Predicting preterm birth using electronic medical records from multiple prenatal visits.

This study aimed to predict preterm birth in nulliparous women using machine learning and easily accessible variables from prenatal visits. Elastic net regularized logistic regression models were developed and evaluated using 5-fold cross-validation on data from 8,830 women in the Nulliparous Pregnancy Outcomes Study: New Mothers-to-Be (nuMoM2b) dataset at three prenatal visits: - , - , and - weeks of gestational age (GA). The models' performance, assessed using Area Under the Curve (AUC), sensitivity, specificity, and accuracy, consistently improved with the incorporation of data from later prenatal visits.

Utilizing AI for the Identification and Validation of Novel Therapeutic Targets and Repurposed Drugs for Endometriosis.

Endometriosis affects over 190 million women globally, and effective therapies are urgently needed to address the burden of endometriosis on women's health. Using an artificial intelligence (AI)-driven target discovery platform, two unreported therapeutic targets, guanylate-binding protein 2 (GBP2) and hematopoietic cell kinase (HCK) are identified, along with a drug repurposing target, integrin beta 2 (ITGB2) for the treatment of endometriosis. GBP2, HCK, and ITGB2 are upregulated in human endometriotic specimens.

Study on the early diagnostic value of nanopore sequencing in alveolar lavage fluid smear-negative pulmonary tuberculosis.

The objective of this study was to investigate the early diagnostic value of nanopore sequencing in alveolar lavage smear-negative pulmonary tuberculosis (PTB). A prospective study was conducted on patients hospitalized at Zhuzhou Central Hospital from October 2021 to June 2022 and suspected to have PTB. Alveolar lavage fluid specimens were collected from these patients and simultaneously subjected to centrifugal bacterial collection smear method in a sandwich cup, bifidobacteria solid culture (referred to as culture), Mycobacterium tuberculosis-DNA (TB-DNA), and nanopore sequencing for detection of Mycobacterium tuberculosis.

Sensitive Detection of Using Allosteric Probe and Hairpin Switches-Based Isothermal Transcription Amplification.

Pathogens pose a serious threat to public and population health, leading to serious outbreak and spread of diseases irrespective of the region. The capability to directly, sensitively, and specifically detect viable pathogens in low numbers in food and clinical samples is very desirable but remains a challenge. In this work, we present a novel assay of a combination of an aptamer-based allosteric probe and hairpin switch-controlled T7 RNA polymerase-based isothermal transcription amplification, which enables rapid, ultrasensitive, label-free detection of direct pathogens.

A one-pot isothermal Fluorogenic Mango II arrays-based assay for label-free detection of miRNA.

The capability to detect a small number of miRNAs in clinical samples with simplicity, selectivity, and sensitivity is immensely valuable, yet it remains a daunting task. Here, we described a novel Mango II aptamers-based sensor for the one-pot, sensitive and specific detection of miRNAs. Target miRNA-initiated mediated catalyzed hairpin assembly (CHA) would allow for the production of plenty of DNA duplexes and the formation of the complete T7 promoter, motivating the rolling circle transcription (RCT).

Contrast-enhanced magnetic resonance neurography for diagnosing brachial plexopathy: improved visualization and additional imaging features.

Contrast-enhanced magnetic resonance neurography (CE-MRN) holds promise for diagnosing brachial plexopathy by enhancing nerve visualization and revealing additional imaging features in various lesions. This study aims to validate CE-MRN's efficacy in improving brachial plexus (BP) imaging across different patient cohorts. Seventy-one subjects, including 19 volunteers and 52 patients with BP compression/entrapment, injury, and neoplasms, underwent both CE-MRN and plain MRN.

Continuous prediction and clinical alarm management of late-onset sepsis in preterm infants using vital signs from a patient monitor.

Background And Objective: Continuous prediction of late-onset sepsis (LOS) could be helpful for improving clinical outcomes in neonatal intensive care units (NICU). This study aimed to develop an artificial intelligence (AI) model for assisting the bedside clinicians in successfully identifying infants at risk for LOS using non-invasive vital signs monitoring.

Methods: In a retrospective study from the NICU of the Máxima Medical Center in Veldhoven, the Netherlands, a total of 492 preterm infants less than 32 weeks gestation were included between July 2016 and December 2018.

The shared mechanism and potential diagnostic markers for premature ovarian failure and dry eye disease.

Premature ovarian failure (POF), which is often comorbid with dry eye disease (DED) is a key issue affecting female health. Here, we explored the mechanism underlying comorbid POF and DED to further elucidate disease mechanisms and improve treatment. Datasets related to POF (GSE39501) and DED (GSE44101) were identified from the Gene Expression Omnibus (GEO) database and subjected to weighted gene coexpression network (WGCNA) and differentially expressed genes (DEGs) analyses, respectively, with the intersection used to obtain 158 genes comorbid in POF and DED.

Plasma Surface Treatment and Application of Polyvinyl Alcohol/Polylactic Acid Electrospun Fibrous Hemostatic Membrane.

In this study, an improved PVA/PLA fibrous hemostatic membrane was prepared by electrospinning technology combined with air plasma modification. The plasma treatment was used to modify PLA to enhance the interlayer bonding between the PVA and PLA fibrous membranes first, then modify the PVA to improve the hemostatic capacity. The surfaces of the PLA and PVA were oxidized after air plasma treatment, the fibrous diameter was reduced, and roughness was increased.

Maternal Hypermethylated Genes Contribute to Intrauterine Growth Retardation of Piglets in Rongchang Pigs.

The placenta is a crucial determinant of fetal survival, growth, and development. Deficiency in placental development directly causes intrauterine growth retardation (IUGR). IUGR can lead to fetal growth restriction and an increase in the mortality rate.

Robust arterial compliance estimation with Katz's fractal dimension of photoplethysmography.

Arterial compliance (AC) plays a crucial role in vascular aging and cardiovascular disease. The ability to continuously estimate aortic AC or its surrogate, pulse pressure (PP), through wearable devices is highly desirable, given its strong association with daily activities. While the single-site photoplethysmography (PPG)-derived arterial stiffness indices show reasonable correlations with AC, they are susceptible to noise interference, limiting their practical use.

Risk factors analysis of lateral cervical lymph node metastasis in papillary thyroid carcinoma: a retrospective study of 830 patients.

Objective: The aim of this study is to investigate the risk factors for lateral cervical lymph node metastasis in papillary thyroid carcinoma (PTC).

Methods: Clinicopathological data (age, gender, Hashimoto's thyroiditis, preoperative circulating tumor cells (CTCs), multifocal, maximum lesion diameter, invaded capsule, T stage, and lymph node metastasis) of 830 PTC patients diagnosed and treated in Meizhou People's Hospital from June 2021 to April 2023 were collected. The related factors of lateral cervical lymph node metastasis were analyzed.

A multi-task learning model using RR intervals and respiratory effort to assess sleep disordered breathing.

Background: Sleep-disordered breathing (SDB) affects a significant portion of the population. As such, there is a need for accessible and affordable assessment methods for diagnosis but also case-finding and long-term follow-up. Research has focused on exploiting cardiac and respiratory signals to extract proxy measures for sleep combined with SDB event detection.

Optimizing Paclitaxel Oral Absorption and Bioavailability: TPGS Co-Coating via Supercritical Anti-Solvent Fluidized Bed Technology.

Supercritical anti-solvent fluidized bed (SAS-FB) coating technology has the advantages of reducing particle size, preventing high surface energy particle aggregation, improving the dissolution performance and bioavailability of insoluble drugs. The poor solubility of Biopharmaceutics Classification System (BCS) class IV drugs poses challenges in achieving optimal bioavailability. Numerous anti-cancer drugs including paclitaxel (PTX) belong to the BCS class IV, hindering their therapeutic efficacy.

Noncontact respiration monitoring techniques in young children: A scoping review.

Pediatric sleep-related breathing disorders, or sleep-disordered breathing (SDB), cover a range of conditions, including obstructive sleep apnea, central sleep apnea, sleep-related hypoventilation disorders, and sleep-related hypoxemia disorder. Pediatric SDB is often underdiagnosed, potentially due to difficulties associated with performing the gold standard polysomnography in children. This scoping review aims to: (1) provide an overview of the studies reporting on safe, noncontact monitoring of respiration in young children, (2) describe the accuracy of these techniques, and (3) highlight their respective advantages and limitations.

Early prediction of gestational diabetes mellitus using maternal demographic and clinical risk factors.

Objective: To build and validate an early risk prediction model for gestational diabetes mellitus (GDM) based on first-trimester electronic medical records including maternal demographic and clinical risk factors.

Methods: To develop and validate a GDM prediction model, two datasets were used in this retrospective study. One included data of 14,015 pregnant women from Máxima Medical Center (MMC) in the Netherlands.

A pretrain-finetune approach for improving model generalizability in outcome prediction of acute respiratory distress syndrome patients.

Background: Early prediction of acute respiratory distress syndrome (ARDS) of critically ill patients in intensive care units (ICUs) has been intensively studied in the past years. Yet a prediction model trained on data from one hospital might not be well generalized to other hospitals. It is therefore essential to develop an accurate and generalizable ARDS prediction model adaptive to different hospital or medical centers.

Topical Delivery of microRNA-125b by Framework Nucleic Acids for Psoriasis Treatment.

Purpose: Psoriasis is a chronic and recurrent inflammatory dermatitis characterized by T cell imbalance and abnormal keratinocyte proliferation. MicroRNAs (miRNAs) hold promise as therapeutic agents for this disease; however, their clinical application is hindered by poor stability and limited skin penetration. This study demonstrates the utilization of Framework Nucleic Acid (FNA) for the topical delivery of miRNAs in psoriasis treatment.

Development and Validation of an Interpretable Conformal Predictor to Predict Sepsis Mortality Risk: Retrospective Cohort Study.

Background: Early and reliable identification of patients with sepsis who are at high risk of mortality is important to improve clinical outcomes. However, 3 major barriers to artificial intelligence (AI) models, including the lack of interpretability, the difficulty in generalizability, and the risk of automation bias, hinder the widespread adoption of AI models for use in clinical practice.

Objective: This study aimed to develop and validate (internally and externally) a conformal predictor of sepsis mortality risk in patients who are critically ill, leveraging AI-assisted prediction modeling.

3D MR elastography-based stiffness as a marker for predicting tumor grade and subtype in cervical cancer.

Background: Increasing evidence has indicated that high tissue stiffness (TS) may be a potential biomarker for evaluation of tumor aggressiveness.

Purpose: To investigate the value of magnetic resonance elastography (MRE)-based quantitative parameters preoperatively predicting the tumor grade and subtype of cervical cancer (CC).

Study Type: Retrospective.

PandaOmics: An AI-Driven Platform for Therapeutic Target and Biomarker Discovery.

PandaOmics is a cloud-based software platform that applies artificial intelligence and bioinformatics techniques to multimodal omics and biomedical text data for therapeutic target and biomarker discovery. PandaOmics generates novel and repurposed therapeutic target and biomarker hypotheses with the desired properties and is available through licensing or collaboration. Targets and biomarkers generated by the platform were previously validated in both and studies.

A customised down-sampling machine learning approach for sepsis prediction.

Objective: Sepsis is a life-threatening condition in the ICU and requires treatment in time. Despite the accuracy of existing sepsis prediction models, insufficient focus on reducing alarms could worsen alarm fatigue and desensitisation in ICUs, potentially compromising patient safety. In this retrospective study, we aim to develop an accurate, robust, and readily deployable method in ICUs, only based on the vital signs and laboratory tests.

Detecting central apneas using multichannel signals in premature infants.

. Monitoring of apnea of prematurity, performed in neonatal intensive care units by detecting central apneas (CAs) in the respiratory traces, is characterized by a high number of false alarms. A two-step approach consisting of a threshold-based apneic event detection algorithm followed by a machine learning model was recently presented in literature aiming to improve CA detection.