Genome Mining Reveals a UbiA-Type Prenyltransferase Access to Farnesylation of Diketopiperazines.

UbiA-type prenyltransferases (PTases) are significant enzymes that lead to structurally diverse meroterpenoids. Herein, we report the identification and characterization of an undescribed UbiA-type PTase, FtaB, that is responsible for the farnesylation of indole-containing diketopiperazines (DKPs) through genome mining. Heterologous expression of the fta gene cluster and non-native pathways result in the production of a series of new C2-farnesylated DKPs. This study broadens the reaction scope of UbiA-type PTases and expands the chemical diversity of meroterpenoids.

Graph-based social relation inference with multi-level conditional attention.

Social relation inference intrinsically requires high-level semantic understanding. In order to accurately infer relations of persons in images, one needs not only to understand scenes and objects in images, but also to adaptively attend to important clues. Unlike prior works of classifying social relations using attention on detected objects, we propose a MUlti-level Conditional Attention (MUCA) mechanism for social relation inference, which attends to scenes, objects and human interactions based on each person pair. Then, we develop a transformer-style network to achieve the MUCA mechanism. The novel network named as Graph-based Relation Inference Transformer (i.e., GRIT) consists of two modules, i.e., a Conditional Query Module (CQM) and a Relation Attention Module (RAM). Specifically, we design a graph-based CQM to generate informative relation queries for all person pairs, which fuses local features and global context for each person pair. Moreover, we fully take advantage of transformer-style networks in RAM for multi-level attentions in classifying social relations. To our best knowledge, GRIT is the first for inferring social relations with multi-level conditional attention. GRIT is end-to-end trainable and significantly outperforms existing methods on two benchmark datasets, e.g., with performance improvement of 7.8% on PIPA and 9.6% on PISC.

Deep learning-based accurate diagnosis and quantitative evaluation of microvascular invasion in hepatocellular carcinoma on whole-slide histopathology images.

BACKGROUND: Microvascular invasion (MVI) is an independent prognostic factor that is associated with early recurrence and poor survival after resection of hepatocellular carcinoma (HCC). However, the traditional pathology approach is relatively subjective, time-consuming, and heterogeneous in the diagnosis of MVI. The aim of this study was to develop a deep-learning model that could significantly improve the efficiency and accuracy of MVI diagnosis. MATERIALS AND METHODS: We collected H&E-stained slides from 753 patients with HCC at the First Affiliated Hospital of Zhejiang University. An external validation set with 358 patients was selected from The Cancer Genome Atlas database. The deep-learning model was trained by simulating the method used by pathologists to diagnose MVI. Model performance was evaluated with accuracy, precision, recall, F1 score, and the area under the receiver operating characteristic curve. RESULTS: We successfully developed a MVI artificial intelligence diagnostic model (MVI-AIDM) which achieved an accuracy of 94.25% in the independent external validation set. The MVI positive detection rate of MVI-AIDM was significantly higher than the results of pathologists. Visualization results demonstrated the recognition of micro MVIs that were difficult to differentiate by the traditional pathology. Additionally, the model provided automatic quantification of the number of cancer cells and spatial information regarding MVI. CONCLUSIONS: We developed a deep learning diagnostic model, which performed well and improved the efficiency and accuracy of MVI diagnosis. The model provided spatial information of MVI that was essential to accurately predict HCC recurrence after surgery.

Clinical Performance of a Dedicated Urine-Based Assay for the Detection of Human Papillomavirus and Cervical Intraepithelial Neoplasia.

Objective: The objective of this study is to assess the clinical performance of a urine-based high-risk human papillomavirus (hrHPV) test for the detection of cervical intraepithelial neoplasia grade 2 or higher (CIN2+). Methods: Between September and December 2021, women aged 20 to 65 years referred to colposcopy clinic were prospectively recruited at three clinical centers in China. Paired urine and cervical specimens from all enrolled women were obtained for hrHPV DNA fluorescence quantitative PCR test. The results of liquid-based cytology (LBC), colposcopy and diagnostic biopsies were collected. We evaluated the sensitivity and specificity for CIN and assessed the agreement/kappa value. Results: A total of 732 women (median age, 40 years) with valid results were included in the study, and 130 (17.8%) women were histologically confirmed as CIN2+. The sensitivity of urine and cervical test for CIN2+ and CIN3+ were 87.69% and 85.45%, respectively. The specificity of urine test performed better than cervical test in women with

In situ fabrication of an anisotropic double-layer hydrogel as a bio-scaffold for repairing articular cartilage and subchondral bone injuries.

Articular cartilage is a smooth and elastic connective tissue playing load-bearing and lubricating roles in the human body. Normal articular cartilage comprises no blood vessels, lymphatic vessels, nerves, or undifferentiated cells, so damage self-repair is very unlikely. The injuries of articular cartilage are often accompanied by damage to the subchondral bone. The subchondral bone mainly provides mechanical support for the joint, and the successful repair of articular cartilage depends on the ability of the subchondral bone to provide a suitable environment. Currently, conventional repair treatments for articular cartilage and subchondral bone defects can hardly achieve good results due to the poor self-repairing ability of the cartilage Here, we propose a bioactive injectable double-layer hydrogel to repair articular cartilage and subchondral bone. The hydrogel scaffold mimics the multilayer structure of articular cartilage and subchondral bone. Agarose was used as a common base material for the double-layer hydrogel scaffold, in which a sodium alginate (SA)/agarose layer was used for the repair of artificially produced subchondral bone defects, while a decellularized extracellular matrix (dECM)/agarose layer was used for the repair of articular cartilage defects. The double-layer hydrogel scaffold is injectable, easy to use, and can fill in the damaged area. The hydrogel scaffold is also anisotropic both chemically and structurally. Animal experiments showed that the surface of the new cartilage tissue in the double-layer hydrogel scaffold group was closest to normal articular cartilage, with a structure similar to that of hyaline cartilage and a preliminary calcified layer. Moreover, the new subchondral bone in this group exhibited many regular bone trabeculae, and the new cartilage and subchondral bone were mechanically bound without mutual intrusion and tightly integrated with the surrounding tissue. The continuous double-layer hydrogel scaffold prepared in this study mimics the multilayer structure of articular cartilage and subchondral bone and promotes the functional repair of articular cartilage and subchondral bone, favoring close integration between the newborn tissue and the original tissue.

Clinical evaluation of a multitarget fecal immunochemical test-sDNA test for colorectal cancer screening in a high-risk population: a prospective, multicenter clinical study.

Colorectal cancer (CRC) is a major malignancy threatening the health of people in China and screening could be effective for preventing the occurrence and reducing the mortality of CRC. We conducted a multicenter, prospective clinical study which recruited 4,245 high-risk CRC individuals defined as having positive risk-adapted scores or fecal immunochemical test (FIT) results, to evaluate the clinical performance of the multitarget fecal immunochemical and stool DNA (FIT-sDNA) test for CRC screening. Each participant was asked to provide a stool sample prior to bowel preparation, and FIT-sDNA test and FIT were performed independently of colonoscopy. We found that 186 (4.4%) were confirmed to have CRC, and 375 (8.8%) had advanced precancerous neoplasia among the high CRC risk individuals. The sensitivity of detecting CRC for FIT-sDNA test was 91.9% (95% CI, 86.8-95.3), compared with 62.4% (95% CI, 54.9-69.3) for FIT (P < 0.001). The sensitivity for detecting advanced precancerous neoplasia was 63.5% (95% CI, 58.3-68.3) for FIT-sDNA test, compared with 30.9% (95% CI, 26.3-35.6) for FIT (P < 0.001). Multitarget FIT-sDNA test detected more colorectal advanced neoplasia than FIT. Overall, these findings indicated that in areas with limited colonoscopy resources, FIT-sDNA test could be a promising further risk triaging modality to select patients for colonoscopy in CRC screening.

HepaClear, a blood-based panel combining novel methylated CpG sites and protein markers, for the detection of early-stage hepatocellular carcinoma.

BACKGROUND: Early screening and detection of hepatocellular carcinoma (HCC) can efficiently improve patient prognosis. We aimed to identify a series of hypermethylated DNA markers and develop a blood-based HCC diagnosis panel containing DNA methylation sites and protein markers with improved sensitivity for early-stage HCC detection. RESULTS: Overall, 850K methylation arrays were performed using paired tissue DNA samples from 60 HCC patients. Ten candidate hypermethylated CpG sites were selected for further evaluation by quantitative methylation-specific PCR with 60 pairs of tissue samples. Six methylated CpG sites, along with α-fetoprotein (AFP) and des-gamma-carboxyprothrombin (DCP), were assayed in 150 plasma samples. Finally, an HCC diagnosis panel, named HepaClear, was developed in a cohort consisting of 296 plasma samples and validated in an independent cohort consisting of 198 plasma samples. The HepaClear panel, containing 3 hypermethylated CpG sites (cg14263942, cg12701184, and cg14570307) and 2 protein markers (AFP and DCP), yielded a sensitivity of 82.6% and a specificity of 96.2% in the training set and a sensitivity of 84.7% and a specificity of 92.0% in the validation set. The HepaClear panel had higher sensitivity (72.0%) for early-stage HCC than AFP (≥ 20 ng/mL, 48.0%) and DCP (≥ 40 mAU/mL, 62.0%) and detected 67.5% of AFP-negative HCC patients (AFP ≤ 20 ng/mL). CONCLUSIONS: We developed a multimarker HCC detection panel (HepaClear) that shows high sensitivity for early-stage HCC. The HepaClear panel exhibits high potential for HCC screening and diagnosis from an at-risk population.

Novel Generative Recurrent Neural Network Framework to Produce Accurate, Applicable, and Deidentified Synthetic Medical Data for Patients With Metastatic Cancer.

PURPOSE: Sensitive patient data cannot be easily shared/analyzed, severely limiting the innovative progress of research, specifically for marginalized/under-represented populations. Existing methods of deidentification are subject to data breaches. The objective of this study was to develop a neural network capable of generating a synthetic version of data for patients with novel postoperative metastatic cancer. METHODS: We analyzed a metastatic cancer patient cohort of 167,474 patients obtained from the National Surgical Quality Improvement Program. Twenty-seven clinical features were analyzed. We created a volume-matched synthetic cohort of 167,474 patients and a reduced-size synthetic cohort of 5,000 patients. The volume-matched and reduced-size synthetic cohorts were compared against the ground truth data to analyze differences in principal component distribution, underlying statistical properties/associations, intervariable correlations, and machine learning classifier performance when developed on the synthetic data. RESULTS: Among 167,474 patients with metastatic cancer in the original data, 50,669 (30.3%) died within 30 days of their index surgery. Our model was able to accurately capture underlying statistical properties, principal components, and intervariable correlations within the ground truth data, yielding an accuracy of 93.2% with a loss of 0.21%, and develop synthetic data capable of training accurate machine learning classifiers. The reduced-size synthetic data accurately replicated all categorical variables and every continuous variable with statistically similar records (P > .05), with the sole exception of preoperative albumin (P < .05). The volume-matched synthetic data frame was able to accurately replicate all categorical variables (P > .05). CONCLUSION: This described methodology can be applied to any structured medical data from any setting, significantly expedite scientific analysis/innovation, and be used to develop improved predictive classifiers with boosted tree-based algorithms, serving as the potential new gold standard of medical data sharing and data augmentation.

AuFe3@Pd/γ-Fe2O3 Nanosheets as an In Situ Regenerable and Highly Efficient Hydrogenation Catalyst.

Heterogenous Pd catalysts play a pivotal role in the chemical industry; however, it is plagued by S2- or other strong adsorbates inducing surface poisoning long term. Herein, we report the development of AuFe3@Pd/γ-Fe2O3 nanosheets (NSs) as an in situ regenerable and highly active hydrogenation catalyst. Upon poisoning, the Pd monolayer sites could be fully and oxidatively regenerated under ambient conditions, which is initiated by •OH radicals from surface defect/FeTetra vacancy-rich γ-Fe2O3 NSs via the Fenton-like pathway. Both experimental and theoretical analyses demonstrate that for the electronic and geometric effect, the 2-3 nm AuFe3 intermetallic nanocluster core promotes the adsorption of reactant onto Pd sites; in addition, it lowers Pd's affinity for •OH radicals to enhance their stability during oxidative regeneration. When packed into a quartz sand fixed-bed catalyst column, the AuFe3@Pd/γ-Fe2O3 NSs are highly active in hydrogenating the carbon-halogen bond, which comprises a crucial step for the removal of micropollutants in drinking water and recovery of resources from heavily polluted wastewater, and withstand ten rounds of regeneration. By maximizing the use of ultrathin metal oxide NSs and intermetallic nanocluster and monolayer Pd, the current study demonstrates a comprehensive strategy for developing sustainable Pd catalysts for liquid catalysis.

Comparison of CT Values between Thrombus and Postmortem Clot Based on Cadaveric Pulmonary Angiography.

OBJECTIVES: To explore the difference in CT values between pulmonary thromboembolism and postmortem clot in postmortem CT pulmonary angiography (CTPA) to further improve the application value of virtual autopsy. METHODS: Postmortem CTPA data with the definite cause of death from 2016 to 2019 were collected and divided into pulmonary thromboembolism group (n=4), postmortem clot group (n=5), and control group (n=5). CT values of pulmonary trunk and left and right pulmonary artery contents in each group were measured and analyzed statistically. RESULTS: The average CT value in the pulmonary thromboembolism group and postmortem clot group were (168.4±53.8) Hu and (282.7±78.0) Hu, respectively, which were lower than those of the control group (1 193.0±82.9) Hu (P<0.05). The average CT value of the postmortem clot group was higher than that of the pulmonary thromboembolism group (P<0.05). CONCLUSIONS: CT value is reliable and feasible as a relatively objective quantitative index to distinguish pulmonary thromboembolism and postmortem clot in postmortem CTPA. At the same time, it can provide a scientific basis to a certain extent for ruling out pulmonary thromboembolism deaths.

Preparation of Copper Ion Adsorbed Modified Montmorillonite/Cellulose Acetate Porous Composite Fiber Membrane by Centrifugal Spinning.

The natural adsorption material montmorillonite (MMT) was selected, and cellulose acetate (CA) was used as the loading substrate to design and prepare a kind of green and environment-friendly recyclable porous composite fiber membrane with good heavy metal ion adsorption performance. Acetic acid modified montmorillonite (HCl-MMT), sodium dodecyl sulfonate modified montmorillonite (SDS-MMT), and chitosan modified montmorillonite (CTS-MMT) were prepared by inorganic modification and organic modification, and the porous MMT/CA composite fiber membrane was constructed by centrifugal spinning equipment. The morphological and structural changes of MMT before and after modification and their effects on porous composite fiber membranes were investigated. The morphology, structure, and adsorption properties of the composite fibers were characterized by scanning electron microscopy (SEM) and atomic absorption spectrometry (ASS). The experimental results showed that the maximum adsorption capacity of Cu2+ on the prepared 5 wt% CTS-MMT composite fiber membrane was 60.272 mg/g after 10 h static adsorption. The adsorption of Cu2+ by a porous composite fiber membrane conforms to the quasi-second-order kinetic model and Langmuir isothermal adsorption model. The main factor of the Cu2+ adsorption rate is chemical adsorption, and the adsorption mechanism is mainly monolayer adsorption.

GhCDPK60 positively regulates drought stress tolerance in both transgenic Arabidopsis and cotton by regulating proline content and ROS level.

Calcium-Dependent Protein Kinases (CDPKs) involved in regulating downstream components of calcium signaling pathways play a role in tolerance to abiotic stresses and seed development in plants. However, functions of only a few cotton CDPKs have been clarified at present. In this study, 80 conserved CDPKs in Gossypium hirsutum L. were identified and characterized, which was divided into four subgroups. Among them, the transcript level of GhCDPK60 was significantly upregulated under drought and several hormone treatments. And we found that the expression levels of several stress-inducible genes down-regulated in GhCDPK60-silence cotton and up-regulated in GhCDPK60-overexpressing Arabidopsis. In addition, physiological analyses demonstrated that GhCDPK60 improved drought stress tolerance by improving the osmotic adjustment ability and reducing the accumulation of reactive oxygen species (ROS) in plants. These findings broaden our understanding of the biological roles of GhCDPK60 and mechanisms underlying drought stress tolerance in cotton.

Anisotropic hydrogel fabricated by controlled diffusion as a bio-scaffold for the regeneration of cartilage injury.

Controlled fabrication of anisotropic materials has become a hotspot in materials science, particularly biomaterials, since the next generation of tissue engineering is based on the application of heterogeneous structures that can simulate the original biological complexity of the body. The current fabrication method of producing anisotropic materials involves expensive and highly specialized equipment, and not every conventional method can be applied to preparing anisotropic materials for corresponding tissue engineering. Anisotropic materials can be easily applied to a problem in tissue engineering: cartilage injury repairing. The articular cartilage consists of four spatially distinct regions: superficial, transitional, deep, and calcified. Each region has a specific extracellular matrix composition, mechanical properties, and cellular organization; this calls for the application of an anisotropic hydrogel. Controlled diffusion, under the assistance of buoyancy, has been considered a generalized method to prepare materials using a gradient. The diffusion of two solutions can be controlled through the difference in their densities. In addition to providing anisotropy, this method realizes the in situ formation of an anisotropic hydrogel, and simplifies the preparation process, freeing it from the need for expensive equipment such as 3D printing and microfluidics. Herein, an anisotropic hydrogel based on a decellularized extracellular matrix is fabricated and characterized. The as-prepared scaffold possessed specific chemical composition, physical properties, and physiological factor gradient. In vitro experiments ensured its biocompatibility and biological effectiveness; further in vivo experiments confirmed its application in the effective regeneration of cartilage injury.

Efficacy of Combined Desensitization Therapy Based on Protein A Immunoadsorption on Anti-human Leukocyte Antigen Antibodies in Sensitized Kidney Transplant Recipients: A Retrospective Study.

Background and objectives Protein A immunoadsorption (PA-IA) therapy is an immunoglobulin selective apheresis for pre-transplantation desensitization therapy and treatment of post-transplantation antibody-mediated rejection. There is no unified protocol for the timing of PA-IA therapy or its combination with other drug therapy. This study aimed to investigate and analyze the clearance effects of desensitization therapy on human leukocyte antigen (HLA) antibodies to provide a reference for the formulation of clinical desensitization therapy regimens. Materials and methods Overall, 27 kidney transplant recipients who received preoperative/postoperative desensitization therapy based on PA-IA therapy in combination with drug therapy were enrolled. The pre-treatment mean fluorescence intensity (MFI) of 1324 human leukocyte antigen (HLA) antibody specificities (MFI >2000) and the post-treatment MFI of the corresponding antibody specificities (after one, four, seven, and 10 sessions) were recorded to analyze the changes in antibody level reduction for the different antibody classes and MFI ranges. Results After 10 sessions of PA-IA therapy, the MFI of class I antibodies decreased from 8298.56 to 3196.15 (reduction of 66.80%), while the MFI of class II antibodies decreased from 13,521.09 to 2773.29 (reduction of 71.14%). The pre-treatment level of class II antibodies was significantly higher than that of class I antibodies (p<0.001), whereas the post-treatment levels of class I and II antibodies were comparable (p>0.05). The clearance effects of PA-IA therapy were greater for strongly positive (MFI>10,000) class II antibodies than for strongly positive class I antibodies, showing a reduction of 62.59% (25.17% to 91.04%) and 45.13% (32.70% to 73.94%), respectively (p=0.015). Conclusions We confirmed the removal efficacy of PA-IA for HLA antibodies. The removal efficacy of class II antibodies on PA-IA is not inferior to that of class I. Under an adequate number of treatment sessions, the clearance effect of PA-IA therapy for strongly positive class II antibodies may be greater than that for strongly positive class I antibodies.

Machine learning models to prognose 30-Day Mortality in Postoperative Disseminated Cancer Patients.

Patients with disseminated cancer at higher risk for postoperative mortality see improved outcomes with altered clinical management. Being able to risk stratify patients immediately after their index surgery to flag high risk patients for healthcare providers is vital. The combination of physician uncertainty and a demonstrated optimism bias often lead to an overestimation of patient life expectancy which can precent proper end of life counseling and lead to inadequate postoperative follow up. In this cohort study of 167,474 postoperative patients with multiple types of disseminated cancer, patients at high risk of 30-day postoperative mortality were accurately identified using our machine learning models based solely on clinical features and preoperative lab values. Extreme Gradient Boosting, Random Forest, and Logistic Regression machine learning models were developed on the cohort. Among 167,474 disseminated cancer patients, 50,669 (30.3%) died within 30 days of their index surgery; After preprocessing, 28 features were included in the model development. The cohort was randomly divided into 133,979 patients (80%) for training the models and 33,495 patients (20%) for testing. The extreme gradient boosting model had an AUC of 0.93 (95% CI: 0.926-0.931), the random forest model had an AUC of 0.93 (95% CI: 0.930-0.934), and the logistic regression model had an AUC of 0.90 (95% CI: 0.900-0.906 the index operation. Ultimately, Machine learning models were able to accurately predict short-term postoperative mortality among a heterogenous population of disseminated cancer patients using commonly accessible medical features. These models can be included in electronic health systems to guide clinical judgements that affect direct patient care, particularly in low-resource settings.

Prefrontal projections to the nucleus reuniens signal behavioral relevance of stimuli during associative learning.

The nucleus reuniens (RE) is necessary for memories dependent on the interaction between the medial prefrontal cortex (mPFC) and hippocampus (HPC). One example is trace eyeblink conditioning, in which the mPFC exhibits differential activity to neutral conditioned stimuli (CS) depending on their contingency with an aversive unconditioned stimulus (US). To test if this relevancy signal is routed to the RE, we photometrically recorded mPFC axon terminals within the RE and tracked their changes with learning. As a comparison, we measured prefrontal terminal activity in the mediodorsal thalamus (MD), which lacks connectivity with the HPC. In naïve male rats, prefrontal terminals within the RE were not strongly activated by tone or light. As the rats associated one of the stimuli (CS+) with the US, terminals gradually increased their response to the CS+ but not the other stimulus (CS-). In contrast, stimulus-evoked responses of prefrontal terminals within the MD were strong even before conditioning. They also became augmented only to the CS+ in the first conditioning session; however, the degree of activity differentiation did not improve with learning. These findings suggest that associative learning selectively increased mPFC output to the RE, signaling the behavioral relevance of sensory stimuli.

Logistic Regression Analysis of the Mechanism of Blunt Brain Injury Inference Based on CT Images.

OBJECTIVES: To study the correlation between CT imaging features of acceleration and deceleration brain injury and injury degree. METHODS: A total of 299 cases with acceleration and deceleration brain injury were collected and divided into acceleration brain injury group and deceleration brain injury group according to the injury mechanism. Subarachnoid hemorrhage (SAH) and Glasgow coma scale (GCS), combined with skull fracture, epidural hematoma (EDH), subdural hematoma (SDH) and brain contusion on the same and opposite sides of the stress point were selected as the screening indexes. χ2 test was used for primary screening, and binary logistic regression analysis was used for secondary screening. The indexes with the strongest correlation in acceleration and deceleration injury mechanism were selected. RESULTS: χ2 test showed that skull fracture and EDH on the same side of the stress point; EDH, SDH and brain contusion on the opposite of the stress point; SAH, GCS were correlated with acceleration and deceleration injury (P<0.05). According to binary logistic regression analysis, the odds ratio (OR) of EDH on the same side of the stress point was 2.697, the OR of brain contusion on the opposite of the stress point was 0.043 and the OR of GCS was 0.238, suggesting there was statistically significant (P<0.05). CONCLUSIONS: EDH on the same side of the stress point, brain contusion on the opposite of the stress point and GCS can be used as key indicators to distinguish acceleration and deceleration injury mechanism. In addition, skull fracture on the same side of the stress point, EDH and SDH on the opposite of the stress point and SAH were relatively weak indicators in distinguishing acceleration and deceleration injury mechanism.

Automatic Identification of Brain Injury Mechanism Based on Deep Learning.

OBJECTIVES: To apply the convolutional neural network (CNN) Inception_v3 model in automatic identification of acceleration and deceleration injury based on CT images of brain, and to explore the application prospect of deep learning technology in forensic brain injury mechanism inference. METHODS: CT images from 190 cases with acceleration and deceleration brain injury were selected as the experimental group, and CT images from 130 normal brain cases were used as the control group. The above-mentioned 320 imaging data were divided into training validation dataset and testing dataset according to random sampling method. The model classification performance was evaluated by the accuracy rate, precision rate, recall rate, F1-value and AUC value. RESULTS: In the training process and validation process, the accuracy rate of the model to classify acceleration injury, deceleration injury and normal brain was 99.00% and 87.21%, which met the requirements. The optimized model was used to test the data of the testing dataset, the result showed that the accuracy rate of the model in the test set was 87.18%, and the precision rate, recall rate, F1-score and AUC of the model to recognize acceleration injury were 84.38%, 90.00%, 87.10% and 0.98, respectively, to recognize deceleration injury were 86.67%, 72.22%, 78.79% and 0.92, respectively, to recognize normal brain were 88.57%, 89.86%, 89.21% and 0.93, respectively. CONCLUSIONS: Inception_v3 model has potential application value in distinguishing acceleration and deceleration injury based on brain CT images, and is expected to become an auxiliary tool to infer the mechanism of head injury.

Bibliometrics Analysis in English and Chinese Literature on Drowning in Forensic Medicine from 1991 to 2020.

OBJECTIVES: To explore the research hotspots and development trends of the field of forensic drowning from 1991 to 2020 by bibliometrics methods. METHODS: Based on Web of Science, CNKI database, Wanfang Data knowledge service platform, python 3.9.2, CiteSpace 5.8.R3, Gephi 0.9.2, etc. were used to analyze the publishing trends, countries/regions, institutions, authors and topics of the study on drowning. RESULTS: A total of 631 English literature were obtained, including 59 articles from Chinese authors, and 386 Chinese literature were obtained. The Chinese and English journals with the largest number of related literatures were Chinese Journal of Forensic Science (80 articles) and Forensic Science International (106 articles), respectively. Japan published the most articles in English, and China ranked third. Osaka City Univ (Japan, 28 articles) published the most English articles, and Guangzhou Forens Sci Inst (China, 22 articles) ranked second. Among Chinese literature, Guangzhou Forens Sci Inst (32 articles) published the most. The topic analysis of Chinese and English literature showed that diatom examination, virtual autopsy, postmortem biochemical examination, the nature of death, and postmortem submersion interval were the hot spots of current research, but English literature had more studies on new technologies and methods, while Chinese literature was more inclined to practice, application and experience summary. CONCLUSIONS: The number of literature in forensic medicine on drowning is relatively stable. The scope of international and domestic collaborations in this field is still limited. The automated examination of diatoms, the establishment of diatom DNA barcodes and virtual autopsy will be the most important research hotspots in the coming period and are expected to achieve breakthroughs in drowning diagnosis, drowning location inference, postmortem submersion interval estimation, etc.

Outcome-Locked Cholinergic Signaling Suppresses Prefrontal Encoding of Stimulus Associations.

Acetylcholine (ACh) is thought to control arousal, attention, and learning by slowly modulating cortical excitability and plasticity. Recent studies, however, discovered that cholinergic neurons emit precisely timed signals about the aversive outcome at millisecond precision. To investigate the functional relevance of such phasic cholinergic signaling, we manipulated and monitored cholinergic terminals in the mPFC while male mice associated a neutral conditioned stimulus (CS) with mildly aversive eyelid shock (US) over a short temporal gap. Optogenetic inhibition of cholinergic terminals during the US promoted the formation of the CS-US association. On the contrary, optogenetic excitation of cholinergic terminals during the US blocked the association formation. The bidirectional behavioral effects paralleled the corresponding change in the expression of an activity-regulated gene, c-Fos in the mPFC. In contrast, optogenetic inhibition of cholinergic terminals during the CS impaired associative learning, whereas their excitation had marginal effects. In parallel, photometric recording from cholinergic terminals in the mPFC revealed strong innate phasic responses to the US. With subsequent CS-US pairings, cholinergic terminals weakened the responses to the US while developing strong responses to the CS. The across-session changes in the CS- and US-evoked terminal responses were correlated with associative memory strength. These findings suggest that phasic cholinergic signaling in the mPFC exerts opposite effects on aversive associative learning depending on whether it is emitted by the outcome or the cue.SIGNIFICANCE STATEMENT Drugs compensating for the decline of acetylcholine (ACh) are used for cognitive impairment, such as Alzheimer's disease. However, their beneficial effects are limited, demanding new strategies based on better understandings of how ACh modulates cognition. Here, we report that by manipulating ACh signals in the mPFC, we can control the strength of aversive associative learning in mice. Specifically, the suppression of ACh signals during an aversive outcome facilitated its association with a preceding cue. In contrast, the suppression of ACh signals during the cue impaired learning. Considering that this paradigm depends on the brain regions affected in Alzheimer's disease, our findings indicate that precisely timed control of ACh signals is essential to refine ACh-based strategies for cognitive enhancement.