RNA-seq-based endometrial receptivity assessment for infertility complicated by MEN1.

Background: Preimplantation genetic testing (PGT) serves as a tool to avoid genetic disorders in patients with known genetic conditions. However, once a selected embryo is transferred, implantation success is attained independent of embryo quality. Using PGT alone is unable to tackle implantation failure caused by endometrial receptivity (ER) abnormalities in these patients. Methods: We validated our newly developed RNA-seq-based ER test (rsERT) in a retrospective cohort study including 511 PGT cycles and reported experience in treating an infertile female patient complicated by multiple endocrine neoplasia type 1 (MEN1). Results: Significant improvement in the clinical pregnancy rate was found in the performed personalized embryo transfer (pET) group (CR, 69.7%; P = 0.035). In the rare MEN1 case, pET was done according to the prediction of the optimal time of window of implantation after unaffected blastocysts were obtained by PGT-M, which ultimately led to a healthy live birth. However, none of the mRNA variants identified in the patient showed a strong association with the MEN1 gene. Conclusions: Applying the new rsERT along with PGT improved ART outcomes and brought awareness of the importance of the ER examination in MEN1 infertile female patients. MEN1-induced endocrine disorder rather than MEN1 mutation contributes to the ER abnormality. Trial Registration: Reproductive Medicine Ethics Committee of Xiangya Hospital Registry No.: 2022010.

An endometrium of type C along with an endometrial thickness of < 8 mm are risk factors for ectopic pregnancy after stimulated cycles with fresh embryo transfer.

BACKGROUND: The study investigated whether specific ultrasonographically observed endometrial features (including endometrium type and thickness) were linked to ectopic pregnancy after stimulated cycles with fresh embryo transfer. METHOD: Of 6246 pregnancy cycles after fresh embryo transfer, 6076 resulted in intrauterine pregnancy and 170 in ectopic pregnancy. The primary outcome of the study was ectopic pregnancy, with the main variables being endometrium type and endometrial thickness. Univariate and subsequent multiple-stepwise logistic regression analyses were used to identify the risk factors of ectopic pregnancy. RESULTS: 1. Compared with patients with an endometrial thickness ≥ 8 mm, the adjusted odds ratio for those with an endometrial thickness < 8 mm was 3.368 (P < 0.001). The adjusted odds ratio for women with a type-C endometrium was 1.897 (P = 0.019) compared with non-type C. 2. A larger dose of gonadotropin used during controlled ovarian hyperstimulation was a protective factor against ectopic pregnancy (P = 0.008). 3. The GnRH antagonist protocol (P = 0.007) was a risk factor for ectopic pregnancy, compared with the use of GnRH agonists. CONCLUSION: (1) An endometrial thickness < 8 mm coupled with a type C endometrium significantly increased the risk of ectopic pregnancy after fresh embryo transfer. (2) A thin endometrial thickness and a type C endometrium could be further related to an abnormal endometrial receptivity/peristaltic wave. (3) Patients at a high risk of ectopic pregnancy should therefore be given special attention, with early diagnosis during the peri-transplantation period may assist in the prevention of ectopic pregnancy.

Two-Level Attention Module Based on Spurious-3D Residual Networks for Human Action Recognition.

In recent years, deep learning techniques have excelled in video action recognition. However, currently commonly used video action recognition models minimize the importance of different video frames and spatial regions within some specific frames when performing action recognition, which makes it difficult for the models to adequately extract spatiotemporal features from the video data. In this paper, an action recognition method based on improved residual convolutional neural networks (CNNs) for video frames and spatial attention modules is proposed to address this problem. The network can guide what and where to emphasize or suppress with essentially little computational cost using the video frame attention module and the spatial attention module. It also employs a two-level attention module to emphasize feature information along the temporal and spatial dimensions, respectively, highlighting the more important frames in the overall video sequence and the more important spatial regions in some specific frames. Specifically, we create the video frame and spatial attention map by successively adding the video frame attention module and the spatial attention module to aggregate the spatial and temporal dimensions of the intermediate feature maps of the CNNs to obtain different feature descriptors, thus directing the network to focus more on important video frames and more contributing spatial regions. The experimental results further show that the network performs well on the UCF-101 and HMDB-51 datasets.

Serologic reactivity of unidentified specificity in antenatal testing and hemolytic disease of the fetus and newborn: The BEST collaborative study.

BACKGROUND: The clinical significance of serologic reactivity of unidentified specificity (SRUS) in pregnancy is not clear based on available literature. The aim of this study is to determine if SRUS is associated with hemolytic disease of the fetus and newborn (HDFN). STUDY DESIGN AND METHODS: Retrospective data were collected from eight institutions over an 11-year study period (2010-2020), when available (5/8 sites). The outcome of the pregnancies with SRUS-no, mild, moderate, or severe HDFN-was determined. RESULTS: SRUS was demonstrated in 589 pregnancies. After excluding those with incomplete data, a total of 284 pregnancies were included in the primary HDFN outcome analysis. SRUS was detected in 124 (44%) pregnancies in isolation, and none were affected by HDFN. Of 41 pregnancies with SRUS and ABO incompatibility, 37 (90%) were unaffected, and 4 (10%) were associated with mild HDFN. Of 98 pregnancies with SRUS and concurrent identifiable antibody reactivity(s), 80 (81%) were unaffected, and 19 (19%) were associated with mild to severe HDFN. There was 1 case of mild HDFN and 1 case of severe HDFN in the 21 pregnancies with SRUS, ABO incompatibility, and concurrent identifiable antibody reactivity(s), and 19 (90%) were unaffected by HDFN. Among all patients with repeat testing, newly identified alloantibodies or other antibodies were identified in 63 of 212 (30%) patients. Although most were not clinically significant, on occasion SRUS preceded clinically significant antibody(s) associated with HDFN (3%, 5/188). CONCLUSION: The antenatal serologic finding of SRUS in isolation is not associated with HDFN but may precede clinically significant antibodies.

Low Pt loading for high-performance fuel cell electrodes enabled by hydrogen-bonding microporous polymer binders.

A key challenge for fuel cells based on phosphoric acid doped polybenzimidazole membranes is the high Pt loading, which is required due to the low electrode performance owing to the poor mass transport and severe Pt poisoning via acid absorption on the Pt surface. Herein, these issues are well addressed by design and synthesis of effective catalyst binders based on polymers of intrinsic microporosity (PIMs) with strong hydrogen-bonding functionalities which improve phosphoric acid binding energy, and thus preferably uphold phosphoric acid in the vicinity of Pt catalyst particles to mitigate the adsorption of phosphoric acid on the Pt surface. With combination of the highly mass transport microporosity, strong hydrogen-bonds and high phosphoric acid binding energy, the tetrazole functionalized PIM binder enables an H2-O2 cell to reach a high Pt-mass specific peak power density of 3.8 W mgPt-1 at 160 °C with a low Pt loading of only 0.15 mgPt cm-2.

Psychological stress recognition from heart rate variability parameters based on field programmable gate arrays.

Psychological stress is a big threat to people's health. Early detection of psychological stress is important. The design of a stress recognition device based on the ECG (electrocardiograph) signal is presented in this paper. The device features intelligence, precision, portability, fast response, and low power consumption. In the design, the ECG signals are acquired by the AD8232 ECG module and processed by a low power consumption FPGA (Field Programmable Gated Array) development board PYNQ-Z2. Meanwhile, a modified Deep Forest model named Aw-Deep Forest (Adaptive Weight Deep Forest) is proposed. The Aw-Deep Forest has better performance than the Deep Forest model because it improves the fitting quality of the forests. By implementing the Aw-Deep Forest model on the FPGA, the device can assess people's state of psychological stress by analyzing the HRV (heart rate variability) parameters from ECG data. This paper mainly introduces the detailed process of ECG signal collecting, filtering, analog signal to digital signal conversion, HRV parameter analysis, and psychological stress recognition with Aw-Deep Forest. The final accuracy is 81.39%.

A Low Complexity Persistent Reconnaissance Algorithm for FANET.

In recent years, with the rapid progress of unmanned aerial vehicle (UAV) technology, UAV-based systems have been widely used in both civilian and military applications. Researchers have proposed various network architectures and routing protocols to address the network connectivity problems associated with the high mobility of UAVs, and have achieved considerable results in a flying ad hoc network (FANET). Although scholars have noted various threats to UAVs in practical applications, such as local magnetic field variation, acoustic interference, and radio signal hijacking, few studies have taken into account the dynamic nature of these threat factors. Moreover, the UAVs' high mobility combined with dynamic threats makes it more challenging to ensure connectivity while adapting to ever-changing scenarios. In this context, this paper introduces the concept of threat probability density function (threat PDF) and proposes a particle swarm optimization (PSO)-based threat avoidance and reconnaissance FANET construction algorithm (TARFC), which enables UAVs to dynamically adapt to avoid high-risk areas while maintaining FANET connectivity. Inspired by the graph editing distance, the total edit distance (TED) is defined to describe the alterations of the FANET and threat factors over time. Based on TED, a dynamic threat avoidance and continuous reconnaissance FANET operation algorithm (TA&CRFO) is proposed to realize semi-distributed control of the network. Simulation results show that both TARFC and TA&CRFO are effective in maintaining network connectivity and avoiding threats in dynamic scenarios. The average threat value of UAVs using TARFC and TA&CRFO is reduced by 3.99~27.51% and 3.07~26.63%, respectively, compared with the PSO algorithm. In addition, with limited distributed moderation, the complexity of the TA&CRFO algorithm is only 20.08% of that of TARFC.

Investigation of the Negative Emotions Exhibited in Patients with Coronary Heart Disease After PCI and Any Influencing Factors.

Purpose: To understand existing negative emotions in patients with coronary heart disease after percutaneous coronary stent implantation (PCI) and analyse its influencing factors. Methods: Patients with coronary heart disease after PCI in three tertiary hospitals in Changsha City from April to September 2018 were selected as the research subjects. The self-designed general information questionnaire assessed irritability, depression and anxiety (IDA) on a self-rating scale. It was used to examine patients' existing negative emotions with coronary heart disease after PCI and analyse the influencing factors. Results: 203 questionnaires were distributed, and 202 valid questionnaires were recovered, with an effective recovery rate of 99.5%. The IDA score of patients with coronary heart disease after PCI was 17.01±7.60 points, the incidence of negative emotions was 63.8%, and the incidences of depression, anxiety and irritability were 39.6%, 8.4% and 15.8%, respectively. Negative emotion was taken as the dependent variable and a patient's general data, such as lifestyle and disease, as the independent variables. A univariate analysis was conducted to obtain gender, age, educational level, marital status, work status, per capita monthly household income, sleep status, etc. Seven factors were identified as the influencing factors of negative emotions in patients with coronary heart disease after PCI, and the difference was statistically significant (P<0.05). Conclusion: Most patients with coronary heart disease after PCI tend to exhibit negative emotions such as anxiety and depression. Medical staff should attach great importance to evaluating any negative feelings in this group and take timely targeted intervention measures to prevent and mitigate the occurrence and development of these adverse emotions in patients with coronary heart disease after PCI.

Phosphoric-Acid Retention in High-Temperature Proton-Exchange Membranes.

Great efforts have been conducted to develop high temperature proton exchange membrane fuel cell (HT-PEMFC) due to its features of enhanced electrocatalyst reactivity, simplified hydrothermal management system and high CO tolerance of catalysts, and remarkable progress has been achieved. However, the easy leaching of phosphoric acid (PA) from the membranes during operation limits its commercial scale-up in complicated environments. This concept here mainly focuses on the recent developments for mitigation of PA loss in PEMs. The probable mechanisms of PA loss are proposed. The approaches to improve PA retention for example via introduction of phosphonic acid by covalent bond, using ion-pairs interaction and siphoning effect, and blending with inorganic nanoparticles are described in detail. Among these strategies, the siphoning effect from the intrinsic microporous PEMs is the most efficient and enables the cell to operate flexibly within a broad temperature range. Therefore, this concept may provide new ideas for the scientists to retain PA, to improve the cell performance and expand the potential applications of PA doped PEMs at elevated humidity and wide temperature range.

An Improved Q-Learning-Based Sensor-Scheduling Algorithm for Multi-Target Tracking.

Target tracking is an essential issue in wireless sensor networks (WSNs). Compared with single-target tracking, how to guarantee the performance of multi-target tracking is more challenging because the system needs to balance the tracking resource for each target according to different target properties and network status. However, the balance of tracking task allocation is rarely considered in those prior sensor-scheduling algorithms, which may result in the degradation of tracking accuracy for some targets and additional system energy consumption. To address this issue, we propose in this paper an improved Q-learning-based sensor-scheduling algorithm for multi-target tracking (MTT-SS). First, we devise an entropy weight method (EWM)-based strategy to evaluate the priority of targets being tracked according to target properties and network status. Moreover, we develop a Q-learning-based task allocation mechanism to obtain a balanced resource scheduling result in multi-target-tracking scenarios. Simulation results demonstrate that our proposed algorithm can obtain a significant enhancement in terms of tracking accuracy and energy efficiency compared with the existing sensor-scheduling algorithms.

Hospital red blood cell and platelet supply and utilization from March to December of the first year of the COVID-19 pandemic: The BEST collaborative study.

BACKGROUND: At the start of the coronavirus disease 2019 (COVID-19) pandemic, widespread blood shortages were anticipated. We sought to determine how hospital blood supply and blood utilization were affected by the first wave of COVID-19. STUDY DESIGN AND METHODS: Weekly red blood cell (RBC) and platelet (PLT) inventory, transfusion, and outdate data were collected from 13 institutions in the United States, Brazil, Canada, and Denmark from March 1st to December 31st of 2020 and 2019. Data from the sites were aligned based on each site's local first peak of COVID-19 cases, and data from 2020 (pandemic year) were compared with data from the corresponding period in 2019 (pre-pandemic baseline). RESULTS: RBC inventories were 3% lower in 2020 than in 2019 (680 vs. 704, p < .001) and 5% fewer RBCs were transfused per week compared to 2019 (477 vs. 501, p < .001). However, during the first COVID-19 peak, RBC and PLT inventories were higher than normal, as reflected by deviation from par, days on hand, and percent outdated. At this time, 16% fewer inpatient beds were occupied, and 43% fewer surgeries were performed compared to 2019 (p < .001). In contrast to 2019 when there was no correlation, there was, in 2020, significant negative correlations between RBC and PLT days on hand and both percentage occupancy of inpatient beds and percentage of surgeries performed. CONCLUSION: During the COVID-19 pandemic in 2020, RBC and PLT inventories remained adequate. During the first wave of cases, significant decreases in patient care activities were associated with excess RBC and PLT supplies and increased product outdating.

Learning to Discover Explainable Clinical Features With Minimum Supervision.

Purpose: To compare supervised transfer learning to semisupervised learning for their ability to learn in-depth knowledge with limited data in the optical coherence tomography (OCT) domain. Methods: Transfer learning with EfficientNet-B4 and semisupervised learning with SimCLR are used in this work. The largest public OCT dataset, consisting of 108,312 images and four categories (choroidal neovascularization, diabetic macular edema, drusen, and normal) is used. In addition, two smaller datasets are constructed, containing 31,200 images for the limited version and 4000 for the mini version of the dataset. To illustrate the effectiveness of the developed models, local interpretable model-agnostic explanations and class activation maps are used as explainability techniques. Results: The proposed transfer learning approach using the EfficientNet-B4 model trained on the limited dataset achieves an accuracy of 0.976 (95% confidence interval [CI], 0.963, 0.983), sensitivity of 0.973 and specificity of 0.991. The semisupervised based solution with SimCLR using 10% labeled data and the limited dataset performs with an accuracy of 0.946 (95% CI, 0.932, 0.960), sensitivity of 0.941, and specificity of 0.983. Conclusions: Semisupervised learning has a huge potential for datasets that contain both labeled and unlabeled inputs, generally, with a significantly smaller number of labeled samples. The semisupervised based solution provided with merely 10% labeled data achieves very similar performance to the supervised transfer learning that uses 100% labeled samples. Translational Relevance: Semisupervised learning enables building performant models while requiring less expertise effort and time by using to good advantage the abundant amount of available unlabeled data along with the labeled samples.

Entropy Measures of Electroencephalograms towards the Diagnosis of Psychogenic Non-Epileptic Seizures.

Psychogenic non-epileptic seizures (PNES) may resemble epileptic seizures but are not caused by epileptic activity. However, the analysis of electroencephalogram (EEG) signals with entropy algorithms could help identify patterns that differentiate PNES and epilepsy. Furthermore, the use of machine learning could reduce the current diagnosis costs by automating classification. The current study extracted the approximate sample, spectral, singular value decomposition, and Renyi entropies from interictal EEGs and electrocardiograms (ECG)s of 48 PNES and 29 epilepsy subjects in the broad, delta, theta, alpha, beta, and gamma frequency bands. Each feature-band pair was classified by a support vector machine (SVM), k-nearest neighbour (kNN), random forest (RF), and gradient boosting machine (GBM). In most cases, the broad band returned higher accuracy, gamma returned the lowest, and combining the six bands together improved classifier performance. The Renyi entropy was the best feature and returned high accuracy in every band. The highest balanced accuracy, 95.03%, was obtained by the kNN with Renyi entropy and combining all bands except broad. This analysis showed that entropy measures can differentiate between interictal PNES and epilepsy with high accuracy, and improved performances indicate that combining bands is an effective improvement for diagnosing PNES from EEGs and ECGs.

Automated feature-based grading and progression analysis of diabetic retinopathy.

BACKGROUND: In diabetic retinopathy (DR) screening programmes feature-based grading guidelines are used by human graders. However, recent deep learning approaches have focused on end to end learning, based on labelled data at the whole image level. Most predictions from such software offer a direct grading output without information about the retinal features responsible for the grade. In this work, we demonstrate a feature based retinal image analysis system, which aims to support flexible grading and monitor progression. METHODS: The system was evaluated against images that had been graded according to two different grading systems; The International Clinical Diabetic Retinopathy and Diabetic Macular Oedema Severity Scale and the UK's National Screening Committee guidelines. RESULTS: External evaluation on large datasets collected from three nations (Kenya, Saudi Arabia and China) was carried out. On a DR referable level, sensitivity did not vary significantly between different DR grading schemes (91.2-94.2.0%) and there were excellent specificity values above 93% in all image sets. More importantly, no cases of severe non-proliferative DR, proliferative DR or DMO were missed. CONCLUSIONS: We demonstrate the potential of an AI feature-based DR grading system that is not constrained to any specific grading scheme.

Associations between dyslipidaemia and pregnancy outcomes in the first complete cycle of IVF/ICSI: a real-world analysis.

RESEARCH QUESTION: Are there associations between dyslipidaemia and pregnancy outcomes in the first complete cycle of IVF/intracytoplasmic sperm injection (ICSI)? DESIGN: This long-term, retrospective real-world analysis involved 5030 infertile women who underwent a first complete IVF/ICSI cycle between January 2015 and October 2020. They were categorized into dyslipidaemia (n = 1903) and control (n = 3127) groups according to serum lipid concentrations before ovarian stimulation. Propensity score matching and multivariable logistic regression were used to control for confounding variables. RESULTS: In the raw cohort, women with dyslipidaemia had a significantly increased late miscarriage rate (P = 0.039), decreased term birth rate (P = 0.002) and decreased live birth rate (P = 0.005) compared with non-dyslipidaemic women. In the propensity score-matched cohort, the term birth rate (P = 0.038) and live birth rate (P = 0.044) were significantly lower in the dyslipidaemia group (n = 1686) than the controls (n = 1686). Multivariable logistic regression indicated that infertile women with dyslipidaemia (P = 0.026) and elevated serum total cholesterol concentrations (total cholesterol ≥5.20 mmol/l; P = 0.028) were significantly less likely to have a live birth. Rates of late miscarriage (P = 0.027), term birth (P = 0.003) and live birth (P = 0.010) differed significantly among women with normal, borderline increased and increased serum lipid concentrations. Compared with controls, women with increased serum lipid concentrations had a significantly higher late miscarriage rate, lower term birth rate and lower live birth rate. Women with increased serum lipid concentrations were significantly less likely than controls to have a live birth. CONCLUSIONS: Dyslipidaemia, total cholesterol ≥5.20 mmol/l and degrees of elevated serum lipid concentrations are negatively associated with live birth rate in the first complete IVF/ICSI cycle in infertile women.

Enhanced recovery after surgery promotes the postoperative recovery of lung and gastrointestinal function of pseudomyxoma peritonei. / åŠ é€Ÿåº·å¤å¤–ç§‘ä¿ƒè¿›è ¹è†œå‡é»æ¶²ç˜¤æœ¯åŽè‚ºä¸Žèƒƒè‚ é“åŠŸèƒ½çš„åº·å¤.

OBJECTIVES: Pseudomyxoma peritonei (PMP) is a rare low-grade malignant tumor, which is difficult to operate with many postoperative complications. In recent years, enhanced recovery after surgery (ERAS) has been greatly developed in the perioperative management of surgical diseases, and it plays an important role in improving the postoperative prognosis of surgical patients. This study was conducted to explore the application of ERAS in the perioperative management of PMP patients, and to study the effect of ERAS on postoperative respiratory and digestive tract complications. METHODS: We retrospectively analyzed clinical data of patients with PMP from January 2014 to December 2018. These patients were treated with surgery in our center and they were divided into an observation group and a control group. The patients in the control group didn't perform ERAS in perioperative period, and patients in the observation group was performed ERAS. Then, we analyzed and compared the postoperative pulmonary complications (PPC) and gastrointestinal function between the 2 groups. RESULTS: There was no significant difference in the incidence of atelectasis, pleural effusion, pulmonary infection and acute respiratory distress syndrome (ARDS) between the two groups, but the total incidence of PPC in the observation group was significantly lower than that in the control group (P=0.032). The incidence of postoperative gastrointestinal dysfunction (PGID) in the observation group was significantly lower than that in the control group (P=0.025), and the postoperative first exhaust time, first defecation time, oral feeding time, and albumin level in the observation group were all better than those in the control group (all P<0.05). CONCLUSIONS: ERAS can significantly reduce the incidence of postoperative PPC and PGID in the PMP patients and improve their postoperative recovery.

Synaptosomal Actin Dynamics in the Developmental Visual Cortex Regulate Behavioral Visual Acuity in Rats.

Purpose: Synaptosomal actin dynamics are essential for synaptic structural stability. Whether actin dynamics are involved in structural and functional synaptic plasticity within the primary visual cortex (V1) or behavioral visual acuity in rats has still not been thoroughly investigated. Methods: Synaptosome preparation and western blot analysis were used to analyze synaptosomal actin dynamics. Transmission electron microscopy was used to detect synaptic density and mitochondrial area alterations. A visual water maze task was applied to assess behavioral visual acuity. Microinjection of the actin polymerization inhibitor or stabilizer detected the effect of actin dynamics on visual function. Results: Actin dynamics, the mitochondrial area, and synaptic density within the area of V1 are increased during the critical period for the development of binocularity. Microinjection of the actin polymerization inhibitor cytochalasin D into the V1 decreased the mitochondrial area, synaptic density, and behavioral visual acuity. Long-term monocular deprivation reduced actin dynamics, the mitochondrial area, and synaptic density within the V1 contralateral to the deprived eye compared with those ipsilateral to the deprived eye and impaired visual acuity in the amblyopic eye. In addition, the mitochondrial area, synaptic density, and behavioral visual acuity were improved by stabilization of actin polymerization by jasplakinolide microinjection. Conclusions: During the critical period of visual development of binocularity, synaptosomal actin dynamics regulate synaptic structure and function and play roles in behavioral visual acuity in rats.

Facilitating diabetic retinopathy screening using automated retinal image analysis in underresourced settings.

AIM: To evaluate an automated retinal image analysis (ARIA) of indigenous retinal fundus images against a human grading comparator for the classification of diabetic retinopathy (DR) status. METHODS: Indigenous Australian adults with type 2 diabetes (n = 410) from three remote and very remote primary-care services in the Northern Territory, Australia, underwent teleretinal DR screening. A single, central retinal fundus photograph (opportunistic mydriasis) for each eye was later regraded using a single ARIA and a UK human grader and national DR classification system. The sensitivity and specificity of ARIA were assessed relative to the comparator. Proportionate agreement and a Kappa statistic were also computed. RESULTS: Retinal images from 391 and 393 participants were gradable for 'Any DR' by the human grader and ARIA grader, respectively. 'Any DR' was detected by the human grader in 185 (47.3%) participants and by ARIA in 202 (48.6%) participants (agreement =88.0%, Kappa = 0.76,), whereas proliferative DR was detected in 31 (7.9%) and 37 (9.4%) participants (agreement = 98.2%, Kappa = 0.89,), respectively. The ARIA software had 91.4 (95% CI, 86.3-95.0) sensitivity and 85.0 (95% CI, 79.3-89.5) specificity for detecting 'Any DR' and 96.8 (95% CI, 83.3-99.9) sensitivity and 98.3 (95% CI, 96.4-99.4) specificity for detecting proliferative DR. CONCLUSIONS: This ARIA software has high sensitivity for detecting 'Any DR', hence could be used as a triage tool for human graders. High sensitivity was also found for detection of proliferative DR by ARIA. Future versions of this ARIA should include maculopathy and referable DR (CSME and/or PDR). Such ARIA software may benefit diabetes care in less-resourced regions.