RSU-Net: U-net based on residual and self-attention mechanism in the segmentation of cardiac magnetic resonance images.

BACKGROUND: Automated segmentation techniques for cardiac magnetic resonance imaging (MRI) are beneficial for evaluating cardiac functional parameters in clinical diagnosis. However, due to the characteristics of unclear image boundaries and anisotropic resolution anisotropy produced by cardiac magnetic resonance imaging technology, most of the existing methods still have the problems of intra-class uncertainty and inter-class uncertainty. However, due to the irregularity of the anatomical shape of the heart and the inhomogeneity of tissue density, the boundaries of its anatomical structures become uncertain and discontinuous. Therefore, fast and accurate segmentation of cardiac tissue remains a challenging problem in medical image processing. METHODOLOGY: We collected cardiac MRI data from 195 patients as training set and 35patients from different medical centers as external validation set. Our research proposed a U-net network architecture with residual connections and a self-attentive mechanism (Residual Self-Attention U-net, RSU-Net). The network relies on the classic U-net network, adopts the U-shaped symmetric architecture of the encoding and decoding mode, improves the convolution module in the network, introduces skip connections, and improves the network's capacity for feature extraction. Then for solving locality defects of ordinary convolutional networks. To achieve a global receptive field, a self-attention mechanism is introduced at the bottom of the model. The loss function employs a combination of Cross Entropy Loss and Dice Loss to jointly guide network training, resulting in more stable network training. RESULTS: In our study, we employ the Hausdorff distance (HD) and the Dice similarity coefficient (DSC) as metrics for assessing segmentation outcomes. Comparsion was made with the segmentation frameworks of other papers, and the comparison results prove that our RSU-Net network performs better and can make accurate segmentation of the heart. New ideas for scientific research. CONCLUSION: Our proposed RSU-Net network combines the advantages of residual connections and self-attention. This paper uses the residual links to facilitate the training of the network. In this paper, a self-attention mechanism is introduced, and a bottom self-attention block (BSA Block) is used to aggregate global information. Self-attention aggregates global information, and has achieved good segmentation results on the cardiac segmentation dataset. It facilitates the diagnosis of cardiovascular patients in the future.

Immune infiltration associated C1q acts as a novel prognostic biomarker of cutaneous melanoma.

C1q (complement C1q A chain, complement C1q B chain, and complement C1q C chain) is a recognized component of the classical complement pathway that influences the prognosis of various cancers. However, the effects of C1q on cutaneous melanoma (SKCM) outcomes and immune infiltration remain unknown. Gene expression profiling interactive analysis 2 and the human protein atlas were used to evaluate differential expression of C1q mRNA and protein. The relationship between C1q expression and clinicopathological features was also examined. The genetic alterations of C1q and their impact on survival were analyzed using the cbioportal database. The Kaplan-Meier approach was used to assess the significance of C1q in individuals with SKCM. The cluster profiler R package and the cancer single-cell state atlas database were used to investigate the function and mechanism of C1q in SKCM. The relationship between C1q and immune cell infiltration was estimated using single-sample gene set enrichment analysis. C1q expression was increased, and predicted a favorable prognosis. High C1q expression correlated with clinicopathological T stage, pathological stage, overall survival, and disease specific survival events. Moreover, C1q genetic alterations range from 2.7% to 4%, with no impact on prognosis. According to the enrichment analysis, C1q and immune-related pathways were closely connected. The link between complement C1q B chain and the functional state of inflammation was determined using the cancer single-cell state atlas database. In particular, C1q expression was significantly associated with infiltration of most immune cells and checkpoints PDCD1, CD274, and HAVCR2. The results of this study suggest that C1q is associated with prognosis and immune cell infiltration, supporting its value as a diagnostic and prognostic biomarker.

Propofol exerts anti-anhedonia effects via inhibiting the dopamine transporter.

Lasker's award-winning drug propofol is widely used in general anesthesia. The recreational use of propofol is reported to produce a well-rested feeling and euphoric state; yet, the neural mechanisms underlying such pleasant effects remain unelucidated. Here, we report that propofol actively and directly binds to the dopamine transporter (DAT), but not the serotonin transporter (SERT), which contributes to the rapid relief of anhedonia. Then, we predict the binding mode of propofol by molecular docking and mutation of critical binding residues on the DAT. Fiber photometry recording on awake freely moving mice and [18F] FP-CIT-PET scanning further establishes that propofol administration evokes rapid and lasting dopamine accumulation in nucleus accumbens (NAc). The enhanced dopaminergic tone drives biased activation of dopamine-receptor-1-expressing medium spiny neurons (D1-MSNs) in NAc and reverses anhedonia in chronically stressed animals. Collectively, these findings suggest the therapeutic potential of propofol against anhedonia, which warrants future clinical investigations.

GSDMD knockdown exacerbates hippocampal damage and seizure susceptibility by crosstalk between pyroptosis and apoptosis in kainic acid-induced temporal lobe epilepsy.

BACKGROUND: Neuronal loss is a vital pathological feature of temporal lobe epilepsy (TLE). However, the exact mechanism of neuronal loss in TLE is not fully understood. Pyroptosis, a novel form of programmed cell death (PCD), has been considered a contributor to the pathogenesis of TLE. However, recent studies have implicated extensive molecular crosstalk among pyroptosis, apoptosis, and necroptosis in various diseases, and they can be transformed to each other according to different contexts. This study aimed to investigate whether gasdermin D (GSDMD)-mediated pyroptosis is involved in the pathogenesis of TLE and whether crosstalk exists in the process of the modulation of pyroptosis. METHODS: The TLE model was established by intra-amygdala injection of kainic acid. The Racine score and local field potential (LFP) recordings were used to assess seizure severity. Western blotting and immunofluorescence were applied to detect the levels and cellular localization of GSDMD. The neuronal loss and type of neuronal death in the bilateral hippocampus were assessed by Nissl staining and flow cytometry analysis. The underlying crosstalk among pyroptosis, apoptosis, and necroptosis was explored by western blot and verified by VX765. RESULTS: GSDMD was significantly upregulated and mainly expressed within the neurons of the hippocampus in the TLE model. Inhibition of pyroptosis by GSDMD knockdown triggered caspase-3-mediated apoptosis, leading to excess neuronal loss and deterioration of epileptic behaviors. Blocking caspase-1 markedly inhibited caspase-3-mediated apoptosis and improved epileptic behaviors under GSDMD knockdown. CONCLUSIONS: Our results demonstrate that GSDMD-mediated pyroptosis is involved in the pathogenesis of TLE. However, inhibition of GSDMD triggers caspase-1-mediated crosstalk between pyroptosis and apoptosis, which exacerbates neuronal loss and seizure susceptibility. Therefore, the complex crosstalk among different forms of PCD should be considered when a potential molecular target in the single PCD pathway is modulated. On the other hand, along with further studies of molecular crosstalk among the PCD pathways, taking advantage of crosstalk to attenuate neuronal loss may provide new insight for the clinical therapy of TLE.

How information processing and risk/benefit perception affect COVID-19 vaccination intention of users in online health communities.

Objective: To investigate the relationship among information processing, risk/benefit perception and the COVID-19 vaccination intention of OHCs users with the heuristic-systematic model (HSM). Methods: This study conducted a cross-sectional questionnaire via an online survey among Chinese adults. A structural equation model (SEM) was used to examine the research hypotheses. Results: Systematic information processing positively influenced benefit perception, and heuristic information processing positively influenced risk perception. Benefit perception had a significant positive effect on users' vaccination intention. Risk perception had a negative impact on vaccination intention. Findings revealed that differences in information processing methods affect users' perceptions of risk and benefit, which decide their vaccination intention. Conclusion: Online health communities can provide more systematic cues and users should process information systematically to increase their perceived benefits, consequently increase their willingness to receive COVID-19 vaccine.

Impact of Contextual-Level Social Determinants of Health on Newer Antidiabetic Drug Adoption in Patients with Type 2 Diabetes.

BACKGROUND: We aimed to investigate the association between contextual-level social determinants of health (SDoH) and the use of novel antidiabetic drugs (ADD), including sodium-glucose cotransporter-2 inhibitors (SGLT2i) and glucagon-like peptide-1 receptor agonists (GLP1a) for patients with type 2 diabetes (T2D), and whether the association varies across racial and ethnic groups. METHODS: Using electronic health records from the OneFlorida+ network, we assembled a cohort of T2D patients who initiated a second-line ADD in 2015-2020. A set of 81 contextual-level SDoH documenting social and built environment were spatiotemporally linked to individuals based on their residential histories. We assessed the association between the contextual-level SDoH and initiation of SGTL2i/GLP1a and determined their effects across racial groups, adjusting for clinical factors. RESULTS: Of 28,874 individuals, 61% were women, and the mean age was 58 (±15) years. Two contextual-level SDoH factors identified as significantly associated with SGLT2i/GLP1a use were neighborhood deprivation index (odds ratio [OR] 0.87, 95% confidence interval [CI] 0.81-0.94) and the percent of vacant addresses in the neighborhood (OR 0.91, 95% CI 0.85-0.98). Patients living in such neighborhoods are less likely to be prescribed with newer ADD. There was no interaction between race-ethnicity and SDoH on the use of newer ADD. However, in the overall cohort, the non-Hispanic Black individuals were less likely to use newer ADD than the non-Hispanic White individuals (OR 0.82, 95% CI 0.76-0.88). CONCLUSION: Using a data-driven approach, we identified the key contextual-level SDoH factors associated with not following evidence-based treatment of T2D. Further investigations are needed to examine the mechanisms underlying these associations.

Rational design of formamidine tin-based perovskite solar cell with 30% potential efficiency via 1-D device simulation.

As a promising photovoltaic technology, halide perovskite solar cells (PSCs) have recently attracted wide attention. This work presents a systematic simulation of low bandgap formamidinium tin iodide (FASnI3)-based p-n heterojunction PSCs to investigate the effects of multiple optoelectronic variations on the photovoltaic performance. The structures of the simulated devices are n-i-p, electron transport layer-free (ETL-free), hole transport layer-free (HTL-free), and inverted HTL-free. The simulation is conducted with the Solar Cell Capacitance Simulator (SCAPS-1D). The power conversion efficiencies (PCEs) dramatically decrease when the acceptor doping density (NA) of the absorber layer exceeds 1016 cm-3. For all devices, the photovoltaic parameters dramatically decrease when the absorber defect density (Nt) is over 1015 cm-3, and the best absorber layer thickness is 1000 nm. It should be pointed out that the Nt and the interface defect layer (IDL) are the primary culprits that seriously affect the device performance. When the interfacial defect density (Nit) exceeds 1012 cm-3, PCEs begin to decline significantly. Therefore, paying attention to these defect layers is necessary to improve the PCE. Furthermore, the proper conduction band offset (CBO) between the electron transport layer (ETL) and absorber layer positively affects PSCs' performance. These simulation results help fabricate highly efficient and environment-friendly narrow bandgap PSCs.

Boosting wheat functional genomics via an indexed EMS mutant library of KN9204.

A better understanding of wheat functional genomics can improve targeted breeding for better agronomic traits and environmental adaptation. However, the lack of gene-indexed mutants and low transformation efficiency in wheat limits in-depth gene functional studies and genetic manipulation for breeding. In this study, we created a library of KN9204, a popular wheat variety in northern China, with a reference genome, transcriptome, and epigenome of different tissues, using ethyl methyl sulfonate (EMS) mutagenesis. This library contains a vast developmental diversity of critical tissues and transition stages. Exome capture sequencing of 2,090 mutant lines, using KN9204 genome-designed probes revealed that 98.79% of coding genes had mutations, and each line had an average of 1,383 EMS-type SNPs. We identified new allelic variations for crucial agronomic trait-related genes, such as Rht-D1, Q, TaTB1, and WFZP. We tested 100 lines with severe mutations in 80 NAC TFs under drought and salinity stresses and identified 13 lines with altered sensitivity. Further analysis of three lines using transcriptome and chromatin accessibility data revealed hundreds of direct targets of NAC with altered transcriptional patterns under salt or drought stress, including SNAC1, DREB2B, CML16, and ZFP182, factors known to respond to abiotic stress. Thus, we have generated and indexed KN9204 EMS mutant library, which would facilitate functional genomics research and offer resources for genetic manipulation in wheat.

Quantification of Oculomotor Responses and Accommodation through Instrumentation and Analysis Toolboxes.

Through the purposeful stimulation and recording of eye movements, the fundamental characteristics of the underlying neural mechanisms of eye movements can be observed. VisualEyes2020 (VE2020) was developed based on the lack of customizable software-based visual stimulation available for researchers that does not rely on motors or actuators within a traditional haploscope. This new instrument and methodology have been developed for a novel haploscope configuration utilizing both eye tracking and autorefractor systems. Analysis software that enables the synchronized analysis of eye movement and accommodative responses provides vision researchers and clinicians with a reproducible environment and shareable tool. The Vision and Neural Engineering Laboratory's (VNEL) Eye Movement Analysis Program (VEMAP) was established to process recordings produced by VE2020's eye trackers, while the Accommodative Movement Analysis Program (AMAP) was created to process the recording outputs from the corresponding autorefractor system. The VNEL studies three primary stimuli: accommodation (blur-driven changes in the convexity of the intraocular lens), vergence (inward, convergent rotation and outward, divergent rotation of the eyes), and saccades (conjugate eye movements). The VEMAP and AMAP utilize similar data flow processes, manual operator interactions, and interventions where necessary; however, these analysis platforms advance the establishment of an objective software suite that minimizes operator reliance. The utility of a graphical interface and its corresponding algorithms allow for a broad range of visual experiments to be conducted with minimal required prior coding experience from its operator(s).

Inflammation-the role of TRPA1 channel.

Recently, increasing numbers of studies have demonstrated that transient receptor potential ankyrin 1 (TRPA1) can be used as a potential target for the treatment of inflammatory diseases. TRPA1 is expressed in both neuronal and non-neuronal cells and is involved in diverse physiological activities, such as stabilizing of cell membrane potential, maintaining cellular humoral balance, and regulating intercellular signal transduction. TRPA1 is a multi-modal cell membrane receptor that can sense different stimuli, and generate action potential signals after activation via osmotic pressure, temperature, and inflammatory factors. In this study, we introduced the latest research progress on TRPA1 in inflammatory diseases from three different aspects. First, the inflammatory factors released after inflammation interacts with TRPA1 to promote inflammatory response; second, TRPA1 regulates the function of immune cells such as macrophages and T cells, In addition, it has anti-inflammatory and antioxidant effects in some inflammatory diseases. Third, we have summarized the application of antagonists and agonists targeting TRPA1 in the treatment of some inflammatory diseases.

Protective effect of Nr4a2 (Nurr1) against LPS-induced depressive-like behaviors via regulating activity of microglia and CamkII neurons in anterior cingulate cortex.

Neuroinflammation is tightly associated with onset of depression. The nuclear receptor related 1 protein (Nurr1, also called Nr4a2), its roles in dopaminergic neurons is well understood, which can alleviate inflammation. Nevertheless, potential effects of Nr4a2 on neuroinflammation associated with depression still remains unclear. Chronic lipopolysaccharides (LPS) stress induced depressive-behaviors were confirmed via behavioral tests. Differentially expressed genes were detected by using RNA-sequencing. The anterior cingulate cortex (ACC) tissues were collected for biochemical experiments. The Golgi-Cox staining and virus labeling were used to evaluate the dendritic spines. We applied fluoxetine (FLX) and amodiaquine dihydrochloride (AQ, a highly selective agonist of Nr4a2) in mice. Overexpression experiments were performed by injecting with AAV-Nr4a2-EGFP into ACC. Chemogenetic activation of CamkII neurons via injecting the hM3Dq virus. Mice treated with LPS displayed depressive- and anxiety-like behaviors. The reduction of Nr4a2 and FosB induced by LPS were rescued by pretreatment with FLX or AQ. More importantly, LPS-induced behavior deficits in mice were also alleviated via fluoxetine treatment and pharmacological activation the expression of Nr4a2. Meanwhile, enhancing the level of Nr4a2 could improve dendritic spines loss of neuron and morphological changes in microglia. Overexpression of Nr4a2 in ACC reversed the depressive- and anxiety-like behaviors caused by LPS administration. Activation of CamkII neurons in ACC could robustly increase the expression of Nr4a2 and improve LPS-induced behavior deficits. Our findings demonstrate that the Nr4a2 may regulate depressive-like behaviors via alleviating the impairment of morphology and function on microglia and CamkII neurons induced by chronic neuroinflammation.

Eosinophil and IFN-γ associated with immune-related adverse events as prognostic markers in patients with non-small cell lung cancer treated with immunotherapy.

Objectives: Immune checkpoint inhibitors (ICIs) alone or combined with other antitumor agents are largely used in lung cancer patients, which show both positive effects and side effects in particular subjects. Our study aims to identify biomarkers that can predict response to immunotherapy or risk of side effects, which may help us play a positive role and minimize the risk of adverse effects in clinical practice. Methods: We retrospectively collected data from patients with advanced non-small cell lung cancer (NSCLC) treated with ICIs at our center. Patients who received initial ICI therapy for >1 year without progression of disease were classified as long-term treatment (LT) group, while others were classified as the non-long-term treatment (NLT) group. Multivariate logistic analysis was performed to identify independent risk factors of progression-free survival (PFS) and immune-related adverse events (irAEs). Results: A total of 83 patients (55.7%) had irAEs. The median PFS for patients in grades 1-2 of irAEs vs. grades 3-4 vs non-irAEs groups was (undefined vs. 12 vs. 8 months; p = 0.0025). The 1-year PFS rate for multisystem vs. single vs. non-irAE groups was 63%, 56%, and 31%, respectively. Signal transduction of inflammatory cytokines improves clinical prognosis through immunomodulatory function, but the benefit is also limited by the resulting organ damage, making it a complex immune balance. Serum biomarkers including EOS% of ≥ 1.15 (HR: 8.30 (95% CI, 2.06 to 33.42); p = 0.003) and IFN-γ of ≥ 3.75 (HR: 5.10 (95% CI, 1.29 to 20.15), p = 0.02) were found to be predictive for irAEs. Conclusion: EOS% of ≥1.15% and IFN-γ of ≥3.75 ng/L were considered peripheral-blood markers for irAEs and associated with improved clinical outcomes for immunotherapy in patients with advanced NSCLC.

Relationship of the levels of reactive oxygen species in the fertilization medium with the outcome of in vitro fertilization following brief incubation.

Embryo selection in in vitro fertilization-embryo transfer (IVF-ET) mostly relies on morphological assessment using a conventional microscope or the time-lapse monitoring system, which is not comprehensive. Inappropriate levels of reactive oxygen species (ROS) in the fertilization medium may cause damage to gametes, eventually leading to adverse IVF outcomes. The present study aimed to identify the optimal oxidation-reduction level in the fertilization medium for IVF outcomes by measuring the static oxidation-reduction potential (sORP) using a highly accurate and sensitive MiOXSYS system. A total of 136 patients undergoing IVF following brief incubation were divided equally into 4 groups in this prospective cohort study. The sORP value in the fertilization medium was detected using the MiOXSYS system, and its relationship with IVF outcomes was analyzed. The primary outcome was pregnancy outcomes, including live birth rate (LBR), clinical pregnancy rate (CPR), biochemical pregnancy rate (BPR), and implantation rate (IR). The secondary outcome was embryo quality, including fertilization rate (FR), cleavage rate (CR), available embryo rate (AER), and good-quality embryo rate (GQER). Group II (sORP: 228.7-235.3 mV) showed a higher LBR, CPR, BPR, and IR compared with Group III (sORP: 235.4-242.7 mV), presented as follows: LBR (32.0% for Group II vs 3.6% for Group III, P = 0.033), CPR (32.0% for Group II vs 3.6% for Group III, P = 0.033), BPR (36.0% for Group II vs 3.6% for Group III, P = 0.019), and IR (31.3% for Group II vs 2.7% for Group III, P = 0.003). The FR in Groups I and II had lower significant differences compared with that in Groups III and IV (71.7% and 70.3% for Groups I and II vs 83.5% and 80.4% for Groups III and IV, P = 0.000). The GQER in Group I to Group IV was 32.7%, 37.4%, 26.5%, and 33.3%, respectively (P = 0.056). This study indicated that the sORP value in the fertilization medium might be a potential indicator of embryo quality and pregnancy outcome.

Ischemic stroke prediction of patients with carotid atherosclerotic stenosis via multi-modality fused network.

Carotid atherosclerotic stenosis of the carotid artery is an important cause of ischemic cerebrovascular disease. The aim of this study was to predict the presence or absence of clinical symptoms in unknown patients by studying the existence or lack of symptoms of patients with carotid atherosclerotic stenosis. First, a deep neural network prediction model based on brain MRI imaging data of patients with multiple modalities is constructed; it uses the multi-modality features extracted from the neural network as inputs and the incidence of diagnosis as output to train the model. Then, a machine learning-based classification algorithm is developed to utilize the clinical features for comparison and evaluation. The experimental results showed that the deep learning model using imaging data could better predict the clinical symptom classification of patients. As part of preventive medicine, this study could help patients with carotid atherosclerosis narrowing to prepare for stroke prevention based on the prediction results.

Caregiver Stress Is Improved and Sustained Following Transcatheter Aortic Valve Replacement.

Background: Little is known about the impact of symptomatic aortic stenosis and subsequent transcatheter aortic valve replacement (TAVR) on stress and health for the caregiver. In this prospective cohort study, we measured caregiver stress before and after TAVR. Methods: We interviewed 34 primary caregivers for patients undergoing outpatient TAVR at an academic institution. Caregiver stress was measured using the Kingston Caregiver Stress Scale (KCSS) and the Caregiver Self-Assessment Questionnaire (CSAQ) before TAVR and at one and six months after. Mean scores were compared pre- and post-TAVR using the Wilcoxon signed-rank test. Results: There was significant improvement in KCSS caregiver stress at one month that was sustained at six months post-TAVR (mean change -1.91 ± 2.50 for six months, p-value 0.01). This was primarily driven by improvement in caregiving issues rather than family or financial issues. There was also significant improvement in CSAQ self-assessed health/illness at one and six months (mean change -2.78 ± 4.01 for six months, p-value 0.016). Conclusions: Our findings support further investigation of caregiver outcomes in shared decision making before TAVR.

Associations of circulation levels of cytokines with birthweight, preterm birth, spontaneous miscarriages, and stillbirth: A Mendelian randomization analysis.

Background: The association between immune imbalances and adverse pregnancy outcomes has been extensive investigated by observational studies, but remain unclear. Thus, this study aimed to establish the causality of the circulation levels of cytokines on adverse pregnancy outcomes, such as offspring's birthweight (BW), preterm birth (PTB), spontaneous miscarriage (SM), and stillbirth (SB). Methods: Two-sample Mendelian randomization (MR) analysis was employed to investigate potential causal relations between 41 cytokines and pregnancy outcomes on the basis of previously published GWAS datasets. Multivariable MR (MVMR) analysis was implemented to investigate the effect of the composition of cytokine networks on the pregnancy outcomes. Potential risk factors were further estimated to explore the potential mediators. Results: Genetic correlation analysis based on large GWAS data sources revealed that genetically predicted MIP1b (ß = -0.027, S.E. = 0.010, p = 0.009) and MCSF (ß = -0.024, S.E. = 0.011, p = 0.029) were associated with reduced offspring's BW, MCP1 (OR: 0.90, 95% CI: 0.83-0.97, p = 0.007) was associated with reduced SM risk, SCF (ß = -0.014, S.E. = 0.005, p = 0.012) associated with decreased number of SB in MVMR. The univariable MR showed that GROa (OR: 0.92, 95% CI: 0.87-0.97, p = 0.004) was associated with decreased PTB risk. Except for the MCSF-BW association, all above associations surpassed the Bonferroni corrected threshold. The MVMR results revealed that MIF, SDF1a, MIP1b, MCSF and IP10 composed cytokine networks, associated with offspring's BW. Risk factors analysis indicated that the above causal associations might be mediated by smoking behaviors. Conclusion: These findings suggest the causal associations of several cytokines with adverse pregnancy outcomes, which were potentially mediated by smoking and obesity. Some of the results did not been corrected through multiple tests and larger samples verification is required in further studies.

Thick quilt may severely impact the estimation of postmortem interval using forensic entomology-based methods -two case reports.

This paper introduces two indoor cases in China, characterized by bodies lying on a mattress, covered with thick quilts and wearing clothes. There were obvious deviation in the estimated minimum postmortem interval (PMImin) of the corpses using entomological methods. Based on the forensic entomology evidence from the scene, the PMImin estimated using temperature data from the nearest weather station was longer than the actual postmortem interval (PMI) based on the police investigation and the security camera footage. The most probable cause of the errors in PMImin estimation was the hindrance in heat dissipation since the corpses were covered with thick quilts while lying on the mattress. Therefore, the heat generated by the decomposition process and larval activity was hard to lose, resulting in the rapid development of insects. These case reports emphasize the importance of temperature collection in forensic entomological investigations. Our findings call for standardized temperature acquisition procedures, including which temperature measurements (body, microenvironment or ambient temperature) should be used in forensic entomological investigations when handling similar cases.

Physics-informed sinogram completion for metal artifact reduction in CT imaging.

Objective.Metal artifacts in the computed tomography (CT) imaging are unavoidably adverse to the clinical diagnosis and treatment outcomes. Most metal artifact reduction (MAR) methods easily result in the over-smoothing problem and loss of structure details near the metal implants, especially for these metal implants with irregular elongated shapes. To address this problem, we present the physics-informed sinogram completion (PISC) method for MAR in CT imaging, to reduce metal artifacts and recover more structural textures.Approach.Specifically, the original uncorrected sinogram is firstly completed by the normalized linear interpolation algorithm to reduce metal artifacts. Simultaneously, the uncorrected sinogram is also corrected based on the beam-hardening correction physical model, to recover the latent structure information in metal trajectory region by leveraging the attenuation characteristics of different materials. Both corrected sinograms are fused with the pixel-wise adaptive weights, which are manually designed according to the shape and material information of metal implants. To furtherly reduce artifacts and improve the CT image quality, a post-processing frequency split algorithm is adopted to yield the final corrected CT image after reconstructing the fused sinogram.Main results.We qualitatively and quantitatively evaluated the presented PISC method on two simulated datasets and three real datasets. All results demonstrate that the presented PISC method can effectively correct the metal implants with various shapes and materials, in terms of artifact suppression and structure preservation.Significance.We proposed a sinogram-domain MAR method to compensate for the over-smoothing problem existing in most MAR methods by taking advantage of the physical prior knowledge, which has the potential to improve the performance of the deep learning based MAR approaches.

Early prediction of Alzheimer's disease and related dementias using real-world electronic health records.

INTRODUCTION: This study aims to explore machine learning (ML) methods for early prediction of Alzheimer's disease (AD) and related dementias (ADRD) using the real-world electronic health records (EHRs). METHODS: A total of 23,835 ADRD and 1,038,643 control patients were identified from the OneFlorida+ Research Consortium. Two ML methods were used to develop the prediction models. Both knowledge-driven and data-driven approaches were explored. Four computable phenotyping algorithms were tested. RESULTS: The gradient boosting tree (GBT) models trained with the data-driven approach achieved the best area under the curve (AUC) scores of 0.939, 0.906, 0.884, and 0.854 for early prediction of ADRD 0, 1, 3, or 5 years before diagnosis, respectively. A number of important clinical and sociodemographic factors were identified. DISCUSSION: We tested various settings and showed the predictive ability of using ML approaches for early prediction of ADRD with EHRs. The models can help identify high-risk individuals for early informed preventive or prognostic clinical decisions.

ClusterSeg: A crowd cluster pinpointed nucleus segmentation framework with cross-modality datasets.

The detection and segmentation of individual cells or nuclei is often involved in image analysis across a variety of biology and biomedical applications as an indispensable prerequisite. However, the ubiquitous presence of crowd clusters with morphological variations often hinders successful instance segmentation. In this paper, nuclei cluster focused annotation strategies and frameworks are proposed to overcome this challenging practical problem. Specifically, we design a nucleus segmentation framework, namely ClusterSeg, to tackle nuclei clusters, which consists of a convolutional-transformer hybrid encoder and a 2.5-path decoder for precise predictions of nuclei instance mask, contours, and clustered-edges. Additionally, an annotation-efficient clustered-edge pointed strategy pinpoints the salient and error-prone boundaries, where a partially-supervised PS-ClusterSeg is presented using ClusterSeg as the segmentation backbone. The framework is evaluated with four privately curated image sets and two public sets with characteristic severely clustered nuclei across a variety range of image modalities, e.g., microscope, cytopathology, and histopathology images. The proposed ClusterSeg and PS-ClusterSeg are modality-independent and generalizable, and superior to current state-of-the-art approaches in multiple metrics empirically. Our collected data, the elaborate annotations to both public and private set, as well the source code, are released publicly at https://github.com/lu-yizhou/ClusterSeg.