Genomics of hybrid parallel origin in Aquilegia ecalcarata.

BACKGROUND: The parallel evolution of similar traits or species provides strong evidence for the role of natural selection in evolution. Traits or species that evolved repeatedly can be driven by separate de novo mutations or interspecific gene flow. Although parallel evolution has been reported in many studies, documented cases of parallel evolution caused by gene flow are scarce by comparison. Aquilegia ecalcarata and A. kansuensis belong to the genus of Aquilegia, and are the closest related sister species. Mutiple origins of A. ecalcarata have been reported in previous studies, but whether they have been driven by separate de novo mutations or gene flow remains unclear. RESULTS: In this study, We conducted genomic analysis from 158 individuals of two repeatedly evolving pairs of A. ecalcarata and A. kansuensis. All samples were divided into two distinct clades with obvious geographical distribution based on phylogeny and population structure. Demographic modeling revealed that the origin of the A. ecalcarata in the Eastern of China was caused by gene flow, and the Eastern A. ecalcarata occurred following introgression from Western A. ecalcarata population. Analysis of Treemix and D-statistic also revealed that a strong signal of gene flow was detected from Western A. ecalcarata to Eastern A. ecalcarata. Genetic divergence and selective sweep analyses inferred parallel regions of genomic divergence and identified many candidate genes associated with ecologically adaptive divergence between species pair. Comparative analysis of parallel diverged regions and gene introgression confirms that gene flow contributed to the parallel evolution of A. ecalcarata. CONCLUSIONS: Our results further confirmed the multiple origins of A. ecalcarata and highlighted the roles of gene flow. These findings provide new evidence for parallel origin after hybridization as well as insights into the ecological adaptation mechanisms underlying the parallel origins of species.

Qualitative and quantitative MRI analysis of alveolar soft part sarcoma: correlation with histological grade and Ki-67 expression.

OBJECTIVE: To investigate the correlation between MRI findings and histological features for preoperative prediction of histological grading and Ki-67 expression level in alveolar soft part sarcoma (ASPS). METHODS: A retrospective analysis was conducted on 63 ASPS patients (Jan 2017-May 2023). All patients underwent 3.0-T MRI examinations, including conventional sequences, dynamic contrast-enhanced scans with time-intensity curve analysis, and diffusion-weighted imaging with apparent diffusion coefficient (ADC) measurements. Patients were divided into low-grade (histological Grade I) and high-grade (histological Grade II/III) groups based on pathology. Immunohistochemistry was used to assess Ki-67 expression levels in ASPS. Statistical analysis included chi-square tests, Wilcoxon rank-sum test, binary logistic regression analysis, Spearman correlation analysis, and receiver operating characteristic curve analysis of various observational data. RESULTS: There were 29 low-grade and 34 high-grade patients (26 males and 37 females) and a wide age range (5-68 years). Distant metastasis, tumor enhancement characteristics, and ADC values were independent predictors of high-grade ASPS. High-grade ASPS had lower ADC values (p = 0.002), with an area under the curve (AUC), sensitivity, and specificity of 0.723, 79.4%, and 58.6%, respectively, for high-grade prediction. There was a negative correlation between ADC values and Ki-67 expression (r = -0.526; p < 0.001). When the cut-off value of ADC was 0.997 × 10-3 mm²/s, the AUC, sensitivity, and specificity for predicting high Ki-67 expression were 0.805, 65.6%, and 83.9%, respectively. CONCLUSION: Qualitative and quantitative MRI parameters are valuable for predicting histological grading and Ki-67 expression levels in ASPS. CRITICAL RELEVANCE STATEMENT: This study will help provide a more nuanced understanding of ASPS and guide personalized treatment strategies. KEY POINTS: There is limited research on assessing ASPS prognosis through MRI. Metastasis, enhancement, and ADC correlated with histological grade; ADC related to Ki-67 expression. MRI provides clinicians with valuable information on ASPS grading and proliferation activity.

In vivo quantitative characterization of nano adjuvant transport in the tracheal layer by photoacoustic imaging.

Adjuvants are indispensable ingredients in vaccine formulations. Evaluating the in vivo transport processes of adjuvants, particularly for inhalation formulations, presents substantial challenges. In this study, a nanosized adjuvant aluminum hydroxide (AlOOH) was synthesized and labeled with indocyanine green (ICG) and bovine serum albumin (BSA) to achieve strong optical absorption ability and high biocompatibility. The adjuvant nanomaterials (BSA@ICG@AlOOH, BIA) were delivered as an aerosol into the airways of mice, its distribution was monitored using photoacoustic imaging (PAI) in vivo. PAI results illustrated the gradual cross-layer transmission process of BIA in the tracheal layer, traversing approximately 250 µm from the inner layer of the trachea to the outer layer. The results were consistent with pathology. While the intensity of the BIA reduced by approximately 46.8% throughout the transport process. The ability of PAI for quantitatively characterized the dynamic transport process of adjuvant within the tracheal layer may be widely used in new vaccine development.

Incidence and prognosis of olfactory and gustatory dysfunctions related to SARS-CoV-2 Omicron strain infection in China: A national multicenter survey of 35,566 individuals.

Objective: This cross-sectional study aimed to determine the epidemiology of olfactory and gustatory dysfunctions related to COVID-19 in China. Methods: This study was conducted by 45 tertiary Grade-A hospitals in China. Online and offline questionnaire data were obtained from patients infected with COVID-19 between December 28, 2022, and February 21, 2023. The collected information included basic demographics, medical history, smoking and drinking history, vaccination history, changes in olfactory and gustatory functions before and after infection, and other postinfection symptoms, as well as the duration and improvement status of olfactory and gustatory disorders. Results: Complete questionnaires were obtained from 35,566 subjects. The overall incidence of olfactory and taste dysfunction was 67.75%. Being female or being a cigarette smoker increased the likelihood of developing olfactory and taste dysfunction. Having received four doses of the vaccine or having good oral health or being a alcohol drinker decreased the risk of such dysfunction. Before infection, the average olfactory and taste VAS scores were 8.41 and 8.51, respectively; after infection, they decreased to 3.69 and 4.29 and recovered to 5.83 and 6.55 by the time of the survey. The median duration of dysosmia and dysgeusia was 15 and 12 days, respectively, with 0.5% of patients having symptoms lasting for more than 28 days. The overall self-reported improvement rate was 59.16%. Recovery was higher in males, never smokers, those who received two or three vaccine doses, and those that had never experienced dental health issues, or chronic accompanying symptoms. Conclusions: The incidence of dysosmia and dysgeusia following infection with the SARS-CoV-2 virus is high in China. Incidence and prognosis are influenced by several factors, including sex, SARS-CoV-2 vaccination, history of head-facial trauma, nasal and oral health status, smoking and drinking history, and the persistence of accompanying symptoms.

LDHA contributes to nicotine induced cardiac fibrosis through autophagy flux impairment.

Cardiac fibrosis is a typical feature of cardiac pathological remodeling, which is associated with adverse clinical outcomes and has no effective therapy. Nicotine is an important risk factor for cardiac fibrosis, yet its underlying molecular mechanism remains poorly understood. This study aimed to identify its potential molecular mechanism in nicotine-induced cardiac fibrosis. Our results showed nicotine exposure led to the proliferation and transformation of cardiac fibroblasts (CFs) into myofibroblasts (MFs) by impairing autophagy flux. Through the use of drug affinity responsive target stability (DARTS) assay, cellular thermal shift assay (CETSA), and surface plasmon resonance (SPR) technology, it was discovered that nicotine directly increased the stability and protein levels of lactate dehydrogenase A (LDHA) by binding to it. Nicotine treatment impaired autophagy flux by regulating the AMPK/mTOR signaling pathway, impeding the nuclear translocation of transcription factor EB (TFEB), and reducing the activity of cathepsin B (CTSB). In vivo, nicotine treatment exacerbated cardiac fibrosis induced in spontaneously hypertensive rats (SHR) and worsened cardiac function. Interestingly, the absence of LDHA reversed these effects both in vitro and in vivo. Our study identified LDHA as a novel nicotine-binding protein that plays a crucial role in mediating cardiac fibrosis by blocking autophagy flux. The findings suggest that LDHA could potentially serve as a promising target for the treatment of cardiac fibrosis.

Generative learning facilitated discovery of high-entropy ceramic dielectrics for capacitive energy storage.

Dielectric capacitors offer great potential for advanced electronics due to their high power densities, but their energy density still needs to be further improved. High-entropy strategy has emerged as an effective method for improving energy storage performance, however, discovering new high-entropy systems within a high-dimensional composition space is a daunting challenge for traditional trial-and-error experiments. Here, based on phase-field simulations and limited experimental data, we propose a generative learning approach to accelerate the discovery of high-entropy dielectrics in a practically infinite exploration space of over 1011 combinations. By encoding-decoding latent space regularities to facilitate data sampling and forward inference, we employ inverse design to screen out the most promising combinations via a ranking strategy. Through only 5 sets of targeted experiments, we successfully obtain a Bi(Mg0.5Ti0.5)O3-based high-entropy dielectric film with a significantly improved energy density of 156 J cm-3 at an electric field of 5104 kV cm-1, surpassing the pristine film by more than eight-fold. This work introduces an effective and innovative avenue for designing high-entropy dielectrics with drastically reduced experimental cycles, which could be also extended to expedite the design of other multicomponent material systems with desired properties.

Effects of probiotic supplementation on 12 min run performance, mood management, body composition and gut microbiota in amateur marathon runners: A double-blind controlled trial.

Background: Probiotic supplementation has a positive effect on endurance exercise performance and body composition in athletes, but the underlying mechanisms remain unclear. Gut microbiota can provide measurable markers of immune function in athletes, and microbial composition analysis may be sensitive enough to detect stress and metabolic disorders caused by exercise. Methods: Nineteen healthy active amateur marathon runners (15 male and 4 female) with a mean age of 29.11 years volunteered to participate in this double-blind controlled study. Based on the performance of the Cooper 12-min running test (CRT), the participants were allocated into two groups to receive either a probiotic formulation comprising lactobacillus acidophilus and bifidobacterium longum (n = 10) or placebo containing maltodextrin (n = 9) for five weeks. Consistency of diet and exercise was ensured throughout the experimental period. Before and after the intervention, all participants were assessed for CRT, emotional stability and gastrointestinal symptoms, gut microbiota composition, body composition and magnetic resonance imaging (MRI) indicators of skeletal muscle microcirculation. Results: Compared to before the intervention, the probiotics group showed an increase in CRT score (2.88 ± 0.57 vs 3.01 ± 0.60 km, P＜0.05), significant improvement in GSRS and GIQLI (9.20 ± 4.64 vs 7.40 ± 3.24, 118.90 ± 12.30 vs 127.50 ± 9.85, P＜0.05), while these indicators remained unchanged in the control group, with a significant time-group interaction effect on gastrointestinal symptoms. Additionally, some MRI metabolic cycling indicators of the thigh skeletal muscle also changed in the probiotics group (P＜0.05). Regarding microbiota abundance, the probiotics group exhibited a significant increase in the abundance of beneficial bacteria and a significant decrease in the abundance of harmful bacteria post-intervention (P＜0.05). Conclusion: As a sports nutritional supplement, probiotics have the potential to improve athletic performance by optimizing the balance of gut microbiota, alleviating gastrointestinal symptoms.

The Role of Astrocytes in Migraine with Cortical Spreading Depression: Protagonists or Bystanders? A Narrative Review.

Cortical spreading depression (CSD) is a slow wave of cortical depolarization closely associated with migraines with an aura. Previously, it was thought that CSD depolarization was mainly driven by neurons, with characteristic changes in neuronal swelling and increased extracellular potassium (K+) and glutamate. However, the role of astrocytes, a member of the neurovascular unit, in migraine with CSD has recently received increasing attention. In the early stages of CSD, astrocytes provide neurons with energy support and clear K+ and glutamate from synaptic gaps. However, in the late stages of CSD, astrocytes release large amounts of lactic acid to exacerbate hypoxia when the energy demand exceeds the astrocytes' compensatory capacity. Astrocyte endfoot swelling is a characteristic of CSD, and neurons are not similarly altered. It is primarily due to K+ influx and abnormally active calcium (Ca2+) signaling. Aquaporin 4 (AQP-4) only mediates K+ influx and has little role as an aquaporin. Astrocytes endfoot swelling causes perivascular space closure, slowing the glymphatic system flow and exacerbating neuroinflammation, leading to persistent CSD. Astrocytes are double-edged swords in migraine with CSD and may be potential targets for CSD interventions.

Mental health and insomnia problems in healthcare workers after the COVID-19 pandemic: A multicenter cross-sectional study.

BACKGROUND: Healthcare workers (HCWs) are at increased risk of contracting coronavirus disease 2019 (COVID-19) as well as worsening mental health problems and insomnia. These problems can persist for a long period, even after the pandemic. However, less is known about this topic. AIM: To analyze mental health, insomnia problems, and their influencing factors in HCWs after the COVID-19 pandemic. METHODS: This multicenter cross-sectional, hospital-based study was conducted from June 1, 2023 to June 30, 2023, which was a half-year after the end of the COVID-19 emergency. Region-stratified population-based cluster sampling was applied at the provincial level for Chinese HCWs. Symptoms such as anxiety, depression, and insomnia were evaluated by the Generalized Anxiety Disorder-7, Patient Health Questionnaire-9, and Insomnia Severity Index. Factors influencing the symptoms were identified by multivariable logistic regression. RESULTS: A total of 2000 participants were invited, for a response rate of 70.6%. A total of 1412 HCWs [618 (43.8%) doctors, 583 (41.3%) nurses and 211 (14.9%) nonfrontline], 254 (18.0%), 231 (16.4%), and 289 (20.5%) had symptoms of anxiety, depression, and insomnia, respectively; severe symptoms were found in 58 (4.1%), 49 (3.5%), and 111 (7.9%) of the participants. Nurses, female sex, and hospitalization for COVID-19 were risk factors for anxiety, depression, and insomnia symptoms; moreover, death from family or friends was a risk factor for insomnia symptoms. During the COVID-19 outbreak, most [1086 (76.9%)] of the participating HCWs received psychological interventions, while nearly all [994 (70.4%)] of them had received public psychological education. Only 102 (7.2%) of the HCWs received individual counseling from COVID-19. CONCLUSION: Although the mental health and sleep problems of HCWs were relieved after the COVID-19 pandemic, they still faced challenges and greater risks than did the general population. Identifying risk factors would help in providing targeted interventions. In addition, although a major proportion of HCWs have received public psychological education, individual interventions are still insufficient.

Zinc deficiency deteriorates ovarian follicle development and function by inhibiting mitochondrial function.

Zinc (Zn) is a crucial trace element essential for human growth and development, particularly for reproductive health. Previous research has shown a decrease in serum zinc concentration with age and individuals with conditions such as polycystic ovary syndrome (PCOS) and diabetes mellitus. However, the specific effects of zinc deficiency on the female reproductive system, especially ovarian function, are not fully understood. In our study, we observed a significant reduction in the total number of follicles and mature follicles in the zinc deficiency group. This reduction correlated with decreased level of anti-Mullerian hormone (AMH) and abnormal gene expression affecting hormone secretion regulation. Furthermore, we found that zinc deficiency disrupted mitochondrial dynamics, leading to oxidative stress in the ovaries, which further inhibited autophagy and increased ovarian apoptosis. These changes ultimately resulted in the failure of germinal vesicle breakdown (GVBD) and reduced oocyte quality. Meanwhile, administration of zinc glycine effectively alleviated the oocyte meiotic arrest caused by dietary zinc deficiency. In conclusion, our findings demonstrated that dietary zinc deficiency can affect hormone secretion and follicle maturation by impairing mitochondrial function and autophagy.

GNNGL-PPI: multi-category prediction of protein-protein interactions using graph neural networks based on global graphs and local subgraphs.

Most proteins exert their functions by interacting with other proteins, making the identification of protein-protein interactions (PPI) crucial for understanding biological activities, pathological mechanisms, and clinical therapies. Developing effective and reliable computational methods for predicting PPI can significantly reduce the time-consuming and labor-intensive associated traditional biological experiments. However, accurately identifying the specific categories of protein-protein interactions and improving the prediction accuracy of the computational methods remain dual challenges. To tackle these challenges, we proposed a novel graph neural network method called GNNGL-PPI for multi-category prediction of PPI based on global graphs and local subgraphs. GNNGL-PPI consisted of two main components: using Graph Isomorphism Network (GIN) to extract global graph features from PPI network graph, and employing GIN As Kernel (GIN-AK) to extract local subgraph features from the subgraphs of protein vertices. Additionally, considering the imbalanced distribution of samples in each category within the benchmark datasets, we introduced an Asymmetric Loss (ASL) function to further enhance the predictive performance of the method. Through evaluations on six benchmark test sets formed by three different dataset partitioning algorithms (Random, BFS, DFS), GNNGL-PPI outperformed the state-of-the-art multi-category prediction methods of PPI, as measured by the comprehensive performance evaluation metric F1-measure. Furthermore, interpretability analysis confirmed the effectiveness of GNNGL-PPI as a reliable multi-category prediction method for predicting protein-protein interactions.

Predicting Lung Cancer Survival to the Future: Population-Based Cancer Survival Modeling Study.

BACKGROUND: Lung cancer remains the leading cause of cancer-related mortality globally, with late diagnoses often resulting in poor prognosis. In response, the Lung Ambition Alliance aims to double the 5-year survival rate by 2025. OBJECTIVE: Using the Taiwan Cancer Registry, this study uses the survivorship-period-cohort model to assess the feasibility of achieving this goal by predicting future survival rates of patients with lung cancer in Taiwan. METHODS: This retrospective study analyzed data from 205,104 patients with lung cancer registered between 1997 and 2018. Survival rates were calculated using the survivorship-period-cohort model, focusing on 1-year interval survival rates and extrapolating to predict 5-year outcomes for diagnoses up to 2020, as viewed from 2025. Model validation involved comparing predicted rates with actual data using symmetric mean absolute percentage error. RESULTS: The study identified notable improvements in survival rates beginning in 2004, with the predicted 5-year survival rate for 2020 reaching 38.7%, marking a considerable increase from the most recent available data of 23.8% for patients diagnosed in 2013. Subgroup analysis revealed varied survival improvements across different demographics and histological types. Predictions based on current trends indicate that achieving the Lung Ambition Alliance's goal could be within reach. CONCLUSIONS: The analysis demonstrates notable improvements in lung cancer survival rates in Taiwan, driven by the adoption of low-dose computed tomography screening, alongside advances in diagnostic technologies and treatment strategies. While the ambitious target set by the Lung Ambition Alliance appears achievable, ongoing advancements in medical technology and health policies will be crucial. The study underscores the potential impact of continued enhancements in lung cancer management and the importance of strategic health interventions to further improve survival outcomes.

X-ray Structure-Guided Discovery of a Potent Benzimidazole Glutaminyl Cyclase Inhibitor That Shows Activity in a Parkinson's Disease Mouse Model.

The secretory glutaminyl cyclase (sQC) and Golgi-resident glutaminyl cyclase (gQC) are responsible for N-terminal protein pyroglutamation and associated with various human diseases. Although several sQC/gQC inhibitors have been reported, only one inhibitor, PQ912, is currently undergoing clinic trials for the treatment of Alzheimer's disease. We report an X-ray crystal structure of sQC complexed with PQ912, revealing that the benzimidazole makes "anchor" interactions with the active site zinc ion and catalytic triad. Structure-guided design and optimization led to a series of new benzimidazole derivatives exhibiting nanomolar inhibition for both sQC and gQC. In a MPTP-induced Parkinson's disease (PD) mouse model, BI-43 manifested efficacy in mitigating locomotor deficits through reversing dopaminergic neuronal loss, reducing microglia, and decreasing levels of the sQC/gQC substrates, α-synuclein, and CCL2. This study not only offers structural basis and new leads for drug discovery targeting sQC/gQC but also provides evidence supporting sQC/gQC as potential targets for PD treatment.

[Interaction between central nervous system and immune system after ischemic stroke and its therapeutic significance of traditional Chinese medicine].

Ischemic stroke is divided into acute phase, subacute phase, and recovery phase, with different pathological and physiological characteristics manifested at each stage. Among them, immune and inflammatory reactions persist for several days and weeks after ischemia. Ischemic stroke not only triggers local inflammation in damaged brain regions but also induces a disorder in the immune system, thereby promoting neuroinflammation and exacerbating brain damage. Therefore, conducting an in-depth analysis of the interaction between the central nervous system and the immune system after ischemic stroke, intervening in the main factors of the interaction between them, blocking pathological cascades, and thereby reducing brain inflammation have become the treatment strategies for ischemic stroke. This study summarizes and sorts out the interaction pathways between the central nervous system and the immune system. The impact of the central nervous system on the immune system can be analyzed from the perspective of the autonomic nervous system, the hypothalamic-pituitary-adrenal axis(HPA), and local inflammatory stimulation. The impact of the immune system on the central nervous system can be analyzed from the dynamic changes of immune cells. At the same time, the relevant progress in the prevention and treatment of traditional Chinese medicine(TCM) is summarized, so as to provide new insights for the analysis of complex mechanisms of TCM in preventing and treating ischemic stroke.

Machine learning reveals the selection pressure exerted by nonantibiotic pharmaceuticals at environmentally relevant concentrations on antibiotic resistance genotypes.

The emergence and increasing prevalence of antibiotic resistance pose a global public risk for human health, and nonantimicrobial pharmaceuticals play an important role in this process. Herein, five nonantimicrobial pharmaceuticals, including acetaminophen (ACT), clofibric acid (CA), carbamazepine (CBZ), caffeine (CF) and nicotine (NCT), tetracycline-resistant strains, five ARGs (sul1, sul2, tetG, tetM and tetW) and one integrase gene (intI1), were detected in 101 wastewater samples during two typical sewage treatment processes including anaerobic-oxic (A/O) and biological aerated filter (BAF) in Harbin, China. The impact of nonantibiotic pharmaceuticals at environmentally relevant concentrations on both the resistance genotypes and resistance phenotypes were explored. The results showed that a significant impact of nonantibiotic pharmaceuticals at environmentally relevant concentrations on tetracycline resistance genes encoding ribosomal protection proteins (RPPs) was found, while no changes in antibiotic phenotypes, such as minimal inhibitory concentrations (MICs), were observed. Machine learning was applied to further sort out the contribution of nonantibiotic pharmaceuticals at environmentally relevant concentrations to different ARG subtypes. The highest contribution and correlation were found at concentrations of 1400-1800 ng/L for NCT, 900-1500 ng/L for ACT and 7000-10,000 ng/L for CF for tetracycline resistance genes encoding RPPs, while no significant correlation was found between the target compounds and ARGs when their concentrations were lower than 500 ng/L for NCT, 100 ng/L for ACT and 1000 ng/L for CF, which were higher than the concentrations detected in effluent samples. Therefore, the removal of nonantibiotic pharmaceuticals in WWTPs can reduce their selection pressure for resistance genes in wastewater.

Fusion of memristor and digital compute-in-memory processing for energy-efficient edge computing.

Artificial intelligence (AI) edge devices prefer employing high-capacity nonvolatile compute-in-memory (CIM) to achieve high energy efficiency and rapid wakeup-to-response with sufficient accuracy. Most previous works are based on either memristor-based CIMs, which suffer from accuracy loss and do not support training as a result of limited endurance, or digital static random-access memory (SRAM)-based CIMs, which suffer from large area requirements and volatile storage. We report an AI edge processor that uses a memristor-SRAM CIM-fusion scheme to simultaneously exploit the high accuracy of the digital SRAM CIM and the high energy-efficiency and storage density of the resistive random-access memory memristor CIM. This also enables adaptive local training to accommodate personalized characterization and user environment. The fusion processor achieved high CIM capacity, short wakeup-to-response latency (392 microseconds), high peak energy efficiency (77.64 teraoperations per second per watt), and robust accuracy (<0.5% accuracy loss). This work demonstrates that memristor technology has moved beyond in-lab development stages and now has manufacturability for AI edge processors.

PIII/PV-Catalyzed Beckmann Reaction and Sequential [2,3]-Sigmatropic Rearrangement to Construct 2-Amidopyridines.

An organophosphorus catalytic method for the synthesis of substituted 2-amidopyridines is reported. The method employs a small-ring organophosphorus-based catalyst and a hydrosilane reductant to drive the conversion of ketoximes and pyridine-N-oxides into 2-amidopyridines through sequential Beckmann rearrangement followed by [2,3]-sigmatropic rearrangement. The readily available ketoximes could be activated to nitrilium ions in PIII/PV redox catalysis and could efficiently participate in the domino reaction of pyridine-N-oxides, thus providing various substituted 2-amidopyridines in moderate to excellent yields. This presented strategy features excellent functional group tolerance and a broad substrate scope.

Cognitive-behavioral therapy for patients with somatoform disorders: A pilot preliminary randomized controlled trial.

Background and objective: Cognitive-behavioral therapy (CBT) for somatoform disorders (SFDs) is understudied in China. Western findings may not be applicable to Chinese culture. This preliminary study evaluated the efficacy of CBT for patients in China, relative to treatment-as-usual (TAU). Methods: Seventy patients with SFDs randomly received either combined CBT and TAU (CBT + TAU), or TAU alone between January 2018 to May 2019. The CBT + TAU group received 12 weekly individual 50-minute CBT sessions. Participants were blindly assessed at 4 timepoints (baseline, week 6, end of treatment: week 12; 12 weeks post-treatment: week 24) using the following outcome measures: SQSS (Self-screening Questionnaire for Somatic Symptoms); PHQ-15 (Patient-Health-Questionnaire-15) and the WI (Whiteley Index); GAD-7 (General Anxiety Disorder-7); HAMD-17 (Hamilton Depression Rating Scale-17); Family Burden Interview Schedule (FBIS); Sheehan Disability Scale (SDS); and the Short Form of Quality-of-Life Enjoyment and Satisfaction Questionnaire (Q-LES-Q-SF). The primary endpoint was the difference between the SQSS total score at week 24 and the baseline. A mixed model for repeated measures was used to analyze inter- and intra-group changes from the baseline. Results: At week 24, The least-squares mean (LSM) change of the total score on the SQSS was -18.87 points and -9.69 points, respectively in the CBT + TAU group and in the TAU group (LSM difference, -9.18 points; 95% confidence interval, -15.72 to -2.64; P = 0.0068). At week 24, the LSM changes from baseline in the WI, HAMD, PHQ15, FBIS and SDS total scores were significantly different between the two groups, however, there was no significant difference in the Q-LES-Q-SF. The SQSS of group effect sizes were 0.63 at 24 weeks. The dropout rates of the CBT + TAU and TAU groups were comparable (22.9% and 19.3%). Conclusions: These preliminary findings suggest that CBT may be helpful for improving the symptoms of patients with SFDs in China.

The combination of deep learning and pseudo-MS image improves the applicability of metabolomics to congenital heart defect prenatal screening.

To investigate the metabolic alterations in maternal individuals with fetal congenital heart disease (FCHD), establish the FCHD diagnostic models, and assess the performance of these models, we recruited two batches of pregnant women. By metabolomics analysis using Ultra High-performance Liquid Chromatography-Mass/Mass (UPLC-MS/MS), a total of 36 significantly altered metabolites (VIP >1.0) were identified between FCHD and non-FCHD groups. Two logistic regression models and four support vector machine (SVM) models exhibited strong performance and clinical utility in the training set (area under the curve (AUC) = 1.00). The convolutional neural network (CNN) model also demonstrated commendable performance and clinical utility (AUC = 0.89 in the training set). Notably, in the validation set, the performance of the CNN model (AUC = 0.66, precision = 0.714) exhibited better robustness than the six models above (AUC≤0.50). In conclusion, the CNN model based on pseudo-MS images holds promise for real-world and clinical applications due to its better repeatability.