Functional parcellation of the neonatal cortical surface.

The cerebral cortex is organized into distinct but interconnected cortical areas, which can be defined by abrupt differences in patterns of resting state functional connectivity (FC) across the cortical surface. Such parcellations of the cortex have been derived in adults and older infants, but there is no widely used surface parcellation available for the neonatal brain. Here, we first demonstrate that existing parcellations, including surface-based parcels derived from older samples as well as volume-based neonatal parcels, are a poor fit for neonatal surface data. We next derive a set of 283 cortical surface parcels from a sample of n = 261 neonates. These parcels have highly homogenous FC patterns and are validated using three external neonatal datasets. The Infomap algorithm is used to assign functional network identities to each parcel, and derived networks are consistent with prior work in neonates. The proposed parcellation may represent neonatal cortical areas and provides a powerful tool for neonatal neuroimaging studies.

Machine Learning for Early Prediction of Major Adverse Cardiovascular Events After First Percutaneous Coronary Intervention in Patients With Acute Myocardial Infarction: Retrospective Cohort Study.

BACKGROUND: The incidence of major adverse cardiovascular events (MACEs) remains high in patients with acute myocardial infarction (AMI) who undergo percutaneous coronary intervention (PCI), and early prediction models to guide their clinical management are lacking. OBJECTIVE: This study aimed to develop machine learning-based early prediction models for MACEs in patients with newly diagnosed AMI who underwent PCI. METHODS: A total of 1531 patients with AMI who underwent PCI from January 2018 to December 2019 were enrolled in this consecutive cohort. The data comprised demographic characteristics, clinical investigations, laboratory tests, and disease-related events. Four machine learning models-artificial neural network (ANN), k-nearest neighbors, support vector machine, and random forest-were developed and compared with the logistic regression model. Our primary outcome was the model performance that predicted the MACEs, which was determined by accuracy, area under the receiver operating characteristic curve, and F1-score. RESULTS: In total, 1362 patients were successfully followed up. With a median follow-up of 25.9 months, the incidence of MACEs was 18.5% (252/1362). The area under the receiver operating characteristic curve of the ANN, random forest, k-nearest neighbors, support vector machine, and logistic regression models were 80.49%, 72.67%, 79.80%, 77.20%, and 71.77%, respectively. The top 5 predictors in the ANN model were left ventricular ejection fraction, the number of implanted stents, age, diabetes, and the number of vessels with coronary artery disease. CONCLUSIONS: The ANN model showed good MACE prediction after PCI for patients with AMI. The use of machine learning-based prediction models may improve patient management and outcomes in clinical practice.

Increasing hub disruption parallels dementia severity in autosomal dominant Alzheimer disease.

Hub regions in the brain, recognized for their roles in ensuring efficient information transfer, are vulnerable to pathological alterations in neurodegenerative conditions, including Alzheimer Disease (AD). Given their essential role in neural communication, disruptions to these hubs have profound implications for overall brain network integrity and functionality. Hub disruption, or targeted impairment of functional connectivity at the hubs, is recognized in AD patients. Computational models paired with evidence from animal experiments hint at a mechanistic explanation, suggesting that these hubs may be preferentially targeted in neurodegeneration, due to their high neuronal activity levels-a phenomenon termed "activity-dependent degeneration". Yet, two critical issues were unresolved. First, past research hasn't definitively shown whether hub regions face a higher likelihood of impairment (targeted attack) compared to other regions or if impairment likelihood is uniformly distributed (random attack). Second, human studies offering support for activity-dependent explanations remain scarce. We applied a refined hub disruption index to determine the presence of targeted attacks in AD. Furthermore, we explored potential evidence for activity-dependent degeneration by evaluating if hub vulnerability is better explained by global connectivity or connectivity variations across functional systems, as well as comparing its timing relative to amyloid beta deposition in the brain. Our unique cohort of participants with autosomal dominant Alzheimer Disease (ADAD) allowed us to probe into the preclinical stages of AD to determine the hub disruption timeline in relation to expected symptom emergence. Our findings reveal a hub disruption pattern in ADAD aligned with targeted attacks, detectable even in pre-clinical stages. Notably, the disruption's severity amplified alongside symptomatic progression. Moreover, since excessive local neuronal activity has been shown to increase amyloid deposition and high connectivity regions show high level of neuronal activity, our observation that hub disruption was primarily tied to regional differences in global connectivity and sequentially followed changes observed in Aß PET cortical markers is consistent with the activity-dependent degeneration model. Intriguingly, these disruptions were discernible 8 years before the expected age of symptom onset. Taken together, our findings not only align with the targeted attack on hubs model but also suggest that activity-dependent degeneration might be the cause of hub vulnerability. This deepened understanding could be instrumental in refining diagnostic techniques and developing targeted therapeutic strategies for AD in the future.

Functional parcellation of the neonatal brain.

The cerebral cortex is organized into distinct but interconnected cortical areas, which can be defined by abrupt differences in patterns of resting state functional connectivity (FC) across the cortical surface. Such parcellations of the cortex have been derived in adults and older infants, but there is no widely used surface parcellation available for the neonatal brain. Here, we first demonstrate that adult- and older infant-derived parcels are a poor fit with neonatal data, emphasizing the need for neonatal-specific parcels. We next derive a set of 283 cortical surface parcels from a sample of n=261 neonates. These parcels have highly homogenous FC patterns and are validated using three external neonatal datasets. The Infomap algorithm is used to assign functional network identities to each parcel, and derived networks are consistent with prior work in neonates. The proposed parcellation may represent neonatal cortical areas and provides a powerful tool for neonatal neuroimaging studies.

Big data on the prevalence of injury deaths among 187 488 elderly Chinese in the past 20 years (2000-2020): a systematic review and meta-analysis.

BACKGROUND: This study systematically reviewed injury death and causes in the elderly population in China from 2000 to 2020, to prevent or reduce the occurrence of injuries and death. METHODS: The CNKI, VIP, Wan Fang, MEDLINE, Embase, SinoMed, and Web of Science databases were searched to collect epidemiological characteristics of injury death among elderly over 60 years old in China from January 2000 to December 2020. Random effects meta-analysis was performed to pool injury mortality rate and identify publication bias, with study quality assessed using the AHRQ risk of bias tool. RESULTS: (1) A total of 41 studies with 187 488 subjects were included, covering 125 million elderly. The pooled injury mortality rate was 135.58/105 [95%CI: (113.36 to 162.14)/105], ranking second in the total death cause of the elderly. (2)Subgroup analysis showed that male injury death (146.00/105) was significantly higher than that of females (127.90/105), and overall injury mortality increased exponentially with age (R2 = 0.957), especially in those over 80 years old; the spatial distribution shows that the injury death rate in the central region is higher than that in the east and west and that in the countryside is higher than that in the city; the distribution of death time shows that after entering an aging society (2000-2020) is significantly higher than before (1990-2000). (3) There are more than 12 types of injury death, and the top three are falling, traffic accidents, and suicide. CONCLUSIONS: China's elderly injury death rate is at a high level in the world, with more males than females, especially after the age of 80. There are regional differences. The main types of injury death are falling, traffic, and suicide. During the 14th Five-Year Plan period, for accidental injuries and death, a rectification list for aging and barrier-free environments was issued. PROSPERO REGISTRATION: The systematic review was registered in PROSPERO under protocol number CRD42022359992.

Brain network decoupling with increased serum neurofilament and reduced cognitive function in Alzheimer's disease.

Neurofilament light chain, a putative measure of neuronal damage, is measurable in blood and CSF and is predictive of cognitive function in individuals with Alzheimer's disease. There has been limited prior work linking neurofilament light and functional connectivity, and no prior work has investigated neurofilament light associations with functional connectivity in autosomal dominant Alzheimer's disease. Here, we assessed relationships between blood neurofilament light, cognition, and functional connectivity in a cross-sectional sample of 106 autosomal dominant Alzheimer's disease mutation carriers and 76 non-carriers. We employed an innovative network-level enrichment analysis approach to assess connectome-wide associations with neurofilament light. Neurofilament light was positively correlated with deterioration of functional connectivity within the default mode network and negatively correlated with connectivity between default mode network and executive control networks, including the cingulo-opercular, salience, and dorsal attention networks. Further, reduced connectivity within the default mode network and between the default mode network and executive control networks was associated with reduced cognitive function. Hierarchical regression analysis revealed that neurofilament levels and functional connectivity within the default mode network and between the default mode network and the dorsal attention network explained significant variance in cognitive composite scores when controlling for age, sex, and education. A mediation analysis demonstrated that functional connectivity within the default mode network and between the default mode network and dorsal attention network partially mediated the relationship between blood neurofilament light levels and cognitive function. Our novel results indicate that blood estimates of neurofilament levels correspond to direct measurements of brain dysfunction, shedding new light on the underlying biological processes of Alzheimer's disease. Further, we demonstrate how variation within key brain systems can partially mediate the negative effects of heightened total serum neurofilament levels, suggesting potential regions for targeted interventions. Finally, our results lend further evidence that low-cost and minimally invasive blood measurements of neurofilament may be a useful marker of brain functional connectivity and cognitive decline in Alzheimer's disease.

Metabolic reprogramming of immune cells in pancreatic cancer progression.

Abnormal intracellular metabolism not only provides nutrition for tumor occurrence and development, but also sensitizes the function of various immune cells in the immune microenvironment to promote tumor immune escape. This review discusses the emerging role of immune cells in the progress of pancreatic cancer, acrossing metabolic reprogramming and key metabolic pathways present in different immune cell types. At present, the hotspots of metabolic reprogramming of immune cells in pancreatic cancer progression mainly focuses on glucose metabolism, lipid metabolism, tricarboxylic acid cycle and amino acid metabolism, which affect the function of anti-tumor immune cells and immunosuppressive cells in the microenvironment, such as macrophages, dendritic cells, T cells, myeloid-derived suppressor cells, neutrophils and B cells by a series of key metabolic signaling pathways, such as PI3K/AKT, mTOR, AMPK, HIF-1α, c-Myc and p53. Drugs that target the tumor metabolism pathways for clinical treatment of pancreatic cancer are also systematically elaborated, which may constitute food for others' projects involved in clinical anti-cancer research.

Health Impacts of High BMI in China: Terrible Present and Future.

Temporal trends and future expectations of health effects due to high body mass index (BMI) remain uncertain in China. The trends of high-BMI-related death in China were evaluated and predicted until 2040 using data and methods from the Global Burden of Disease study. The absolute numbers and age-standardized rates of death and disability-adjusted life years (DALYs) were also calculated by age, gender, and cause. From 1990 to 2019, the high-BMI-related death percent, number and rate were all significantly increased in China, and death rate may exceed that globally in the next 10 years. High BMI caused more deaths and DALYs for men than for women, and the gap appeared to increase over time. In 2019, the burden of high BMI among ages 0−14 and 15−19 for children and adolescents were lower than that among adults (>20 years). The most common cause of death associated with high BMI was stroke, followed by ischemic heart disease and hypertensive heart disease. High BMI burden is a significant public health challenges in China. BMI surveillance and evaluation of evidence-based preventive strategies should be immediately initiated in Chinese residents due to the rapid increase in the burden of high BMI.

Global research trends of artificial intelligence applied in esophageal carcinoma: A bibliometric analysis (2000-2022) via CiteSpace and VOSviewer.

Objective: Using visual bibliometric analysis, the application and development of artificial intelligence in clinical esophageal cancer are summarized, and the research progress, hotspots, and emerging trends of artificial intelligence are elucidated. Methods: On April 7th, 2022, articles and reviews regarding the application of AI in esophageal cancer, published between 2000 and 2022 were chosen from the Web of Science Core Collection. To conduct co-authorship, co-citation, and co-occurrence analysis of countries, institutions, authors, references, and keywords in this field, VOSviewer (version 1.6.18), CiteSpace (version 5.8.R3), Microsoft Excel 2019, R 4.2, an online bibliometric platform (http://bibliometric.com/) and an online browser plugin (https://www.altmetric.com/) were used. Results: A total of 918 papers were included, with 23,490 citations. 5,979 authors, 39,962 co-cited authors, and 42,992 co-cited papers were identified in the study. Most publications were from China (317). In terms of the H-index (45) and citations (9925), the United States topped the list. The journal "New England Journal of Medicine" of Medicine, General & Internal (IF = 91.25) published the most studies on this topic. The University of Amsterdam had the largest number of publications among all institutions. The past 22 years of research can be broadly divided into two periods. The 2000 to 2016 research period focused on the classification, identification and comparison of esophageal cancer. Recently (2017-2022), the application of artificial intelligence lies in endoscopy, diagnosis, and precision therapy, which have become the frontiers of this field. It is expected that closely esophageal cancer clinical measures based on big data analysis and related to precision will become the research hotspot in the future. Conclusions: An increasing number of scholars are devoted to artificial intelligence-related esophageal cancer research. The research field of artificial intelligence in esophageal cancer has entered a new stage. In the future, there is a need to continue to strengthen cooperation between countries and institutions. Improving the diagnostic accuracy of esophageal imaging, big data-based treatment and prognosis prediction through deep learning technology will be the continuing focus of research. The application of AI in esophageal cancer still has many challenges to overcome before it can be utilized.

Multicentric tracking of multiple agents by anterior cingulate cortex during pursuit and evasion.

Successful pursuit and evasion require rapid and precise coordination of navigation with adaptive motor control. We hypothesize that the dorsal anterior cingulate cortex (dACC), which communicates bidirectionally with both the hippocampal complex and premotor/motor areas, would serve a mapping role in this process. We recorded responses of dACC ensembles in two macaques performing a joystick-controlled continuous pursuit/evasion task. We find that dACC carries two sets of signals, (1) world-centric variables that together form a representation of the position and velocity of all relevant agents (self, prey, and predator) in the virtual world, and (2) avatar-centric variables, i.e. self-prey distance and angle. Both sets of variables are multiplexed within an overlapping set of neurons. Our results suggest that dACC may contribute to pursuit and evasion by computing and continuously updating a multicentric representation of the unfolding task state, and support the hypothesis that it plays a high-level abstract role in the control of behavior.

Rules warp feature encoding in decision-making circuits.

We have the capacity to follow arbitrary stimulus-response rules, meaning simple policies that guide our behavior. Rule identity is broadly encoded across decision-making circuits, but there are less data on how rules shape the computations that lead to choices. One idea is that rules could simplify these computations. When we follow a rule, there is no need to encode or compute information that is irrelevant to the current rule, which could reduce the metabolic or energetic demands of decision-making. However, it is not clear if the brain can actually take advantage of this computational simplicity. To test this idea, we recorded from neurons in 3 regions linked to decision-making, the orbitofrontal cortex (OFC), ventral striatum (VS), and dorsal striatum (DS), while macaques performed a rule-based decision-making task. Rule-based decisions were identified via modeling rules as the latent causes of decisions. This left us with a set of physically identical choices that maximized reward and information, but could not be explained by simple stimulus-response rules. Contrasting rule-based choices with these residual choices revealed that following rules (1) decreased the energetic cost of decision-making; and (2) expanded rule-relevant coding dimensions and compressed rule-irrelevant ones. Together, these results suggest that we use rules, in part, because they reduce the costs of decision-making through a distributed representational warping in decision-making circuits.

The neural basis of predictive pursuit.

It remains unclear whether and, if so, how nonhuman animals make on-the-fly predictions during pursuit. Here we used a novel laboratory pursuit task that incentivizes the prediction of future prey positions. We trained three macaques to perform a joystick-controlled pursuit task in which prey follow intelligent escape algorithms. Subjects aimed toward the likely future positions of the prey, which indicated that they generate internal predictions and use these to guide behavior. We then developed a generative model that explains real-time pursuit trajectories and showed that our subjects use prey position, velocity and acceleration to make predictions. We identified neurons in the dorsal anterior cingulate cortex whose responses track these three variables. These neurons multiplexed prediction-related variables with a distinct and explicit representation of the future position of the prey. Our results provide a clear demonstration that the brain can explicitly represent future predictions and highlight the critical role of anterior cingulate cortex for future-oriented cognition.

Inhibition of brain-type glycogen phosphorylase ameliorates high glucose-induced cardiomyocyte apoptosis via Akt-HIF-1α activation.

Brain-type glycogen phosphorylase (pygb) is one of the rate-limiting enzymes in glycogenolysis that plays a crucial role in the pathogenesis of type 2 diabetes mellitus. Here we investigated the role of pygb in high-glucose (HG)-induced cardiomyocyte apoptosis and explored the underlying mechanisms, by using the specific pygb inhibitors or pygb siRNA. Our results show that inhibition of pygb significantly attenuates cell apoptosis and oxidative stress induced by HG in H9c2 cardiomyocytes. Inhibition of pygb improved glucose metabolism in cardiacmyocytes, as evidenced by increased glycogen content, glucose consumption, and glucose transport. Mechanistically, pygb inhibition activates the Akt-GSK-3ß signaling pathway and suppresses the activation of NF-κB in H9c2 cells exposed to HG. Additionally, pygb inhibition promotes the expression and the translocation of hypoxia-inducible factor-1α (HIF-1α) after HG stimulation. However, the changes in glucose metabolism and HIF-1α activation mediated by pygb inhibition are significantly reversed in the presence of the Akt inhibitor MK2206. In conclusion, this study found that inhibition of pygb prevents HG-induced cardiomyocyte apoptosis via activation of Akt-HIF-α.

Ventromedial Prefrontal Cortex Tracks Multiple Environmental Variables during Search.

To make efficient foraging decisions, we must combine information about the values of available options with nonvalue information. Some accounts of ventromedial PFC (vmPFC) suggest that it has a narrow role limited to evaluating immediately available options. We examined responses of neurons in area 14 (a putative macaque homolog of human vmPFC) as 2 male macaques performed a novel foraging search task. Although many neurons encoded the values of immediately available offers, they also independently encoded several other variables that influence choice, but that are conceptually distinct from offer value. These variables include average reward rate, number of offers viewed per trial, previous offer values, previous outcome sizes, and the locations of the currently attended offer. We conclude that, rather than serving as specialized economic value center, vmPFC plays a broad role in integrating relevant environmental information to drive foraging decisions.SIGNIFICANCE STATEMENT Decision makers must often choose whether to take an immediately available option or continue to search for a better one. We hypothesized that this process, which is integral to foraging theory, leaves neural signatures in the brain region ventromedial PFC. Subjects performed a novel foraging task in which they searched through differently valued options and attempted to balance their reward threshold with various time costs. We found that neurons not only encode the values of immediately available offers, but multiplexed these with environmental variables, including reward rate, number of offers viewed, previous offer values, and spatial information. We conclude that vmPFC plays a rich role in encoding and integrating multiple foraging-related variables during economic decisions.

Expression and transcriptional regulation of gsdf in spotted scat (Scatophagus argus).

Gonadal soma-derived factor (Gsdf) is critical for testicular differentiation and early germ cell development in teleosts. The spotted scat (Scatophagus argus), with a stable XX-XY sex-determination system and the candidate sex determination gene dmrt1, provides a good model for understanding the mechanism of sex determination and differentiation in teleosts. In this study, we analyzed spotted scat gsdf tissue distribution and gene expression patterns in gonads, as well as further analysis of transcriptional regulation. Tissue distribution analysis showed that gsdf was only expressed in testis and ovary. Real-time PCR showed that both gsdf and dmrt1 were expressed significantly higher in testes at different phases (phase III, IV and V) compared to ovaries at phase II, III and IV, while gsdf was expressed significantly higher in phase II ovaries than those of phase III and IV. Western blot analysis also showed that Gsdf was more highly expressed in the testis than ovary. Immunohistochemistry analysis showed that Gsdf was expressed in Sertoli cells surrounding spermatogonia in the testis, while it was expressed in the somatic cells surrounding the oogonia of the ovary. Approximately 2.7 kb of the 5' upstream region of gsdf was cloned from the spotted scat genomic DNA and in silico promoter analysis revealed the putative transcription factor binding sites of Dmrt1 and Sf1. The luciferase reporter assay, using the human embryonic kidney cells, demonstrated that Dmrt1 activated gsdf expression in a dose-dependent manner in the presence of Sf1 in spotted scat. These results suggest that Gsdf could play a role in regulating the development of spermatogonia and oogonia, and also participate in male sex differentiation by acting as a downstream gene of Dmrt1 in spotted scat.