Cell polarization in ischemic stroke: molecular mechanisms and advances.

Ischemic stroke is a cerebrovascular disease associated with high mortality and disability rates. Since the inflammation and immune response play a central role in driving ischemic damage, it becomes essential to modulate excessive inflammatory reactions to promote cell survival and facilitate tissue repair around the injury site. Various cell types are involved in the inflammatory response, including microglia, astrocytes, and neutrophils, each exhibiting distinct phenotypic profiles upon stimulation. They display either proinflammatory or anti-inflammatory states, a phenomenon known as 'cell polarization.' There are two cell polarization therapy strategies. The first involves inducing cells into a neuroprotective phenotype in vitro, then reintroducing them autologously. The second approach utilizes small molecular substances to directly affect cells in vivo. In this review, we elucidate the polarization dynamics of the three reactive cell populations (microglia, astrocytes, and neutrophils) in the context of ischemic stroke, and provide a comprehensive summary of the molecular mechanisms involved in their phenotypic switching. By unraveling the complexity of cell polarization, we hope to offer insights for future research on neuroinflammation and novel therapeutic strategies for ischemic stroke.

Cadherin-18 loss in prospermatogonia and spermatogonial stem cells enhances cell adhesion through a compensatory mechanism.

Extracellular membrane proteins are crucial for mediating cell attachment, recognition, and signal transduction in the testicular microenvironment, particularly germline stem cells. Cadherin 18 (CDH18), a type II classical cadherin, is primarily expressed in the nervous and reproductive systems. Here, we investigated the expression of CDH18 in neonatal porcine prospermatogonia (ProSGs) and murine spermatogonial stem cells (SSCs). Disruption of CDH18 expression did not adversely affect cell morphology, proliferation, self-renewal, or differentiation in cultured porcine ProSGs, but enhanced cell adhesion and prolonged cell maintenance. Transcriptomic analysis indicated that the down-regulation of CDH18 in ProSGs significantly up-regulated genes and signaling pathways associated with cell adhesion. To further elucidate the function of CDH18 in germ cells, Cdh18 knockout mice were generated, which exhibited normal testicular morphology, histology, and spermatogenesis. Transcriptomic analysis showed increased expression of genes associated with adhesion, consistent with the observations in porcine ProSGs. The interaction of CDH18 with ß-catenin and JAK2 in both porcine ProSGs and murine SSCs suggested an inhibitory effect on the canonical Wnt and JAK-STAT signaling pathways during CDH18 deficiency. Collectively, these findings highlight the crucial role of CDH18 in regulating cell adhesion in porcine ProSGs and mouse SSCs. Understanding this regulatory mechanism provides significant insights into the testicular niche.

Microaerobic-mediated suppression of Klebsiella pneumoniae mucoviscosity is restored by rmpD overexpression.

AIMS: Hypervirulent Klebsiella pneumoniae (hvKp) causes invasive community-acquired infections in healthy individuals, and hypermucoviscosity (HMV) is the main phenotype associated with hvKp. This study investigates the impact of microaerobic environment availability on the mucoviscosity of K. pneumoniae. METHODS AND RESULTS: By culturing 25 clinical strains under microaerobic and aerobic environments, we observed a notable reduction in mucoviscosity in microaerobic environments. RNA sequencing and qRT-PCR revealed downregulated expressions of capsule synthesis genes (galf, orf2, wzi, wza, wzb, wzc, wcaj, manC, manB, and ugd) and regulatory genes (rmpA, rmpD, and rmpC) under microaerobic conditions. Transmission electron microscopy and Indian ink staining analysis were performed, revealing that the capsular thickness of K. pneumoniae decreased by half in microaerobic conditions compared to aerobic conditions. Deletion of rmpD and rmpC caused the loss of the HMV phenotype in both aerobic and microaerobic conditions. However, compared to wild-type strain in microaerobic condition, only rmpD overexpression strain, and not rmpC overexpression strain, displayed a significant increase in capsule thickness in microaerobic conditions. CONCLUSIONS: Microaerobic conditions can suppress the mucoviscosity of K. pneumoniae, but this suppression can be overcome by altering the expression of rmpD, indicating a specific function for rmpD in the oxygen environmental adaptation of K. pneumoniae.

Association between cardiometabolic index and asthma in adults: evidence from NHANES 2005-2018.

OBJECTIVES: Cardiometabolic Index (CMI) is a surrogate marker for metabolic disorders. It is associated with various chronic diseases. This study aims to investigate the relationship between CMI and asthma. METHODS: Data from seven consecutive National Health and Nutrition Examination Survey cycles between 2005 and 2018 were used. The study included adults with self-reported asthma diagnoses and complete information for CMI calculation. The formula for CMI is CMI = [WC (cm)/height (cm)] × [TG (mg/dL)/HDL-C (mg/dL)]. A multivariate logistic regression model was employed to examine the linear relationship between CMI and asthma. Subgroup analyses were conducted to explore potential influencing factors. Additionally, smooth curve fitting and threshold effect analysis were used to describe the non-linear relationship. RESULTS: A higher CMI was possibly associated with an increased prevalence of asthma. After adjusting for various covariates including marital status, Poverty Income Ratio, Body Mass Index, hypertension, diabetes, smoking, alcohol consumption, heart attack, and stroke, the results remained significant (OR = 1.03; 95%CI, 1.00-1.05, p = 0.0178, R2 = 0.52). Participants with the highest CMI had a 38% increased risk of asthma prevalence compared to those with the lowest CMI (OR = 1.38; 95%CI, 1.19-1.60, p < 0.0001). CONCLUSION: The findings reveal that elevated CMI levels correlate with an increased risk of asthma, highlighting CMI's potential as a predictive marker for asthma, particularly in populations with a CMI below 1.97. These results suggest that interventions aimed at improving metabolic health may prove effective in managing or preventing asthma.

Realm of hepatitis E: Challenges and opportunities.

Hepatitis E virus (HEV), responsible for widespread viral hepatitis, infects approximately 2.3 billion individuals globally, with a significant mortality burden in Asia. The virus, primarily transmitted through contaminated water and undercooked meat, is often underdiagnosed, particularly in immunocompromised patients. Current HEV treatments, while effective, are limited by adverse effects, necessitating research into safer alternatives. Moreover, HEV's extrahepatic manifestations, impacting the nervous and renal systems, remain poorly understood. This study underscores the imperative for enhanced HEV research, improved diagnostic methods, and more effective treatments, coupled with increased public health awareness and preventive strategies.

Mechanisms of regulation of glycolipid metabolism by natural compounds in plants: effects on short-chain fatty acids.

BACKGROUND: Natural compounds can positively impact health, and various studies suggest that they regulate glucose‒lipid metabolism by influencing short-chain fatty acids (SCFAs). This metabolism is key to maintaining energy balance and normal physiological functions in the body. This review explores how SCFAs regulate glucose and lipid metabolism and the natural compounds that can modulate these processes through SCFAs. This provides a healthier approach to treating glucose and lipid metabolism disorders in the future. METHODS: This article reviews relevant literature on SCFAs and glycolipid metabolism from PubMed and the Web of Science Core Collection (WoSCC). It also highlights a range of natural compounds, including polysaccharides, anthocyanins, quercetins, resveratrols, carotenoids, and betaines, that can regulate glycolipid metabolism through modulation of the SCFA pathway. RESULTS: Natural compounds enrich SCFA-producing bacteria, inhibit harmful bacteria, and regulate operational taxonomic unit (OTU) abundance and the intestinal transport rate in the gut microbiota to affect SCFA content in the intestine. However, most studies have been conducted in animals, lack clinical trials, and involve fewer natural compounds that target SCFAs. More research is needed to support the conclusions and to develop healthier interventions. CONCLUSIONS: SCFAs are crucial for human health and are produced mainly by the gut microbiota via dietary fiber fermentation. Eating foods rich in natural compounds, including fruits, vegetables, tea, and coarse fiber foods, can hinder harmful intestinal bacterial growth and promote beneficial bacterial proliferation, thus increasing SCFA levels and regulating glucose and lipid metabolism. By investigating how these compounds impact glycolipid metabolism via the SCFA pathway, novel insights and directions for treating glucolipid metabolism disorders can be provided.

Effects of PGE1 on the ERS pathway in neonatal rats with hyperoxic lung injury.

BACKGROUND: With the increase in the number of low birth weight infants, oxygen therapy is more widely used. However, chronic high-concentration oxygen environments lead to hyperoxic lung injury in children, which in turn leads to bronchopulmonary dysplasia (BPD). PGE1 is widely used in the clinic for its ability to inhibit inflammation and improve circulation. Therefore, we further investigated whether PGE-1 has a therapeutic effect on hyperoxic lung injury. METHODS: Hyperoxic lung injury model was adopted for investigating the interventional effects and underlying mechanisms of intraperitoneal injection of prostaglandin E1 (PGE-1) on hyperoxic lung injury in newborn rats via relevant experimental techniques, such as Diff-Quick staining, lung wet dry specific gravity measurements, HE staining, TUNEL staining, ELISA, and the Western blot method. RESULTS: Inflammatory and apoptotic cells in the PGE1-treated group were significantly lower than those in the hyperoxic lung injury group (p < 0.05); and the contents of IL-1ß, IL-6 and TNF-α in the treated group were significantly lower than those in the model group (p < 0.05). Caspase-3, CHOP, GRP78 and Bcl-2/Bax protein expression in the treatment group was significantly lower than that in the model group (p < 0.05). CONCLUSION: PGE-1 has a therapeutic effect on hyperoxic lung injury in neonatal rats. IMPACT: PGE1 treatment reduces levels of inflammatory cells and pro-inflammatory cytokines and decreases apoptosis. PGE1 has a therapeutic effect on BPD through the endoplasmic reticulum stress pathway. This study offers the possibility of PGE1 for the treatment of BPD.

Effects of moderate/vigorous activity on 3-year body composition changes in postmenopausal women: a target trial emulation.

Background and Objective Postmenopausal women tend to experience significant changes in body composition, particularly abdominal adipose tissue (AAT) deposition patterns, which are hypothesized to be critical factors influencing future cardiometabolic disease risk. Physical activity has a demonstrable effect on body composition and overall health. However, there is little evidence for how different intensities and durations of physical activity over a sustained period of time influence AAT patterns and other measures of body composition in postmenopausal women. We emulated a target trial of physical activity interventions, including the 2018 Physical Activity Guidelines for Americans recommendations, on 3-year changes in AAT and body composition. Methods We analyzed observational data from 4,451 postmenopausal women aged 50-79 years in the Women's Health Initiative (WHI) to emulate a three-year target trial of adhering to increasing minutes of moderate (at least 15, 30, 75, 150, 300 minutes/week) and vigorous (at least 15, 30, 75, 150 minutes/week) physical activity aligned with the physical activity guidelines. All participants had repeated whole body Dual X-Ray Absorptiometry (DXA) scans with derived abdominal visceral (VAT) and subcutaneous adipose tissue (SAT). The measured differences in average levels of VAT, SAT, and other body composition measures determined at end of follow-up were estimated with the parametric-g formula. Results Over 3 years, interventions of increasing minutes of moderate activity would result in dose-dependent reductions in abdominal VAT, SAT, and overall body fat, and increases in lean soft tissue, with the greatest estimated benefit at the 2018 physical activity guideline recommendation of 150 mins/wk or more. Compared to no intervention, if all participants had adhered to at least 150 mins/wk of moderate physical activity, they would have 16.8 cm2 lower VAT (95% CI -23.1, -10.4), 26.8 cm2 lower SAT (95% CI -36.3, -17.3), 1.3% lower total body fat% (95% CI -1.8, -0.7), 1.2 % higher total lean soft tissue% (95% CI 0.7, 1.8), and 2.6 kg lower total bodyweight (95% CI -3.6, -1.5). We saw similar patterns in our vigorous-intensity activity interventions - if all participants adhered to at least 150 mins/wk, they would have experienced 6.7 cm2 lower VAT (95% CI -17.7, 4.3), 13.3 cm2 lower SAT (95% CI -28.8, 2.1), 1.0 % lower total body fat percent (95% CI -2.0, 0.0 ), % higher total lean soft tissue percent (95% CI) and a 0.9 kg lower total bodyweight (95% CI -2.7, 0.8). Conclusion This hypothetical emulated intervention indicated that postmenopausal women who adhere to physical activity guideline recommendations would experience beneficial changes in abdominal VAT, SAT, and overall body composition over 3 years. The study results underscore the imperative to explore further how physical activity may serve as a potential determinant of body composition.

Emergence of mcr-4.3 genes in a novel Shewanella specie isolated from the Arctic environment.

Mobile colistin resistance (mcr) genes are pivotal contributors to last-line of antimicrobial resistance in human infections. Shewanella, historically recognized as a natural environmental bacterium with metal reduction capabilities, recently has been observed in clinical settings. However, limited knowledge has been explored on genetic differences between strains from non-clinical and clinical strains. In this study, we conducted the whole genome sequencing on six Arctic strains, illustrated the phylogenetic relationships on published 393 Shewanella strains that categorized the genus into four lineages (L1 to L4). Over 86.4% of clinical strain group (CG) strains belonged to L1 and L4, carrying mcr-4 genes and a complete metal-reduction pathways gene cluster. Remarkably, a novel Arctic Shewanella strain in L3, exhibits similar genetic characteristics with CG strains that carried both mcr-4 genes and a complete metal reduction pathway gene cluster. It raised concerns about the transmission ability from environment to clinic setting causing in the potential infections, and emphasized the need for monitoring the emerging strains with human infections.

A multi-ancestry GWAS meta-analysis of facial features and its application in predicting archaic human features.

Facial morphology, a complex trait influenced by genetics, holds great significance in evolutionary research. However, due to limited fossil evidence, the facial characteristics of Neanderthals and Denisovans have remained largely unknown. In this study, we conducted a large-scale multi-ethnic meta-analysis of the genome-wide association study (GWAS), including 9674 East Asians and 10,115 Europeans, quantitatively assessing 78 facial traits using 3D facial images. We identified 71 genomic loci associated with facial features, including 21 novel loci. We developed a facial polygenic score (FPS) that enables the prediction of facial features based on genetic information. Interestingly, the distribution of FPSs among populations from diverse continental groups exhibited relevant correlations with observed facial features. Furthermore, we applied the FPS to predict the facial traits of seven Neanderthals and one Denisovan using ancient DNA and aligned predictions with the fossil records. Our results suggested that Neanderthals and Denisovans likely shared similar facial features, such as a wider but shorter nose and a wider endocanthion distance. The decreased mouth width was characterized specifically in Denisovans. The integration of genomic data and facial trait analysis provides valuable insights into the evolutionary history and adaptive changes in human facial morphology.

Nitrogen-Doped Porous Carbons Derived from Peanut Shells as Efficient Electrodes for High-Performance Supercapacitors.

The doping of porous carbon materials with nitrogen is an effective approach to enhance the electrochemical performance of electrode materials. In this study, nitrogen-doped porous carbon derived from peanut shells was prepared as an electrode for supercapacitors. Melamine, urea, urea phosphate, and ammonium dihydrogen phosphate were employed as different nitrogen dopants. The optimized electrode material PA-1-1 prepared by peanut shells, with ammonium dihydrogen phosphate as a nitrogen dopant, exhibited a N content of 3.11% and a specific surface area of 602.7 m2/g. In 6 M KOH, the PA-1-1 electrode delivered a high specific capacitance of 208.3 F/g at a current density of 1 A/g. Furthermore, the PA-1-1 electrode demonstrated an excellent rate performance with a specific capacitance of 170.0 F/g (retention rate of 81.6%) maintained at 20 A/g. It delivered a capacitance of PA-1-1 with a specific capacitance retention of 98.8% at 20 A/g after 5000 cycles, indicating excellent cycling stability. The PA-1-1//PA-1-1 symmetric supercapacitor exhibited an energy density of 17.7 Wh/kg at a power density of 2467.0 W/kg. This work not only presents attractive N-doped porous carbon materials for supercapacitors but also offers a novel insight into the rational design of biochar carbon derived from waste peelings.

HT-2 toxin impairs porcine oocyte in vitro maturation through disruption of endomembrane system.

HT-2 toxin is a type of mycotoxin which is shown to affect gastric and intestinal lesions, hematopoietic and immunosuppressive effects, anorexia, lethargy, nausea. Recently, emerging evidences indicate that HT-2 also disturbs the reproductive system. In this study, we investigated the impact of HT-2 toxin exposure on the organelles of porcine oocytes. Our results found that the abnormal distribution of endoplasmic reticulum increased after HT-2 treatment, with the perturbation of ribosome protein RPS3 and GRP78 expression; Golgi apparatus showed diffused localization pattern and GM130 localization was also impaired, thereby affecting the Rab10-based vesicular transport; Due to the impairment of ribosomes, ER, and Golgi apparatus, the protein supply to lysosomes was hindered, resulting in lysosomal damage, which further disrupted the LC3-based autophagy. Moreover, the results indicated that the function and distribution of mitochondria were also affected by HT-2 toxin, showing with fragments of mitochondria, decreased TMRE and ATP level. Taken together, our study suggested that HT-2 toxin exposure induces damage to the organelles for endomembrane system, which further inhibited the meiotic maturation of porcine oocytes.

Sound as a bell: a deep learning approach for health status classification through speech acoustic biomarkers.

BACKGROUND: Human health is a complex, dynamic concept encompassing a spectrum of states influenced by genetic, environmental, physiological, and psychological factors. Traditional Chinese Medicine categorizes health into nine body constitutional types, each reflecting unique balances or imbalances in vital energies, influencing physical, mental, and emotional states. Advances in machine learning models offer promising avenues for diagnosing conditions like Alzheimer's, dementia, and respiratory diseases by analyzing speech patterns, enabling complementary non-invasive disease diagnosis. The study aims to use speech audio to identify subhealth populations characterized by unbalanced constitution types. METHODS: Participants, aged 18-45, were selected from the Acoustic Study of Health. Audio recordings were collected using ATR2500X-USB microphones and Praat software. Exclusion criteria included recent illness, dental issues, and specific medical histories. The audio data were preprocessed to Mel-frequency cepstral coefficients (MFCCs) for model training. Three deep learning models-1-Dimensional Convolution Network (Conv1D), 2-Dimensional Convolution Network (Conv2D), and Long Short-Term Memory (LSTM)-were implemented using Python to classify health status. Saliency maps were generated to provide model explainability. RESULTS: The study used 1,378 recordings from balanced (healthy) and 1,413 from unbalanced (subhealth) types. The Conv1D model achieved a training accuracy of 91.91% and validation accuracy of 84.19%. The Conv2D model had 96.19% training accuracy and 84.93% validation accuracy. The LSTM model showed 92.79% training accuracy and 87.13% validation accuracy, with early signs of overfitting. AUC scores were 0.92 and 0.94 (Conv1D), 0.99 (Conv2D), and 0.97 (LSTM). All models demonstrated robust performance, with Conv2D excelling in discrimination accuracy. CONCLUSIONS: The deep learning classification of human speech audio for health status using body constitution types showed promising results with Conv1D, Conv2D, and LSTM models. Analysis of ROC curves, training accuracy, and validation accuracy showed all models robustly distinguished between balanced and unbalanced constitution types. Conv2D excelled with good accuracy, while Conv1D and LSTM also performed well, affirming their reliability. The study integrates constitution theory and deep learning technologies to classify subhealth populations using noninvasive approach, thereby promoting personalized medicine and early intervention strategies.

Magnetic excitations in strained infinite-layer nickelate PrNiO2 films.

Strongly correlated materials respond sensitively to external perturbations such as strain, pressure, and doping. In the recently discovered superconducting infinite-layer nickelates, the superconducting transition temperature can be enhanced via only ~ 1% compressive strain-tuning with the root of such enhancement still being elusive. Using resonant inelastic x-ray scattering (RIXS), we investigate the magnetic excitations in infinite-layer PrNiO2 thin films grown on two different substrates, namely SrTiO3 (STO) and (LaAlO3)0.3(Sr2TaAlO6)0.7 (LSAT) enforcing different strain on the nickelates films. The magnon bandwidth of PrNiO2 shows only marginal response to strain-tuning, in sharp contrast to the enhancement of the superconducting transition temperature Tc in the doped superconducting samples. These results suggest the bandwidth of spin excitations of the parent compounds is similar under strain while Tc in the doped ones is not, and thus provide important empirics for the understanding of superconductivity in infinite-layer nickelates.

Highly confined epsilon-near-zero and surface phonon polaritons in SrTiO3 membranes.

Recent theoretical studies have suggested that transition metal perovskite oxide membranes can enable surface phonon polaritons in the infrared range with low loss and much stronger subwavelength confinement than bulk crystals. Such modes, however, have not been experimentally observed so far. Here, using a combination of far-field Fourier-transform infrared (FTIR) spectroscopy and near-field synchrotron infrared nanospectroscopy (SINS) imaging, we study the phonon polaritons in a 100 nm thick freestanding crystalline membrane of SrTiO3 transferred on metallic and dielectric substrates. We observe a symmetric-antisymmetric mode splitting giving rise to epsilon-near-zero and Berreman modes as well as highly confined (by a factor of 10) propagating phonon polaritons, both of which result from the deep-subwavelength thickness of the membranes. Theoretical modeling based on the analytical finite-dipole model and numerical finite-difference methods fully corroborate the experimental results. Our work reveals the potential of oxide membranes as a promising platform for infrared photonics and polaritonics.

Coexistence of Interacting Charge Density Waves in a Layered Semiconductor.

Coexisting orders are key features of strongly correlated materials and underlie many intriguing phenomena from unconventional superconductivity to topological orders. Here, we report the coexistence of two interacting charge-density-wave (CDW) orders in EuTe_{4}, a layered crystal that has drawn considerable attention owing to its anomalous thermal hysteresis and a semiconducting CDW state despite the absence of perfect Fermi surface nesting. By accessing unoccupied conduction bands with time- and angle-resolved photoemission measurements, we find that monolayers and bilayers of Te in the unit cell host different CDWs that are associated with distinct energy gaps. The two gaps display dichotomous evolutions following photoexcitation, where the larger bilayer CDW gap exhibits less renormalization and faster recovery. Surprisingly, the CDW in the Te monolayer displays an additional momentum-dependent gap renormalization that cannot be captured by density-functional theory calculations. This phenomenon is attributed to interlayer interactions between the two CDW orders, which account for the semiconducting nature of the equilibrium state. Our findings not only offer microscopic insights into the correlated ground state of EuTe_{4} but also provide a general nonequilibrium approach to understand coexisting, layer-dependent orders in a complex system.

Rapid and accurate identification of yeast subspecies by MALDI-MS combined with a cell membrane disruption reagent.

Matrix-assisted laser desorption/ionization mass spectrometry (MALDI-MS) has been widely used for microbial analysis. However, due to the impenetrable shell of fungi the direct identification of fungi remains challenges. Targeting on this problem, the yeast Saccharomyces cerevisiae (S. cerevisiae) was selected as a model fungus, and a new fungal cell membrane disruption reagent C18-G1 was used before MALDI-MS detection. As a result, much more intensive peaks which distributed in wider m/z range of S. cerevisiae have been identified in comparison with the use of traditional fungal pretreatment methods. Furthermore, a differential peak at m/z 4993 between two subspecies of S. cerevisiae has been identified. The corresponding protein with exclusive sequence of the specific peak was obtained based on MS/MS fragments and database searching. In addition, the method was successfully applied for the discrimination of four commercial yeasts.

The Phenotypic Variation in Moso Bamboo and the Selection of Key Traits.

This research aimed to explore the diverse phenotypic characteristics of moso bamboo in China and pinpoint essential characteristics of moso bamboo. In this study, 63 grids were selected using the grid method to investigate 28 phenotypic traits of moso bamboo across the entire distribution area of China. The results suggest that the phenotypic traits of moso bamboo exhibit rich diversity, with coefficients of variation ranging from 5.87% to 36.57%. The phenotypic traits of moso bamboo showed varying degrees of correlation. A principal component analysis was used to identify seven main phenotypic trait indicators: diameter at breast height (DBH), leaf area (LA), leaf weight (LW), branch-to-leaf ratio (BLr), leaf moisture content (Lmc), wall-to-cavity ratio (WCr), and node length at breast height (LN), which accounted for 81.64% of the total information. A random forest model was used, which gave good results to validate the results. The average combined phenotypic trait value (D-value) of most germplasm was 0.563. The highest D-value was found in Wuyi 1 moso in Fujian (0.803), while the lowest D-value was observed in Pingle 2 moso in Guangxi (0.317). The clustering analysis of phenotypic traits classified China's moso bamboo germplasm into four groups. Group I had the highest D-value and is an important candidate germplasm for excellent germplasm screening.

Ancestral ribonucleases back in motion for evolutionary-dynamics guided protein design.

The dynamics behavior of a protein is essential for its functionality. Here, Doucet et al. demonstrate how the evolutionary analysis of conformational pathways within a protein family serves to identify common core scaffolds that accommodate branch-specific functional regions controlled by flexibility switches, offering a model for evolutionary-dynamics based protein design.

A sensitivity indicator screening and intelligent classification method for the diagnosis of T2D-CHD.

Background: The prevalence of Type 2 Diabetes Mellitus (T2D) and its significant role in increasing Coronary Heart Disease (CHD) risk highlights the urgent need for effective CHD screening within this population. Despite current advancements in T2D management, the complexity of cardiovascular complications persists. Our study aims to develop a comprehensive CHD screening model for T2D patients, employing multimodal data to improve early detection and management, addressing a critical gap in clinical practice. Methods: We analyzed data from 699 patients, including 471 with CHD (221 of these also had T2D) and a control group of 228 without CHD. Employing strict diagnostic criteria, we conducted significance testing and multivariate analysis to identify key indicators for T2D-CHD diagnosis. This led to the creation of a neural network model using 21 indicators and a logistic regression model based on an 8-indicator subset. External validation was performed with an independent dataset from an additional 212 patients to confirm the models' generalizability. Results: The neural network model achieved an accuracy of 90.7%, recall of 90.78%, precision of 90.83%, and an F-1 score of 0.908. The logistic regression model demonstrated an accuracy of 90.13%, recall of 90.1%, precision of 90.22%, and an F-1 score of 0.9016. External validation reinforced the models' reliability and effectiveness in broader clinical settings. Conclusion: Our AI-driven diagnostic models significantly enhance early CHD detection and management in T2D patients, offering a novel, efficient approach to addressing the complex interplay between these conditions. By leveraging advanced analytics and comprehensive patient data, we present a scalable solution for improving clinical outcomes in this high-risk population, potentially setting a new standard in personalized care and preventative medicine.