Treatment of lumbar spondylolysis in young adults using modified intravertebral screw-rod fixation system for single vertebral body combined with autologous cancellous bone graft: a technical note and preliminary report.

Background: There is currently no consensus on the surgical treatment of lumbar spondylolysis in young adults, and the nonunion rate remains relatively high even after surgery. Therefore, in this study, we proposed a modified intravertebral screw-rod fixation technique within a single vertebral segment and investigated the clinical efficacy of this modified fixation system combined with autologous cancellous bone grafting in the treatment of lumbar spondylolysis in young adults.

Methods: This study included 28 young adults with lumbar spondylolysis who were treated at our center between 2021 and 2023.

A blueprint for broadly effective bacteriophage-antibiotic cocktails against bacterial infections.

Bacteriophage (phage) therapy is a promising therapeutic modality for multidrug-resistant bacterial infections, but its application is mainly limited to personalized therapy due to the narrow host range of individual phages. While phage cocktails targeting all possible bacterial receptors could theoretically confer broad coverage, the extensive diversity of bacteria and the complexity of phage-phage interactions render this approach challenging. Here, using screening protocols for identifying "complementarity groups" of phages using non-redundant receptors, we generate effective, broad-range phage cocktails that prevent the emergence of bacterial resistance.

Exploring the influence of environmental indicators and forecasting influenza incidence using ARIMAX models.

Background: Influenza is a respiratory infection that poses a significant health burden worldwide. Environmental indicators, such as air pollutants and meteorological factors, play a role in the onset and propagation of influenza. Accurate predictions of influenza incidence and understanding the factors influencing it are crucial for public health interventions.

Causal relationship between green tea intake and gastrointestinal disorders: a two-sample Mendelian randomization study.

Background: The precise association between green tea intake and gastrointestinal disorders remains controversial. This study aimed to investigate the potential causal association between green tea intake and gastrointestinal disorders through a two-sample Mendelian randomization (MR) study.

Methods: Utilizing publicly accessible data from genome-wide association studies (GWAS), we identified SNPs strongly linked with the study variables from multiple large databases to serve as instrumental variables (IVs).

Enhanced machine learning approaches for OSA patient screening: model development and validation study.

Age, gender, body mass index (BMI), and mean heart rate during sleep were found to be risk factors for obstructive sleep apnea (OSA), and a variety of methods have been applied to predict the occurrence of OSA. This study aimed to develop and evaluate OSA prediction models using simple and accessible parameters, combined with multiple machine learning algorithms, and integrate them into a cloud-based mobile sleep medicine management platform for clinical use. The study data were obtained from the clinical records of 610 patients who underwent polysomnography (PSG) at the Sleep Medicine Center of the Second Affiliated Hospital of Fujian Medical University between January 2021 and December 2022.

Identification and experimental validation of programmed cell death- and mitochondria-associated biomarkers in osteoporosis and immune microenvironment.

Prior research has demonstrated that programmed cell death (PCD) and mitochondria assume pivotal roles in controlling cellular metabolism and maintaining bone cell equilibrium. Nonetheless, the comprehensive elucidation of their mode of operation in osteoporosis (OP) warrants further investigation. Therefore, this study aimed at analyzing the role of genes associated with PCD (PCD-RGs) and mitochondria (mortality factor-related genes; MRGs) in OP.

Recent advancements in machine learning for bone marrow cell morphology analysis.

As machine learning progresses, techniques such as neural networks, decision trees, and support vector machines are being increasingly applied in the medical domain, especially for tasks involving large datasets, such as cell detection, recognition, classification, and visualization. Within the domain of bone marrow cell morphology analysis, deep learning offers substantial benefits due to its robustness, ability for automatic feature learning, and strong image characterization capabilities. Deep neural networks are a machine learning paradigm specifically tailored for image processing applications.

Prediction of influenza outbreaks in Fuzhou, China: comparative analysis of forecasting models.

Background: Influenza is a highly contagious respiratory disease that presents a significant challenge to public health globally. Therefore, effective influenza prediction and prevention are crucial for the timely allocation of resources, the development of vaccine strategies, and the implementation of targeted public health interventions.

Method: In this study, we utilized historical influenza case data from January 2013 to December 2021 in Fuzhou to develop four regression prediction models: SARIMA, Prophet, Holt-Winters, and XGBoost models.

A Machine Learning Prediction Model of Adult Obstructive Sleep Apnea Based on Systematically Evaluated Common Clinical Biochemical Indicators.

Objective: Obstructive sleep apnea (OSA) is a common and potentially fatal sleep disorder. The purpose of this study was to construct an objective and easy-to-promote model based on common clinical biochemical indicators and demographic data for OSA screening.

Methods: The study collected the clinical data of patients who were referred to the Sleep Medicine Center of the Second Affiliated Hospital of Fujian Medical University from December 1, 2020, to July 31, 2023, including data for demographics, polysomnography (PSG), and 30 biochemical indicators.

Causal association between air pollution and autoimmune diseases: a two-sample Mendelian randomization study.

Background: In recent years, an increasing number of observational studies have reported the impact of air pollution on autoimmune diseases (ADs). However, no Mendelian randomization (MR) studies have been conducted to investigate the causal relationships. To enhance our understanding of causality, we examined the causal relationships between particulate matter (PM) and nitrogen oxides (NO) and ADs.

Evaluating the risk of developing hyperuricemia in patients with type 2 diabetes mellitus using least absolute shrinkage and selection operator regression and machine learning algorithm.

Background: Hyperuricemia is a common complication of type 2 diabetes mellitus and can lead to serious consequences such as gout and kidney disease.

Methods: Patients with type 2 diabetes mellitus from six different communities in Fuzhou were recruited from June to December 2022. Questionnaires, physical examinations, and laboratory tests were conducted to collect data on various variables.

Pf bacteriophages hinder sputum antibiotic diffusion via electrostatic binding.

Despite great progress in the field, chronic () infections remain a major cause of morbidity and mortality in patients with cystic fibrosis, necessitating treatment with inhaled antibiotics. Pf phage is a filamentous bacteriophage produced by that has been reported to act as a structural element in biofilms. Pf presence has been associated with resistance to antibiotics and poor outcomes in cystic fibrosis, though the underlying mechanisms are unclear.

A study on the impact of marital status on the survival status of prostate cancer patients based on propensity score matching.

Marital status is an independent prognostic factor for survival in many types of cancers, but its prognostic impact on patients with prostate cancer (PCa) has not been established. The aim of this study was to explore the independent prognostic factors of PCa and to investigate the effect of marital status on survival outcomes in patients with different stratified by PCa. Using the surveillance, epidemiology, and end results (SEER) database, we collected data on 584,655 PCa patients diagnosed between 1975 and 2019.

Studying the impact of marital status on diagnosis and survival prediction in pancreatic ductal carcinoma using machine learning methods.

Pancreatic cancer is a commonly occurring malignant tumor, with pancreatic ductal carcinoma (PDAC) accounting for approximately 95% of cases. According of its poor prognosis, identifying prognostic factors of pancreatic ductal carcinoma can provide physicians with a reliable theoretical foundation when predicting patient survival. This study aimed to analyze the impact of marital status on survival outcomes of PDAC patients using propensity score matching and machine learning.

Exploring the spatiotemporal relationship between influenza and air pollution in Fuzhou using spatiotemporal weighted regression model.

Air pollution has become a significant concern for human health, and its impact on influenza, has been increasingly recognized. This study aims to explore the spatiotemporal heterogeneity of the impacts of air pollution on influenza and to confirm a better method for infectious disease surveillance. Spearman correlation coefficient was used to evaluate the correlation between air pollution and the influenza case counts.

Antimicrobial activity of a natural compound and analogs against multi-drug-resistant Gram-positive pathogens.

Unlabelled: The increasing prevalence of methicillin-resistant (MRSA) has sparked global concern due to the dwindling availability of effective antibiotics. To increase our treatment options, researchers have investigated naturally occurring antimicrobial compounds and have identified MC21-A (C58), which has potent antimicrobial activity against MRSA. Recently, we have devised total synthesis schemes for C58 and its chloro-analog, C59.

An artificial neural network model for evaluating the risk of hyperuricaemia in type 2 diabetes mellitus.

Type 2 diabetes with hyperuricaemia may lead to gout, kidney damage, hypertension, coronary heart disease, etc., further aggravating the condition of diabetes as well as adding to the medical and financial burden. To construct a risk model for hyperuricaemia in patients with type 2 diabetes mellitus based on artificial neural network, and to evaluate the effectiveness of the risk model to provide directions for the prevention and control of the disease in this population.

Rapid assessment of changes in phage bioactivity using dynamic light scattering.

Extensive efforts are underway to develop bacteriophages as therapies against antibiotic-resistant bacteria. However, these efforts are confounded by the instability of phage preparations and a lack of suitable tools to assess active phage concentrations over time. In this study, we use dynamic light scattering (DLS) to measure changes in phage physical state in response to environmental factors and time, finding that phages tend to decay and form aggregates and that the degree of aggregation can be used to predict phage bioactivity.

The relationship between sleep quality and daytime dysfunction among college students in China during COVID-19: a cross-sectional study.

Objective: College Students' sleep quality and daytime dysfunction have become worse since the COVID-19 outbreak, the purpose of this study was to explore the relationship between sleep quality and daytime dysfunction among college students during the COVID-19 (Corona Virus Disease 2019) period.

Methods: This research adopts the form of cluster random sampling of online questionnaires. From April 5 to 16 in 2022, questionnaires are distributed to college students in various universities in Fujian Province, China and the general information questionnaire and PSQI scale are used for investigation.