bluuebunny/crossref_metadata_2025_split huggingface dataset:
Expression of microRNAs related to apoptosis in the aqueous humor and lens capsule of patients with glaucoma
Authors: Yu Hyo Seon , Hong Eun Hee , Kang Ji Hye , Lee Yong Woo , Lee Won June , Kang Min Ho , Cho Heeyoon , Shin Yong Un , Seong Mincheol 
Published: February 2024

BackgroundThe aim of this study is to investigate the expression profiles of microRNAs (miRNAs) related to apoptosis in the aqueous humor (AH) and lens capsule (LC) of patients with glaucoma.MethodsAH and LC samples were collected from patients with open-angle glaucoma and control participants who were scheduled for cataract surgery. A miRNA PCR array comprising 84 miRNAs was used to analyze the AH (glaucoma, n = 3; control, n = 3) and LC samples (glaucoma, n = 3; control, n = 4). Additionally, the AH and LC samples (glaucoma, n = 3; control, n = 4) were subjected to quantitative real-time PCR to validate the differentially expressed miRNAs determined using the PCR array. Bioinformatics analysis was performed to identify the interactions between miRNAs and diseases. Additionally, the differential expression of these miRNAs and the target gene was validated through in vitro experiments using a retinal ganglion cell (RGC) model.ResultsExpression levels of 19 and 3 miRNAs were significantly upregulated in the AH and LC samples of the glaucoma group, respectively (p &lt; 0.05). Of these, the expression levels of hsa-miR-193a-5p and hsa-miR-222-3p showed significant differences in both AH and LC samples. Bioinformatics analysis showed experimentally validated 8 miRNA:gene pairs. Among them, PTEN was selected to analyze the expression level in AH and LC from separate cohort (glaucoma, n = 5; control, n = 4). The result showed downregulation of PTEN concurrent with upregulation of the two miRNAs in LC samples of glaucoma group. In vitro experiments validated that the expression levels of hsa-miR-193a-5p and hsa-miR-222-3p were significantly upregulated, and that of PTEN was significantly downregulated in the H2O2-treated RGC, while the level of PTEN was recovered through co-treatment with miR-193a inhibitor or miR-222 inhibitor.ConclusionThis is the first study to investigate the differential expression of apoptosis-related miRNAs in the AH and LC of patients with glaucoma. Hsa-miR-193a-5p and hsa-miR-222-3p, which were upregulated in both AH and LC, may be considered potential biomarkers for glaucoma.
Prevention of ventilator-associated pneumonia with probiotics: an overview of systematic reviews
Authors: Han Minjuan , Wang Ke , Sun Lei , Liu Wang , Dong Wanhu 
Published: February 2024

BackgroundProbiotics has been used as an adjuvant therapy for the prevention of ventilator-associated pneumonia (VAP). This study aimed to systematically compile, evaluate, and synthesize previous systematic reviews (SRs) and meta-analyses (MAs) on the prevention of VAP with probiotics.MethodsThe methodological quality, reporting quality, and evidence quality of enrolled studies were, respectively evaluated by Assessment of Multiple Systematic Reviews 2 (AMSTAR-2) tool, Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) checklists, and Grading of Recommendations, Assessment, Development, and Evaluation (GRADE) system.ResultsThirteen eligible publications were analyzed in this overview. The included studies were rated as generally low methodological quality owing to the lack of a registered protocol or a list of exclusion criteria. The inadequate quality of the reports was demonstrated by the lack of reporting on the registration protocols, the lack of reporting on the search strategy, and the lack of reporting on the additional analyses. For GRADE, there were 36.17% (17/47) outcomes graded to be of moderate quality, 42.55% (20/47) to be of low quality, and 21.28% (10/47) to be of very low quality.ConclusionProbiotics may be associated with reduced incidence of VAP. However, caution should be exercised when recommending probiotics for the prevention of VAP owing to the poor quality of the current evidence.
Long-term follow-up of an attenuated presentation of NAXE-related disease, a potentially actionable neurometabolic disease: a case report
Authors: Almudhry Montaha , Prasad Chitra , Rupar C. Anthony , Tay Keng Yeow , Prasad Asuri N. 
Published: February 2024

BackgroundEarly-onset progressive encephalopathy with brain edema and/or leukoencephalopathy (PEBEL-1) is an autosomal recessive disorder whereby a fluctuating clinical course is exacerbated by febrile illnesses. Pathogenic NAD(P)HX epimerase (NAXE) gene mutations underpin this disorder. This mutation damages the metabolite repair system involved in regenerating crucial redox carriers. Longer survival has rarely been reported in this potentially actionable entity.ObjectivesThis case study aims to report a milder phenotype of a patient with NAXE gene mutation and his longitudinal follow-up of more than 20 years.Case reportA 24-year-old man first became symptomatic in infancy with frequent initial neurological decompensations in the setting of infections with subsequent clinical improvement followed by stability with residual cerebellar dysfunction. Clinical features noted over the years include chronic ataxia, nystagmus, ptosis, mild spasticity of lower limbs, and neuropsychiatric symptoms. Cerebellar and spinal cord atrophy were noted in cranial and spinal MR imaging. Biallelic homozygous variants in the NAXE gene (c.733 A&gt;C) were identified on whole exome sequencing. Symptom management included the initiation of a mitochondrial cocktail with carnitine, coenzyme Q, and thiamine. Subsequently, niacin (Vitamin B3), which is involved in the cellular biosynthesis of NAD+, was added, given its potentially beneficial therapeutic impact.ConclusionA missense homozygous variant in the NAXE gene is described in this patient with a milder clinical phenotype of the disease. Supplementation with niacin in addition to a mitochondrial cocktail presents a potential supportive therapeutic option to reduce disease progression.
Sleep duration and perceptions of sleep quality in British Army recruits during basic training - an observational analysis
Authors: Rawcliffe Alex J. , Tyson Hayley , Hinde Katrina , Jacka Kimberley , Holland Rachel , Chapman Shaun , Roberts Andrew J. 
Published: February 2024

IntroductionSleep is critical to the health, wellbeing and performance of military personnel during basic training. This two-part study evaluated sleep-wake patterns and sleep disturbances in junior soldiers (JS) and infantry recruits in Autumn 2021 (study 1), and non-infantry recruits in spring 2022 (study 2).MethodsDuring studies 1 and 2, validated wearable technology combined with a sleep diary was used to quantify sleep-wake indices, sleep disturbances and perceptions of sleep quality. Sleep diary data was analysed descriptively. A series of repeated-measures ANOVAs examined differences in objective sleep-wake indices. Correlation analysis determined associations between time in bed (TIB) and total sleep time (TST).ResultsSignificant (p &lt; 0.05) differences in most sleep-wake indices were observed between weeks of basic training for all cohorts. Strong positive correlations between TIB and TST were observed for each cohort across basic training (r = 0.681 – 0.970, p &lt; 0.001), with longer TST associated with greater TIB. The mean±SD sleep duration (hours and mins [hm]) for JS (06:22 ± 00:27hm), non-infantry (05:41 ± 00:47hm) and infantry (05:46 ± 00:34hm) recruits across basic training was consistently below national recommendations. The mean±SD bed and wake times for JS (bedtime: 23:01 ± 00:32hm; awake: 05:34 ± 00:10hm), non-infantry (bedtime: 23:38 ± 01:09hm; awake: 04:47 ± 00:58hm), and infantry (bedtime: 23:13 ± 00:29hm; awake: 05:38 ± 00:26hm) recruits varied across weeks of basic training, with over 80% reporting “fairly bad” or “very bad” sleep quality and frequent periods of “dozing off” during daytime activity. The most commonly reported sleep disturbing factors identified during basic training involved: late-night military admin (e.g., ironing, boot cleaning, kit set up etc), early morning wake times, extraneous noise, light and hot room temperatures within the primary sleeping environment, bed/mattress discomfort, muscle soreness and feelings of stress and anxiety.Discussion/ConclusionOur findings contribute to the existing evidence that long-term sleep loss is pervasive during initial military training programmes. The average sleep durations indicate chronic and unrecoverable sleep loss which would be expected to significantly impair physical and cognitive military performance, and increase the risk of injury, illness and attrition rates during basic training. Changes in the design and scheduling of basic training programmes to enable, at the least, minimum sleep recommendations to be met, and to improve sleep hygiene in the primary sleeping environment are warranted.
Empirical comparison of deep learning models for fNIRS pain decoding
Authors: Fernandez Rojas Raul , Joseph Calvin , Bargshady Ghazal , Ou Keng-Liang 
Published: February 2024

IntroductionPain assessment is extremely important in patients unable to communicate and it is often done by clinical judgement. However, assessing pain using observable indicators can be challenging for clinicians due to the subjective perceptions, individual differences in pain expression, and potential confounding factors. Therefore, the need for an objective pain assessment method that can assist medical practitioners. Functional near-infrared spectroscopy (fNIRS) has shown promising results to assess the neural function in response of nociception and pain. Previous studies have explored the use of machine learning with hand-crafted features in the assessment of pain.MethodsIn this study, we aim to expand previous studies by exploring the use of deep learning models Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), and (CNN-LSTM) to automatically extract features from fNIRS data and by comparing these with classical machine learning models using hand-crafted features.ResultsThe results showed that the deep learning models exhibited favourable results in the identification of different types of pain in our experiment using only fNIRS input data. The combination of CNN and LSTM in a hybrid model (CNN-LSTM) exhibited the highest performance (accuracy = 91.2%) in our problem setting. Statistical analysis using one-way ANOVA with Tukey's (post-hoc) test performed on accuracies showed that the deep learning models significantly improved accuracy performance as compared to the baseline models.DiscussionOverall, deep learning models showed their potential to learn features automatically without relying on manually-extracted features and the CNN-LSTM model could be used as a possible method of assessment of pain in non-verbal patients. Future research is needed to evaluate the generalisation of this method of pain assessment on independent populations and in real-life scenarios.
Deep learning-enabled detection of hypoxic–ischemic encephalopathy after cardiac arrest in CT scans: a comparative study of 2D and 3D approaches
Authors: Molinski Noah S. , Kenda Martin , Leithner Christoph , Nee Jens , Storm Christian , Scheel Michael , Meddeb Aymen 
Published: February 2024

ObjectiveTo establish a deep learning model for the detection of hypoxic–ischemic encephalopathy (HIE) features on CT scans and to compare various networks to determine the best input data format.Methods168 head CT scans of patients after cardiac arrest were retrospectively identified and classified into two categories: 88 (52.4%) with radiological evidence of severe HIE and 80 (47.6%) without signs of HIE. These images were randomly divided into a training and a test set, and five deep learning models based on based on Densely Connected Convolutional Networks (DenseNet121) were trained and validated using different image input formats (2D and 3D images).ResultsAll optimized stacked 2D and 3D networks could detect signs of HIE. The networks based on the data as 2D image data stacks provided the best results (S100: AUC: 94%, ACC: 79%, S50: AUC: 93%, ACC: 79%). We provide visual explainability data for the decision making of our AI model using Gradient-weighted Class Activation Mapping.ConclusionOur proof-of-concept deep learning model can accurately identify signs of HIE on CT images. Comparing different 2D- and 3D-based approaches, most promising results were achieved by 2D image stack models. After further clinical validation, a deep learning model of HIE detection based on CT images could be implemented in clinical routine and thus aid clinicians in characterizing imaging data and predicting outcome.
EEG-based major depressive disorder recognition by neural oscillation and asymmetry
Authors: Liu Xinyu , Zhang Haoran , Cui Yi , Zhao Tong , Wang Bin , Xie Xiaomeng , Liang Sixiang , Sha Sha , Yan Yuxiang , Zhao Xixi , Zhang Ling 
Published: February 2024

BackgroundMajor Depressive Disorder (MDD) is a pervasive mental health issue with significant diagnostic challenges. Electroencephalography (EEG) offers a non-invasive window into the neural dynamics associated with MDD, yet the diagnostic efficacy is contingent upon the appropriate selection of EEG features and brain regions.MethodsIn this study, resting-state EEG signals from both eyes-closed and eyes-open conditions were analyzed. We examined band power across various brain regions, assessed the asymmetry of band power between the hemispheres, and integrated these features with clinical characteristics of MDD into a diagnostic regression model.ResultsRegression analysis found significant predictors of MDD to be beta2 (16–24 Hz) power in the Prefrontal Cortex (PFC) with eyes open (B = 20.092, p = 0.011), beta3 (24–40 Hz) power in the Medial Occipital Cortex (MOC) (B = −12.050, p &lt; 0.001), and beta2 power in the Right Medial Frontal Cortex (RMFC) with eyes closed (B = 24.227, p &lt; 0.001). Asymmetries in beta1 (12–16 Hz) power with eyes open (B = 28.047, p = 0.018), and in alpha (8–12 Hz, B = 9.004, p = 0.013) and theta (4–8 Hz, B = −13.582, p = 0.008) with eyes closed were also significant predictors.ConclusionThe study confirms the potential of multi-region EEG analysis in improving the diagnostic precision for MDD. By including both neurophysiological and clinical data, we present a more robust approach to understanding and identifying this complex disorder.LimitationsThe research is limited by the sample size and the inherent variability in EEG signal interpretation. Future studies with larger cohorts and advanced analytical techniques are warranted to validate and refine these findings.
Cardiovascular risk factors and development of nomograms in an Italian cohort of patients with suspected coronary artery disease undergoing SPECT or PET stress myocardial perfusion imaging
Authors: Megna Rosario , Petretta Mario , Nappi Carmela , Assante Roberta , Zampella Emilia , Gaudieri Valeria , Mannarino Teresa , D’Antonio Adriana , Green Roberta , Cantoni Valeria , Panico Mariarosaria , Acampa Wanda , Cuocolo Alberto 
Published: February 2024

IntroductionSingle-photon emission computed tomography (SPECT) and positron emission tomography (PET) are non-invasive nuclear medicine techniques that can identify areas of abnormal myocardial perfusion. We assessed the prevalence of cardiovascular risk factors in patients with suspected coronary artery disease (CAD) undergoing SPECT or PET stress myocardial perfusion imaging (MPI). Based on significant risk factors associated with an abnormal MPI, we developed a nomogram for each cohort as a pretest that would be helpful in decision-making for clinicians.MethodsA total of 6,854 patients with suspected CAD who underwent stress myocardial perfusion imaging by SPECT or PET/CT was studied. As part of the baseline examination, clinical teams collected information on traditional cardiovascular risk factors: age, gender, body mass index, angina, dyspnea, diabetes, hypertension, hyperlipidemia, family history of CAD, and smoking.ResultsThe prevalence of cardiovascular risk factors was different in the two cohorts of patients undergoing SPECT (n = 4,397) or PET (n = 2,457) myocardial perfusion imaging. A statistical significance was observed in both cohorts for age, gender, and diabetes. At multivariable analysis, only age and male gender were significant covariates in both cohorts. The risk of abnormal myocardial perfusion imaging related to age was greater in patients undergoing PET (odds ratio 4% vs. 1% per year). In contrast, male gender odds ratio was slightly higher for SPECT compared to PET (2.52 vs. 2.06). In the SPECT cohort, smoking increased the risk of abnormal perfusion of 24%. Among patients undergoing PET, diabetes and hypertension increased the risk of abnormal perfusion by 63% and 37%, respectively. For each cohort, we obtained a nomogram by significant risk factors at multivariable logistic regression. The area under the receiver operating characteristic curve associated with the nomogram was 0.67 for SPECT and 0.73 for the PET model.ConclusionsPatients with suspected CAD belonging to two different cohorts undergoing SPECT or PET stress myocardial perfusion imaging can have different cardiovascular risk factors associated with a higher risk of an abnormal MPI study. As crude variables, age, gender, and diabetes were significant for both cohorts. Net of the effect of other covariates, age and gender were the only risk factors in common between the two cohorts. Furthermore, smoking and type of stress test were significant for the SPECT cohort, where as diabetes and hypertension were significant for the PET cohort. Nomograms obtained by significant risk factors for the two cohorts can be used by clinicians to evaluate the risk of an abnormal study.
18F-FDG PET/CT imaging in pulmonary sarcomatoid carcinoma
Authors: Luo Zhe-Huang , Luo Xiao-Yan , Qi Wan-Ling , Liu Qian 
Published: February 2024

BackgroundPulmonary sarcomatoid carcinoma (PSC) is a rare highly aggressive and poorly differentiated non-small cell carcinoma, and little is known about the information on the usefulness of 18F-fluorodeoxyglucose (18F-FDG) positron emission tomography/computed tomography (PET/CT). We investigated the clinical and 18F-FDG PET/CT features of PSC.MethodsWe retrospectively analyzed 25 consecutive PSC patients who had undergone 18F-FDG PET/CT. Demographic data, PET/CT findings before treatment, pathological features, and prognosis in these patients were investigated to define correlates between maximal standard uptake value (SUVmax) and clinicopathological parameters.ResultsFrom March 2017 to January 2023, twenty-five eligible patients with PSC were identified. There were 23 (92%) men, aged 68.5 ± 8.5 (range 56-90) years. Eighteen (72%) patients had a frequent smoking history. The mean size of PSCs was 59.3 ± 18.6 (range 29-97) mm, and 23 (92%) PSCs were Stage IV tumors. 20 (80%) lesions were located in the upper lung and 19 (76%) cases belonged to the peripheral type. Necrotic foci appeared in 21(84%) tumors. 11 (44%) PSCs invaded the pleura. All PSCs were FDG avid, and the mean of SUVmax was 11.8 ± 5.3 (range 4.8-25.5). Metastases were found on PET/CT in 24(96%) patients. The SUVmax of the lesions ≥ 5cm was higher than that of the lesions &lt; 5cm (p=0.004), and the SUVmax of lesions with TTF-1 expression was higher than those of lesions without TTF-1 expression (p=0.009). All of the 25 primary lesions were considered malignant and confirmative, probable, and possible diagnosis of PSC was made in 2 (8%), 4 (16%), and 5(20%) patients, respectively on PET/CT. PSC was not considered in 14 (56%) patients, in PET/CT. The survival of patients with surgery didn’t demonstrate a significantly good prognosis as compared with those without surgery (p=0.675).ConclusionAll PSCs had obvious FDG avidity. Although imaging diagnosis is still difficult, combined clinical and imaging features more than 40% of primary lesions were considered for the possibility of PSC in our group. Early histopathological diagnosis is necessary to help develop a reasonable regimen.
Pediatric obstructive sleep apnea diagnosis: leveraging machine learning with linear discriminant analysis
Authors: Qin Han , Zhang Liping , Li Xiaodan , Xu Zhifei , Zhang Jie , Wang Shengcai , Zheng Li , Ji Tingting , Mei Lin , Kong Yaru , Jia Xinbei , Lei Yi , Qi Yuwei , Ji Jie , Ni Xin , Wang Qing , Tai Jun 
Published: February 2024

ObjectiveThe objective of this study was to investigate the effectiveness of a machine learning algorithm in diagnosing OSA in children based on clinical features that can be obtained in nonnocturnal and nonmedical environments.Patients and methodsThis study was conducted at Beijing Children's Hospital from April 2018 to October 2019. The participants in this study were 2464 children aged 3–18 suspected of having OSA who underwent clinical data collection and polysomnography(PSG). Participants’ data were randomly divided into a training set and a testing set at a ratio of 8:2. The elastic net algorithm was used for feature selection to simplify the model. Stratified 10-fold cross-validation was repeated five times to ensure the robustness of the results.ResultsFeature selection using Elastic Net resulted in 47 features for AHI ≥5 and 31 features for AHI ≥10 being retained. The machine learning model using these selected features achieved an average AUC of 0.73 for AHI ≥5 and 0.78 for AHI ≥10 when tested externally, outperforming models based on PSG questionnaire features. Linear Discriminant Analysis using the selected features identified OSA with a sensitivity of 44% and specificity of 90%, providing a feasible clinical alternative to PSG for stratifying OSA severity.ConclusionsThis study shows that a machine learning model based on children's clinical features effectively identifies OSA in children. Establishing a machine learning screening model based on the clinical features of the target population may be a feasible clinical alternative to nocturnal OSA sleep diagnosis.