Association of normalization, non-differentially expressed genes and data source with machine learning performance in intra-dataset or cross-dataset modelling of transcriptomic and clinical data.

Cross-dataset testing is critical for examining machine learning (ML) model's performance. However, most studies on modelling transcriptomic and clinical data only conducted intra-dataset testing. It is also unclear whether normalization and non-differentially expressed genes (NDEG) can improve cross-dataset modeling performance of ML.

A CRISPR mediated point-of-care assay for the detection of mucosal calprotectin in an animal model of ulcerative colitis.

Inflammatory bowel disease (IBD) is a chronic disorder associated with inflammation in the gastrointestinal tract, leading to a range of debilitating symptoms. Fecal calprotectin is an established biomarker for ulcerative colitis (UC), one of the main IBD diseases, which provides indications of the presence and severity of inflammation in the digestive tract. Enzyme-Linked Immunosorbent Assay (ELISA) as a gold standard approach for fecal calprotectin detection is time-consuming and impractical in point-of-care settings.

Point-of-care testing of Procalcitonin using lateral flow assay.

This study presents a novel gold nano shells (GNS)-assisted paper-based lateral flow assay (LFA) for the detection of procalcitonin (PCT). PCT is a biomarker of infection, originating from the C cells of the thyroid gland and functioning as a precursor to the hormone calcitonin. Its concentration in the blood is known to increase in line with the progression of infectious diseases.

Flexible chip-based wearable sensors enhanced by gold nanoparticles for IFN-γ detection.

Cytokines, essential regulatory proteins orchestrating various physiological processes and immune responses, play a pivotal role in assessing health status. Conventional cytokine detection methods, such as enzyme-linked immunosorbent assays, are reliable but time-consuming. Electrochemical aptamer-based sensors offer rapid response and higher selectivity, but fall short of achieving non-invasive detection.

Towards machine learning fairness in classifying multicategory causes of deaths in colorectal or lung cancer patients.

Classification of patient multicategory survival outcomes is important for personalized cancer treatments. Machine Learning (ML) algorithms have increasingly been used to inform healthcare decisions, but these models are vulnerable to biases in data collection and algorithm creation. ML models have previously been shown to exhibit racial bias, but their fairness towards patients from different age and sex groups have yet to be studied.

Emerging Arboviral Diseases in Pakistan: Epidemiology and Public Health Implications.

Arboviruses pose significant public health challenges globally, particularly in Pakistan, where deforestation, climate change, urbanization, inadequate sanitation, and natural disasters have all contributed to the spread of mosquito-borne flavivirus diseases like dengue fever. The lack of a thorough national surveillance system has made it difficult to determine the extent and distribution of these diseases. Concern has been raised by recent outbreaks of West Nile virus (WNV) and chikungunya (CHIKV) epidemics, which may lead to Zika virus (ZIKV) outbreaks in the future.

Liquid-Metal-Driven Synthesis of Mesoporous Noble Metal Thin Films and Micropatterns for Biosensing.

Nanoporous thin films (NPTFs) of noble metals are known for their exceptional durability, biocompatibility, and enlarged yet modifiable surface in broad biosensing applications. However, it remains challenging to devise a viable NPTF fabrication method that is generally applicable to different types of noble metals and for generating micro/nanopatterns. Here, we present a liquid-metal-based approach for transforming noble metal thin films and their lithographically formed patterns into NPTFs with mesoscale pores.

Normalization and selecting non-differentially expressed genes improve machine learning modelling of cross-platform transcriptomic data.

Normalization is a critical step in quantitative analyses of biological processes. Recent works show that cross-platform integration and normalization enable machine learning (ML) training on RNA microarray and RNA-seq data, but no independent datasets were used in their studies. Therefore, it is unclear how to improve ML modelling performance on independent RNA array and RNA-seq based datasets.

Topological segregation of stress sensors along the gut crypt-villus axis.

The crypt-villus structure of the small intestine serves as an essential protective barrier. The integrity of this barrier is monitored by the complex sensory system of the gut, in which serotonergic enterochromaffin (EC) cells play an important part. These rare sensory epithelial cells surveil the mucosal environment for luminal stimuli and transmit signals both within and outside the gut.

The Topographic Relationships and Geographic Distribution of Prevascular Vitreous Fissures and Cisterns Assessed by Ultrawidefield En Face Vitreous Images.

Purpose: To determine the topographic relationships and geographic distribution of prevascular vitreous fissures (PVFs) and cisterns across the entire posterior vitreous membrane in healthy subjects, using ultrawidefield en face and cross-sectional swept-source OCT (SS-OCT) images.

Design: Observational cross-sectional study.

Participants: Ninety-six eyes of 96 healthy participants (age range, 20-49 years) without posterior vitreous detachment.

Evolutionary dynamics of SARS-CoV-2 variants under strict non-pharmaceutical interventions - a population-based study.

Introduction: China implemented a dynamic zero-COVID strategy to curb viral transmission in response to the coronavirus disease 2019 (COVID-19) pandemic. This strategy was designed to inhibit mutation of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the virus responsible for COVID-19. This study explores the dynamics of viral evolution under stringent non-pharmaceutical interventions (NPIs) through real-world observations.

Construction and validation of a prediction model for arteriovenous fistula thrombosis in patients with AVF using Lasso regression.

Objective: The primary objective of this study is to develop and validate a high-risk model for Arteriovenous Fistula Thrombosis (AVFT) in patients undergoing autogenous arteriovenous fistula surgery for hemodialysis.

Methods: Retrospectively, we collected general information, clinical characteristics, laboratory examinations, and dialysis-related factors from a cohort of 1465 patients who received continuous arteriovenous fistula surgery at the Hemodialysis Access Center of Sichuan Provincial People's Hospital between January 2019 and June 2022. The patients were randomly divided into a training set and a validation set in a 2:1 ratio.

The efficacy and safety of patient-controlled intravenous analgesia with esketamine after total hip arthroplasty: a randomized controlled trial.

Purpose: To evaluate the efficacy and safety of esketamine-based patient-controlled intravenous analgesia following total hip arthroplasty.

Methods: A total of 135 total hip arthroplasty patients were randomly assigned to one of the three treatment groups: esketamine, sufentanil or continuous fascia iliaca compartment block (FICB) group. The primary endpoint was the postoperative visual analogue scale (VAS) pain scores at rest and on movement.

Associations of fat, bone, and muscle indices with disease severity in patients with obstructive sleep apnea hypopnea syndrome.

Purpose: To investigate the relationship between obstructive sleep apnea hypopnea syndrome (OSAHS) severity and fat, bone, and muscle indices.

Methods: This study included 102 patients with OSAHS and retrospectively reviewed their physical examination data. All patients underwent polysomnography, body composition analysis, dual-energy X-ray absorptiometry, computed tomography (CT) and blood test.

Structural insights into hybridoma-derived neutralizing monoclonal antibodies against Omicron BA.5 and XBB.1.16 variants of SARS-CoV-2.

Unlabelled: The emergence of novel variants of severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) continues to pose an ongoing challenge for global public health services, highlighting the urgent need for effective therapeutic interventions. Neutralizing monoclonal antibodies (mAbs) are a major therapeutic strategy for the treatment of COVID-19 and other viral diseases. In this study, we employed hybridoma technology to generate mAbs that target the BA.

The Known and Unknown of Global Tick-Borne Viruses.

Ticks are crucial vectors for various pathogens associated with human and animal diseases, including viruses. Nevertheless, significant knowledge gaps prevail in our understanding of tick-borne viruses (TBVs). We here examined existing studies on TBVs, uncovering 870 documented virus species across 28 orders, 55 families, and 66 genera.

multiplex imaging of dynamic neurochemical networks with designed far-red dopamine sensors.

Neurochemical signals like dopamine (DA) play a crucial role in a variety of brain functions through intricate interactions with other neuromodulators and intracellular signaling pathways. However, studying these complex networks has been hindered by the challenge of detecting multiple neurochemicals simultaneously. To overcome this limitation, we developed a single-protein chemigenetic DA sensor, HaloDA1.

Seroprevalence and risk factors for Crimean-Congo hemorrhagic fever virus exposure among febrile patients in northern Tanzania.

Background: Crimean-Congo hemorrhagic fever is a tick-borne zoonotic disease that may be severe and is present in many African countries. We aimed to understand the seroprevalence and risk for Crimean-Congo hemorrhagic fever virus in Tanzania by testing archived serum samples from patients enrolled in a prospective cohort study.

Methods: We prospectively enrolled febrile inpatients and outpatients from 2012 through 2014 at two referral hospitals in northern Tanzania.

A recurrent sigma pi sigma neural network.

In this paper, a novel recurrent sigma‒sigma neural network (RSPSNN) that contains the same advantages as the higher-order and recurrent neural networks is proposed. The batch gradient algorithm is used to train the RSPSNN to search for the optimal weights based on the minimal mean squared error (MSE). To substantiate the unique equilibrium state of the RSPSNN, the characteristic of stability convergence is proven, which is one of the most significant indices for reflecting the effectiveness and overcoming the instability problem in the training of this network.

Comparative analysis between genotypes of adenovirus isolates from hospitalized children with acute respiratory tract infections and clinical manifestations in Wuhan, China, from June 2022 to September 2023.

Acute respiratory tract infections (ARTIs) are among the leading causes of morbidity and mortality in children worldwide. Human adenovirus (HAdV) infections are estimated to account for at least 5% of pediatric ARTIs. The circulated genotypes of HAdV and the correlation between genotype and clinical manifestations in Wuhan, China, before and after the complete relaxation of nonpharmaceutical interventions against severe acute respiratory syndrome coronavirus 2, remain unknown.

The "Rule of 4" ultrasound diagnostic criteria at 6 weeks postoperatively was more appropriate for clinical determination of arteriovenous fistula maturation.

Objective: investigate the timing of assessing the maturation of arteriovenous fistula (AVF) and the diagnostic efficacy of two ultrasound assessment criteria in determining the maturation of AVF.

Methods: We collected clinical data on 227 patients with end-stage renal disease who were first diagnosed at our institution between February 1, 2023 and February 1, 2024, and were followed up regularly for 12 weeks post-AVF creation, with the diameter of the draining vein and the brachial artery volume flow (VF) recorded at 1 day, 2, 4, 6, 8, 10, and 12 weeks post-operation. We focused on the time of AVF maturation, and ultrasonographic diagnostic criteria 1 (the vein diameter was ≥4 mm, and the brachial artery VF was >500 mL/min, referred to as the "Rule of 4") and ultrasonographic diagnostic criteria 2 (the vein diameter was ≥5 mm, and the brachial artery VF was> 500 mL/min, referred to as the "Rule of 5") were used to evaluate the maturation of AVF.

Artesunate protects against a mouse model of cerulein and lipopolysaccharide‑induced acute pancreatitis by inhibiting TLR4‑dependent autophagy.

Severe acute pancreatitis (SAP) is a severe clinical condition associated with high rates of morbidity and mortality. Multiple organ dysfunction syndrome that follows systemic inflammatory response syndrome is the leading cause of SAP‑related death. Since the inflammatory mechanism of SAP remains unclear, there is currently a lack of effective drugs available for its treatment.

Correction: Sharma et al. Modulation of Renal Injury by Variable Expression of Myo-Inositol Oxygenase (MIOX) via Perturbation in Metabolic Sensors. 2020, , 217.

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Machine learning-based optimization of photogrammetric JRC accuracy.

To improve the accuracy of photogrammetric joint roughness coefficient (JRC) estimation, this study proposes two optimization models based on ground sample distance (GSD), point density, and the root mean square error (RMSE) of checkpoints. First, an algorithm that automatically generates spatial positions for equipment based on the convergence strategy was developed, using principles of Structure from Motion and Multi-View Stereo (SfM-MVS) and the shooting parameter selection algorithm (SPSA). Second, a portable positioning plate containing ground control points and checkpoints was designed based on optical principles, and a moving camera capture strategy guided by SPSA was proposed.