Clinical Impact of Metformin Therapy on Gestational Diabetes Mellitus and Maternal-Infant Health Outcomes.

Objective: This study aimed to assess the impact of metformin treatment on clinical parameters (blood glucose, inflammation, hormone levels) and outcomes for both mothers and infants in cases of gestational diabetes mellitus (GDM). Methods: A comparative study with a retrospective cohort design was conducted. A total of 96 patients diagnosed with gestational diabetes mellitus over the past three years in our hospital were included. The participants were divided into two groups: a control group receiving insulin treatment and a study group receiving metformin treatment. We compared the clinical effects between the two groups. Results: After treatment, the levels of postprandial 2-hour blood glucose, fasting blood glucose, and glycosylated hemoglobin significantly improved in both groups compared to pre-treatment levels. Moreover, the study group exhibited superior outcomes compared to the control group (P < .05). The levels of interleukin-8 (IL-8), tumor necrosis factor-α (TNF-α), and interleukin-1ß (IL-1ß) demonstrated improvement in both groups, with the study group outperforming the control group (P < .05). Additionally, the levels of Cystatin C (CysC) and Homocysteine (Hcy) in both groups improved post-treatment, with the study group showing better results than the control group (P < .05). Notably, the study group exhibited a lower incidence of adverse outcomes than the control group (P < .05). Conclusions: Metformin therapy demonstrated a significant clinical impact on gestational diabetes mellitus. Compared to insulin therapy, metformin showed superior effects on blood glucose, inflammation, hormone levels, and maternal and infant outcomes, suggesting its adoption for patient consideration.

Effectiveness of exercise-based cardiac rehabilitation for patients with left ventricular assist device: A systematic review and meta-analysis.

PURPOSE: Exercise-based cardiac rehabilitation (EBCR) improves functional capacity in heart failure (HF). However, data on the effect of EBCR in patients with advanced HF and left ventricular assist devices (LVADs) are limited. This meta-analysis aimed to evaluate the impact of EBCR on the functional ability of LVAD patients by comparing the corresponding outcome indicators between the EBCR and ST groups. METHODS: PubMed, Embase, Clinical Trials, and Cochrane Library databases were searched for studies assessing and comparing the effects of EBCR and standard therapy (ST) in patients following LVAD implantation. Using pre-defined criteria, appropriate studies were identified and selected. Data from selected studies were extracted in a standardized fashion, and a meta-analysis was performed using a fixed-effects model. The protocol was registered on INPLASY (202340073). RESULTS: In total, 12 trials involving 477 patients were identified. The mean age of the participants was 52.9 years, and 78.6% were male. The initiation of EBCR varied from LVAD implantation during the index hospitalization to 11 months post-LVAD implantation. The median rehabilitation period ranged from 2 weeks to 18 months. EBCR was associated with improved peak oxygen uptake (VO2) in all trials. Quantitative analysis was performed in six randomized studies involving 214 patients (EBCR: n = 130, ST: n = 84). EBCR was associated with a significantly high peak VO2 (weighted mean difference [WMD] = 1.64 mL/kg/min; 95% confidence interval [CI], 0.20-3.08; p = .03). Similarly, 6-min walk distance (6MWD) showed significantly greater improvement in the EBCR group than in the ST group (WMD = 34.54 m; 95% CI, 12.47-56.42; p = .002) in 266 patients (EBCR, n = 140; ST, n = 126). Heterogeneity was low among the included trials. None of the included studies reported serious adverse events related to EBCR, indicating the safety of EBCR after LVAD implantation. CONCLUSION: This study demonstrated that EBCR following LVAD implantation is associated with greater improvement in functional capacity compared with ST as reflected by the improved peak VO2 and 6MWD values. Considering the small number of patients in this analysis, further research on the clinical impact of EBCR in LVAD patients is warranted.

A Multitask Dynamic Graph Attention Autoencoder for Imbalanced Multilabel Time Series Classification.

Graph learning is widely applied to process various complex data structures (e.g., time series) in different domains. Due to multidimensional observations and the requirement for accurate data representation, time series are usually represented in the form of multilabels. Accurately classifying multilabel time series can provide support for personalized predictions and risk assessments. It requires effectively capturing complex label relevance and overcoming imbalanced label distributions of multilabel time series. However, the existing methods are unable to model label relevance for multilabel time series or fail to fully exploit it. In addition, the existing multilabel classification balancing strategies suffer from limitations, such as disregarding label relevance, information loss, and sampling bias. This article proposes a dynamic graph attention autoencoder-based multitask (DGAAE-MT) learning framework for multilabel time series classification. It can fully and accurately model label relevance for each instance by using a dynamic graph attention-based graph autoencoder to improve multilabel classification accuracy. DGAAE-MT employs a dual-sampling strategy and cooperative training approach to improve the classification accuracy of low-frequency classes while maintaining the classification accuracy of high-frequency and mid-frequency classes. It avoids information loss and sampling bias. DGAAE-MT achieves a mean average precision (mAP) of 0.955 and an F1 score of 0.978 on a mixed medical time series dataset. It outperforms state-of-the-art works in the past two years.

A review of machine learning-based methods for predicting drug-target interactions.

The prediction of drug-target interactions (DTI) is a crucial preliminary stage in drug discovery and development, given the substantial risk of failure and the prolonged validation period associated with in vitro and in vivo experiments. In the contemporary landscape, various machine learning-based methods have emerged as indispensable tools for DTI prediction. This paper begins by placing emphasis on the data representation employed by these methods, delineating five representations for drugs and four for proteins. The methods are then categorized into traditional machine learning-based approaches and deep learning-based ones, with a discussion of representative approaches in each category and the introduction of a novel taxonomy for deep neural network models in DTI prediction. Additionally, we present a synthesis of commonly used datasets and evaluation metrics to facilitate practical implementation. In conclusion, we address current challenges and outline potential future directions in this research field.

A bimodal feature fusion convolutional neural network for detecting obstructive sleep apnea/hypopnea from nasal airflow and oximetry signals.

The most prevalent sleep-disordered breathing condition is Obstructive Sleep Apnea (OSA), which has been linked to various health consequences, including cardiovascular disease (CVD) and even sudden death. Therefore, early detection of OSA can effectively help patients prevent the diseases induced by it. However, many existing methods have low accuracy in detecting hypopnea events or even ignore them altogether. According to the guidelines provided by the American Academy of Sleep Medicine (AASM), two modal signals, namely nasal pressure airflow and pulse oxygen saturation (SpO2), offer significant advantages in detecting OSA, particularly hypopnea events. Inspired by this notion, we propose a bimodal feature fusion CNN model that primarily comprises of a dual-branch CNN module and a feature fusion module for the classification of 10-second-long segments of nasal pressure airflow and SpO2. Additionally, an Efficient Channel Attention mechanism (ECA) is incorporated into the second module to adaptively weight feature map of each channel for improving classification accuracy. Furthermore, we design an OSA Severity Assessment Framework (OSAF) to aid physicians in effectively diagnosing OSA severity. The performance of both the bimodal feature fusion CNN model and OSAF is demonstrated to be excellent through per-segment and per-patient experimental results, based on the evaluation of our method using two real-world datasets consisting of polysomnography (PSG) recordings from 450 subjects.

Rebaudioside B Attenuates Lung Ischemia-reperfusion Injury-associated Apoptosis and Inflammation.

OBJECTIVE: At present, no proven effective treatment is available for Lung Ischemiareperfusion Injury (LIRI). Natural compounds offer promising prospects for developing new drugs to address various diseases. This study sought to explore the potential of Rebaudioside B (Reb B) as a treatment compound for LIRI, both in vivo and in vitro. METHODS: This study involved utilizing the human pulmonary alveolar cell line A549, consisting of epithelial type II cells, subjected to Oxygen-glucose Deprivation/recovery (OGD/R) for highthroughput in vitro cell viability screening. The aim was to identify the most promising candidate compounds. Additionally, an in vivo rat model of lung ischemia-reperfusion was employed to evaluate the potential protective effects of Reb B. RESULTS: Through high-throughput screening, Reb B emerged as the most promising natural compound among those tested. In the A549 OGD/R models, Reb B exhibited a capacity to enhance cell viability by mitigating apoptosis. In the in vivo LIRI model, pre-treatment with Reb B notably decreased apoptotic cells, perivascular edema, and neutrophil infiltration within lung tissues. Furthermore, Reb B demonstrated its ability to attenuate lung inflammation associated with LIRI primarily by elevating IL-10 levels while reducing levels of IL-6, IL-8, and TNF-α. CONCLUSION: The comprehensive outcomes strongly suggest Reb B's potential as a protective agent against LIRI. This effect is attributed to its inhibition of the mitochondrial apoptotic pathway and its ability to mitigate the inflammatory response.

A reliable and quick method for screening alternative splicing variants for low-abundance genes.

Alternative splicing (AS) is a universal phenomenon in eukaryotes, and it is still challenging to identify AS events. Several methods have been developed to identify AS events, such as expressed sequence tags (EST), microarrays and RNA-seq. However, EST has limitations in identifying low-abundance genes, while microarray and RNA-seq are high-throughput technologies, and PCR-based technology is needed for validation. To overcome the limitations of EST and shortcomings of high-throughput technologies, we established a method to identify AS events, especially for low-abundance genes, by reverse transcription (RT) PCR with gene-specific primers (GSPs) followed by nested PCR. This process includes two major steps: 1) the use of GSPs to amplify as long as the specific gene segment and 2) multiple rounds of nested PCR to screen the AS and confirm the unknown splicing variants. With this method, we successfully identified three new splicing variants, namely, GenBank Accession No. HM623886 for the bdnf gene (GenBank GeneID: 12064), GenBank Accession No. JF417977 for the trkc gene (GenBank GeneID: 18213) and GenBank Accession No. HM623888 for the glb-18 gene (GenBank GeneID: 172485). In addition to its reliability and simplicity, the method is also cost-effective and labor-intensive. In conclusion, we developed an RT-nested PCR method using gene-specific primers to efficiently identify known and novel AS variants. This approach overcomes the limitations of existing methods for detecting rare transcripts. By enabling the discovery of new isoforms, especially for low-abundance genes, this technique can aid research into aberrant splicing in disease. Future studies can apply this method to uncover AS variants involved in cancer, neurodegeneration, and other splicing-related disorders.

Correction: The role of the Basic Public Health Service program in the control of hypertension in China: Results from a cross-sectional health service interview survey.

[This corrects the article DOI: 10.1371/journal.pone.0217185.].

Non-invasive decision support for clinical treatment of non-small cell lung cancer using a multiscale radiomics approach.

BACKGROUND: Selecting therapeutic strategies for cancer patients is typically based on key target-molecule biomarkers that play an important role in cancer onset, progression, and prognosis. Thus, there is a pressing need for novel biomarkers that can be utilized longitudinally to guide treatment selection. METHODS: Using data from 508 non-small cell lung cancer (NSCLC) patients across three institutions, we developed and validated a comprehensive predictive biomarker that distinguishes six genotypes and infiltrative immune phenotypes. These features were analyzed to establish the association between radiological phenotypes and tumor genotypes/immune phenotypes and to create a radiological interpretation of molecular features. In addition, we assessed the sensitivity of the models by evaluating their performance at five different voxel intervals, resulting in improved generalizability of the proposed approach. FINDINGS: The radiomics model we developed, which integrates clinical factors and multi-regional features, outperformed the conventional model that only uses clinical and intratumoral features. Our combined model showed significant performance for EGFR, KRAS, ALK, TP53, PIK3CA, and ROS1 mutation status with AUCs of 0.866, 0.874, 0.902, 0.850, 0.860, and 0.900, respectively. Additionally, the predictive performance for PD-1/PD-L1 was 0.852. Although the performance of all models decreased to different degrees at five different voxel space resolutions, the performance advantage of the combined model did not change. CONCLUSIONS: We validated multiscale radiomic signatures across tumor genotypes and immunophenotypes in a multi-institutional cohort. This imaging-based biomarker offers a non-invasive approach to select patients with NSCLC who are sensitive to targeted therapies or immunotherapy, which is promising for developing personalized treatment strategies during therapy.

Exploring non-invasive precision treatment in non-small cell lung cancer patients through deep learning radiomics across imaging features and molecular phenotypes.

BACKGROUND: Accurate prediction of tumor molecular alterations is vital for optimizing cancer treatment. Traditional tissue-based approaches encounter limitations due to invasiveness, heterogeneity, and molecular dynamic changes. We aim to develop and validate a deep learning radiomics framework to obtain imaging features that reflect various molecular changes, aiding first-line treatment decisions for cancer patients. METHODS: We conducted a retrospective study involving 508 NSCLC patients from three institutions, incorporating CT images and clinicopathologic data. Two radiomic scores and a deep network feature were constructed on three data sources in the 3D tumor region. Using these features, we developed and validated the 'Deep-RadScore,' a deep learning radiomics model to predict prognostic factors, gene mutations, and immune molecule expression levels. FINDINGS: The Deep-RadScore exhibits strong discrimination for tumor molecular features. In the independent test cohort, it achieved impressive AUCs: 0.889 for lymphovascular invasion, 0.903 for pleural invasion, 0.894 for T staging; 0.884 for EGFR and ALK, 0.896 for KRAS and PIK3CA, 0.889 for TP53, 0.895 for ROS1; and 0.893 for PD-1/PD-L1. Fusing features yielded optimal predictive power, surpassing any single imaging feature. Correlation and interpretability analyses confirmed the effectiveness of customized deep network features in capturing additional imaging phenotypes beyond known radiomic features. INTERPRETATION: This proof-of-concept framework demonstrates that new biomarkers across imaging features and molecular phenotypes can be provided by fusing radiomic features and deep network features from multiple data sources. This holds the potential to offer valuable insights for radiological phenotyping in characterizing diverse tumor molecular alterations, thereby advancing the pursuit of non-invasive personalized treatment for NSCLC patients.

THEMIS is a substrate and allosteric activator of SHP1, playing dual roles during T cell development.

THEMIS plays an indispensable role in T cells, but its mechanism of action has remained highly controversial. Using the systematic proximity labeling methodology PEPSI, we identify THEMIS as an uncharacterized substrate for the phosphatase SHP1. Saturated mutagenesis assays and mass spectrometry analysis reveal that phosphorylation of THEMIS at the evolutionally conserved Tyr34 residue is oppositely regulated by SHP1 and the kinase LCK. Similar to THEMIS-/- mice, THEMISY34F/Y34F knock-in mice show a significant decrease in CD4 thymocytes and mature CD4 T cells, but display normal thymic development and peripheral homeostasis of CD8 T cells. Mechanistically, the Tyr34 motif in THEMIS, when phosphorylated upon T cell antigen receptor activation, appears to act as an allosteric regulator, binding and stabilizing SHP1 in its active conformation, thus ensuring appropriate negative regulation of T cell antigen receptor signaling. However, cytokine signaling in CD8 T cells fails to elicit THEMIS Tyr34 phosphorylation, indicating both Tyr34 phosphorylation-dependent and phosphorylation-independent roles of THEMIS in controlling T cell maturation and expansion.

A Survey on Differential Privacy for Medical Data Analysis.

Machine learning methods promote the sustainable development of wise information technology of medicine (WITMED), and a variety of medical data brings high value and convenience to medical analysis. However, the applications of medical data have also been confronted with the risk of privacy leakage that is hard to avoid, especially when conducting correlation analysis or data sharing among multiple institutions. Data security and privacy preservation have recently played an essential role in the field of secure and private medical data analysis, where many differential privacy strategies are applied to medical data publishing and mining. In this paper, we survey research work on the applications of differential privacy for medical data analysis, discussing the necessity of medical privacy-preserving, the advantages of differential privacy, and their applications to typical medical data, such as genomic data and wearable device data. Furthermore, we discuss the challenges and potential future research directions for differential privacy in medical applications.

Growth and exopolysaccharide production by Streptococcus thermophilus ST1 in skim milk

To analyze the exopolysaccharide (EPS) production by Streptococcus thermophilus ST1, cultures were cultivated in 10 percent (w/v) reconstituted skim milk under different growth conditions including various temperatures and pHs of growth medium, supplementation of the medium with various carbon sources (glucose, lactose, sucrose, galactose and fructose) and nitrogen source (whey protein concentrate, or WPC). The results showed that most EPS production by strain ST1 was obtained at a temperature (42°C) and pH (6.5) optimal for its growth. Supplementation of the skim milk medium with either carbohydrates or WPC increased both growth and polymer formation by different extents, with sucrose being most effective among the carbon sources tested. Under the optimal cultural conditions, i.e. pH 6.5, 42°C with 2 percent (w/v) sucrose and 0.5 percent (w/v) WPC, 135.80 mg l-1 of EPS was produced by strain ST1. The monosaccharide composition of the EPS was determined to be glucose and galactose (2:1), and the molecular mass of the EPS was 3.97 × 10(6) Da. The aqueous solution of the EPS at 1 percent (w/v) showed relatively high viscosity, indicating the potential of this EPS-producing S. thermophilus strain for applications in the improvement of physical properties of fermented milk products.