Search Results (363)

Background and Objectives: Acute metastatic cord compression (AMSCC) and femoral impending/pathological fracture negatively impact a patient’s quality of life, morbidity and survival, and are considered significant life events. This study aims to compare AMSCC and FMD as distinct yet overlapping metastatic orthopedic emergencies, addressing whether they represent sequential disease stages or distinct patient subpopulations—an analysis critical for prognosis and treatment planning. Materials and Methods: Records of all patients who underwent surgery for a femoral metastatic disease (FMD) over a decade (2004–2015) and patients who were treated for acute metastatic spinal compression (AMSCC) (2007–2017) were retrieved. There were no patients lost to follow-up. Results: The treatment cohorts were similar in terms of age, gender, tumour origin, and the number of spinal metastases. Fifty-four patients were diagnosed with AMSCC. Following treatment, the Frankel muscle grading improved by 0.5 ± 0.8 grades. Two hundred and eighteen patients underwent surgical intervention for FMD. Seventy percent of femoral metastases were located in the femoral neck and trochanteric area. Impending fractures accounted for 52% of the cohort. The FMD cohort, including impending and pathological fractures, was similar to the AMSCC cohort in terms of age and the time interval between cancer diagnosis and surgery (56.7 ± 74.2 vs. 51.6 ± 69.6, respectively, p = 0.646). The Karnofsky functional score was higher for the FMD cohort (63.3 ± 16.2) than for the AMSCC cohort (48.5 ± 19.5; p < 0.001). The mean survival time for the FMD cohort was double that of the AMSCC, at 18.4 ± 23.5 months versus 9.1 ± 13.6 months, respectively (p = 0.006). Conclusions: In conclusion, this study is novel in proposing that FMD and AMSCC are distinct clinical entities, differing in their impact on patient function and, most importantly, on patient survival. Full article

Background: Among all ambulatory antibiotic prescriptions, about 20% are non-visit-based (ordered outside of an in-person clinical encounter), and about 30% are not associated with an infection-related diagnosis code. Objective/Methods: To identify the rationale for ambulatory antibiotic prescribing, we queried the electronic health record (EHR) of a single, large health system in the Midwest United States to identify all oral antibiotics prescribed from November 2018 to February 2019 and examined visit, procedure, lab, department, and diagnosis codes. For the remaining antibiotic prescriptions—mostly non-visit-based, no-infectious-diagnosis-documented—we randomly selected and manually reviewed the EHR to identify a prescribing rationale and, if none was present, surveyed prescribers for their rationale. Results: During the study period, there were 47,619 antibiotic prescriptions from 1177 clinicians to 41,935 patients, of which 2608 (6%) were eligible non-visit-based, no-infectious-diagnosis-documented. We randomly selected 2298. There was a documented rationale for 2116 (92%) prescriptions. The most common documented reasons—not mutually exclusive—were patient-reported symptoms (71%), persistence of symptoms after initial management (18%), travel (17%), and responding to lab or imaging results (11%). We contacted 160 clinicians who did not document any prescribing rationale in the EHR and received responses from 62 (39%). Clinicians’ stated reasons included upcoming or current patient travel (19%), the antibiotic was for the prescriber’s own family member (19%), or the clinician made a diagnosis but did not document it in the EHR (18%). Conclusions: Non-visit-based, no-infectious-diagnosis-documented antibiotic prescriptions were most often in response to patient-reported symptoms, though they also occur for a variety of other reasons, some problematic, like in the absence of documentation or for a family member. Full article

Background: Early identification of sepsis is critical for improving outcomes, particularly in low-resource emergency settings. In Thai community hospitals, where physicians may not always be available, triage is often nurse-led. Selecting accurate and practical sepsis screening tools is essential not only for timely clinical decision-making but also for timely diagnosis and promoting appropriate antibiotic use. Methods: This cross-sectional study analyzed 475 adult patients with suspected sepsis who presented to the emergency department of a Thai community hospital, using retrospective data from January 2021 to December 2022. Six screening tools were evaluated: Systemic Inflammatory Response Syndrome (SIRS), Quick Sequential Organ Failure Assessment (qSOFA), Modified Early Warning Score (MEWS), National Early Warning Score (NEWS), National Early Warning Score version 2 (NEWS2), and Search Out Severity (SOS). Diagnostic accuracy was assessed using International Classification of Diseases, Tenth Revision (ICD-10) codes as the reference standard. Performance metrics included sensitivity, specificity, predictive values, likelihood ratios, and the area under the receiver operating characteristic (AUROC) curve, all reported with 95% confidence intervals. Results: SIRS had the highest sensitivity (84%), while qSOFA demonstrated the highest specificity (91%). NEWS2, NEWS, and MEWS showed moderate and balanced diagnostic accuracy. SOS also demonstrated moderate accuracy. Conclusions: A two-step screening approach—using SIRS for initial triage followed by NEWS2 for confirmation—is recommended. This strategy enhances nurse-led screening and optimizes limited resources in emergency care. Early sepsis detection through accurate screening tools constitutes a feasible public health intervention to support appropriate antibiotic use and mitigate antimicrobial resistance, especially in resource-limited community hospital settings. Full article

Background/Objectives: Tongue thrust (TT) occurs when abnormal tongue movements cause anterior tongue placement with pressure and contact against or between the teeth, potentially affecting the oral phase of swallowing, impacting eating, breathing and speaking. There is limited literature on the diagnostic and treatment approaches for TT, as well as involvement of health practitioners in its management. This study aims to examine the current knowledge and practices related to TT diagnosis and treatment among health professionals in Australia. Methods: A two-phase explanatory sequential mixed methods approach was adopted, comprising an online survey that collected participants’ demographic information and details on assessment, diagnosis, management, referral practices, and relevant experience and training. Phase one involved 47 health professionals from various disciplines in Australia who completed an online survey in its entirety. Phase two included in-depth interviews with seven speech-language pathologists (SLPs) to gain further insights into their experiences in managing TT. Survey data were analysed descriptively, and interview data was analysed thematically. Results: Most participants diagnosed TT using clinical assessments, such as general observation and oral motor examinations. Treatment approaches commonly included orofacial myofunctional therapy and the use of myofunctional devices. Interviews with SLPs identified four key themes: tongue thrust as a symptom rather than a diagnosis, facilitators to effective treatment, multidisciplinary approaches to management, and training and education gaps in clinical practice. Conclusions: This study provides valuable insights into how TT is identified, assessed, diagnosed, and managed by health professionals in Australia. It highlights the perspectives of SLPs on treatment approaches, as well as their views on the availability and adequacy of training and education in this field. The findings suggest the need for a broader understanding of TT management, emphasising the importance of multidisciplinary collaboration and professional development. These insights are globally relevant, as they stress the shared challenges and the value of international collaboration in improving TT diagnosis and treatment practices. Full article

Objectives: The early detection of low bone mineral density (BMD) is essential for preventing osteoporosis and related complications. While dual-energy X-ray absorptiometry (DXA) remains the gold standard for diagnosis, its cost and limited availability restrict its use in large-scale screening. This study investigated whether raw bioelectrical impedance analysis (BIA) data combined with explainable machine learning (ML) models could accurately classify osteopenia in women aged 40 to 55. Methods: In a cross-sectional design, 138 women underwent same-day BIA and DXA assessments. Participants were categorized as osteopenic (T-score between −1.0 and −2.5; n = 33) or normal (T-score ≥ −1.0) based on DXA results. Overall, 24.1% of the sample were classified as osteopenic, and 32.85% were postmenopausal. Raw BIA outputs were used as input features, including impedance values, phase angles, and segmental tissue parameters. A sequential forward feature selection (SFFS) algorithm was employed to optimize input dimensionality. Four ML classifiers were trained using stratified five-fold cross-validation, and SHapley Additive exPlanations (SHAP) were applied to interpret feature contributions. Results: The neural network (NN) model achieved the highest classification accuracy (92.12%) using 34 selected features, including raw impedance measurements, derived body composition indices such as regional lean mass estimates and the edema index, as well as a limited number of categorical variables, including self-reported physical activity status. SHAP analysis identified muscle mass indices and fluid distribution metrics, features previously associated with bone health, as the most influential predictors in the current model. Other classifiers performed comparably but with lower precision or interpretability. Conclusions: ML models based on raw BIA data can classify osteopenia with high accuracy and clinical transparency. This approach provides a cost-effective and interpretable alternative for the early identification of individuals at risk for low BMD in resource-limited or primary care settings. Full article

Background: HM is a rare, severe form of migraine with aura, characterised by motor weakness and strongly influenced by genetic factors affecting the brain. While pathogenic variants in CACNA1A, ATP1A2, and SCN1A genes have been implicated in familial HM, approximately 75% of cases lack known pathogenic variants in these genes, suggesting a more complex genetic basis. Methods: To advance our understanding of HM, we applied a variant prioritisation approach using whole-exome sequencing (WES) data from patients referred for HM diagnosis (n = 184) and utilised PathVar, a bioinformatics pipeline designed to identify pathogenic variants. Our analysis incorporated two strategies for association testing: (1) PathVar-identified single nucleotide variants (SNVs) and (2) PathVar SNVs combined with missense and rare variants. Principal component analysis (PCA) was performed to adjust for ancestral and other unknown differences between cases and controls. Results: Our results reveal a sequential reduction in the number of genes significantly associated with HM, from 20 in the first strategy to 11 in the second, which highlights the unique contribution of PathVar SNVs to the genetic architecture of HM. PathVar SNVs were more distinctive in the case cohort, suggesting a closer link to the functional changes underlying HM compared to controls. Notably, novel genes, such as SLC38A10, GCOM1, and NXPH2, which were previously not implicated in HM, are now associated with the disorder, advancing our understanding of its genetic basis. Conclusions: By prioritising PathVar SNVs, we identified a broader set of genes potentially contributing to HM. Given that HM is a rare condition, our findings, utilising a sample size of 184, represent a unique contribution to the field. This iterative analysis demonstrates that integrating diverse variant schemes provides a more comprehensive view of the genetic factors driving HM. Full article

The diagnosis-related group (DRG) system classifies patients into different groups in order to facilitate decisions regarding medical insurance payments. Currently, more than 600 standard DRGs exist in China. Payment details represented by DRG weights must be adjusted during decision-making. After modeling the DRG weight-determining process as a parameter-searching and optimization-solving problem, we propose a stochastic gradient tracking algorithm (SGT) and compare it with a genetic algorithm and sequential quadratic programming. We describe diagnosis-related groups in China using several statistics based on sample data from one city. We explored the influence of the SGT hyperparameters through numerous experiments and demonstrated the robustness of the best SGT hyperparameter combination. Our stochastic gradient tracking algorithm finished the parameter search in only 3.56 min when the insurance payment rate was set at 95%, which is acceptable and desirable. As the main medical insurance payment scheme in China, DRGs require quantitative evidence for policymaking. The optimization algorithm proposed in this study shows a possible scientific decision-making method for use in the DRG system, particularly with regard to DRG weights. Full article

Conventional applications of the Galvanostatic Intermittent Titration Technique (GITT) and EIS for estimating chemical diffusivity in battery electrodes face issues such as insufficient relaxation time to reach equilibrium, excessively long pulse durations that violate the short-time diffusion assumption, and the assumption of sequential electrode reaction and diffusion processes. In this work, a quasi-equilibrium criterion of 0.1 mV h⁻¹ was applied to NMC622 electrodes, yielding 8–9 h relaxations below 3.8 V, but above 3.8 V, voltage decayed linearly and indefinitely, even upon discharging titration, showing unusual nonmonotonic relaxation behavior. The initial 36-s transients of a 10-min galvanostatic pulse and diffusion impedance in series with the electrode reaction yielded consistent diffusivity values. However, solid-state diffusion in spherical active particles within porous electrodes, where ambipolar diffusion occurs in the pore electrolyte with $t_{+}=0.3$, requires a physics-based three-rail transmission line model (TLM). The corrected diffusivity may be three to four times higher. An analytic two-rail TLM approximating the three-rail numerical model was applied to temperature- and frequency-dependent EIS data. This approach mitigates parameter ambiguity and unphysical correlations in EIS. Physics-based EIS enables the identification of multistep energetics and the diagnosis of performance and degradation mechanisms. Full article

Deep learning-based fault diagnosis methods have gained extensive attention in recent years due to their outstanding performance. The model input can take the form of multiple domains, such as the time domain, frequency domain, and time–frequency domain, with commonalities and differences between them. Fusing multimodal features is crucial for enhancing diagnostic effectiveness. In addition, original signals typically exhibit nonstationary characteristics influenced by varying working conditions. In this paper, a dual-attentive multimodal fusion method combining a multiscale dilated CNN (DAMFM-MD) is proposed for rotating machinery fault diagnosis. Firstly, multimodal data are constructed by combining original signals, FFT-based frequency spectra, and STFT-based time–frequency images. Secondly, a three-branch multiscale CNN is developed for discriminative feature learning to consider nonstationary factors. Finally, a two-stage sequential fusion is designed to achieve multimodal complementary fusion considering the features with commonality and differentiation. The performance of the proposed method was experimentally verified through a series of industrial case analyses. The proposed DAMFM-MD method achieves the best F-score of 99.95%, an accuracy of 99.96%, and a recall of 99.95% across four sub-datasets, with an average fault diagnosis response time per sample of 1.095 milliseconds, outperforming state-of-the-art methods. Full article

Background/Objectives: The clinical forms of coronavirus disease 2019 (COVID-19) vary widely in severity, ranging from asymptomatic or moderate cases to severe pneumonia that can lead to acute respiratory failure, acute respiratory distress syndrome, multiple organ dysfunction syndrome, and death. Our main objective was to determine the prevalence of bacterial and fungal secondary infections in an intensive care unit (ICU). Secondary objectives included analyzing the impact of these infections on mortality and medical resource utilization, as well as assessing antimicrobial resistance in this context. Methods: We conducted a retrospective cohort study that included critically ill severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) patients treated in an ICU and analyzed the prevalence of co-infections and superinfections. Results: A multivariate analysis of mortality found that the presence of superinfections increased the odds of death by more than 15-fold, while the Sequential Organ Failure Assessment (SOFA) score and C-reactive protein (adjusted for confounders) increased the odds of mortality by 51% and 13%, respectively. The antibiotic resistance profile of microorganisms indicated a high prevalence of resistant strains. Carbapenems, glycopeptides, and oxazolidinones were the most frequently used classes of antibiotics. Among patients, 27.9% received a single antibiotic, 47.5% received two from different classes, and 24.4% were treated with three or more. Conclusions: The incidence and spectrum of bacterial and fungal superinfections are higher in critically ill ICU patients, leading to worse outcomes in COVID-19 cases. Multidrug-resistant pathogens present significant challenges for ICU and public health settings. Early screening, accurate diagnosis, and minimal use of invasive devices are essential to reduce risks and improve patient outcomes. Full article

Parkinson’s disease (PD) is a progressive neurodegenerative disorder characterized by motor and cognitive impairments. Early detection is critical for effective intervention, but current diagnostic methods often lack accuracy and generalizability. Electroencephalography (EEG) offers a noninvasive means to monitor neural activity, revealing abnormal brain oscillations linked to PD pathology. However, deep learning models for EEG analysis frequently struggle to balance high accuracy with robust generalization across diverse patient populations. To overcome these challenges, this study proposes a convolutional transformer enhanced sequential model (CTESM), which integrates convolutional neural networks, transformer attention blocks, and long short-term memory layers to capture spatial, temporal, and sequential EEG features. Enhanced by biologically informed feature extraction techniques, including spectral power analysis, frequency band ratios, wavelet transforms, and statistical measures, the model was trained and evaluated on a publicly available EEG dataset comprising 31 participants (15 with PD and 16 healthy controls), recorded using 40 channels at a 500 Hz sampling rate. The CTESM achieved an exceptional classification accuracy of 99.7% and demonstrated strong generalization on independent test datasets. Rigorous evaluation across distinct training, validation, and testing phases confirmed the model’s robustness, stability, and predictive precision. These results highlight the CTESM’s potential for clinical deployment in early PD diagnosis, enabling timely therapeutic interventions and improved patient outcomes. Full article

Background: Methylmalonic acidemia combined with homocystinuria (cblC) can lead to infantile maculopathy. Although significant visual deterioration is commonly reported in early-onset cblC, we found poor awareness regarding formal assessments of ocular complications, especially in newborns, and of how these complications relate to the timing of therapy initiation. In this work, we present our experience and perform a literature review. Methods: We performed sequential fundus examinations, optical coherence tomography (OCT) and full-field electroretinography (ERG) under sedation following detection of signs of retinal degeneration. We also assessed visual fields using kinetic attraction perimetry. Results: We report a newborn who was referred on the eighth day of life, following a diagnosis of cblC through newborn screening (NBS), and who began treatment that same day. Close monitoring of retinal changes through fundus examinations allowed the detection of signs of retinal degeneration at 3 months, which progressed when checked at 5 months. At 7 months, OCT showed retinal thinning with the appearance of bull’s eye maculopathy in the corresponding region on fundoscopy; ERG revealed a reduction in the amplitude of both scotopic and photopic components, whereas kinetic attraction perimetry showed no abnormalities. Genetic investigation confirmed the disease, compound heterozygous for a nonsense variant in MMACHC and a splicing one in PRDX1. Conclusions: In cblC, retinal degeneration occurs in the first months of life despite timely treatment and adequate biochemical control, and it may manifest before any signs of visual deprivation appear. However, there is an early, narrow window during which therapy may slow down retinal degeneration enough to prevent sensory nystagmus. We recommend initiating therapy immediately after biochemical diagnosis, along with close ophthalmological monitoring, before the appearance of any signs. Full article

Background/Objectives: Sepsis, as a major cause of mortality worldwide, requires timely diagnosis and prompt treatment to improve patient outcomes. In this study, we evaluated sepsis management strategies and their impact on clinical outcomes in hospitalized patients. Methods: A retrospective study was conducted by analyzing clinical and treatment data from the electronic records of sepsis patients who had been admitted to tertiary care hospitals in eastern Saudi Arabia. Using systematic sampling, the details of eligible patients were obtained. Data were collected on patient demographics, vital signs, Sequential Organ Failure Assessment (SOFA) and laboratory parameters, treatment (antibiotic therapy, vasopressor use, or fluid resuscitation), and outcomes (survival in hospital). Statistical analyses were performed to assess the association between clinical and treatment strategies and patient outcomes. Results: A total of 234 sepsis cases were analyzed, of which 70.9% were survivors and 29.1% were non-survivors. Patients aged 60 years and above were the most affected. Statistically significant differences were observed across all of the measured vital sign variables and outcomes (p < 0.0001). Based on SOFA scores, 56.41% of patients were assessed as having a moderate risk. Through our comparison of clinical and laboratory parameters between survivors and non-survivors, significant differences were found in all of the measured variables (p < 0.0001). The odds of survival were significantly higher in those who received early administration of broad-spectrum antibiotics (OR = 4.9449, p = 0.0001), vasopressor therapy (OR = 1.9408, p = 0.0262), and fluid resuscitation OR = 11.035, p = 0.0001). Conclusions: The results of this study highlight the importance of early sepsis recognition, prompt antibiotic therapy, and standardized protocol adherence in improving patient outcomes and reducing mortality and morbidity. Full article

Gastrointestinal submucosal tumors (SMTs) are difficult to diagnose accurately due to their deep location and the limitations of traditional biopsy tools. To address these issues, we propose a multifunctional capsule-shaped puncture biopsy robot (PBR) with capabilities for tissue sampling, thermal hemostasis, and multi-stage drug delivery. The PBR measures 27 mm in length and 13 mm in diameter, integrating a micro-scale electro-permanent magnetic system with a 60-turn dual-layer coil (wire diameter: 0.6 mm) to drive an 8 mm-depth puncture needle. A graphene–carbon nanotube composite heating film enables rapid and safe temperature elevation, achieving effective hemostasis and triggering sequential drug release using paraffin-based phase-change materials. Heating remains within the clinical safety range. Experiments demonstrated successful tissue penetration, precise magnetic control, and reliable staged pigment release simulating drug delivery. Tests on an ex vivo porcine stomach confirmed adaptability to irregular gastric surfaces. This compact PBR provides an integrated and minimally invasive approach to both the diagnosis and treatment of gastrointestinal lesions. Full article

A cyclonic ocean eddy (COE) exhibited an extraordinarily prolonged response to sequential typhoons Hinnamnor (1 September 2022) and Muifa (11 September 2022), reaching its peak strength 20 days post-typhoon (1 October 2022), almost double the typical 7–14-day latency for mesoscale eddies. In this study, we use a functional analysis apparatus, namely the multiscale window transform (MWT) and the MWT-based theory of canonical transfer and multiscale energetics analysis, to investigate the dynamics underlying this phenomenon. The original fields, which are obtained from HYCOM reanalysis data, are initially decomposed into three parts in three different scale windows, respectively, with the eddy-scale window (or COE window) lying in between. By examining the evolution of eddy kinetic energy (EKE), the response can be divided into two stages. From the energetic diagnosis, the COE’s response is not only visible at the surface but was even strengthened through interactions between the subsurface and surface, with vertical transport playing a crucial role. This response can be categorized into two stages: The energetics of the long-delayed response is in the first stage due to the storage of the eddy-scale available potential energy (EAPE) from the high-frequency scale window, where the typhoon injects energy through an inverse canonical transfer. The resulting EAPE is transported downward to the sub-surface. In the second stage, the subsurface EKE is carried upward to the surface via pressure work, leading to an explosive growth of the COE. These findings illuminate the significance of subsurface–surface interactions in modulating long-delayed eddy responses. Full article